Skip to content

Advertisement

  • Research
  • Open Access

Effects of familial hypercholesterolemia-associated genes on the phenotype of premature myocardial infarction

Contributed equally
Lipids in Health and Disease201918:95

https://doi.org/10.1186/s12944-019-1042-3

  • Received: 30 January 2019
  • Accepted: 1 April 2019
  • Published:

Abstract

Background

The incidence of premature myocardial infarction (PMI) has gradually increased in recent years. Genetics plays a central role in the development of PMI. Familial hypercholesterolemia (FH) is one of the most common genetic disorders of cholesterol metabolism leading to PMI.

Objective

This study investigated the relationship between FH-associated genes and the phenotype of PMI to clarify the genetic spectrum of PMI diseases.

Method

This study enrolled PMI patients (n = 225) and detected the mutations in their FH-associated genes (LDLR, APOB, PCSK9, LDLRAP1) by Sanger sequencing. At the same time, patients free of PMI (non-FH patients, n = 56) were enrolled as control, and a logistic regression analysis was used to identify risk factors associated with PMI. The diagnosis of FH was confirmed using “2018 Chinese expert consensus of FH screening and diagnosis” before the prevalence and clinical features of FH were analyzed.

Results

Pathogenic mutations in LDLR, APOB, PCSK9 and LDLRAP1 genes were found in 17 of 225 subjects (7.6%), and all mutations were loss of function (LOF) and heterozygous. The genotype-phenotype relationship of patients carrying FH-associated mutations showed high heterogeneity. The logistic regression analysis showed that the smoking history, obesity and the family history of premature CHD were independent risk factors of PMI. In this study, a total of 19 patients (8.4%) were diagnosed as FH, and the proportion of smoking subjects in FH patients was higher than that in non-FH patients.

Conclusions

FH-associated gene mutations were present in about 7.6% of Chinese patients with PMI. In addition to genetic factors, smoking history, lifestyle and other environmental factors may play a synergistic role in determining the phenotype of PMI.

Trial registration

Essential gene mutation of cholesterol metabolism in patients with premature myocardial infarction. ChiCTR-OCH-12002349.Registered 26 December 2014, http://www.chictr.org.cn/showproj.aspx?proj=7201.

Keywords

  • Premature myocardial infarction
  • Cholesterol metabolism
  • Familial hypercholesterolemia
  • Gene mutation

Introduction

As the most severe type of coronary artery diseases, myocardial infarction (MI) has posed a threat to public health because of its high morbidity and mortality. More than 600,000 people suffer from MI, which leads to 180,000 deaths yearly in China [1, 2]. In recent years, the incidence of premature myocardial infarction (PMI) has gradually increased [3]. It has been shown that genetics plays a central role in the development of PMI, with its heritability estimated at approximately 63% [4]. It was also reported that about 10–15% of PMI cases were caused by essential mutations in genes related to cholesterol metabolism [5]. Familial hypercholesterolemia (FH) is one of the most common genetic disorders of cholesterol metabolism [6], and the mutations in FH-associated genes, such as low-density lipoprotein receptor (LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9) and low-density lipoprotein receptor adaptor protein 1 (LDLRAP1), can increase the plasma levels of low-density lipoprotein cholesterol (LDL-C) and lead to PMI.

A previous study carried out by the author of this study showed that the prevalence of FH diagnosed by genetic testing was 4.4% [7]. However, it was also found that not all clinical phenotypes of PMI matched gene mutations. Therefore, this article aimed to investigate the relationship between FH-associated gene and the phenotype of PMI to clarify the genetic spectrum of PMI.

Materials and methods

Study population

All MI patients were enrolled at Peking University People’s Hospital between May 1, 2015 and March 31, 2017. MI, including ST-segment elevation MI and non-ST-segment elevation MI, which was defined according to the Third Universal Definition of Myocardial Infarction [8]. PMI patients (age at the first MI onset: males of ≤55 years old, or females of ≤60 years old) were included as the experimental group, while gender matched patients free of PMI (non-PMI, age at the first MI onset: males of ≤55 years old, or females of ≤60 years old) were enrolled as the control group. PMI patients with incomplete clinical data or with no blood samples were excluded. The investigational protocol of this study was approved by the ethics review board of Peking University People’s Hospital and was registered into the Chinese Clinical Trial Register (registration number: ChiCTR-OCH-12002349, registry URL: http://www.chictr.org.cn/showproj.aspx?proj=7201). All subjects provided written informed consent at the time of their enrollment. The investigational protocol was designed in accordance with CONSORT2010.

