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Decreased lipid levels in adult with congenital heart disease: a systematic review and Meta-analysis

BMC Cardiovascular Disorders volume 23, Article number: 523 (2023) Cite this article

Abstract

Background

Metabolic disorders were a health problem for many adults with congenital heart disease, however, the differences in metabolic syndrome-related metabolite levels in adults with congenital heart disease compared to the healthy population were unknown.

Methods

We collected 18 studies reporting metabolic syndrome-associated metabolite levels in patients with congenital heart disease. Data from different studies were combined under a random-effects model using Cohen’s d values.

Results

The results found that the levels of total cholesterol (Cohen’s d -0.68, 95% CI: -0.91 to -0.45), high-density lipoprotein cholesterol (Cohen’s d -0.63, 95% CI: -0.89 to -0.37), and low-density lipoprotein cholesterol (Cohen’s d -0.32, 95% CI: -0.54 to -0.10) were significantly lower in congenital heart disease patients compared with controls. Congenital heart disease patients also had a lower body mass index (Cohen’s d -0.27, 95% CI: -0.42 to -0.12) compared with controls. On the contrary, congenital heart disease patients had higher levels of hemoglobin A1c (Cohen’s d 0.93, 95% CI: 0.17 to 1.70) than controls. Meanwhile, there were no significant differences in triglyceride (Cohen’s d 0.07, 95% CI: -0.09 to 0.23), blood glucose (Cohen’s d -0.12, 95% CI: -0.94 to 0.70) levels, systolic (Cohen’s d 0.07, 95% CI: -0.30 to 0.45) and diastolic blood pressure (Cohen’s d -0.10, 95% CI: -0.39 to 0.19) between congenital heart disease patients and controls.

Conclusions

The lipid levels in patients with congenital heart disease were significantly lower than those in the control group. These data will help in the health management of patients with congenital heart disease and guide clinicians.

PROSPERO registration number

CRD42022228156.

Peer Review reports

Introduction

Congenital heart disease (CHD) is the most common cause of congenital abnormalities [1]. In recent years, significant advances in pediatric cardiology and cardiovascular surgery have improved the survival rates of CHD patients into adulthood and have greatly increased the number of adult patients with CHD [2]. Nevertheless, the heart malformation of most patients cannot be completely cured by surgery, and many patients continue to suffer from hemodynamic abnormalities [3], such as coronary artery replantation during transposition repair can lead to abnormal coronary blood flow reserve [4]. Adult CHD patients were also susceptible to cardiac-related complications, including heart failure, arrhythmia, endocarditis, cardiac conduit obstructions, thrombosis, aortic disease and pulmonary hypertension [5, 6]. As a result, the health condition of these patients needs to be monitored throughout their lives.

Metabolic syndrome (MS) usually includes a combination of any three of the following metabolic disorders: including obesity, dyslipidemia, hyperglycemia, and hypertension [7, 8]. Clinical evidence showed that the incidence of MS was higher in adults with CHD than in the general population, which means that adult CHD patients were at higher risk of metabolic disorders than the general population [9]. Autonomic dysfunction, which was more pronounced in the obese population, increases cardiovascular workload, hemodynamic stress, severe arrhythmias, and significant cardiac pathology, so obesity may complicate the management of patients with adult CHD who were already at risk for ventricular dysfunction, arrhythmias, and heart failure [10]. Hypertension may increase the risk of postoperative aortic dilatation in patients with CHD. Dellberg et al. reported that type 2 diabetes were more common in adults CHD population, and another study showed a higher prevalence of impaired fasting glucose in patients with CHD than in the general population [11]. Studies have indicated a higher prevalence of MS in patients with CHD. However, contrasting findings have been reported regarding lipid levels. Some studies have observed lower levels of total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C) in acyanotic CHD (ACHD) patients compared to control groups [12,13,14,15]. On the other hand, cyanotic CHD (CCHD) patients did not have significantly lower levels of TC and LDL-C compared to controls, but they had significantly lower levels of HDL-C [16, 17]. Moreover, a separate study concluded that HDL-C levels in cyanotic CHD patients were significantly lower than in ACHD patients [12]. Dyslipidemia is a common manifestation of MS, and the identification and treatment of dyslipidemia are crucial for improving the overall health status in adults with CHD [12, 18, 19]. Abnormal metabolism was accompanied by an increased risk of cardiovascular disease (CVD), which was probably the leading cause of death in most adult CHD patients [20, 21]. Therefore, monitoring of lipids, BMI, glucose and blood pressure in CHD patients is beneficial for the management of health status in CHD patients. Studies have shown that patients with CHD are more likely to have obesity and hypertension compared to the general population [22, 23].

Despite this, there is still a lack of systematic review and meta-analysis of the differences in MS-related metabolite levels between adults with CHD and healthy controls. In this study, we systematically assessed the differences in blood lipid (TC, LDL-C, HDL-C, TG), HbA1c, glucose levels and blood pressure between adult CHD patients and the normal population through the meta-analysis. Our findings will help guide the future management of adult CHD and provide guidance for clinicians.

Methods

Search Strategy

We used PubMed [24] and Web of Science [25] to search the literature on metabolite levels of patients with CHD published before July 2022. Titles and abstracts were searched according to specific filters appropriate for the different databases. The main search terms used were [1] congenital heart disease, congenital heart defect, CHD, heart abnormality, heart malformation; and [2] adults; and [3] lipid, glucose, blood pressure and BMI. By screening the titles and abstracts (FD Ma and PQ Li), studies that meet the requirements were filtered out and evaluated by reading the full text. The guarantors of the review were XD Xie.

Study selection

Studies on CHD published in English were included in the title and abstract screening stage. The full texts of all relevant literature were further evaluated. If the study was [1] English literature, [2] adult congenital heart disease, [3] case-control study or cohort study, [4] human study, and [5] reports data on metabolism-related indicators, including TC, HDL-C, LDL-C, triglycerides (TG), hemoglobin A1c (HbA1c), and glucose, as well as blood pressure data were included. Conversely, we excluded the following studies [1] Syndromic congenital heart disease (i.e. syndromic disorders associated with the development of congenital heart disease, such as Down syndrome, Marfan syndrome), [2] Ischemic heart disease and Coronary heart disease, [3] Maternal and child congenital heart disease, [4] reviews, conference papers, case reports, [5] published data were incomplete, [6] repeated publications.

