Characteristics of Genetically Confirmed Familial Hypercholesterolemia in Chinese Patients with Coronary Heart Disease

Characteristics of Genetically Confirmed Familial Hypercholesterolemia in Chinese Patients with Coronary Heart Disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Characteristics of Genetically Confirmed Familial Hypercholesterolemia in Chinese Patients with Coronary Heart Disease Yihan Wang, Chuang Li, Wenshu Zhao, Ying Dong, Peijia Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5243180/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted 11 You are reading this latest preprint version Abstract Background Familial hypercholesterolemia (FH) is a genetically inherited disorder caused by monogenic mutations or polygenic deleterious variants. Patients with FH innate with significantly elevated risks for coronary heart disease (CHD). FH prevalence based on genetic testing in Chinese CHD patients is missing. Whether classical index of coronary atherosclerosis severity can be used as indicators of FH needs to be explored. To investigate the FH prevalence in Chinese CHD patients and the association of SYNTAX I score with FH genotype. Methods The monogenic and polygenic FH related genes were genotyped in 400 consecutively enrolled CHD patients. The clinical characteristics and SYNTAX I scores were analyzed in a retrospective nested case-control study. Results The prevalence of genetically confirmed FH in our CHD cohort was 8.75%. The cLDL-C level, SYNTAX I scores and incidences of triple vessel lesions in FH patients were significantly higher, while cLDL-C and SYNTAX I scores were independent risk factors for FH. Furthermore, cLDL-C levels of polygenic FH were significantly lower than monogenic FH, while their severity of coronary atherosclerosis was comparable. Conclusions Our study revealed a genetically confirmed FH prevalence of 8.75% in a Chinese CHD cohort. Additionally, the SYNTAX I score was an independent risk factor for FH. Besides, polygenic origin of FH should be taken into consideration for CHD patients suspected of FH. Familial hypercholesterolemia prevalence SYNTAX I score monogenic polygenic Figures Figure 1 Figure 2 1. Introduction Familial hypercholesterolemia (FH) is a genetically inherited disorder affecting lipid metabolism, characterized by markedly elevated levels of low-density lipoprotein cholesterol (LDL-C) 1 . Four primary genes—LDL receptor (LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9), and LDLR adaptor protein 1 (LDLRAP1)—are widely recognized as the pathogenic genes for FH. Mutations in these genes account for over 99% of the reported causative mutations for FH 2 , 3 . In contrast, mutations in other potential causative genes such as Apolipoprotein (APOE), signal transducing adaptor protein family 1 (STAP1), and lysosomal acid lipase (LIPA) 4 – 8 are rare or contentious 9 , 10 . Although clinical criteria exist for diagnosing FH, genetic screening is increasingly viewed as a gold standard. However, a significant proportion of clinically diagnosed FH cases cannot be genetically confirmed. Approximately 20%-30% of clinically suspected FH cases might be attributable to polygenic variants 1 , termed as polygenic FH 11 . Polygenic FH is defined using an algorithm based on a weighted score of 12 key single nucleotide polymorphisms (12-SNP) genotypes 12 . The prevalence of heterozygous FH (HeFH), defined as patients harbouring one single mutations on either allel of the four FH related genes, in the general population is estimated to be between 0.4%-0.5% 13 , with substantially higher rates (1.6–20.3%) 13–17 observed in patients with coronary heart disease (CHD). Variations in reported HeFH rates may be due to inconsistent diagnostic criteria, varying LDL-C levels considered, and the age of first CHD incidence. Notably, only a few studies on HeFH prevalence in CHD patients have been verified through genetic testing 14 , 15 , especially in the Chinese population 14 , 15 . Furthermore, none of these studies have considered polygenic FH. This study aims to investigate the prevalence of genetically diagnosed FH in Chinese patients with CHD. The risk of myocardial infarction in FH patients is elevated by 12.5 times compared to non-FH individuals 18 . This increased risk can be attributed to the higher lifelong cumulative exposure to LDL-C, also known as the LDL-C burden, which is the sum of LDL-C levels multiplied by the number of years. This prolonged exposure significantly accelerates the development of atherosclerosis 19 , 20 . Furthermore, FH patients typically exhibit more severe atherosclerosis, characterized by multiple vessel diseases and more diffuse atherosclerotic lesions 14 , 21 , 22 . Previous research predominantly focused on clinically diagnosed FH patients, using diagnostic criteria such as the Dutch Lipid Clinic Network (DLCN) and Simon Broome Register (SBR), to evaluate the severity of atherosclerosis. However, there is a noticeable gap in studies examining the extent of atherosclerosis in genetically diagnosed FH patients. Therefore, our study aims to explore the correlation between genetic diagnoses of FH and the severity of coronary atherosclerosis. The severity can be quantitatively assessed using the Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery (SYNTAX) I score. We hypothesize that the SYNTAX I score is linked with both monogenic and polygenic forms of FH. It may also serve as a predictive tool to identify FH-related variants in CHD inpatients. The use of the SYNTAX I score as a novel index could aid in the early identification, diagnosis, and treatment of FH in CHD patients, as well as facilitate cascade screening of their relatives. 2. Method 2.1. Study population A total of 400 consecutive patients with CHD were enrolled in this study from March 2019 to January 2021. All participants underwent genetic testing. Those data were accessed for research purposes at March 2023. A control group, matched for age and sex, was established from patients with positive genetic test results, using a 1:4 matching ratio (Fig. 1 ). The inclusion criteria for participation were: (1) age between 18 and 85 years, (2) a diagnosis of CHD confirmed by angiography, and (3) a corrected LDL-C (cLDL-C) level exceeding 3.3 mmol/L (which is above the diagnosis criteria of hypercholesterolemia) or DLCN criteria score of more than 3 points, indicating at least a “Possible FH” according to DLCN criteria. Exclusion criteria included any of the following conditions: severe renal insufficiency (estimated glomerular filtration rate < 50 ml/min/1.73 m²), thyroid dysfunction, severe liver insufficiency (evidenced by alanine transaminase or aspartate aminotransferase levels exceeding three times the normal upper limit), systemic inflammatory diseases, myelomatosis, and other conditions leading to secondary hyperlipidemia or influencing the use of lipid-lowering drugs. This study adhered to the principles of the Declaration of Helsinki and received approval from the Beijing Chaoyang Hospital Ethics Committee (Ethics number: 2021-Sec-513). Written informed consent was obtained from all participants. 2.2. Clinical diagnosis of FH. The clinical diagnosis of FH was determined using the DLCN criteria 23 , excluding DNA analysis. The cLDL-C was defined as the highest value recorded before the initiation of lipid-lowering treatment (LLT). This value was either obtained from the hospital’s medical system dating back to January 1, 2000, if available, or estimated based on the potency and dosage of the LLT, using the formula cLDL-C = LDL-C × correction factor 24 . 2.3. Genetic sequencing and in silico analysis 2.3.1 Multiplex PCR and trget sequencing Genomic DNA was extracted from a 2 ml sample of peripheral blood using a DNA extraction kit (Tiangen Biotech, Beijing, China). The concentration of the extracted DNA was measured using a Qubit™ 3.0 Fluorometer (Thermo Fisher Scientific, USA). The four major genes associated with FH were individually synthesized into library DNA molecules. These molecules then underwent multiple rounds of PCR amplification and subsequent purification. The PCR amplification process was conducted using a ProFlex PCR System (Applied Biosystems, USA). For library preparation, the MultipSeq® Custom Panel (Cat: IGMU209V1) from iGeneTech Bioscience (Beijing, China) was employed, adhering to the manufacturer’s standard protocols. Sequencing by synthesis was carried out on the Illumina NovaSeq 6000 System platform. 2.3.2 Sanger sequencing Specific primers for 12-SNP sites associated with polygenic FH were designed, resulting in 12 primer pairs. These primers were used to amplify the target fragments of the sample genomic DNA using 2×Taq MasterMix (Dye) reagent (CoWin Biosciences, Jiangsu, China). Following PCR amplification, the products underwent purification. For sequencing, the BigDye™ Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, USA) was employed alongside the specific primers to synthesize DNA molecules. The sequencing process was conducted using an ABI 3730XL Genetic Analyzer. Additionally, Sanger sequencing was utilized to validate any novel mutations discovered in the target sequencing process. This approach is robust in confirming the presence and nature of genetic variations pertinent to polygenic FH. 2.3.3 Data analysis The bioinformatics analysis of the multiplex PCR sequencing data was conducted using the “Bestnovo Cloud Gene Detection and Automatic Analysis Platform”. The raw image data generated by the Illumina NovaSeq 6000 system were converted into sequence data via Base Calling and stored in the FASTQ file format. This sequencing data was then aligned with the human genome reference sequence (hg19) from the UCSC database using Burrows-Wheeler Aligner (BWA) software. The coverage of the target region and the quality of sequencing were assessed. Variant identification was performed using the HaplotypeCaller tool from the Genome Analysis Toolkit (GATK) software. Identified variants were filtered based on stringent criteria, and ANNOVAR software was used to provide relevant annotation information. For 12-SNP types, Sanger sequencing results were analyzed using Chromas 2.4.1 software (Technelysium). This comprehensive approach ensured the accurate identification and annotation of genetic variants relevant to the study. 2.3.4 Genetic analysis report Genetic analysis was performed for all participants at four exon mutation loci—namely LDLR, APOB, PCSK9, and LDLRAP1—as well as for 12-SNP detailed in Supplementary Table 1. A mutation locus was classified as pathogenic if it was listed in disease-specific databases such as the Human Gene Mutation Database (HGMD) and ClinVar. For loci not recorded in these databases and with an allele frequency of less than 0.01 in population databases, three common in silico analyses were employed: PolyPhen-2, Sorting Tolerant From Intolerant (SIFT), and Mutation Taster. To be deemed pathogenic, non-synonymous mutations needs to meet at least two of the following three criteria: (1) classified as probably damaging (D) or possibly damaging (P) in PolyPhen-2 25 , (2) deemed deleterious (D) in SIFT 26 , and (3) identified as disease causing automatic (A) or disease causing (D) in Mutation Taster 27 . Participants were categorized based on decile ranges of their 12-SNP weighted scores: a score of ≤ 0.73 points indicated low risk for polygenic hypercholesterolemia 28 ; a score between > 0.73 and < 1.16 points indicated medium risk; and a score of ≥ 1.16 points indicated high risk. All loci were validated through Sanger sequencing, and interpretations of the reports were conducted under the supervision of a sophisticated geneticist. A diagnosis of FH was made if patients exhibited at least one of the four major monogenic pathogenic mutations or had a 12-SNP weighted score of ≥ 1.16 points 28 . Conversely, a diagnosis of non-FH was assigned to patients lacking monogenic pathogenic mutations and with a 12-SNP weighted score of < 0.73 points. 