Collection of clinical and laboratory data

Data of PMI patients were retrieved from previously published studies to assess the clinical characteristics of PMI [7]. The clinical characteristics of non-PMI patients, including age, sex, body mass index (BMI), and family history of premature coronary heart disease (CHD), were collected via their medical records. Laboratory examination results, such as those of routine blood test and biochemical test carried out during the first 24 h after hospital admission, were also obtained. The severity of CHD was assessed according to the Gensini score system described in a previous study [9]. A family history of premature CHD was defined as males of < 55 years old or females of < 60 years old in the first-degree relatives.

Diagnostic criteria for FH

FH was diagnosed using “2018 Chinese expert consensus of FH screening and diagnosis” [10]. Adults who met 2 of the following criteria could be diagnosed as FH: (1) untreated LDL-C ≥ 4.7 mmol/L (180 mg/dl); (2) skin/tendon xanthoma or corneal arcus in a person of < 45 years old; (3) A first-degree relative with FH or premature arteriosclerotic cardiovascular disease (ASCVD). In addition, FH was also diagnosed by detecting the pathogenic mutations in LDLR, APOB, PCSK9 and LDLRAP1 genes.

Collection of blood cell samples

Peripheral venous blood samples were collected after patients were admitted into the hospital and were processed within 30 min of collection. Blood cells was isolated by centrifugation at 3000 rpm/min for 10 min and then transferred into new tubes, which were stored at − 80 °C until use.

DNA extraction

Genomic DNA was extracted from blood cell samples using a DNeasy Blood Kit (Tianyihuyuan, Beijing, China) according to the manufacturer’s protocol.

Sequencing and analysis of mutations

The entire exon region of LDLR, PCSK9, and LDLRAP1 genes, as well as the exon 26 of APOB gene (from 100 bp to 200 bp of p.Arg3500) located at the LDL receptor-binding site, were studied by Sanger sequencing using an ABI 3730-XL Genetic Analyzer (ABI, Foster City, CA).

The LOVD database ( https://databases.lovd.nl/shared/genes/ ), the NCBI-ClinVar database ( https://www.ncbi.nlm.nih.gov/clinvar/ ), and the NCBI-Pubmed database ( https://www.ncbi.nlm.nih.gov/pubmed ) were used to determine whether the sequenced mutations were “pathogenic” or “potentially pathogenic” mutations that have been reported. Polyphen-2 software was used to predict whether a newly found mutation was pathogenic.

Statistical analysis

SPSS19.0 software was used for analysis. Measurement data with a normal distribution were represented by X ± S and examined using independent samples t-test. Count data were expressed by (RQ) and examined using X2 tests. A logistic regression analysis model was used to evaluate the correlation between various risk factors and PMI. P < 0.05 indicated significant difference.

Results

Genetic phenotypes of PMI patients

A total of 225 PMI patients meeting the inclusion criteria were collected, including 188 males (83.6%) and 37 females (16.4%). 19 non-synonymous variants were identified in these PMI patients, including 12 pathogenic mutations and 7 benign variants (Table 1). Among these variants, 5 pathogenic variants (LDLR c.129G > C, c.1867A > T; PCSK9 c.1792G > A; LDLRAP1 c.65G > C, c.274G > A) and 4 benign variants (LDLR c.928A > T, c.2320G > A; PCSK9 c.517C > T, c.1954A > G) were discovered for the first time.
Table 1

Genetic phenotypes of PMI patients

Gene

Function

cDNA position

Protein position

Significance

 