Data extraction

Evaluation and extraction of the data contained in the literature were performed independently by 2 authors and in consultation with the third author in case of disagreement. The following data were mainly extracted: authors, year of study publication, geographic area of the research, type of study design, the period for collecting cases, sample size, age, gender and body mass index (BMI) of the participant, whether the case group was operated or not, metabolite level data and classification of congenital heart disease.

Study Quality assessments

The quality of studies included was guaranteed by using the Newcastle-Ottawa Scale (NOS), which is a tool recommended by the Cochrane Collaboration for observational studies to assess the risk of bias [26]. The star system with a range from zero to 9 stars was used to assess the quality of study [27]. The criteria included 8 items with a maximum of 9 stars. We included studies with a quality level above 6 stars for meta-analysis [28].

Statistical analyses

In this meta-analysis, the method of standardized mean difference (i.e., Cohen’s d value) was used as the effect size to compare the differences in TC, HDL-C, LDL-C, TG, HbA1c, blood glucose levels, blood pressure and BMI between CHD patients and healthy controls under a random-effect model or a fixed-effect model. The units of metabolite levels reported in all studies were normalized to mg/dl before analysis. The normal control ranges for human lipid levels are: Total cholesterol, ideal value: <200 mg/dl; critical value: 200-239 mg/dl; excessive value: >240 mg/dl. Triglycerides, ideal value: <150 mg/dl; critical value: 150-199 mg/dl; excessive value: >200 mg/dl. HDL cholesterol, ideal value: >50 mg/dl; critical value: 35-50 mg/dl; risk value: <35 mg/dl; LDL cholesterol, ideal value: <130 mg/dl; critical value: 130-159 mg/dl; excess value: >160 mg/dl. Cohen’s d is a parameter used to compare the magnitude of the mean difference effect, which can indicate the magnitude of the difference between the overall means under different treatments and can be compared across studies. Cohen’s d values were considered a small effect size at 0.2, a moderate effect size at 0.5, and a large effect size at 0.8 [29]. The effect size and the corresponding 95% confidence interval (CI) were calculated by the online tool [30]-a practical meta-analysis effect size calculator used the mean ± standard deviation (SD) of related metabolite indicators reported in the literature. To determine which model to use, the heterogeneity between studies was assessed by the P-value and I2 statistics corresponding to the Cochran Q test. The I2 statistic was used to assess the percentage of total variation across studies that is due to heterogeneity rather than chance (I2 > 75% indicates high heterogeneity, 51–75% indicates substantial heterogeneity, 26-50% indicates moderate heterogeneity, and ≤ 25% indicates low heterogeneity). If P-value < 0.05 and I2 > 50%, the analysis was performed using a random-effects model, and if P-value > 0.05 and I2 < 50%, the analysis was performed using a fixed-effects model. To explore the source of heterogeneity, subgroup analysis was carried out by dividing the different geographical areas where the study was located. The symmetrical distribution of the funnel chart and the Egger’s test assessed publication bias (P < 0.05 indicates that the results were significant and there was publication bias). All statistical analyses were conducted in R software, version 4.0.4.

Results

Identification of studies

At first, a total of 7257 articles meeting the requirements were collected with 5783 of them from PubMed and 1474 of them from the Web of Science. Among them, 188 duplicate studies were excluded, and then 6961 studies including reviews, conference papers, case reports, or studies that did not meet our inclusion criteria were excluded by reading the titles and abstracts. After reading and evaluating the full texts of the remaining 108 studies, 18 of them met the inclusion criteria and were included for meta-analysis (Fig. 1).

Fig. 1
figure 1

Flow chart of the literature search

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Characteristics of studies

Eventually, the 18 studies, which involved 3613 CHD patients and 5664 healthy controls, met the inclusion and exclusion criteria and were included in this meta-analysis (Table 1). All literatures included were published between 2001 and 2021. There were 1993 male CHD patients and 1620 female CHD patients matched 2782 male controls and 2882 female controls. Almost all studies were based on adult CHD patients except for 5 studies including Santos 2001 (age 27.0 ± 12.0), Katayama 2006 (age 15.3 ± 11.9), Martínez 2010 and 2014 (selection criteria were 14 years and older), Jan 2015 (age 27.3 ± 12.1). A total of 13 studies reported the BMI of subjects, 10 studies were matched for BMI between cases and controls. Among the CHD patients, 637 were without surgery, 760 were treated with surgery, and 2216 were unclear whether they were treated with surgery or not. The candidate studies were 3 retrospective case-control studies, 3 prospective case-control studies, 1 observational case-control study, 1 Cross-sectional cohort study and 1 randomized case-control study. Besides these, 9 studies were conducted in Europe (Netherlands, Italy, Spain, Belgium, Sweden, Poland, Germany), 4 studies were conducted in Asia (Japan), 3 studies in the Americas (Brazil, United States), and 2 studies in Europe combined with Oceania (Denmark, Sweden, Norway, Australia).

In terms of all CHD patients, there were 872 patients with CCHD, accounting for 24.14%, and 2741 patients with ACHD, accounting for 75.86% (Fig. 2A). For the detailed phenotypes, tetralogy of Fallot (TOF), coarctation of the aorta (COA), aortic valve disease (AVD), and ventricular septal defect (VSD) were the 4 main phenotypes, accounted for 16.14%, 13.51%, 11.02%, and 10.55% respectively. Other phenotypes include transposition of the great arteries (TGA), atrial septal defect (ASD), pulmonary valve disease (PVD), atrioventricular septal defect (AVSD), double outlet of the right ventricle (DORV), Eisenmenger syndrome (ES), etc. (Fig. 2B).