2.4. Laboratory measurement On the first day following admission, 5 mL of venous blood samples which were collected from fasting patients and analyzed with a Dimension RxLMax™ automated analyzer. All biochemical indices, including the cLDL-C levels, were measured using a Hitachi 7600 automated analyzer. The cLDL-C levels were either retrieved from the hospital’s medical system, if available, or estimated using a correction factor. This process ensured accurate and standardized measurement of biochemical parameters critical for the study. 2.5. Coronary features analysis Patients with CHD were identified based on the presence of coronary stenosis: ≥50% in the left main coronary artery or ≥ 70% in at least one of the major coronary arteries, as determined by angiography 29 , 30 . The severity of CHD was assessed using the SYNTAX I score, calculated via a software tool available at www.SYNTAXscore.com . The SYNTAX I scores were independently calculated by two physicians, P.J.W. and L.C., and the average of these scores was used to minimize bias in the assessment. This method ensured a more objective and reliable evaluation of CHD severity in the study participants. 2.6. Statistical analysis Data were presented as means ± standard deviation (SD) or medians (with upper and lower quartiles) for continuous variables, and as counts (percentages) for categorical variables. For quantitative data conforming to a normal distribution, the t-test was employed for comparisons between two independent groups. In cases where the data did not follow a normal distribution, the Mann-Whitney U test was used instead. The Kruskal-Wallis test was used to compare multiple independent variables, with using Bonferroni correction, significance threshold was 0.0167 (0.05/3). Qualitative data were analyzed using the χ2-test, supplemented by Fisher’s exact probability test for correction as necessary. Univariate and multivariate logistic regression analyses were conducted to identify independent factors associated with FH. Continuous independent factors were transformed into categorical variables using cut-off values derived from receiver operating characteristic (ROC) curve analysis. These categorical variables were then utilized to develop a new diagnostic predictive model for FH using a logistic regression model. The accuracy of the cLDL-C group, SYNTAX I group, and predicted probability 1 (Pred1) as predictors of FH was compared using ROC curves to obtain the area under the curve (AUC), cut-off value, sensitivity, and specificity. A p-value of < 0.05 was considered statistically significant. All statistical analyses were performed using SPSS version 26.0 software and MedCalc version 22.016 software. 3. Result Prevalence of FH in Chinese CHD patients In this study, 400 patients with CHD were initially enrolled. Genetic testing revealed that 26 patients (6.5%) had monogenic FH as per ACMG criteria, and 9 patients (2.25%) polygenic FH. Consequently, these 35 patients (8.75%) were classified into the FH group (monogenic or high-risk polygenic FH), serving as the case group. Following adjustment for age, sex and using a matching ratio of 1:4, a control group of 140 non-FH patients was established, including 126 patients with medium-risk polygenic FH and 14 patients with low-risk polygenic FH. In total, 175 patients (83.9% of man and 17.1% of women) were included. The average age of enrolled CHD patients was 63.4 ± 9.2 years, and the mean cLDL-C level was 3.99 ± 1.01 mmol/L. SYNTAX I score could serve as potential indicators for FH diagnosis in coronary heart disease patients The baseline characteristics of the FH and non-FH groups are detailed in Table 1 . Compared to non-FH patients, the cLDL-C level in FH patients was significantly higher (4.70 ± 1.53 mmol/L) (3.81 ± 0.74 mmol/L, P = 0.002). It is worth noting that FH patients had significantly higher SYNTAX I scores (24.0 (18.0, 34.0) vs. 17.0 (10.0, 22.0), P < 0.001) and a greater prevalence of triple vessel lesions (77.1% vs. 54.3%, P = 0.014). Table 1 Baseline characteristics analysis of matched subset (N = 175) Variables FH (35) non-FH (140) P value Basic informations Age (y) 63.5 ± 9.4 63.4 ± 9.1 0.931 Male 29 (82.9%) 116 (82.9%) 1.000 BMI (kg/m 2 ) 26.1 ± 3.0 26.7 ± 4.4 0.409 Smoking 16 (45.7%) 52 (37.1%) 0.352 HR (BPM) 73.0 ± 8.5 75.1 ± 12.4 0.348 SBP (mmHg) 138.3 ± 18.4 137.7 ± 18.1 0.850 DBP (mmHg) 76.6 ± 10.6 77.8 ± 11.3 0.581 Tendon xanthoma 0 (0.0%) 0 (0.0%) - Arcus corneae 0 (0.0%) 0 (0.0%) - Family history of premature ASCVD 8 (22.9%) 42 (30.0%) 0.403 History LLT 22 (62.9%) 90 (64.3%) 0.875 Comorbidities Premature ASCVD 14 (40.0%) 77 (55.0%) 0.112 Previous MI 6 (17.1%) 20 (14.3%) 0.671 PCI history 10 (28.6%) 28 (20.0%) 0.271 CABG history 1 (2.9%) 2 (1.4%) 0.490 Stroke 7 (20.0%) 19 (13.6%) 0.339 PVD 4 (11.4%) 6 (4.3%) 0.115 Hypertension 25 (71.4%) 102 (72.9%) 0.865 Diabetes mellitus 18 (51.4%) 59 (42.1%) 0.322 Heart failure 7 (20.0%) 16 (11.4%) 0.260 Hyperuricemia 2 (5.7%) 9 (6.4%) 1.000 Biological data TG (mmol/L) 1.24 (0.88, 1.65) 1.26 (0.97, 2.01) 0.495 Treated LDL-C (mmol/L) 3.14 ± 2.07 2.44 ± 0.95 0.059 Corrected LDL-C (mmol/L) 4.70 ± 1.53 3.81 ± 0.74 0.002 Lp (a) (mg/dL) 18.10 (11.00, 28.20) 16.95 (9.83, 33.10) 0.761 HbA1C (%) 6.83 ± 1.26 6.83 ± 1.23 0.985 UA (µmol/L) 383.0 (317.0, 440.0) 344.0 (285.0, 410.8) 0.140 eGFR (ml/min/1.73m 2 ) 93.9 (83.7, 104.7) 95.0 (85.0, 101.0) 0.840 Imaging indexs IMT 2 (5.7%) 17 (12.1%) 0.372 Carotid plaque 5 (14.3%) 15 (10.7%) 0.557 SYNTAX I score 24.0 (18.0, 34.0) 17.0 (10.0, 22.0) <0.001 Triple vessel lesions 27 (77.1%) 76 (54.3%) 0.014 FH related indicators 12-SNP score 0.99 (0.58, 1.16) 0.98 (0.90, 1.04) 0.655 DLCN score 1* 2 (1, 3) 2 (0, 3) 0.350 DLCN score 2✝ 9 (2, 11) 2 (0, 3) <0.001 FH indicates familial hypercholesterolemia; BMI, body mass index; HR, heart rate; BPM, beats per minute; BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; ASCVD, atherosclerotic cardiovascular disease; LLT, lipid-lowering treatment; MI, myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; PVD, peripheral vascular disease; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; Lp (a), lipoprotein (a); HbA1C, glycated hemoglobin; UA, uric acid; eGFR, estimated glomerular filtration rate; IMT, intima-media thickness; SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery; SNP, single nucleotide polymorphism and DLCN, dutch lipid clinic network. * DLCN score 1: not include DNA analysis score in criteria. ✝DLCN score 2: include DNA analysis score in criteria. Subsequent logistic regression analysis was conducted to determine whether the parameters showing significant differences were independently associated with FH diagnosis. The univariate logistic regression analysis (Table 2 ) revealed that both the cLDL-C concentration (Odds Ratio [OR]: 2.204, P = 0.010), the SYNTAX I score (OR: 1.145, P < 0.001) as well as the presence of triple vessel lesions (OR: 2.842, P < 0.017) were significantly associated with FH. These associations persisted in the multivariate logistic regression analysis, except for the presence of triple vessel lesions (P = 0.878) (Table 2 ). The cLDL-C concentration (OR: 1.778, P < 0.001) and the SYNTAX I score (OR: 1.124, P < 0.001) maintained their statistical significance, thus confirming them as independent risk factors for an FH diagnosis. This analysis underscores the importance of these parameters in the diagnostic process for FH. Table 2 Logistic regression analysis of the influence of variables on the monogenic FH (FH = 35) Univariate logistic regression Multivariate logistic regression Variables β OR (95% CI) P β OR (95% CI) P Corrected LDL-C 0.790 2.204 (1.476–3.292) < 0.001 0.576 1.778 (1.149–2.752) 0.010 SYNTAX I score 0.135 1.145 (1.083–1.211) < 0.001 0.117 1.124 (1.059–1.193) < 0.001 Triple vessel lesions No Reference Reference Yes 1.045 2.842 (1.207–6.691) 0.017 0.078 1.081 (0.400-2.915) 0.878 FH indicates familial hypercholesterolemia; LDL-C, low-density lipoprotein cholesterol; and SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery. The study utilized ROC curve analysis to evaluate the predictive power of the cLDL-C level and SYNTAX I score in diagnosing FH (Fig. 2 a). The AUC for the cLDL-C level was 0.667 (P = 0.0023), with a determined cut-off value of 4.02 mmol/L for identifying carriers of FH variants (monogenic or polygenic FH). This cut-off value yielded a sensitivity of 57.14% and a specificity of 75.00%. The AUC for the SYNTAX I score was 0.759 (P < 0.001), with a cut-off value of 29.5, and demonstrated a sensitivity of 48.57% and a specificity of 100.00%. There was no significant difference between the AUCs of these two indicators (P = 0.1953). Based on the integer part of the cut-off values, the two predictors were converted from continuous to categorical variables (defined as cLDL-C ≥ 4 mmol/L = 1, cLDL-C < 4 mmol/L = 0, SYNTAX I score ≥ 29 = 1, SYNTAX I score < 29 = 0). A new multivariate logistic regression analysis was then performed to establish a predictive probability model. Both the cLDL-C group (P < 0.019) and the SYNTAX I group (P < 0.001) were statistically significant. The Pred1 formula was: Y = -2.491 + 1.140 × cLDL-C group + 4.098 × SYNTAX I group. Figure 2 b presented the ROC curve analysis of the cLDL-C group, SYNTAX I group, and Pred1 in predicting FH. The AUC for Pred1 was 0.795 (P < 0.001), with a cut-off value of 0.21, and a sensitivity and specificity of 48.57% and 98.57%, respectively. The AUC of Pred1 was significantly higher than that of the cLDL-C group (P < 0.001). However, there were no significant differences between the Pred1 and SYNTAX I group (P = 0.0738), or between the cLDL-C group and SYNTAX I group (P = 0.1469). Polygenic FH patients share with monogenic FH patients regarding CHD risk notwithstanding lower cLDL-C levels Within the FH group, monogenic FH patients had significantly higher cLDL-C levels (4.99 ± 1.63 vs 3.85 ± 0.73, P = 0.008), as compared to polygenic FH patients. However, their SYNTAX I scores were similar [23.3 (18.0, 32.1) vs 30.0 (16.5, 48.3), P = 0.516] (Table 3 ), suggesting similar pro-atherosclerotic tendencies between the two groups. Table 3 Characteristics analysis of monogenic and polygenic FH groups (N = 35) Variables monogenic FH (26) polygenic FH (9) P value Corrected LDL-C (mmol/L) 4.99 ± 1.63 3.85 ± 0.73 0.008 SYNTAX I score 23.3 (18.0, 32.1) 30.0 (16.5, 48.3) 0.516 DLCN score 1* 2.0 (1.0, 4.0 ) 1.0 (0.0, 2.0) 0.046 DLCN score 2✝ 10.0 (9.0, 12.3) 1.0 (0.0, 2.0) <0.001 FH indicates familial hypercholesterolemia; LDL-C, low-density lipoprotein cholesterol; SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery; and DLCN, dutch lipid clinic network. * DLCN score 1: not include DNA analysis score in criteria. ✝DLCN score 2: include DNA analysis score in criteria. Additionally, while the cLDL-C levels of high-risk polygenic FH patients were not significantly higher than those of non-FH patients, polygenic FH group exhibited significantly higher SYNTAX I scores than non-FH groups (P = 0.006 and P = 0.003, respectively, lower than the Bonferroni correctehd threshold of 0.0167) (Table 4 a and 4 b). This indicates that polygenic FH patients indeed have a higher risk than non-FH patients regarding pro-atherosclerotic tendencies. Table 4 a. Characteristics of different risk groups of polygenic FH (N = 149) Variables Corrected LDL-C (mmol/L) SYNTAX I score high risk (9) 3.85 ± 0.73 30.0 (16.5, 48.3) middle risk (126) 3.84 ± 0.75 17.0 (10.0, 22.6) low risk (14) 3.58 ± 0.61 14.0 (7.8, 20.3) P value 0.653 0.008 FH indicates familial hypercholesterolemia; LDL-C, low-density lipoprotein cholesterol; and SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery. Table 4 b. Comparison of SYNTAX I score between three risk groups of polygenic FH (N = 149) Variables P value* high risk vs. middle risk 0.006 high risk vs. low risk 0.003 middle risk vs. low risk 0.257 FH indicates familial hypercholesterolemia; and SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery. * P value is compared with Bonferroni corrected threshold of 0.0167. 