LDLR

Missense

c.129G > C

p.Lys43Asn

likely pathogenic

Putative

LDLR

Missense

c.241C > T

p.Arg81Cys

pathogenic

Known

LDLR

Missense

c.292G > A

p.Gly98Ser

pathogenic

Known

LDLR

Missense

c.1525A > G

p.Lys509Glu

pathogenic

Known

LDLR

Missense

c.1691A > G

p.Asn564Ser

pathogenic

Known

LDLR

Missense

c.1691A > G

p.Asn564Ser

pathogenic

Known

LDLR

Missense

c.1867A > T

p.Ile623Phe

likely pathogenic

Putative

LDLR

Missense

c.2054C > T

p.Pro685Leu

pathogenic

Known

LDLR

Missense

c.2054C > T

p.Pro685Leu

pathogenic

Known

LDLR

 

c.928A > T

p.Ile310Phe

benign

Putative

LDLR

 

c.1057G > A

p.Glu353Lys

benign

Known

LDLR

 

c.1171G > A

p.Ala391Thr

benign

Known

LDLR

 

c.1516G > A

p.Val306Met

benign

Known

LDLR

 

c.2320G > A

p.Asp774Asn

benign

Putative

PCSK9

Missense

c.277C > T

p.Arg93Cys

pathogenic

Known

PCSK9

Missense

c.277C > T

p.Arg93Cys

pathogenic

Known

PCSK9

Missense

c.277C > T

p.Arg93Cys

pathogenic

Known

PCSK9

Missense

c.277C > T

p.Arg93Cys

pathogenic

Known

PCSK9

Missense

c.277C > T

p.Arg93Cys

pathogenic

Known

PCSK9

Missense

c.277C > T

p.Arg93Cys

pathogenic

Known

PCSK9

Missense

c.1792G > A

p.Ala598Thr

likely pathogenic

Putative

PCSK9

 

c.517C > T

p.Pro173Ser

benign

Putative

PCSK9

 

c.1954A > G

p.Asn652Asp

benign

Putative

APOB

Missense

c.10579C > T

p. Arg3527Trp

pathogenic

Known

LDLRAP1

Missense

c.65G > C

p.Trp22Ser

likely pathogenic

Putative

LDLRAP1

Missense

c.274G > A

p.Val92Met

likely pathogenic

Putative

LDLR low-density lipoprotein receptor, APOB apolipoprotein B, PCSK9 proprotein convertase subtilisin/kexin type 9, LDLRAP1 low-density lipoprotein receptor adaptor protein 1

Pathogenic mutations in LDLR, APOB, PCSK9 and LDLRAP1 genes were found in 17 of the 225 subjects (7.6%), and all pathogenic mutations were loss of function (LOF) and heterozygous. However, these mutations also included 7 PCSK9 LOF mutations. In contrast to LOF mutations in LDLR, APOB and LDLRAP1, PCSK9 LOF mutations could increase hepatic LDLR expressions and reduce circulating levels of LDL. One patient carried LDLR(c.129G > C), PCSK9(c.277C > T) and LDLRAP1(c.274G > A) mutations, and all other patients carried a single gene mutation. As shown in a previous study [7], the prevalence of FH was 4.4% in PMI patients diagnosed by genetic testing.

Clinical phenotypes of PMI patients

Among the 225 PMI patients, their age at MI onset was (46.64 ± 7.21) years old. In addition, the average age at MI onset in 56 non-PMI patients was (73.73 ± 6.97) years old.

Compared to non-PMI patients, the PMI patients had a higher level of LDL-C or body mass index (BMI), and were more likely to have a smoking history and a family history of premature CAD (Table 2). The logistic regression analysis showed that the differentially expressed risk factors were independent predictive factors for patients with PMI (Table 3). Although the genetic factor (a family history of premature CHD) was associated with PMI (OR = 2.840; 95% CI: 1.075–7.503; P = 0.035), its impact on PMI was relatively weak, because PMI might also be affected by BMI, smoking and other factors.
Table 2

Clinical phenotypes of patients with PMI

 

Patients with PMI (n = 225)

Non-PMI patients (n = 56)

P value

Male, n(%)

188 (83.6)

45 (80.4)

0.569

BMI (kg/m2)

26.71 ± 3.51

24.58 ± 4.12

0.001

Age of MI onset (years)

46.64 ± 7.21

73.73 ± 6.97

< 0.001

Family history of PCHD, n (%)