Table 1 Selected characteristics of 18 studies included in the meta-analysis
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Fig. 2
figure 2

Classification of congenital heart disease. (A) CHD Classification I CCHD, cyanotic congenital heart disease; ACHD, acyanotic congenital heart disease. (B) CHD Classification II detailed types of CHD. TOF, Tetralogy of Fallot; COA, Coarctation of the aorta; AVD, Aortic valve disease; VSD, Ventricular septal defect; TGA, Transposition of the great arteries; ASD, Atrial septal defect; IS, Isolated shunts; PVD, Pulmonary valve disease; AVSD, Atrioventricular septal defect; PS, Pulmonary stenosis; DORV, Double outlet right ventricle; ES, Eisenmenger syndrome; TA, Tricuspid atresia; DOLV, Double outlet left ventricle

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Assessment of Study Quality

The quality of all studies was evaluated by using the Newcastle-Ottawa scale (Table 2). All literature included ranks above 6 stars, indicating the high quality of the studies and meeting the requirements of meta-analysis. 88.9% of studies (16/18) provided a clear case definition and underwent independent validation of the case in the original studies. Subjects included were diagnosed at least 2 times or based on at least 2 diagnostic methods, including echocardiography and/or cardiac magnetic resonance and/or cardiac catheterization; or by looking up original records such as hospital medical records. The cases collected in all studies were representative, eligible CHD patients were collected within a specified time, or all cases came from a specific hospital. Controls in 66.7% of studies (12/18) were selected from normal communities, and another 33.3% of studies also clearly defined selected controls. In 15 studies, the case and control groups were matched for sex and/or age. A total of 13 studies reported data on lipid levels. For the measurement of blood lipid levels, 6 studies did not report detailed methods, 3 studies used spectrophotometry, 2 studies used enzymatic colorimetry, and the other 2 studies used laboratory or hospital standard methods.

Table 2 Overview of research quality assessment
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The levels of TC, HDL-C, LDL-C were significantly lower in CHD patients

The meta-analysis demonstrated that the levels of TC, HDL-C, and LDL-C in CHD patients were significantly lower than controls. The Cohen’s d value for TC calculated by meta-analysis was − 0.68 with a range of -1.22 to 0.23 (95% CI: -0.91 to -0.45), I2 = 88%, P < 0.01 (Fig. 3A). The Cohen’s d range of HDL-C was − 1.53 to -0.08, and the combined Cohen’s d value was − 0.63 (95% CI: -0.89 to -0.37), I2 = 89%, P < 0.01(Fig. 3B). The results showed a significant medium effect size on decreased the levels of TC and HDL-C, compared with the control groups. Similar results were obtained for LDL-C levels, which were also reduced in CHD patients [combined Cohen’s d value: -0.32(95% CI: -0.54 to -0.10), I2 = 87%, P < 0.01] (Fig. 3C). As far as HbA1c was concerned, there were only 5 studies. Compared with controls, the levels of HbA1c were higher in CHD patients. The combined Cohen’s d was 0.93 (95% CI: 0.17 to 1.70), I2 = 91%, P < 0.01 (Fig. 3D). We also found that CHD patients had significantly lower BMI than controls, although the combined Cohen’s d value had only a small effect size [combined Cohen’s d value: -0.27 (95% CI: -0.42 to -0.12]), I2 = 69%, P < 0.01] (Fig. 3E).

Fig. 3
figure 3

Forest plot of blood lipid levels between CHD patients and healthy controls. (A) Total cholesterol; (B) High-density lipoprotein cholesterol; (C) Low-density lipoprotein cholesterol; (D) HbA1c; (E) BMI. These studies were listed by year of publication. The data was expressed as a Cohen’s d value. The blue square represents the Cohen’s d value of a single study, the gray diamond represents the Cohen’s d value from the fixed-effects model meta-analysis, and the red diamond represents the Cohen’s d value from the random-effects model meta-analysis. The horizontal line represents 95% CI. Abbreviation: CI, confidence interval

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Differently, there were no significant differences in TG, blood glucose levels, diastolic and systolic blood pressure between CHD patients and controls. Cohen’s d values for TG levels combined were 0.07 (95% CI: -0.09 to 0.23), I2 = 69%, P < 0.01 (Fig. 4A). The combined Cohen’s d values for blood glucose levels reported in the 6 articles were − 0.12 (95% CI: -0.94 to 0.70), I2 = 97%, P < 0.01 (Fig. 4B). The combined Cohen’s d value for systolic blood pressure was 0.07 (95% CI: -0.30 to 0.45), I2 = 87%, P < 0.01 (Fig. 4C). Cohen’s d values for diastolic blood pressure combined were − 0.10 (95% CI: -0.39 to 0.19), I2 = 91%, P < 0.01 (Fig. 4D).

Fig. 4
figure 4

Forest plot of metabolites levels and blood pressure between CHD patients and healthy controls. (A)Triglycerides; (B) Glucose; (C) Systolic blood pressure; (D) Diastolic blood pressure. These studies were listed by year of publication. The data was expressed as a Cohen’s d value. The blue square represents the Cohen’s d value of a single study, the gray diamond represents the Cohen’s d value from the fixed-effects model meta-analysis, and the red diamond represents the Cohen’s d value from the random-effects model meta-analysis. The horizontal line represents 95% CI. Abbreviation: CI, confidence interval

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Sensitivity analysis and publication Bias

To explore whether the heterogeneity between different studies was attributable to the inclusion of extreme research results, we conducted a sensitivity analysis. The results were presented in Supplementary Fig. 1. Sensitivity analyses were performed separately for the different biomarkers, and each biomarker was analyzed by excluding one study in turn and using the remaining studies, the overall results did not change significantly, indicating that there were no extreme phenomena in the studies included. In the analysis of publication bias, Egger’s test showed no evidence of publication bias for TC (P = 0.1308) (Supplementary Fig. 2A), LDL-C (P = 0.6761) (Supplementary Fig. 2C), TG (P = 0.095) (Supplementary Fig. 2E), HbA1c (P = 0.0633) (Supplementary Fig. 2G), Glucose (P = 0.8673) (Supplementary Fig. 2I) levels, and diastolic (P = 0.5120) (Supplementary Fig. 2K) and systolic blood pressure (P = 0.8641) (Supplementary Fig. 2M). Also, no publication bias was observed by visual inspection of funnel plots (Supplementary Fig. 2B, 2D,2 F, 2 H, 2 J, 2 L and 2 N). But for HDL-C levels (P = 0.0453) (Supplementary Fig. 3A and 3B) and BMI (P = 0.0118) (Supplementary Fig. 3D and 3E), both Egger’s test and funnel chart showed that there may be publication bias (P < 0.05). Nonetheless, further analysis using the trim and fill method showed that the comprehensive effect size of HDL-C (Cohen’s d = -0.3201) and BMI (Cohen’s d = -0.1115) were consistent with the results before the analysis (Supplementary Fig. 3C and 3 F), which could be explained to some extent, the comprehensive effect size was not be affected by publication bias in HDL-C levels and BMI.