4. Discussion This study delineates the clinical characteristics of FH in patients with CHD, focusing on genetically diagnosed cases. Initially, we report the prevalence of FH, including both monogenic and polygenic forms, in a Chinese CHD cohort. Furthermore, leveraging coronary angiography data, we explore the relationship between the SYNTAX I score and FH diagnosis. Notably, patients with FH exhibited significantly more severe atherosclerosis, as evidenced by higher SYNTAX I scores and a greater incidence of triple vessel disease. Additionally, the study highlights the similar severity of atherosclerosis in polygenic FH patients compared to those with monogenic FH, underscoring the necessity of further evaluating polygenic FH in patients without mutations in the classical FH-related genes. The prevalence of FH in our CHD cohort, at 8.75% (including polygenic cases), exceeds previously reported figures. However, these variations in prevalence are attributable to differing diagnostic criteria. A meta-analysis encompassing over 10 million subjects reported FH prevalence rates of 3.2% in CHD, rising to 6.7% in cases of premature CHD 13 . The inconsistency in diagnostic criteria, which often blend clinical and genetic assessments, complicates these findings. Genetic testing, considered more reliable than clinical diagnosis due to often ambiguous clinical histories, reveals different insights 31 . For example, tendon xanthomas, a significant component in the DLCN criteria, are infrequently evaluated in clinical practice. Moreover, the widespread use of statins over the past three decades has reduced the physical manifestations of FH, such as xanthomas 13 . Furthermore, some patients may experience premature CHD before receiving a later CHD diagnosis, potentially leading to an underestimation of the DLCN score prior to genetic testing. In line with this, Amor-Salamanca et al. reported an 8.7% prevalence of FH in a cohort of acute coronary syndrome patients using genetic testing, paralleling our findings and suggesting the need for larger cohorts for further validation 32 . Additionally, data on FH prevalence among Chinese CHD patients are limited, with previous studies indicating FH prevalence rates ranging from 4.4–7.6% in premature myocardial infarction cases 15 , 33 . Our study is the first to present the prevalence of genetically diagnosed FH in a general CHD patient population. This study also observed more severe atherosclerosis in FH patients, as indicated by higher SYNTAX I scores. Additionally, FH patients exhibited more diffuse lesions, although this difference was not statistically significant following logistic regression analysis. It is well-documented that FH patients often experience more severe CHD than non-FH individuals, characterized by multiple vessel involvement 21 and more severe atherosclerosis 14 , 22 . For instance, Ranshaka Auckle et al. 21 demonstrated that among 498 patients with premature ST-segment-elevation myocardial infarction (STEMI), those with possible, probable, or definite FH (according to DLCN criteria) had a higher likelihood of multivessel stenosis compared to those unlikely to have FH. Similarly, Jian-Jun Li et al. 14 found a positive correlation between lesion severity, as assessed by the Gensini score (GS), and the degree of clinically diagnosed FH. Michel Farnier et al. 22 showed that patients with probable or definite FH had significantly higher SYNTAX scores than those unlikely to have FH in a study of 233 patients with acute myocardial infarction. In our study, genetic testing, which is the gold standard for diagnosing FH, was used, enhancing the validity of these associations. However, genetic testing is not routinely employed in clinical practice, leading to a low diagnosis rate of genetic FH (less than 1%) 34 . Therefore, the SYNTAX I score could serve as a simple yet effective indicator for identifying potential FH patients, enabling physicians to initiate appropriate diagnostic procedures for FH and determine LDL-C management goals. Our study found that monogenic and polygenic FH accounted for 74.3% and 25.7% of FH cases within CHD patients, respectively. Notably, while average LDL-C levels were higher in monogenic FH patients, SYNTAX I scores did not significantly differ between monogenic and polygenic FH patients. Moreover, SYNTAX I scores were significantly higher in polygenic FH patients compared to non-FH patients. This finding suggests that, in addition to monogenic FH, polygenic FH should not be overlooked. In clinical practice, a substantial number of patients present with significantly elevated LDL-C levels but lack monogenic pathogenic mutations 1 . It is estimated that only 60–70% of patients classified as probable or definite FH according to DLCN criteria actually harbor FH-related mutations 35 . The observation that polygenic FH patients exhibit a similar severity of atherosclerosis as monogenic FH patients underscores the importance of screening for polygenic FH in patients with probable or definite FH or CHD patients with severe atherosclerosis. 5. Limitations Firstly, it is important to note that not all pre-treatment LDL-C concentrations were accessible in this study. For patients without available objective pre-treatment LDL-C data, an estimated formula, as outlined in the methods section, was utilized to calculate the cLDL-C. This estimation could potentially impact the accuracy of the LDL-C levels. However, such estimation techniques are commonly accepted in cholesterol-related research and are generally considered reliable proxies for true LDL-C levels. Secondly, the SYNTAX I score can exhibit variability due to intra-observer and inter-observer heterogeneity 35 , 3 . However, in this study, no significant differences were observed between the SYNTAX I scores assessed by two different physicians. The use of an average SYNTAX I score value in our analysis aimed to mitigate this potential source of bias. Lastly, it should be acknowledged that this was a single-center retrospective study. Consequently, the calculated prevalence of FH may not fully represent the broader population. Further research involving larger and more diverse cohorts is needed to validate and refine these findings. 6. Conclusions The study revealed that the prevalence of genetically diagnosed FH patients in the cohort was 8.75%, encompassing both monogenic and polygenic forms of the condition. Furthermore, the SYNTAX I score was identified as an independent predictor of the FH genotype. Notably, patients with polygenic FH demonstrated a similar severity of atherosclerosis when compared to those with monogenic FH, indicating the importance of considering both types in the evaluation and management of patients with suspected FH. Abbreviations FH: Familial hypercholesterolemia; CHD: coronary heart disease; LDLR: LDL receptor; APOB: apolipoprotein B; PCSK9: proprotein convertase subtilisin/kexin type 9; LDLRAP1: LDLR adaptor protein 1; APOE: Apolipoprotein; STAP1: signal transducing adaptor protein family 1; LIPA: lysosomal acid lipase; HeFH: heterozygous FH; DLCN: Dutch Lipid Clinic Network; SBR: Simon Broome Register; SYNTAX I: Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery I Declarations Acknowledgements Not applicable. Author’s contributions P.W and Y.D contributed to the conception and design of the work. Y.W and P.W drafted the manuscript. Y. W and C. L contributed to the data collection for the work. Y.D and W.Z contributed to the data analysis and interpretation for the work. W. Z, and Y.D critically revised the manuscript. All authors critically reviewed the manuscript and gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy. Funding None. Availability of data and materials The datasets generated and/or analysed during the current study are not publicly available due to the restrictions of human genetics data policy of Beijing Chaoyang Hospital Ethics Committee, but are available from the corresponding author on reasonable request. Ethics approval and consent for participate The study protocol was approved by the ethics committee of Beijing Chaoyang Hospital, Capital Medical University and performed in accordance with the ethical standers laid down in the 1964 Declaration of Helsinki and its later amendments. Written informed consents were obtained from all participants. Consent for publication Not applicable. Clinical trial number Not applicable. Competing interests None . References Berberich AJ, Hegele RA. The complex molecular genetics of familial hypercholesterolaemia. Nat Rev Cardiol. 2019;16:9-20. Fellin R, Arca M, Zuliani G, Calandra S, Bertolini S. The history of Autosomal Recessive Hypercholesterolemia (ARH). From clinical observations to gene identification. Gene. 2015;555:23-32. Futema M, Plagnol V, Whittall RA, et al. Use of targeted exome sequencing as a diagnostic tool for Familial Hypercholesterolaemia. J Med Genet. 2012;49:644-649. Marduel M, Ouguerram K, Serre V, et al. Description of a large family with autosomal dominant hypercholesterolemia associated with the APOE p.Leu167del mutation. Hum Mutat. 2013;34:83-87. Fouchier SW, Dallinga-Thie GM, Meijers JC, et al. Mutations in STAP1 are associated with autosomal dominant hypercholesterolemia. Circ Res. 2014;115:552-555. 