49 (21.8)

5 (8.9)

0.029

eGFR (ml/min/1.73m2)

96.72 (82.49, 104.27)

78.45 (67.71, 89.32)

< 0.001

LDL-C (mmol/L)

3.63 (2.97, 4.35)

3.29 (2.49, 3.86)

0.005

LVEF (%)

60.93 ± 10.25

60.67 ± 8.75

0.861

Gensini score

54 (34, 79)

58.5 (45.5, 83.5)

0.107

Multivessel lesion, n(%)

176 (78.2)

48 (85.7)

0.212

Smoking, n (%)

153 (68.0)

27 (48.2)

0.006

Hyperlipemia, n (%)

75 (33.3)

15 (26.8)

0.347

Hypertension, n (%)

116 (51.6)

35 (62.5)

0.142

Diabetes, n (%)

83 (36.9)

14 (25.0)

0.094

PMI premature myocardial infarction, BMI body mass index, PCHD premature coronary heart disease, eGFR estimated glomerular filtration rate, LDL-C low-density lipoprotein cholesterol, LVEF left ventricular ejection fraction; All P values represented the comparisons between PMI patients and non-PMI patients. Comparisons between groups were performed with the student’s t-test for continuous variables and Chi-square test for categorical variables. P < 0.05 was considered statistically significant

Table 3

Logistic regression analysis of PMI patients

Risk factors

B

OR

95%CI

P

LDL-C (mmol/L)

0.404

1.498

1.127–1.991

0.005

Family history of PCHD, n (%)

1.044

2.840

1.075–7.503

0.035

BMI (kg/m2)

0.172

1.188

1.079–1.307

< 0.001

Smoking, n (%)

0.732

2.080

1.128–3.835

0.019

PMI premature myocardial infarction, LDL-C low-density lipoprotein cholesterol, PCHD premature coronary heart disease, BMI body mass index

In this study, PMI patients were divided into 4 groups according to their age (Table 4). The proportions of males and patients with a smoking history were both > 50% in all age groups. However, with the increase in age, both the proportion of males and the proportion of patients with a smoking history decreased. Besides, patients younger than 30 years old had the highest level of LDL-C, highest Gensini scores and the highest incidence with a family history of PCHD.
Table 4

Age-related Characteristics of PMI patients

 

Age ≤ 30 (n = 9)

30 ≤ age < 40 (n = 34)

40 ≤ age < 50 (n = 105)

50 ≤ age < 60 (n = 77)

Male, n(%)

9 (100)

34 (100)

93 (88.6)

52 (67.5)

Age at MI onset (years)

28.33 ± 1.58

37.74 ± 3.02

46.33 ± 2.91

53.57 ± 2.21

BMI (kg/m2)

29.17 ± 4.53

28.42 ± 3.90

26.81 ± 3.64

25.52 ± 2.44

Family history of PCHD, n (%)

5 (55.6)

11 (32.4)

17 (16.2)

16 (20.8)

Smoking, n (%)

9 (100)

27 (79.4)

73 (69.5)

44 (57.1)

LDL-C (mmol/L)

3.98 (3.87,5.42)

3.67 (3.18,4.36)

3.61 (2.79,4.34)

3.65 (3.10, 4.40)

LVEF (%)

52.16 ± 11.96

58.92 ± 10.25

60.96 ± 10.56

62.81 ± 9.04

Gensini scores

78 (38,90)

55 (25.25,86.76)

62 (56.35,68.15)

53 (35,78)

Multivessel lesion, n(%)

6 (66.7)

27 (79.4)

84 (80)

59 (76.6)

PMI premature myocardial infarction, BMI body mass index, PCHD premature coronary heart disease, LDL-C low-density lipoprotein cholesterol, LVEF left ventricular ejection fraction

Clinical phenotypes of patients carrying mutations

Patients carrying mutations in different genes or different mutations of the same gene showed different levels of LDL-C and CHD severity (Table 5). The LDL-C level and Gensini scores were the highest in patients carrying LDLR mutations, followed by patients carrying APOB mutations (Fig. 1).
Table 5