Subgroup Analysis by Geographic Region

We found varying degrees of heterogeneity among the studies, so further subgroup analyses were performed. Subgroup analyses were performed according to different geographical regions, including the Americas, Asia, Europe, and Europe combined with Oceania. This is because there are differences in genetic factors and dietary habits in different geographic regions, both of which can have an impact on lipid levels. In terms of TC levels, the CHD patients from the America (Cohen’s d, -0.79; 95% CI: -1.37 to -0.21; I2 = 95%), Europe (Cohen’s d, -0.70; 95% CI: -0.93 to -0.46; I2 = 83%), and Europe combined with Oceania (Cohen’s d, -0.90; 95% CI: -1.17 to -0.64; I2 = 0%) were significantly lower than controls, but not in Asians (Cohen’s d, -0.36; 95% CI: -1.06 to 0.35; I2 = 85%) (Supplementary Fig. 4A). The HDL-C levels of CHD patients from Asia (Cohen’s d, -0.76; 95% CI: -1.27 to -0.25; I2 = 67%), Europe (Cohen’s d, -0.49; 95% CI: -0.80 to -0.18; I2 = 82%), and Europe combined with Oceania (Cohen’s d, -0.62; 95% CI: -0.96 to -0.28; I2 = 40%) were significantly lower than control groups, while the HDL-C levels of the Americans (Cohen’s d, -0.84; 95% CI: -2.18 to 0.51; I2 = 98%) was not significantly different between CHD patients and the control groups (Supplementary Fig. 4B). LDL-C levels of CHD patients was significantly lower than control groups only in Europe (Cohen’s d, -0.51; 95% CI: -0.78 to -0.24; I2 = 81%). There were no significant differences in LDL-C levels between CHD patients from Asia (Cohen’s d, 0.03; 95% CI: -0.56 to 0.62; I2 = 80%), America (Cohen’s d, -0.43; 95% CI: -0.89 to 0.03; I2 = 89%), and Europe combined with Oceania (Cohen’s d, -0.17; 95% CI: -0.42 to 0.09; I2 = 0%) and the controls (Supplementary Fig. 5A). The relationship between TG levels and CHD was complicated in different populations. TG levels were decreased in American (Cohen’s d, -0.49; 95% CI: -0.91 to -0.07) patients and increased in Europe combined with Oceania (Cohen’s d, 0.47; 95% CI: 0.21 to 0.73; I2 = 0%) patients. There were no significant differences in TG levels between CHD patients and controls in Asian (Cohen’s d, 0.14; 95% CI: -0.08 to 0.37; I2 = 0%) and European (Cohen’s d, -0.05; 95% CI: -0.17 to 0.07; I2 = 33%) (Supplementary Fig. 5B).

Additionally, the CHD patients in Asia had higher HbA1c levels and lower glucose levels compared with the control groups (Cohen’s d, 1.52; 95% CI: 0.85 to 2.20; I2 = 70% and Cohen’s d, -1.20; 95% CI: -1.61 to -0.78; I2 = 0%). The HbA1c levels in the Europe combined with Oceania (Cohen’s d, 0.14; 95% CI: -0.12 to 0.39; I2 = 0%) population and the glucose levels in the European populations (Cohen’s d, 0.39; 95% CI: -0.38to 1.16; I2 = 98%) were not significantly different between CHD patients and the control groups (Supplementary Fig. 6A and 6B). In terms of BMI, patients with CHD in Europe (Cohen’s d, -0.18; 95% CI: -0.31 to -0.06; I2 = 41%) and Asia (Cohen’s d, -1.24; 95% CI: -1.97 to -0.50; I2 = 65%) had a lower BMI, while in the Americas (Cohen’s d, -0.08; 95% CI: -0.28 to 0.13; I2 = 49%) and Europe combined with Oceania (Cohen’s d, -0.24; 95% CI: -0.50 to 0.01; I2 = 0%) had a BMI that did not differ from the control groups (Supplementary Fig. 6C). In European CHD patients, blood pressure was not significantly different from healthy controls (Systolic blood pressure: Cohen’s d, 0.23; 95% CI: -0.42 to 0.89; I2 = 89%; Diastolic blood pressure: Cohen’s d, 0.10; 95% CI: -0.34 to 0.55; I2 = 83%), but in Europe combined with Oceania populations CHD patients had lower blood pressure (systolic blood pressure: Cohen’s d, -0.41; 95% CI: -0.66 to -0.15; I2 = 0%; Diastolic blood pressure: Cohen’s d, -0.50; 95% CI: -0.76 to -0.24; I2 = 0%)( Supplementary Fig. 7A and 7B).

Discussion

We investigated MS-related metabolite levels in 3613 CHD patients and 5664 controls in 18 studies. Although studies suggest that MS is more common in patients with CHD, our results contradict these findings. The results of this meta-analysis indicated that among CHD patients, lipid levels including TC, HDL-C, and LDL-C levels, and BMI were significantly lower than healthy controls. HbA1clevels were elevated in CHD patients, while triglyceride, glucose levels and blood pressure were not significantly different from healthy controls. To the best of our knowledge, this study is the first meta-analysis to evaluate the difference in MS-related metabolite levels between CHD patients and healthy controls, which can provide advantageous information for clinicians and CHD survivors, as well as guide the clinical treatment of CHD patients. Our results showed that CHD patients have lower lipid levels than controls. Meanwhile, other studies have shown an association between CHD and cardiovascular disease risk in later life. CHD patients are found to have a higher risk of developing cardiovascular disease, including stroke, heart failure, and coronary artery heart disease and more [28]. It has been demonstrated that mature cardiomyocytes take fatty acids as the primary substrates to generate ATP [31]. Thus, the reduced lipid levels might have a negative effect on energy metabolism of cardiomyocyte and potentially contribute to cardiac dysfunction, which might serve as one of the risk factors for increased cardiovascular risk in adult CHD. In the future, sophisticated basic and clinical studies will help to uncover how hypolipidemia affects the cardiomyocyte energy production and its association with cardiovascular risk.