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Teslovich TM MK, Smith AV, Edmondson AC, Stylianou IM, Koseki M, Pirruccello JP, Ripatti S, Chasman DI, Willer CJ, Johansen CT, Fouchier SW, Isaacs A, Peloso GM, Barbalic M, Ricketts SL, Bis JC, Aulchenko YS, Thorleifsson G, Feitosa MF, Chambers J, Orho-Melander M, Melander O, Johnson T, Li X, Guo X, Li M, Shin Cho Y, Jin Go M, Jin Kim Y, Lee JY, Park T, Kim K, Sim X, Twee-Hee Ong R, Croteau-Chonka DC, Lange LA, Smith JD, Song K, Hua Zhao J, Yuan X, Luan J, Lamina C, Ziegler A, Zhang W, Zee RY, Wright AF, Witteman JC, Wilson JF, Willemsen G, Wichmann HE, Whitfield JB, Waterworth DM, Wareham NJ, Waeber G, Vollenweider P, Voight BF, Vitart V, Uitterlinden AG, Uda M, Tuomilehto J, Thompson JR, Tanaka T, Surakka I, Stringham HM, Spector TD, Soranzo N, Smit JH, Sinisalo J, Silander K, Sijbrands EJ, Scuteri A, Scott J, Schlessinger D, Sanna S, Salomaa V, Saharinen J, Sabatti C, Ruokonen A, Rudan I, Rose LM, Roberts R, Rieder M, Psaty BM, Pramstaller PP, Pichler I, Perola M, Penninx BW, Pedersen NL, Pattaro C, Parker AN, Pare G, Oostra BA, O'Donnell CJ, Nieminen MS, Nickerson DA, Montgomery GW, Meitinger T, McPherson R, McCarthy MI, McArdle W, Masson D, Martin NG, Marroni F, Mangino M, Magnusson PK, Lucas G, Luben R, Loos RJ, Lokki ML, Lettre G, Langenberg C, Launer LJ, Lakatta EG, Laaksonen R, Kyvik KO, Kronenberg F, König IR, Khaw KT, Kaprio J, Kaplan LM, Johansson A, Jarvelin MR, Janssens AC, Ingelsson E, Igl W, Kees Hovingh G, Hottenga JJ, Hofman A, Hicks AA, Hengstenberg C, Heid IM, Hayward C, Havulinna AS, Hastie ND, Harris TB, Haritunians T, Hall AS, Gyllensten U, Guiducci C, Groop LC, Gonzalez E, Gieger C, Freimer NB, Ferrucci L, Erdmann J, Elliott P, Ejebe KG, Döring A, Dominiczak AF, Demissie S, Deloukas P, de Geus EJ, de Faire U, Crawford G, Collins FS, Chen YD, Caulfield MJ, Campbell H, Burtt NP, Bonnycastle LL, Boomsma DI, Boekholdt SM, Bergman RN, Barroso I, Bandinelli S, Ballantyne CM, Assimes TL, Quertermous T, Altshuler D, Seielstad M, Wong TY, Tai ES, Feranil AB, Kuzawa CW, Adair LS, Taylor HA Jr, Borecki IB, Gabriel SB, Wilson JG, Holm H, Thorsteinsdottir U, Gudnason V, Krauss RM, Mohlke KL, Ordovas JM, Munroe PB, Kooner JS, Tall AR, Hegele RA, Kastelein JJ, Schadt EE, Rotter JI, Boerwinkle E, Strachan DP, Mooser V, Stefansson K, Reilly MP, Samani NJ, Schunkert H, Cupples LA, Sandhu MS, Ridker PM, Rader DJ, van Duijn CM, Peltonen L, Abecasis GR, Boehnke M, Kathiresan S. Biological, clinical and population relevance of 95 loci for blood lipids. Nature. 2010;466:707-713. Futema M, Bourbon M, Williams M, Humphries SE. Clinical utility of the polygenic LDL-C SNP score in familial hypercholesterolemia. Atherosclerosis. 2018;277:457-463. Beheshti SO, Madsen CM, Varbo A, Nordestgaard BG. Worldwide Prevalence of Familial Hypercholesterolemia: Meta-Analyses of 11 Million Subjects. J Am Coll Cardiol. 2020;75:2553-2566. Li JJ, Li S, Zhu CG, et al. Familial Hypercholesterolemia Phenotype in Chinese Patients Undergoing Coronary Angiography. Arterioscler Thromb Vasc Biol. 2017;37:570-579. Cui Y, Li S, Zhang F, et al. Prevalence of familial hypercholesterolemia in patients with premature myocardial infarction. Clinical Cardiology. 2019;42:385-390. Nanchen D, Gencer B, Auer R, et al. Prevalence and management of familial hypercholesterolaemia in patients with acute coronary syndromes. Eur Heart J. 2015;36:2438-2445. Rallidis LS, Triantafyllis AS, Tsirebolos G, et al. Prevalence of heterozygous familial hypercholesterolaemia and its impact on long-term prognosis in patients with very early ST-segment elevation myocardial infarction in the era of statins. Atherosclerosis. 2016;249:17-21. Schmidt EB, Hedegaard BS, Retterstol K. Familial hypercholesterolaemia: history, diagnosis, screening, management and challenges. Heart. 2020;106:1940-1946. Vuorio A, Docherty KF, Humphries SE, Kuoppala J, Kovanen PT. Statin treatment of children with familial hypercholesterolemia-trying to balance incomplete evidence of long-term safety and clinical accountability: are we approaching a consensus? Atherosclerosis. 2013;226:315-320. Ference BA, Ginsberg HN, Graham I, et al. Low-density lipoproteins cause atherosclerotic cardiovascular disease. 1. Evidence from genetic, epidemiologic, and clinical studies. A consensus statement from the European Atherosclerosis Society Consensus Panel. Eur Heart J. 2017;38:2459-2472. Auckle R, Su B, Li H, et al. Familial hypercholesterolemia in Chinese patients with premature ST-segment-elevation myocardial infarction: Prevalence, lipid management and 1-year follow-up. PLoS One. 2017;12:e0186815. Farnier M, Salignon-Vernay C, Yao H, et al. Prevalence, risk factor burden, and severity of coronary artery disease in patients with heterozygous familial hypercholesterolemia hospitalized for an acute myocardial infarction: Data from the French RICO survey. J Clin Lipidol. 2019;13:601-607. Nordestgaard BG, Chapman MJ, Humphries SE, et al. Familial hypercholesterolaemia is underdiagnosed and undertreated in the general population: guidance for clinicians to prevent coronary heart disease: consensus statement of the European Atherosclerosis Society. Eur Heart J. 2013;34:3478-3490a. Haralambos K, Whatley SD, Edwards R, et al. Clinical experience of scoring criteria for Familial Hypercholesterolaemia (FH) genetic testing in Wales. Atherosclerosis. 2015;240:190-196. Adzhubei IA, Schmidt S, Peshkin L, et al. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7:248-249. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31:3812-3814. Schwarz JM, Rodelsperger C, Schuelke M, Seelow D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods. 2010;7:575-576. Talmud PJ, Shah S, Whittall R, et al. Use of low-density lipoprotein cholesterol gene score to distinguish patients with polygenic and monogenic familial hypercholesterolaemia: a case-control study. The Lancet. 2013;381:1293-1301. Mangla A, Oliveros E, Williams KA, Kalra DK. Cardiac Imaging in the Diagnosis of Coronary Artery Disease. Current Problems in Cardiology. 2017;42:316-366. Task Force M, Montalescot G, Sechtem U, et al. 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J. 2013;34:2949-3003. Kindt I, Mata P, Knowles JW. The role of registries and genetic databases in familial hypercholesterolemia. Current Opinion in Lipidology. 2017;28:152-160. Amor-Salamanca A, Castillo S, Gonzalez-Vioque E, et al. Genetically Confirmed Familial Hypercholesterolemia in Patients With Acute Coronary Syndrome. Journal of the American College of Cardiology. 2017;70:1732-1740. Lee C, Cui Y, Song J, et al. Effects of familial hypercholesterolemia-associated genes on the phenotype of premature myocardial infarction. Lipids in Health and Disease. 2019;18. Nordestgaard BG, Benn M. Genetic testing for familial hypercholesterolaemia is essential in individuals with high LDL cholesterol: who does it in the world? Eur Heart J. 2017;38:1580-1583. Catapano AL, Graham I, De Backer G, et al. 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. Eur Heart J. 2016;37:2999-3058. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Dec, 2024 Read the published version in BMC Cardiovascular Disorders → Version 1 posted Editorial decision: Revision requested 05 Dec, 2024 Reviews received at journal 12 Nov, 2024 Reviewers agreed at journal 12 Nov, 2024 Reviews received at journal 07 Nov, 2024 Reviewers agreed at journal 05 Nov, 2024 Reviewers agreed at journal 05 Nov, 2024 Reviewers invited by journal 04 Nov, 2024 Editor invited by journal 17 Oct, 2024 Editor assigned by journal 17 Oct, 2024 Submission checks completed at journal 17 Oct, 2024 First submitted to journal 10 Oct, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5243180","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":368042971,"identity":"edff6dc9-6ecd-48a3-871b-5cc066d7aeef","order_by":0,"name":"Yihan Wang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yihan","middleName":"","lastName":"Wang","suffix":""},{"id":368042972,"identity":"0032232e-06c0-4017-aac1-40c616e7a2e8","order_by":1,"name":"Chuang Li","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chuang","middleName":"","lastName":"Li","suffix":""},{"id":368042973,"identity":"0c43e076-3cbf-410b-afcf-a34a0edc8ba1","order_by":2,"name":"Wenshu Zhao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenshu","middleName":"","lastName":"Zhao","suffix":""},{"id":368042975,"identity":"03e522fe-2a40-46ea-bca1-8688517ecd78","order_by":3,"name":"Ying Dong","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Dong","suffix":""},{"id":368042977,"identity":"a8b4e036-7368-453f-a1de-8102c1167f23","order_by":4,"name":"Peijia Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYFACxgaDBCjrQUJFjRwbe/sBorUwG3w4c8yYj+dMAtH2sUnObGNOnCfhYIBXmTl7c0PBgxqGxP7Z7RekedjY0tskGBIYflRsw6nFsucg0GHHGBJn3DlTYMzDI5PbJt14gLHnzG2cWgxuJAK1sDHkNtzISUjmkWDLbZM5kMDM2IZHy/2HQC3/GHLnA7Uc5jFgTmeTSDDAr+UGMMQS2xhyN9xIP9g4I4E5gaAWyx6gwxL7GOo33shhZvhw4JhhGzCQD+Lzizn78WeGP74xGMvdSH/+I/Ffjbx8e/vBBz8q8DgMGB1A/B/I5EFExwGc6iFamB9AmOwP8CkcBaNgFIyCEQwAqrheR/UuA90AAAAASUVORK5CYII=","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Peijia","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-10-11 03:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5243180/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5243180/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12872-024-04428-3","type":"published","date":"2024-12-21T15:57:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67288478,"identity":"82be6dd2-02a5-4921-a2a5-a66ee053bcc3","added_by":"auto","created_at":"2024-10-23 09:50:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of patients selection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFH indicates familial hypercholesterolemia; cLDL-C, corrected low-density lipoprotein cholesterol; CHD, coronary heart disease; SNP, single nucleotide polymorphism; and DLCN, dutch lipid clinic network.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5243180/v1/30caa7a804e875af4ccccdc0.png"},{"id":67288487,"identity":"3711d064-0403-4cc5-9c87-61083da6dd01","added_by":"auto","created_at":"2024-10-23 09:50:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":365140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceive operating characteristic (ROC) curve analysis of predictability for FH\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) ROC curve analysis of SYNTAX I score and corrected LDL-C in predcting genetically confirmed FH; (B) ROC curve analysis of SYNTAX I score group, corrected LDL-C group and predicted probability (Pred1) in predcting genetically confirmed FH.\u003c/p\u003e\n\u003cp\u003eFH indicates familial hypercholesterolemia; LDL-C, low-density lipoprotein cholesterol; SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery; and Pred1, predicted probability.