Clinical phenotypes of LDLR, APOB, PCSK9 and LDLRAP1 gene mutations

Case

Gene

Function

Exon

Sites of the Mutation

LDL-C (mmol/L)

Gensini Scores

Case1

LDLR

Missense

3

c.241C > T

4.86

40

Case2

LDLR

Missense

3

c.292G > A

3.89

48

Case3

LDLR

Missense

10

c.1525A > G

8.00

54

Case4

LDLR

Missense

11

c.1691A > G

4.35

128

Case5

LDLR

Missense

11

c.1691A > G

7.25

100

Case6

LDLR

Missense

13

c.1867A > T

5.72

104

Case7

LDLR

Missense

14

c.2054C > T

7.74

78

Case8

LDLR

Missense

14

c.2054C > T

6.37

157

Case9

APOB

Missense

26

c.10579C > T

4.93

57

Case10

PCSK9

Missense

2

c.277C > T

3.26

40

Case11

PCSK9

Missense

2

c.277C > T

2.68

40

Case12

PCSK9

Missense

2

c.277C > T

1.66

168

Case13

PCSK9

Missense

2

c.277C > T

3.46

12

Case14

PCSK9

Missense

2

c.277C > T

3.02

65

Case15

PCSK9

Missense

3

c.1792G > A

3.60

117

Case16

LDLRAP1

Missense

1

c.65G > C

2.66

54

Case17

LDLR+PCSK9+LDLRAP1

   

2.50

62

LDLR low-density lipoprotein receptor, APOB apolipoprotein B, PCSK9 proprotein convertase subtilisin/kexin type 9, LDLRAP1 low-density lipoprotein receptor adaptor protein 1

Fig. 1
Fig. 1

LDL-C levels and Gensini scores of patients carrying mutations. LDLR, low-density lipoprotein receptor; APOB, apolipoprotein B; PCSK9, proprotein convertase subtilisin/kexin type 9; LDLRAP1, low-density lipoprotein receptor adaptor protein 1

The LDL-C level of patients carrying PCSK9 LOF mutations ranged from 1.66 mmol/L to 3.60 mmol/L, but the level of LDL-C did not match the severity of CHD. For example, the LDL-C level in Case 12 was only 1.66 mmol/L, but the Gensini score of this patient was the highest among patients carrying PCSK9 LOF mutations (Table 5). Most of these patients had a smoking history, and some of them suffered from diabetes and hypertension.

Clinical phenotypes of FH patients

Among the 225 PMI patients, 11 (4.9%) patients met at least two of the Chinese criteria for FH diagnosis. Of these patients, 2/11 (1.8%) patients were also diagnosed by genetic testing. At the end, a total of 19 patients (8.4%) were diagnosed as FH. The proportion of smoking subjects in FH patients was higher than that in non-FH patients (Table 6).
Table 6

Clinical phenotypes of FH patients

 

FH patients (n = 19)

Non-FH patients (n = 206)

P value

Male, n(%)

17 (89.5)

171 (83.0)

0.467

BMI (kg/m2)

27.82 ± 5.28

26.60 ± 3.30

0.334

Age at MI onset (years)

46.68 ± 7.97

50.43 ± 7.91

0.050

Family history of PCHD, n (%)

11 (57.9)

38 (18.4)

< 0.001

eGFR (ml/min/1.73m2)

93.95 (76.40, 101.71)

97.37 (84.17, 104.48)

0.435

LDL-C (mmol/L)

4.93 (4.80, 6.37)

3.58 (2.95, 4.21)

< 0.001

LVEF (%)

61.31 ± 12.22

60.90 ± 10.08

0.869

Gensini scores

62 (48, 92)

53.5 (32, 77.25)

0.087

Tendon xanthoma/corneal arcus n(%)

0 (0)

0 (0)

1.000

Multivessel lesion, n(%)

15 (78.9)

161 (78.2)

0.936

Smoking, n (%)

17 (89.5)

136 (66.0)

0.036

Hyperlipemia, n (%)

11 (57.9)

64 (31.1)

0.018

Hypertension, n (%)

4 (21.1)

112 (54.4)

0.005

Diabetes, n (%)

7 (36.8)

76 (36.9)