The underlying causes of dyslipidemia in CHD patients were multifaceted. Surgical intervention was the primary consideration. There was evidence that CHD patients undergoing Fontan surgery would develop a series of liver abnormalities over time, including coagulopathy, cholestasis, liver fibrosis, cirrhosis, and hepatocellular carcinoma [32,33,34]. Thus, liver dysfunction and resulting dyslipidemia may lead to hypolipidemia in CHD patients [35]. Nevertheless, we also found that some patients in our studies without undergoing Fontan surgery still had a low level of blood lipid, suggesting that other factors also played a role in the abnormal lipid metabolism of CHD patients. It is noted that endocrine diseases caused by metabolic abnormalities often appear in adult CHD patients. For instance, the prevalence of subclinical hypothyroidism (SCH) in adult CHD patients (9.6%) is higher than in people without known thyroid diseases (4.6%) [36, 37]. For patients with CCHD, proteinuria is the result of long-term cyanosis [38]. Previous studies had shown that inflammation, proteinuria, and autoimmune diseases contribute to SCH, and that SCH was related to changes in serum cholesterol levels [39, 40]. Hence, the decline in metabolic capacity caused by SHC in CCHD patients may be one of the reasons for their abnormal blood lipid levels. Moreover, the low blood lipid levels of CHD patients may also be related to the decreasing iron storage levels [17, 41], because the low iron storage levels of CHD patients can reduce LDL-C [42]. Malnutrition is also a problem for patients with CHD, and different studies have reported inconsistent levels of malnutrition in patients with CHD. 85% of patients with CHD in the study by Tokel et al. were malnourished, which correlated with the patient’s household income and dietary intake. Results by Blasquez et al. showed that 15% of patients with CHD had moderate or severe malnutrition, with half of them exhibiting low caloric intake with little appropriate nutritional support [43,44,45,46,47]. Therefore, the undernutrition in CHD patients may also be one of the factors for their low levels of blood lipids. Our study also observed that the BMI of CHD patients was lower than healthy controls, which may be associated with developmental delay due to the lower weight gain during their childhood [48]. The lower BMI may partially explain the lower lipid levels of CHD patients, but it is unlikely that the direct determinant of lipid levels in CHD patients is body weight itself [12].

Genetic variants may also be associated with lipid levels. Apolipoprotein (APOB) and lipoprotein-related receptor protein 2 (LRP2) play roles in lipid metabolism as LDL apolipoprotein and transport of cholesterol, respectively [49, 50]. Rare mutations in APOB and LRP2 inhibited the proliferation of cardiomyocytes and were associated with the occurrence of left heart hypoplasia syndrome (HLHS) [51]. These findings suggested that genetic variants in genes related to lipid metabolism may be responsible for affected lipid levels in CHD patients.

Our research did find differences in blood lipid levels of adult CHD between diverse populations. We considered that the living environment and diet were partially responsible for this difference. For example, adherence to the Mediterranean diet could reduce the prevalence of obesity (especially abdominal obesity) and MS [52, 53]. Studies have shown that energy expenditure levels were also associated with lipid levels in CHD patients, with more active young men showing lower TC and TG levels in comparison with their moderately active and sedentary peers [54]. Medication use may also affect blood lipid levels in CHD patients, such as beta-blockers lowering HDL-C levels [55]. Because of the lack of data on the correlation between living environment, dietary habits, energy expenditure levels and medication use with lipid levels in the included literature, we were unable to further analyze the relationship between the above factors and lipid levels in adult patients with CHD. To clarify whether the blood lipid levels of adult CHD patients were affected by these factors needs further study in the future.

Our results also showed that the HbA1c levels of CHD patients were significantly higher than healthy controls. A total of 5 studies reported HbA1c levels of 191 CHD patients, of which 175 were CCHD patients. Research had shown that long-term hypoxemia in CCHD patients could significantly increase the number of red blood cells, thereby prolonging the blood passage time and headed to a reduction in blood rheology [56]. Elevated levels of HbA1c may be related to the above results. In addition, it was reported that CCHD survivors have a significantly increased risk of type 2 diabetes (T2DM), which may also be related to their increased HbA1c levels [57]. Although studies have shown a higher prevalence of hypertension and diabetes in patients with CHD [11, 22], it is important to note that hyperglycemia can also lead to excessive production of superoxide beyond the mitochondrial electron transport chain via different molecular mechanisms. This, in turn, can result in vascular damage and the death of cardiomyocyte [58]. Therefore, the implementation of strict glycemic control through insulin therapy can be cardioprotective by enhancing glucose consumption as well as reducing both circulating levels and myocardial uptake of free fatty acids [59, 60]. However, no significant differences in blood glucose levels and blood pressure between patients with CHD and the general population were found in our study, which may be due to the limited data we collected and inconsistent measurement criteria for relevant biomarkers in the original studies, and more clinical data are needed in the future to elucidate the differences in blood glucose levels and blood pressure between patients with CHD and the general population.

There were some limitations in our study. First, for most results, there was a varying degree of heterogeneity between studies. This is because CHD comprises a spectrum of very different anatomical, physiological and clinical conditions. We hypothesize that the source of heterogeneity may be the specific classifications of CHD and whether surgical interventions were performed in CHD patients, which needs to be determined by more detailed clinical studies in the future. More importantly, the subgroup analysis revealed variations in the levels of relevant biomarkers across different populations. For example, Americans exhibited lower TG levels, although only one study was included. Conducting more clinical studies could help mitigate study bias and provide further insights into the variation in TG levels among CHD patients. Additionally, it can shed light on the impact of genetics and diet on their lipid levels in different regions.

Our study identified significantly lower lipid levels in patients with CHD than in control group. We hypothesize that low lipid levels pose a disadvantage in patients with CHD due to the adult heart’s heavy reliance on mitochondrial oxidative phosphorylation to produce ATP for energy [61], Cardiomyocytes, in particular, prefer lipids as their primary and more energy-efficient substrate, contributing to approximately 70% of total ATP production. This underscores the importance of plasma fatty acid uptake in maintaining cardiac viability [62]. These finding have implications for the health management of patients with CHD and can provide guidance for clinicians. However, further research is needed, both at the basic and clinical levels, are necessary to validate the impact of low-fat conditions on patients with CHD and explore the potential efficacy of nutritional interventions to mitigate this impact.

Conclusions

The lipid levels in patients with congenital heart disease were significantly lower than those in the control group. These data will help in the health management of patients with congenital heart disease and guide clinicians.