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5243180/v1/a3c2f89e26bc89be0b2144e8.png"},{"id":72201886,"identity":"17f48f30-c4b3-4fb2-b2e6-e11d441fe84f","added_by":"auto","created_at":"2024-12-23 16:11:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1118385,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5243180/v1/301a05ec-e7b2-431f-806a-c4476d91ef9d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characteristics of Genetically Confirmed Familial Hypercholesterolemia in Chinese Patients with Coronary Heart Disease","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFamilial hypercholesterolemia (FH) is a genetically inherited disorder affecting lipid metabolism, characterized by markedly elevated levels of low-density lipoprotein cholesterol (LDL-C) \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Four primary genes\u0026mdash;LDL receptor (LDLR), apolipoprotein B (APOB), proprotein convertase subtilisin/kexin type 9 (PCSK9), and LDLR adaptor protein 1 (LDLRAP1)\u0026mdash;are widely recognized as the pathogenic genes for FH. Mutations in these genes account for over 99% of the reported causative mutations for FH\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In contrast, mutations in other potential causative genes such as Apolipoprotein (APOE), signal transducing adaptor protein family 1 (STAP1), and lysosomal acid lipase (LIPA) \u003csup\u003e\u003cspan additionalcitationids=\"CR5 CR6 CR7\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e are rare or contentious\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough clinical criteria exist for diagnosing FH, genetic screening is increasingly viewed as a gold standard. However, a significant proportion of clinically diagnosed FH cases cannot be genetically confirmed. Approximately 20%-30% of clinically suspected FH cases might be attributable to polygenic variants\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, termed as polygenic FH\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Polygenic FH is defined using an algorithm based on a weighted score of 12 key single nucleotide polymorphisms (12-SNP) genotypes\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe prevalence of heterozygous FH (HeFH), defined as patients harbouring one single mutations on either allel of the four FH related genes, in the general population is estimated to be between 0.4%-0.5%\u003csup\u003e13\u003c/sup\u003e, with substantially higher rates (1.6\u0026ndash;20.3%)\u003csup\u003e13\u0026ndash;17\u003c/sup\u003e observed in patients with coronary heart disease (CHD). Variations in reported HeFH rates may be due to inconsistent diagnostic criteria, varying LDL-C levels considered, and the age of first CHD incidence. Notably, only a few studies on HeFH prevalence in CHD patients have been verified through genetic testing\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, especially in the Chinese population\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Furthermore, none of these studies have considered polygenic FH. This study aims to investigate the prevalence of genetically diagnosed FH in Chinese patients with CHD.\u003c/p\u003e \u003cp\u003eThe risk of myocardial infarction in FH patients is elevated by 12.5 times compared to non-FH individuals\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This increased risk can be attributed to the higher lifelong cumulative exposure to LDL-C, also known as the LDL-C burden, which is the sum of LDL-C levels multiplied by the number of years. This prolonged exposure significantly accelerates the development of atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Furthermore, FH patients typically exhibit more severe atherosclerosis, characterized by multiple vessel diseases and more diffuse atherosclerotic lesions\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious research predominantly focused on clinically diagnosed FH patients, using diagnostic criteria such as the Dutch Lipid Clinic Network (DLCN) and Simon Broome Register (SBR), to evaluate the severity of atherosclerosis. However, there is a noticeable gap in studies examining the extent of atherosclerosis in genetically diagnosed FH patients. Therefore, our study aims to explore the correlation between genetic diagnoses of FH and the severity of coronary atherosclerosis. The severity can be quantitatively assessed using the Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery (SYNTAX) I score.\u003c/p\u003e \u003cp\u003eWe hypothesize that the SYNTAX I score is linked with both monogenic and polygenic forms of FH. It may also serve as a predictive tool to identify FH-related variants in CHD inpatients. The use of the SYNTAX I score as a novel index could aid in the early identification, diagnosis, and treatment of FH in CHD patients, as well as facilitate cascade screening of their relatives.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study population\u003c/h2\u003e \u003cp\u003eA total of 400 consecutive patients with CHD were enrolled in this study from March 2019 to January 2021. All participants underwent genetic testing. Those data were accessed for research purposes at March 2023. A control group, matched for age and sex, was established from patients with positive genetic test results, using a 1:4 matching ratio (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe inclusion criteria for participation were: (1) age between 18 and 85 years, (2) a diagnosis of CHD confirmed by angiography, and (3) a corrected LDL-C (cLDL-C) level exceeding 3.3 mmol/L (which is above the diagnosis criteria of hypercholesterolemia) or DLCN criteria score of more than 3 points, indicating at least a \u0026ldquo;Possible FH\u0026rdquo; according to DLCN criteria. Exclusion criteria included any of the following conditions: severe renal insufficiency (estimated glomerular filtration rate\u0026thinsp;\u0026lt;\u0026thinsp;50 ml/min/1.73 m\u0026sup2;), thyroid dysfunction, severe liver insufficiency (evidenced by alanine transaminase or aspartate aminotransferase levels exceeding three times the normal upper limit), systemic inflammatory diseases, myelomatosis, and other conditions leading to secondary hyperlipidemia or influencing the use of lipid-lowering drugs. This study adhered to the principles of the Declaration of Helsinki and received approval from the Beijing Chaoyang Hospital Ethics Committee (Ethics number: 2021-Sec-513). Written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Clinical diagnosis of FH.\u003c/h2\u003e \u003cp\u003eThe clinical diagnosis of FH was determined using the DLCN criteria\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, excluding DNA analysis. The cLDL-C was defined as the highest value recorded before the initiation of lipid-lowering treatment (LLT). This value was either obtained from the hospital\u0026rsquo;s medical system dating back to January 1, 2000, if available, or estimated based on the potency and dosage of the LLT, using the formula cLDL-C\u0026thinsp;=\u0026thinsp;LDL-C \u0026times; correction factor\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Genetic sequencing and in silico analysis\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Multiplex PCR and trget sequencing\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from a 2 ml sample of peripheral blood using a DNA extraction kit (Tiangen Biotech, Beijing, China). The concentration of the extracted DNA was measured using a Qubit\u0026trade; 3.0 Fluorometer (Thermo Fisher Scientific, USA).\u003c/p\u003e \u003cp\u003eThe four major genes associated with FH were individually synthesized into library DNA molecules. These molecules then underwent multiple rounds of PCR amplification and subsequent purification. The PCR amplification process was conducted using a ProFlex PCR System (Applied Biosystems, USA). For library preparation, the MultipSeq\u0026reg; Custom Panel (Cat: IGMU209V1) from iGeneTech Bioscience (Beijing, China) was employed, adhering to the manufacturer\u0026rsquo;s standard protocols. Sequencing by synthesis was carried out on the Illumina NovaSeq 6000 System platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Sanger sequencing\u003c/h2\u003e \u003cp\u003eSpecific primers for 12-SNP sites associated with polygenic FH were designed, resulting in 12 primer pairs. These primers were used to amplify the target fragments of the sample genomic DNA using 2\u0026times;Taq MasterMix (Dye) reagent (CoWin Biosciences, Jiangsu, China). Following PCR amplification, the products underwent purification.\u003c/p\u003e \u003cp\u003eFor sequencing, the BigDye\u0026trade; Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems, USA) was employed alongside the specific primers to synthesize DNA molecules. The sequencing process was conducted using an ABI 3730XL Genetic Analyzer. Additionally, Sanger sequencing was utilized to validate any novel mutations discovered in the target sequencing process. This approach is robust in confirming the presence and nature of genetic variations pertinent to polygenic FH.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Data analysis\u003c/h2\u003e \u003cp\u003eThe bioinformatics analysis of the multiplex PCR sequencing data was conducted using the \u0026ldquo;Bestnovo Cloud Gene Detection and Automatic Analysis Platform\u0026rdquo;. The raw image data generated by the Illumina NovaSeq 6000 system were converted into sequence data via Base Calling and stored in the FASTQ file format. This sequencing data was then aligned with the human genome reference sequence (hg19) from the UCSC database using Burrows-Wheeler Aligner (BWA) software. The coverage of the target region and the quality of sequencing were assessed.\u003c/p\u003e \u003cp\u003eVariant identification was performed using the HaplotypeCaller tool from the Genome Analysis Toolkit (GATK) software. Identified variants were filtered based on stringent criteria, and ANNOVAR software was used to provide relevant annotation information. For 12-SNP types, Sanger sequencing results were analyzed using Chromas 2.4.1 software (Technelysium). This comprehensive approach ensured the accurate identification and annotation of genetic variants relevant to the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Genetic analysis report\u003c/h2\u003e \u003cp\u003eGenetic analysis was performed for all participants at four exon mutation loci\u0026mdash;namely LDLR, APOB, PCSK9, and LDLRAP1\u0026mdash;as well as for 12-SNP detailed in Supplementary Table\u0026nbsp;1. A mutation locus was classified as pathogenic if it was listed in disease-specific databases such as the Human Gene Mutation Database (HGMD) and ClinVar. For loci not recorded in these databases and with an allele frequency of less than 0.01 in population databases, three common in silico analyses were employed: PolyPhen-2, Sorting Tolerant From Intolerant (SIFT), and Mutation Taster. To be deemed pathogenic, non-synonymous mutations needs to meet at least two of the following three criteria: (1) classified as probably damaging (D) or possibly damaging (P) in PolyPhen-2\u003csup\u003e25\u003c/sup\u003e, (2) deemed deleterious (D) in SIFT\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, and (3) identified as disease causing automatic (A) or disease causing (D) in Mutation Taster\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Participants were categorized based on decile ranges of their 12-SNP weighted scores: a score of \u0026le;\u0026thinsp;0.73 points indicated low risk for polygenic hypercholesterolemia\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e; a score between \u0026gt;\u0026thinsp;0.73 and \u0026lt;\u0026thinsp;1.16 points indicated medium risk; and a score of \u0026ge;\u0026thinsp;1.16 points indicated high risk. All loci were validated through Sanger sequencing, and interpretations of the reports were conducted under the supervision of a sophisticated geneticist.\u003c/p\u003e \u003cp\u003eA diagnosis of FH was made if patients exhibited at least one of the four major monogenic pathogenic mutations or had a 12-SNP weighted score of \u0026ge;\u0026thinsp;1.16 points\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Conversely, a diagnosis of non-FH was assigned to patients lacking monogenic pathogenic mutations and with a 12-SNP weighted score of \u0026lt;\u0026thinsp;0.73 points.