0.996

FH familial hypercholesterolemia, LDL-C low-density lipoprotein cholesterol, BMI body mass index, LVEF left ventricular ejection fraction. All P values represent comparisons between PMI patients and non-PMI patients. Comparisons between groups were performed with student’s t-test for continuous variables and with Chi-square test for categorical variables. P < 0.05 was considered statistically significant

Discussion

MI places a heavy psychological and the socioeconomic burden on “young” patients because it greatly affects the patients’ ability to work,. The registration study of acute myocardial infarction in China (CAMI) showed that PMI patients accounted for about 27.7% of all MI patients [11]. Because genetic factors play a great role in PMI, the identification of genetic variants conferring susceptibility to PMI is important for the prevention and management of this condition.

In this study, it was observed that 7.6% PMI patients carried FH-associated mutations and the value was higher than that reported in the study by Khera et al., who showed that a familial hypercholesterolemia mutation was present in 36 of 2081 (1.7%) patients with early-onset myocardial infarction [12]. The above difference may be attributed to the different geographical regions and nationalities studied in the two reports [13]. In this study, LDLR gene mutations made up the vast majority of all mutations. However, because all LDLRAP1 mutations are heterozygous, they are hence not pathogenic.

PCSK9 LOF mutations (PCSK9 c.277C > T, c.1792G > A) were also found in this study. In particular, as a mutation previously reported only in the Japanese population, the c.277C > T mutation can decrease the level of LDL-C and reduce the risk of ASCVD [14]. However, the patients in this study did not show lower levels of LDL-C and its reason might be their lifestyle and other environmental factors. In this study, the logistic regression analysis showed that smoking, obesity and family history of premature CHD were independent risk factors for PMI, suggesting that the lifestyle played an important role in the onset of PMI.

Men dominated PMI patients in most studies on PMI, including this study [15, 16], which may be attributed to the misperception that females are ‘protective’ against cardiovascular diseases. Previous studies have also shown that PMI patients were usually cigarette smokers and the proportion of PMI smokers increased over a decreasing age [17, 18], which was consistent with the result of this research. Since patients younger than 30 years were found to have more serious coronary artery lesions, both genetic (family history of PCHD) and environmental (smoking) factors play an important role in the onset of PMI.

The genotype-phenotype relationship of patients with FH-associated mutations showed high heterogeneity. Numerous studies have demonstrated that the carriers of LDLR mutations had the highest levels of LDL-C [19, 20], which was consist with the result of this research that showed the median LDL-C level in carriers of LDLR variants was 5.72 mmol/L and was higher than that in the carriers of other mutations. In addition, the coronary lesions in carriers of LDLR variants were more severe than those in carriers of other mutations. However, patients carrying the same mutation also showed obviously different levels of LDL-C and CAD severity. Such clinical heterogeneity might be attributed to the following reasons. On the one hand, the penetrance of these genes was not 100%; on the other hand, the different diets and lifestyles adopted by different patients might affect their clinical phenotypes.

In this study, the “2018 Chinese expert consensus of FH screening and diagnosis” was used first to diagnose FH. Only 2/11(1.8%) patients with clinical FH carried FH-associated gene mutations and the percentage was lower than that reported in a previous study, suggesting that different diagnostic criteria of FH may lead to different prevalence values of FH. Most studies have also found that 20–60% of subjects with phenotypic FH did not carry a causative mutation in LDLR, APOB, PCSK9 or LDLRAP1 genes, suggesting that phenotypic FH may also be induced by multiple small-effect common variants, mutations in unknown FH-associated genes, or environmental factors [21, 22]. None of the FH patients in this study had Tendon xanthoma/corneal arcus, which was consistent with the results of previous studies [23, 24].

Nevertheless, several limitations in this study need to be addressed. Firstly, the sample size in this study is small and may cause a statistical bias. Secondly, the approach used in this study to annotate missense variants by prediction algorithms may lead to misclassification in some cases, and the predicted variants need to be validated through in vitro functional experiments. Thirdly, only 4 FH-associated common genes were detected in this study, while some other rare FH-associated genes, including APOE and STAP1, were not detected.