Data Availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Abbreviations

ACHD:

acyanotic congenital heart disease

APOB:

Apolipoprotein B

AVD:

aortic valve disease

BMI:

body mass index

CCHD:

cyanotic CHD

CHD:

congenital heart disease

CI:

confidence interval

COA:

coarctation of the aorta

CVD:

cardiovascular disease

HbA1c:

hemoglobin A1c

HDL-C:

high-density lipoprotein cholesterol

HLHS:

left heart hypoplasia syndrome

LDL-C:

low-density lipoprotein cholesterol

LRP2:

lipoprotein-related receptor protein 2

MS:

metabolic syndrome

SCH:

subclinical hypothyroidism

TC:

total cholesterol

TG:

triglyceride

TOF:

tetralogy of Fallot

VSD:

ventricular septal defect

8 References

  1. Liu Y, Chen S, Zühlke L, Black GC, Choy MK, Li N, et al. Global birth prevalence of congenital heart defects 1970–2017: updated systematic review and meta-analysis of 260 studies. Int J Epidemiol. 2019;48(2):455–63.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Niwa K. Adults with congenital heart disease transition. Curr Opin Pediatr. 2015;27(5):576–80.

    Article  PubMed  Google Scholar 

  3. Orwat S, Diller GP. Congenital heart defects as an intrinsic additional risk factor for the occurrence and outcome of myocardial infarction. Eur Heart J. 2021;42(26):2587–9.

    Article  PubMed  Google Scholar 

  4. Gagliardi MG, Adorisio R, Crea F, Versacci P, Di Donato R, Sanders SP. Abnormal vasomotor function of the epicardial coronary arteries in children five to eight years after arterial switch operation: an angiographic and intracoronary doppler flow wire study. J Am Coll Cardiol. 2005;46(8):1565–72.

    Article  PubMed  Google Scholar 

  5. Niwa K. Metabolic syndrome in adult congenital heart disease. Korean Circulation Journal. 2019;49(8):691–708.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. van der Velde ET, Vriend JW, Mannens MM, Uiterwaal CS, Brand R, Mulder BJ. CONCOR, an initiative towards a national registry and DNA-bank of patients with congenital heart disease in the Netherlands: rationale, design, and first results. Eur J Epidemiol. 2005;20(6):549–57.

    Article  PubMed  Google Scholar 

  7. Mottillo S, Filion KB, Genest J, Joseph L, Pilote L, Poirier P, et al. The metabolic syndrome and cardiovascular risk a systematic review and meta-analysis. J Am Coll Cardiol. 2010;56(14):1113–32.

    Article  PubMed  Google Scholar 

  8. Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR, et al. The metabolic syndrome. Endocr Rev. 2008;29(7):777–822.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Deen JF, Krieger EV, Slee AE, Arslan A, Arterburn D, Stout KK et al. Metabolic syndrome in adults with congenital heart disease. J Am Heart Association. 2016;5(2).

  10. Maser RE, Lenhard MJ. An overview of the effect of weight loss on cardiovascular autonomic function. Curr Diabetes Rev. 2007;3(3):204–11.

    Article  PubMed  Google Scholar 

  11. Dellborg M, Björk A, Pirouzi Fard MN, Ambring A, Eriksson P, Svensson AM, et al. High mortality and morbidity among adults with congenital heart disease and type 2 diabetes. Scandinavian Cardiovasc Journal: SCJ. 2015;49(6):344–50.

    CAS  Google Scholar 

  12. Martínez-Quintana E, Rodríguez-González F, Nieto-Lago V, Nóvoa FJ, López-Rios L, Riaño-Ruiz M. Serum glucose and lipid levels in adult congenital heart disease patients. Metab Clin Exp. 2010;59(11):1642–8.

    Article  PubMed  Google Scholar 

  13. Martínez-Quintana E, Rodríguez-González F. Lipoprotein(a) concentrations in adult congenital heart disease patients. Congenit Heart Dis. 2014;9(1):63–8.

    Article  PubMed  Google Scholar 

  14. Ohuchi H, Miyamoto Y, Yamamoto M, Ishihara H, Takata H, Miyazaki A, et al. High prevalence of abnormal glucose metabolism in young adult patients with complex congenital heart disease. Am Heart J. 2009;158(1):30–9.

    Article  CAS  PubMed  Google Scholar 

  15. Flannery LD, Fahed AC, DeFaria Yeh D, Youniss MA, Barinsky GL, Stefanescu Schmidt AC, et al. Frequency of Guideline-Based statin therapy in adults with congenital heart disease. Am J Cardiol. 2018;121(4):485–90.

    Article  PubMed  Google Scholar 

  16. Tarp JB, Clausen P, Celermajer D, Christoffersen C, Jensen AS, Sørensen K, et al. Vascular function in adults with cyanotic congenital heart disease. Int J Cardiol Heart Vasculature. 2020;30:100632.

    Article  Google Scholar 

  17. Duffels MG, Mulder KM, Trip MD, de Groot E, Gort J, van Dijk AP, et al. Atherosclerosis in patients with cyanotic congenital heart disease. Circulation Journal: Official Journal of the Japanese Circulation Society. 2010;74(7):1436–41.

    Article  CAS  PubMed  Google Scholar 

  18. Sposito AC, Caramelli B, Fonseca FA, Bertolami MC, Afiune Neto A, Souza AD, et al. [IV brazilian Guideline for Dyslipidemia and Atherosclerosis prevention: Department of atherosclerosis of brazilian society of Cardiology]. Arquivos brasileiros de cardiologia. 2007;88(Suppl 1):2–19.

    Article  PubMed  Google Scholar 

  19. Giannakoulas G, Dimopoulos K, Engel R, Goktekin O, Kucukdurmaz Z, Vatankulu MA, et al. Burden of coronary artery disease in adults with congenital heart disease and its relation to congenital and traditional heart risk factors. Am J Cardiol. 2009;103(10):1445–50.

    Article  PubMed  Google Scholar 

  20. Pillutla P, Shetty KD, Foster E. Mortality associated with adult congenital heart disease: Trends in the US population from 1979 to 2005. Am Heart J. 2009;158(5):874–9.

    Article  PubMed  Google Scholar 

  21. Verheugt CL, Uiterwaal CS, van der Velde ET, Meijboom FJ, Pieper PG, van Dijk AP, et al. Mortality in adult congenital heart disease. Eur Heart J. 2010;31(10):1220–9.