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Laboratory measurement\u003c/h2\u003e \u003cp\u003eOn the first day following admission, 5 mL of venous blood samples which were collected from fasting patients and analyzed with a Dimension RxLMax\u0026trade; automated analyzer. All biochemical indices, including the cLDL-C levels, were measured using a Hitachi 7600 automated analyzer. The cLDL-C levels were either retrieved from the hospital\u0026rsquo;s medical system, if available, or estimated using a correction factor. This process ensured accurate and standardized measurement of biochemical parameters critical for the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Coronary features analysis\u003c/h2\u003e \u003cp\u003ePatients with CHD were identified based on the presence of coronary stenosis: \u0026ge;50% in the left main coronary artery or \u0026ge;\u0026thinsp;70% in at least one of the major coronary arteries, as determined by angiography\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. The severity of CHD was assessed using the SYNTAX I score, calculated via a software tool available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.SYNTAXscore.com\" target=\"_blank\"\u003ewww.SYNTAXscore.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.SYNTAXscore.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The SYNTAX I scores were independently calculated by two physicians, P.J.W. and L.C., and the average of these scores was used to minimize bias in the assessment. This method ensured a more objective and reliable evaluation of CHD severity in the study participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eData were presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or medians (with upper and lower quartiles) for continuous variables, and as counts (percentages) for categorical variables. For quantitative data conforming to a normal distribution, the t-test was employed for comparisons between two independent groups. In cases where the data did not follow a normal distribution, the Mann-Whitney U test was used instead. The Kruskal-Wallis test was used to compare multiple independent variables, with using Bonferroni correction, significance threshold was 0.0167 (0.05/3). Qualitative data were analyzed using the χ2-test, supplemented by Fisher\u0026rsquo;s exact probability test for correction as necessary.\u003c/p\u003e \u003cp\u003eUnivariate and multivariate logistic regression analyses were conducted to identify independent factors associated with FH. Continuous independent factors were transformed into categorical variables using cut-off values derived from receiver operating characteristic (ROC) curve analysis. These categorical variables were then utilized to develop a new diagnostic predictive model for FH using a logistic regression model. The accuracy of the cLDL-C group, SYNTAX I group, and predicted probability 1 (Pred1) as predictors of FH was compared using ROC curves to obtain the area under the curve (AUC), cut-off value, sensitivity, and specificity. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant. All statistical analyses were performed using SPSS version 26.0 software and MedCalc version 22.016 software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cp\u003e \u003cb\u003ePrevalence of FH in Chinese CHD patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn this study, 400 patients with CHD were initially enrolled. Genetic testing revealed that 26 patients (6.5%) had monogenic FH as per ACMG criteria, and 9 patients (2.25%) polygenic FH. Consequently, these 35 patients (8.75%) were classified into the FH group (monogenic or high-risk polygenic FH), serving as the case group. Following adjustment for age, sex and using a matching ratio of 1:4, a control group of 140 non-FH patients was established, including 126 patients with medium-risk polygenic FH and 14 patients with low-risk polygenic FH. In total, 175 patients (83.9% of man and 17.1% of women) were included. The average age of enrolled CHD patients was 63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.2 years, and the mean cLDL-C level was 3.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.01 mmol/L.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSYNTAX I score could serve as potential indicators for FH diagnosis in coronary heart disease patients\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe baseline characteristics of the FH and non-FH groups are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Compared to non-FH patients, the cLDL-C level in FH patients was significantly higher (4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53 mmol/L) (3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 mmol/L, P\u0026thinsp;=\u0026thinsp;0.002). It is worth noting that FH patients had significantly higher SYNTAX I scores (24.0 (18.0, 34.0) vs. 17.0 (10.0, 22.0), P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and a greater prevalence of triple vessel lesions (77.1% vs. 54.3%, P\u0026thinsp;=\u0026thinsp;0.014).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics analysis of matched subset (N\u0026thinsp;=\u0026thinsp;175)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFH (35)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003enon-FH (140)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic informations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (y)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.4\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116 (82.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.1\u0026thinsp;\u0026plusmn;\u0026thinsp;3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52 (37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.352\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR (BPM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.348\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.3\u0026thinsp;\u0026plusmn;\u0026thinsp;18.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e137.7\u0026thinsp;\u0026plusmn;\u0026thinsp;18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.6\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTendon xanthoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArcus corneae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history of premature ASCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (22.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistory LLT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90 (64.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePremature ASCVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77 (55.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious MI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCI history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (28.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCABG history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStroke\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (13.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePVD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.115\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (71.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102 (72.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18 (51.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.322\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeart failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperuricemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (6.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.24 (0.88, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.26 (0.97, 2.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreated LDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected LDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLp (a) (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.10 (11.00, 28.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.95 (9.83, 33.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1C (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.83\u0026thinsp;\u0026plusmn;\u0026thinsp;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.985\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUA (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e383.0 (317.0, 440.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344.0 (285.0, 410.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (ml/min/1.73m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.9 (83.7, 104.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95.0 (85.0, 101.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImaging indexs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIMT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (5.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17 (12.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCarotid plaque\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5 (14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.557\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYNTAX I score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.0 (18.0, 34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.0 (10.0, 22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriple vessel lesions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (77.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (54.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFH related indicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12-SNP score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.58, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.90, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCN score 1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (1, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCN score 2✝\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (2, 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFH indicates familial hypercholesterolemia; BMI, body mass index; HR, heart rate; BPM, beats per minute; BP, blood pressure; SBP, systolic blood pressure; DBP, diastolic blood pressure; ASCVD, atherosclerotic cardiovascular disease; LLT, lipid-lowering treatment; MI, myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; PVD, peripheral vascular disease; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; Lp (a), lipoprotein (a); HbA1C, glycated hemoglobin; UA, uric acid; eGFR, estimated glomerular filtration rate; IMT, intima-media thickness; SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery; SNP, single nucleotide polymorphism and DLCN, dutch lipid clinic network.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* DLCN score 1: not include DNA analysis score in criteria.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e✝DLCN score 2: include DNA analysis score in criteria.