In conclusion, FH-associated gene mutations were present in about 7.6% of Chinese PMI patients. In addition to genetic factors, smoking history, lifestyle and other environmental factors may play a synergistic role in determining the phenotype of PMI.

Notes

Abbreviations

APOB: 

Apolipoprotein B

ASCVD: 

Arteriosclerotic cardiovascular disease

BMI: 

Body mass index

CHD: 

Coronary heart disease

FH: 

Familial hypercholesterolemia

LDL-C: 

Low-density lipoprotein cholesterol

LDLR: 

Low-density lipoprotein receptor

LDLRAP1: 

Low-density lipoprotein receptor adaptor protein 1

MI: 

Myocardial infarction

PCSK9: 

Proprotein convertase subtilisin/kexin type 9

PMI: 

Premature myocardial infarction

Declarations

Acknowledgements

The authors would like to acknowledge all staffs in Peking University People’s Hospital for their help.

Funding

This study was supported by the National Natural Science Foundation of China (81770356, 81470473,), Capital Health Research and Development of Special Funds (No. 2016–2-4083).

Availability of data and materials

The datasets used in the current study are available from the corresponding author upon request.

Authors’ contributions

CL and YC wrote the manuscript; SL and JS prepared Tables 1, 2, 3, 4, 5; MW prepared Fig. 1; FZ, LLI and DH modified the manuscript; HC designed the study and modified the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Subjects have given their written informed consent. The study protocol has been approved by the research institute’s committee on human research.

Consent for publication

All authors have given consent for the publication of this paper.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Cardiology, Peking University People’s Hospital, Xizhimen South Rd. No.11, Xicheng district, Beijing, 100044, China
(2)
Beijing Key Laboratory of Early Prediction and Intervention of Acute Myocardial Infarction, Peking University People’s Hospital, Beijing, China
(3)
Center for Cardiovascular Translational Research, Peking University People’s Hospital, Beijing, China