    Article  PubMed  Google Scholar 

  22. Moons P, Van Deyk K, Dedroog D, Troost E, Budts W. Prevalence of cardiovascular risk factors in adults with congenital heart disease. European journal of cardiovascular prevention and rehabilitation: official journal of the European Society of Cardiology, Working Groups on Epidemiology & Prevention and Cardiac Rehabilitation and Exercise Physiology. 2006;13(4):612–6.

  23. Pemberton VL, McCrindle BW, Barkin S, Daniels SR, Barlow SE, Binns HJ, et al. Report of the National Heart, Lung, and Blood Institute’s Working Group on obesity and other cardiovascular risk factors in congenital heart disease. Circulation. 2010;121(9):1153–9.

    Article  PubMed  PubMed Central  Google Scholar 

  24. PubMed database. URL:https://pubmedncbinlmnihgov/.Accessed 8 July 2022.

  25. Web of Science database. URL:http://appswebofknowledgecom.Accessed 19 July 2022.

  26. Wells GA SB, O’Connell D, Peterson J, Welch V, Losos M et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality if nonrandomized studies in meta-analyses. Available from: http://www.ohrica/programs/clinical_epidemiology/oxfordhtm [cited 2009 Oct 19].

  27. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.

    Article  PubMed  Google Scholar 

  28. Wang T, Chen L, Yang T, Huang P, Wang L, Zhao L, et al. Congenital heart Disease and Risk of Cardiovascular Disease: a Meta-analysis of Cohort Studies. J Am Heart Association. 2019;8(10):e012030.

    Article  Google Scholar 

  29. J. C. Statistical power analysis for the behavioral Sciences. Hillsdale, NJ: L Erlbaum Associates. 1988.

  30. meta-analysis effect. size calculator. https://wwwcampbellcollaborationorg/.Accessed 28 July 2022.

  31. Lopaschuk GD, Karwi QG, Tian R, Wende AR, Abel ED. Cardiac Energy Metabolism in Heart failure. Circul Res. 2021;128(10):1487–513.

    Article  CAS  Google Scholar 

  32. Goldberg DJ, Surrey LF, Glatz AC, Dodds K, O’Byrne ML, Lin HC et al. Hepatic fibrosis is Universal following Fontan Operation, and severity is Associated with Time from surgery: a liver biopsy and hemodynamic study. J Am Heart Association. 2017;6(5).

  33. Habib A, Mihas AA, Abou-Assi SG, Williams LM, Gavis E, Pandak WM, et al. High-density lipoprotein cholesterol as an indicator of liver function and prognosis in noncholestatic cirrhotics. Clin Gastroenterol Hepatology: Official Clin Pract J Am Gastroenterological Association. 2005;3(3):286–91.

    Article  CAS  Google Scholar 

  34. Baek JS, Bae EJ, Ko JS, Kim GB, Kwon BS, Lee SY, et al. Late hepatic complications after Fontan operation; non-invasive markers of hepatic fibrosis and risk factors. Heart. 2010;96(21):1750–5.

    Article  CAS  PubMed  Google Scholar 

  35. Whiteside W, Tan M, Yu S, Rocchini A. Low total, low-density lipoprotein, high-density lipoprotein, and non-high-density lipoprotein cholesterol levels in patients with complex congenital heart disease after Fontan palliation. J Pediatr. 2013;162(6):1199–204.

    Article  CAS  PubMed  Google Scholar 

  36. Hollowell JG, Staehling NW, Flanders WD, Hannon WH, Gunter EW, Spencer CA, et al. Serum TSH, T(4), and thyroid antibodies in the United States population (1988 to 1994): National Health and Nutrition Examination Survey (NHANES III). J Clin Endocrinol Metab. 2002;87(2):489–99.

    Article  CAS  PubMed  Google Scholar 

  37. Martínez-Quintana E, Rodríguez-González F, Nieto-Lago V. Subclinical hypothyroidism in grown-up congenital heart disease patients. Pediatr Cardiol. 2013;34(4):912–7.

    Article  PubMed  Google Scholar 

  38. Martínez-Quintana E, Rodríguez-González F, Fábregas-Brouard M, Nieto-Lago V. Serum and 24-hour urine analysis in adult cyanotic and noncyanotic congenital heart disease patients. Congenit Heart Dis. 2009;4(3):147–52.

    Article  PubMed  Google Scholar 

  39. Palmieri EA, Fazio S, Lombardi G, Biondi B. Subclinical hypothyroidism and cardiovascular risk: a reason to treat? Treat Endocrinol. 2004;3(4):233–44.

    Article  PubMed  Google Scholar 

  40. Fatourechi V. Subclinical hypothyroidism: an update for primary care physicians. Mayo Clin Proc. 2009;84(1):65–71.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Mascitelli L, Pezzetta F, Goldstein MR. Reduced body iron stores and atherosclerosis in patients with cyanotic congenital heart disease. Int J Cardiol. 2011;146(1):117.

    Article  PubMed  Google Scholar 

  42. Vinchi F, Porto G, Simmelbauer A, Altamura S, Passos ST, Garbowski M, et al. Atherosclerosis is aggravated by iron overload and ameliorated by dietary and pharmacological iron restriction. Eur Heart J. 2020;41(28):2681–95.

    Article  CAS  PubMed  Google Scholar 

  43. Varan B, Tokel K, Yilmaz G. Malnutrition and growth failure in cyanotic and acyanotic congenital heart disease with and without pulmonary hypertension. Arch Dis Child. 1999;81(1):49–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Vaidyanathan B, Nair SB, Sundaram KR, Babu UK, Shivaprakasha K, Rao SG, et al. Malnutrition in children with congenital heart disease (CHD) determinants and short term impact of corrective intervention. Indian Pediatr. 2008;45(7):541–6.

    PubMed  Google Scholar 

  45. Tokel K, Azak E, Ayabakan C, Varan B, Aşlamaci SA, Mercan S. Somatic growth after corrective surgery for congenital heart disease. Turk J Pediatr. 2010;52(1):58–67.

    PubMed  Google Scholar 

  46. Blasquez A, Clouzeau H, Fayon M, Mouton JB, Thambo JB, Enaud R, et al. Evaluation of nutritional status and support in children with congenital heart disease. Eur J Clin Nutr. 2016;70(4):528–31.