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSubsequent logistic regression analysis was conducted to determine whether the parameters showing significant differences were independently associated with FH diagnosis. The univariate logistic regression analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed that both the cLDL-C concentration (Odds Ratio [OR]: 2.204, P\u0026thinsp;=\u0026thinsp;0.010), the SYNTAX I score (OR: 1.145, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as well as the presence of triple vessel lesions (OR: 2.842, P\u0026thinsp;\u0026lt;\u0026thinsp;0.017) were significantly associated with FH. These associations persisted in the multivariate logistic regression analysis, except for the presence of triple vessel lesions (P\u0026thinsp;=\u0026thinsp;0.878) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The cLDL-C concentration (OR: 1.778, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the SYNTAX I score (OR: 1.124, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) maintained their statistical significance, thus confirming them as independent risk factors for an FH diagnosis. This analysis underscores the importance of these parameters in the diagnostic process for FH.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of the influence of variables on the monogenic FH (FH\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate logistic regression\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate logistic regression\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected LDL-C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.204 (1.476\u0026ndash;3.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.778 (1.149\u0026ndash;2.752)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYNTAX I score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.145 (1.083\u0026ndash;1.211)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.124 (1.059\u0026ndash;1.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTriple vessel lesions\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.842 (1.207\u0026ndash;6.691)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.081 (0.400-2.915)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eFH indicates familial hypercholesterolemia; LDL-C, low-density lipoprotein cholesterol; and SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe study utilized ROC curve analysis to evaluate the predictive power of the cLDL-C level and SYNTAX I score in diagnosing FH (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The AUC for the cLDL-C level was 0.667 (P\u0026thinsp;=\u0026thinsp;0.0023), with a determined cut-off value of 4.02 mmol/L for identifying carriers of FH variants (monogenic or polygenic FH). This cut-off value yielded a sensitivity of 57.14% and a specificity of 75.00%. The AUC for the SYNTAX I score was 0.759 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a cut-off value of 29.5, and demonstrated a sensitivity of 48.57% and a specificity of 100.00%. There was no significant difference between the AUCs of these two indicators (P\u0026thinsp;=\u0026thinsp;0.1953).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the integer part of the cut-off values, the two predictors were converted from continuous to categorical variables (defined as cLDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4 mmol/L\u0026thinsp;=\u0026thinsp;1, cLDL-C\u0026thinsp;\u0026lt;\u0026thinsp;4 mmol/L\u0026thinsp;=\u0026thinsp;0, SYNTAX I score\u0026thinsp;\u0026ge;\u0026thinsp;29\u0026thinsp;=\u0026thinsp;1, SYNTAX I score\u0026thinsp;\u0026lt;\u0026thinsp;29\u0026thinsp;=\u0026thinsp;0). A new multivariate logistic regression analysis was then performed to establish a predictive probability model. Both the cLDL-C group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.019) and the SYNTAX I group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were statistically significant. The Pred1 formula was: Y = -2.491\u0026thinsp;+\u0026thinsp;1.140 \u0026times; cLDL-C group\u0026thinsp;+\u0026thinsp;4.098 \u0026times; SYNTAX I group.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb presented the ROC curve analysis of the cLDL-C group, SYNTAX I group, and Pred1 in predicting FH. The AUC for Pred1 was 0.795 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a cut-off value of 0.21, and a sensitivity and specificity of 48.57% and 98.57%, respectively. The AUC of Pred1 was significantly higher than that of the cLDL-C group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there were no significant differences between the Pred1 and SYNTAX I group (P\u0026thinsp;=\u0026thinsp;0.0738), or between the cLDL-C group and SYNTAX I group (P\u0026thinsp;=\u0026thinsp;0.1469).\u003c/p\u003e \u003cp\u003e \u003cb\u003ePolygenic FH patients share with monogenic FH patients regarding CHD risk notwithstanding lower cLDL-C levels\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWithin the FH group, monogenic FH patients had significantly higher cLDL-C levels (4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63 vs 3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73, P\u0026thinsp;=\u0026thinsp;0.008), as compared to polygenic FH patients. However, their SYNTAX I scores were similar [23.3 (18.0, 32.1) vs 30.0 (16.5, 48.3), P\u0026thinsp;=\u0026thinsp;0.516] (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), suggesting similar pro-atherosclerotic tendencies between the two groups.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics analysis of monogenic and polygenic FH groups (N\u0026thinsp;=\u0026thinsp;35)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003emonogenic FH (26)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003epolygenic FH (9)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorrected LDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.99\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSYNTAX I score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.3 (18.0, 32.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.0 (16.5, 48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.516\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCN score 1*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0 (1.0, 4.0 )\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDLCN score 2✝\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.0 (9.0, 12.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.0 (0.0, 2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eFH indicates familial hypercholesterolemia; LDL-C, low-density lipoprotein cholesterol; SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery; and DLCN, dutch lipid clinic network.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e* DLCN score 1: not include DNA analysis score in criteria.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e✝DLCN score 2: include DNA analysis score in criteria.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, while the cLDL-C levels of high-risk polygenic FH patients were not significantly higher than those of non-FH patients, polygenic FH group exhibited significantly higher SYNTAX I scores than non-FH groups (P\u0026thinsp;=\u0026thinsp;0.006 and P\u0026thinsp;=\u0026thinsp;0.003, respectively, lower than the Bonferroni correctehd threshold of 0.0167) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This indicates that polygenic FH patients indeed have a higher risk than non-FH patients regarding pro-atherosclerotic tendencies.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ea. Characteristics of different risk groups of polygenic FH (N\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrected LDL-C (mmol/L)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSYNTAX I score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh risk (9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.0 (16.5, 48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle risk (126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.0 (10.0, 22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow risk (14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.58\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.0 (7.8, 20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFH indicates familial hypercholesterolemia; LDL-C, low-density lipoprotein cholesterol; and SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eb. Comparison of SYNTAX I score between three risk groups of polygenic FH (N\u0026thinsp;=\u0026thinsp;149)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh risk vs. middle risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehigh risk vs. low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emiddle risk vs. low risk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eFH indicates familial hypercholesterolemia; and SYNTAX, synergy between percutaneous coronary intervention with taxus and cardiac surgery.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*\u003cem\u003eP\u003c/em\u003e value is compared with Bonferroni corrected threshold of 0.0167.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study delineates the clinical characteristics of FH in patients with CHD, focusing on genetically diagnosed cases. Initially, we report the prevalence of FH, including both monogenic and polygenic forms, in a Chinese CHD cohort. Furthermore, leveraging coronary angiography data, we explore the relationship between the SYNTAX I score and FH diagnosis. Notably, patients with FH exhibited significantly more severe atherosclerosis, as evidenced by higher SYNTAX I scores and a greater incidence of triple vessel disease. Additionally, the study highlights the similar severity of atherosclerosis in polygenic FH patients compared to those with monogenic FH, underscoring the necessity of further evaluating polygenic FH in patients without mutations in the classical FH-related genes.\u003c/p\u003e \u003cp\u003eThe prevalence of FH in our CHD cohort, at 8.75% (including polygenic cases), exceeds previously reported figures. However, these variations in prevalence are attributable to differing diagnostic criteria. A meta-analysis encompassing over 10\u0026nbsp;million subjects reported FH prevalence rates of 3.2% in CHD, rising to 6.7% in cases of premature CHD\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The inconsistency in diagnostic criteria, which often blend clinical and genetic assessments, complicates these findings. Genetic testing, considered more reliable than clinical diagnosis due to often ambiguous clinical histories, reveals different insights\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For example, tendon xanthomas, a significant component in the DLCN criteria, are infrequently evaluated in clinical practice. Moreover, the widespread use of statins over the past three decades has reduced the physical manifestations of FH, such as xanthomas\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Furthermore, some patients may experience premature CHD before receiving a later CHD diagnosis, potentially leading to an underestimation of the DLCN score prior to genetic testing. In line with this, Amor-Salamanca et al. reported an 8.7% prevalence of FH in a cohort of acute coronary syndrome patients using genetic testing, paralleling our findings and suggesting the need for larger cohorts for further validation\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Additionally, data on FH prevalence among Chinese CHD patients are limited, with previous studies indicating FH prevalence rates ranging from 4.4\u0026ndash;7.6% in premature myocardial infarction cases\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Our study is the first to present the prevalence of genetically diagnosed FH in a general CHD patient population.