References

  1. Zhou Y, Yao X, Liu G, Jian W, Yip W. Level and variation on quality of care in China: a cross-sectional study for the acute myocardial infarction patients in tertiary hospitals in Beijing. BMC Health Serv Res. 2019;19(1):43.View ArticleGoogle Scholar
  2. Zhang Q, Zhao D, Xie W, Xie X, Guo M, Wang M, et al. Recent trends in hospitalization for acute myocardial infarction in Beijing: increasing overall burden and a transition from ST-segment elevation to non-ST-segment elevation myocardial infarction in a population-based study. Medicine (Baltimore). 2016;95(5):e2677.View ArticleGoogle Scholar
  3. Titov BV, Osmak GJ, Matveeva NA, Kukava NG, Shakhnovich RM, Favorov AV, et al. Genetic risk factors for myocardial infarction more clearly manifest for early age of first onset. Mol Biol Rep. 2017;44(4):315–21.View ArticleGoogle Scholar
  4. Fischer M, Broeckel U, Holmer S, Baessler A, Hengstenberg C, Mayer B, et al. Distinct heritable patterns of angiographic coronary artery disease in families with myocardial infarction. Circulation. 2005;111:855–62.View ArticleGoogle Scholar
  5. Roberts R. Genetics of premature myocardial infarction. Genetics. 2008;10:186–93.Google Scholar
  6. Elkins JC, Fruh S. Early diagnosis and treatment of familial hypercholesterolemia. Nurse Pract. 2019;44(2):18–24.View ArticleGoogle Scholar
  7. Cui Y, Li S, Zhang F, Song J, Lee C, Wu M, et al. Prevalence of familial hypercholesterolemia in patients with premature myocardial infarction. Clin Cardiol. 2019;42(3):385–90.View ArticleGoogle Scholar
  8. White HD, Thygesen K, Alpert JS, Jaffe AS. Clinical implications of the third universal definition of myocardial infarction. Heart. 2014;100(5):424–32.View ArticleGoogle Scholar
  9. Li S, Zhang Y, Xu RX, Guo YL, Zhu CG, Wu NQ, et al. Proprotein convertase subtilisin-kexin type 9 as a biomarker for the severity of coronary artery disease. Ann Med. 2015;47:386–93.View ArticleGoogle Scholar
  10. Gao W, Chen H, An J, Che WL, Chen JY, Dong SH, et al. Chinese expert consensus of FH screening and diagnosis. Chinese J Cardiovasc Dis. 2018;46(2):99–103.Google Scholar
  11. Xiaojin G, Jingang Y, Yuejin Y, Li W, Xu HY, Wu Y, et al. Age-related coronary risk factors in Chinese patients with acute myocardial infarction. Natl Med J China. 2016;96:3251–6.Google Scholar
  12. Khera AV, Chaffin M, Zekavat SM, Collins RL, Roselli C, Natarajan P, et al. Whole-genome sequencing to characterize monogenic and polygenic contributions in patients hospitalized with early-onset myocardial infarction. Circulation. 2019;139(13):1593–1602.View ArticleGoogle Scholar
  13. Sun D, Zhou BY, Li S, Sun NL, Hua Q, Wu SL, et al. Genetic basis of index patients with familial hypercholesterolemia in Chinese population: mutation spectrum and genotype-phenotype correlation. Lipids Health Dis. 2018;17(1):252.View ArticleGoogle Scholar
  14. Miyake Y, Kimura R, Kokubo Y, Okayama A, Tomoike H, Yamamura T, et al. Genetic variants in PCSK9 in the Japanese population: rare genetic variants in PCSK9 might collectively contribute to plasma LDL cholesterol levels in the general population. Atherosclerosis. 2008;196(1):29–36.View ArticleGoogle Scholar
  15. Tamrakar R, Bhatt YD, Kansakar S, Bhattarai M, Shaha KB, Tuladhar E. Acute myocardial infarction in young adults: study of risk factors, angiographic features and clinical outcome. NHJ. 2013;10:12–6.Google Scholar
  16. Abed MA, Eshah NF, Moser DK. Risk profile of myocardial infarction in young versus older adults. Heart Lung. 2018;47(3):226–30.View ArticleGoogle Scholar
  17. Larsen GK, Seth M, Gurm HS. The ongoing importance of smoking as a powerful risk factor for ST-segment elevation myocardial infarction in young patients. JAMA Intern Med. 2013;173:1261–2.View ArticleGoogle Scholar
  18. Shukla AN, Jayaram AA, Doshi D, Patel P, Shah K, Shinde A, et al. The young myocardial infarction study of the Western Indians: Youth Registry Glob Heart 2019.Google Scholar
  19. Raal FJ, Sjouke B, Hovingh GK, Isaac BF. Phenotype diversity among patients with homozygous familial hypercholesterolemia: a cohort study. Atherosclerosis. 2016;248:238–44.View ArticleGoogle Scholar
  20. Cuchel M, Bruckert E, Ginsberg H, Raal FJ, Santos RD, Hegele RA, et al. Homozygous familial hypercholesterolaemia: new insights and guidance for clinicians to improve detection and clinical management. A position paper from the consensus panel on familial Hypercholesterolaemia of the European atherosclerosis society. Eur Heart J. 2014;35:2146–57.View ArticleGoogle Scholar
  21. Bañares V, Corral P, Medeiros A, Araujo MB, Lozada A, Bustamante J, et al. Preliminary spectrum of genetic variants in familial hypercholesterolemia in Argentina. J Clin Lipidol. 2017;11:524–31.View ArticleGoogle Scholar
  22. Durst R, Ibe U, Shpitzen S, Schurr D, Eliav O, Futema M, et al. Molecular genetics of familial hypercholesterolemia in Israel-revisited. Atherosclerosis. 2017;257:55–63.View ArticleGoogle Scholar
  23. Pimstone SN, Sun XM, du Souich C, Frohlich JJ, Hayden MR, Soutar AK. Phenotypic variation in hetero- zygous familial hypercholesterolemia: a comparison of Chinese patients with the same or similar mutations in the LDL receptor gene in China or Canada. Arterioscler Thromb Vasc Biol. 1998;18:309–15.View ArticleGoogle Scholar
  24. Tomlinson B, Hu M, Chow E. Current status of familial hypercholesterolemia in Chinese populations. Curr Opin Lipidol. 2019.Google Scholar

Copyright

© The Author(s). 2019

Advertisement