    Article  CAS  PubMed  Google Scholar 

  47. Ratanachu-Ek S, Pongdara A. Nutritional status of pediatric patients with congenital heart disease: pre- and post cardiac surgery. J Med Association Thailand = Chotmaihet Thangphaet. 2011;94(Suppl 3):133–7.

    Google Scholar 

  48. Hirose Y, Ichida F, Oshima Y. Developmental status of young infants with congenital heart disease. Pediatr Int. 2007;49(4):468–71.

    Article  PubMed  Google Scholar 

  49. Whitfield AJ, Barrett PH, van Bockxmeer FM, Burnett JR. Lipid disorders and mutations in the APOB gene. Clin Chem. 2004;50(10):1725–32.

    Article  CAS  PubMed  Google Scholar 

  50. Christensen EI, Verroust PJ. Megalin and cubilin, role in proximal tubule function and during development. Pediatr Nephrol. 2002;17(12):993–9.

    Article  PubMed  Google Scholar 

  51. Theis JL, Vogler G, Missinato MA, Li X, Nielsen T, Zeng XI et al. Patient-specific genomics and cross-species functional analysis implicate LRP2 in hypoplastic left heart syndrome. eLife. 2020;9.

  52. Martínez-González MA, García-Arellano A, Toledo E, Salas-Salvadó J, Buil-Cosiales P, Corella D, et al. A 14-item Mediterranean diet assessment tool and obesity indexes among high-risk subjects: the PREDIMED trial. PLoS ONE. 2012;7(8):e43134.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Kastorini CM, Milionis HJ, Esposito K, Giugliano D, Goudevenos JA, Panagiotakos DB. The effect of Mediterranean diet on metabolic syndrome and its components: a meta-analysis of 50 studies and 534,906 individuals. J Am Coll Cardiol. 2011;57(11):1299–313.

    Article  CAS  PubMed  Google Scholar 

  54. Vasconcelos IQ, Stabelini Neto A, Mascarenhas LP, Bozza R, Ulbrich AZ, Campos W, et al. Cardiovascular risk factors in adolescents with different levels of energy expenditure. Arquivos brasileiros de cardiologia. 2008;91(4):207–12.

    PubMed  Google Scholar 

  55. Lardinois CK, Neuman SL. The effects of antihypertensive agents on serum lipids and lipoproteins. Arch Intern Med. 1988;148(6):1280–8.

    Article  CAS  PubMed  Google Scholar 

  56. Katayama Y, Horigome H, Murakami T, Takahashi-Igari M, Miyata D, Tanaka K. Evaluation of blood rheology in patients with cyanotic congenital heart disease using a microchannel array flow analyzer. Clin Hemorheol Microcirc. 2006;35(4):499–508.

    PubMed  Google Scholar 

  57. Madsen NL, Marino BS, Woo JG, Thomsen RW, Videbœk J, Laursen HB et al. Congenital heart Disease With and without cyanotic potential and the long-term risk of diabetes Mellitus: a Population-Based follow-up study. J Am Heart Association. 2016;5(7).

  58. Rahman S, Rahman T, Ismail AA, Rashid AR. Diabetes-associated macrovasculopathy: pathophysiology and pathogenesis. Diabetes Obes Metab. 2007;9(6):767–80.

    Article  CAS  PubMed  Google Scholar 

  59. Caturano A, Galiero R, Pafundi PC, Cesaro A, Vetrano E, Palmiero G, et al. Does a strict glycemic control during acute coronary syndrome play a cardioprotective effect? Pathophysiology and clinical evidence. Diabetes Res Clin Pract. 2021;178:108959.

    Article  CAS  PubMed  Google Scholar 

  60. Mehta SR, Yusuf S, Díaz R, Zhu J, Pais P, Xavier D, et al. Effect of glucose-insulin-potassium infusion on mortality in patients with acute ST-segment elevation myocardial infarction: the CREATE-ECLA randomized controlled trial. JAMA. 2005;293(4):437–46.

    Article  PubMed  Google Scholar 

  61. Saddik M, Lopaschuk GD. Myocardial triglyceride turnover and contribution to energy substrate utilization in isolated working rat hearts. J Biol Chem. 1991;266(13):8162–70.

    Article  CAS  PubMed  Google Scholar 

  62. Stanley WC, Chandler MP. Energy metabolism in the normal and failing heart: potential for therapeutic interventions. Heart failure reviews. 2002;7(2):115 − 30.

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Funding

This work was supported by the Fundamental Research Funds for the Central Universities(lzujbky-2018-88), National Natural Science Foundation of China (31670379), The project of innovation service platform for Gansu province(18JR2TA024) and Science and technology planning project of Lanzhou science and Technology Bureau.

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  1. Fengdie Ma and Peiqiang Li contributed equally to this work and share first authorship.

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  1. Institute of Genetics, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China

    Fengdie Ma, Peiqiang Li, Shasha Zhang, Wenjing Shi, Jing Wang, Qinglong Ma, Ziyan Nie, Handan Xiao, Xinyi Chen & Xiaodong Xie

  2. The first people’s hospital of Lanzhou city, Lanzhou, Gansu, China

    Meie Zhao

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Contributions

PL contributed to the conception and design of the study, analyzed the data, and wrote and revised the manuscript. XX contributed to the conception and design of the study. FM organized the database, analyzed the data, and wrote the manuscript. SZ and WS organized the database. QM, JW and MZ performed the statistical analysis. ZN and HX wrote the first draft of the manuscript. CX wrote parts of the manuscript. All authors read and approved the final manuscript.

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This work was supported by the Gansu Provincial Natural Science Foundation of China (23JRRA1046), Peiqiang Li. Fundamental Research Funds for the Central Universities(lzujbky-2018-88), Peiqiang Li. National Natural Science Foundation of China (31670379), Xiaodong Xie. The project of innovation service platform for Gansu province(18JR2TA024), Xiaodong Xie. Science and technology planning project of Lanzhou science and Technology Bureau, Xiaodong Xie.

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Ma, F., Li, P., Zhang, S. et al. Decreased lipid levels in adult with congenital heart disease: a systematic review and Meta-analysis. BMC Cardiovasc Disord 23, 523 (2023). https://doi.org/10.1186/s12872-023-03455-w

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BMC Cardiovascular Disorders

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