\u003c/p\u003e \u003cp\u003eThis study also observed more severe atherosclerosis in FH patients, as indicated by higher SYNTAX I scores. Additionally, FH patients exhibited more diffuse lesions, although this difference was not statistically significant following logistic regression analysis. It is well-documented that FH patients often experience more severe CHD than non-FH individuals, characterized by multiple vessel involvement\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and more severe atherosclerosis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. For instance, Ranshaka Auckle et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e demonstrated that among 498 patients with premature ST-segment-elevation myocardial infarction (STEMI), those with possible, probable, or definite FH (according to DLCN criteria) had a higher likelihood of multivessel stenosis compared to those unlikely to have FH. Similarly, Jian-Jun Li et al.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e found a positive correlation between lesion severity, as assessed by the Gensini score (GS), and the degree of clinically diagnosed FH. Michel Farnier et al.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e showed that patients with probable or definite FH had significantly higher SYNTAX scores than those unlikely to have FH in a study of 233 patients with acute myocardial infarction.\u003c/p\u003e \u003cp\u003eIn our study, genetic testing, which is the gold standard for diagnosing FH, was used, enhancing the validity of these associations. However, genetic testing is not routinely employed in clinical practice, leading to a low diagnosis rate of genetic FH (less than 1%)\u003csup\u003e34\u003c/sup\u003e. Therefore, the SYNTAX I score could serve as a simple yet effective indicator for identifying potential FH patients, enabling physicians to initiate appropriate diagnostic procedures for FH and determine LDL-C management goals.\u003c/p\u003e \u003cp\u003eOur study found that monogenic and polygenic FH accounted for 74.3% and 25.7% of FH cases within CHD patients, respectively. Notably, while average LDL-C levels were higher in monogenic FH patients, SYNTAX I scores did not significantly differ between monogenic and polygenic FH patients. Moreover, SYNTAX I scores were significantly higher in polygenic FH patients compared to non-FH patients. This finding suggests that, in addition to monogenic FH, polygenic FH should not be overlooked. In clinical practice, a substantial number of patients present with significantly elevated LDL-C levels but lack monogenic pathogenic mutations\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is estimated that only 60\u0026ndash;70% of patients classified as probable or definite FH according to DLCN criteria actually harbor FH-related mutations\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The observation that polygenic FH patients exhibit a similar severity of atherosclerosis as monogenic FH patients underscores the importance of screening for polygenic FH in patients with probable or definite FH or CHD patients with severe atherosclerosis.\u003c/p\u003e"},{"header":"5. Limitations","content":"\u003cp\u003eFirstly, it is important to note that not all pre-treatment LDL-C concentrations were accessible in this study. For patients without available objective pre-treatment LDL-C data, an estimated formula, as outlined in the methods section, was utilized to calculate the cLDL-C. This estimation could potentially impact the accuracy of the LDL-C levels. However, such estimation techniques are commonly accepted in cholesterol-related research and are generally considered reliable proxies for true LDL-C levels.\u003c/p\u003e \u003cp\u003eSecondly, the SYNTAX I score can exhibit variability due to intra-observer and inter-observer heterogeneity\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. However, in this study, no significant differences were observed between the SYNTAX I scores assessed by two different physicians. The use of an average SYNTAX I score value in our analysis aimed to mitigate this potential source of bias.\u003c/p\u003e \u003cp\u003eLastly, it should be acknowledged that this was a single-center retrospective study. Consequently, the calculated prevalence of FH may not fully represent the broader population. Further research involving larger and more diverse cohorts is needed to validate and refine these findings.\u003c/p\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThe study revealed that the prevalence of genetically diagnosed FH patients in the cohort was 8.75%, encompassing both monogenic and polygenic forms of the condition. Furthermore, the SYNTAX I score was identified as an independent predictor of the FH genotype. Notably, patients with polygenic FH demonstrated a similar severity of atherosclerosis when compared to those with monogenic FH, indicating the importance of considering both types in the evaluation and management of patients with suspected FH.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eFH: Familial hypercholesterolemia; CHD: coronary heart disease; LDLR: LDL receptor; APOB: apolipoprotein B; PCSK9: proprotein convertase subtilisin/kexin type 9; LDLRAP1: LDLR adaptor protein 1; APOE: Apolipoprotein; STAP1: signal transducing adaptor protein family 1; LIPA: lysosomal acid lipase; HeFH: heterozygous FH; DLCN: Dutch Lipid Clinic Network; SBR: Simon Broome Register; SYNTAX I: Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery I\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eP.W and Y.D contributed to the conception and design of the work. Y.W and P.W drafted the manuscript. Y. W and C. L contributed to the data collection for the work. Y.D and W.Z contributed to the data analysis and interpretation for the work. W. Z, and Y.D critically revised the manuscript. All authors critically reviewed the manuscript and gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available due to the restrictions of human genetics data policy of Beijing Chaoyang Hospital Ethics Committee,\u0026nbsp;but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent for participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the ethics committee of Beijing Chaoyang Hospital, Capital Medical University and performed in accordance with the ethical standers laid down in the 1964 Declaration of Helsinki and its later amendments.\u0026nbsp;Written informed consents were obtained\u0026nbsp;from all participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBerberich AJ, Hegele RA. 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Genetic testing for familial hypercholesterolaemia is essential in individuals with high LDL cholesterol: who does it in the world? \u003cem\u003eEur Heart J.\u003c/em\u003e 2017;38:1580-1583.\u003c/li\u003e\n\u003cli\u003eCatapano AL, Graham I, De Backer G, et al. 2016 ESC/EAS Guidelines for the Management of Dyslipidaemias. \u003cem\u003eEur Heart J.\u003c/em\u003e 2016;37:2999-3058.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Familial hypercholesterolemia, prevalence, SYNTAX I score, monogenic, polygenic","lastPublishedDoi":"10.21203/rs.3.rs-5243180/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5243180/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eFamilial hypercholesterolemia (FH) is a genetically inherited disorder caused by monogenic mutations or polygenic deleterious variants. Patients with FH innate with significantly elevated risks for coronary heart disease (CHD). FH prevalence based on genetic testing in Chinese CHD patients is missing. Whether classical index of coronary atherosclerosis severity can be used as indicators of FH needs to be explored. To investigate the FH prevalence in Chinese CHD patients and the association of SYNTAX I score with FH genotype.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe monogenic and polygenic FH related genes were genotyped in 400 consecutively enrolled CHD patients. The clinical characteristics and SYNTAX I scores were analyzed in a retrospective nested case-control study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of genetically confirmed FH in our CHD cohort was 8.75%. The cLDL-C level, SYNTAX I scores and incidences of triple vessel lesions in FH patients were significantly higher, while cLDL-C and SYNTAX I scores were independent risk factors for FH. Furthermore, cLDL-C levels of polygenic FH were significantly lower than monogenic FH, while their severity of coronary atherosclerosis was comparable.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur study revealed a genetically confirmed FH prevalence of 8.75% in a Chinese CHD cohort. Additionally, the SYNTAX I score was an independent risk factor for FH. Besides, polygenic origin of FH should be taken into consideration for CHD patients suspected of FH.\u003c/p\u003e","manuscriptTitle":"Characteristics of Genetically Confirmed Familial Hypercholesterolemia in Chinese Patients with Coronary Heart Disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-23 09:50:23","doi":"10.21203/rs.3.rs-5243180/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-05T19:37:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-12T12:21:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70795455401418224167159243499323059449","date":"2024-11-12T11:44:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-07T10:28:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48867501759268664072630640967360083837","date":"2024-11-05T13:00:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148170707845273583708009250129703069773","date":"2024-11-05T10:13:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-04T22:45:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-10-17T18:11:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-17T14:07:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-17T14:05:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cardiovascular Disorders","date":"2024-10-11T03:44:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cardiovascular-disorders","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcar","sideBox":"Learn more about [BMC Cardiovascular Disorders](http://bmccardiovascdisord.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcar/default.aspx","title":"BMC Cardiovascular Disorders","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50df1f1f-2746-438a-b58d-c342352392d5","owner":[],"postedDate":"October 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:02:41+00:00","versionOfRecord":{"articleIdentity":"rs-5243180","link":"https://doi.org/10.1186/s12872-024-04428-3","journal":{"identity":"bmc-cardiovascular-disorders","isVorOnly":false,"title":"BMC Cardiovascular Disorders"},"publishedOn":"2024-12-21 15:57:39","publishedOnDateReadable":"December 21st, 2024"},"versionCreatedAt":"2024-10-23 09:50:23","video":"","vorDoi":"10.1186/s12872-024-04428-3","vorDoiUrl":"https://doi.org/10.1186/s12872-024-04428-3","workflowStages":[]},"version":"v1","identity":"rs-5243180","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5243180","identity":"rs-5243180","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}