Clinical Value of Serum APOC1, LMNA, and TPM4 in Predicting Fetal Cardiac Abnormalities in Elderly Parturients

Abstract

Objective

This study aimed to explore the clinical value of serum APOC1, LMNA, and TPM4 in predicting fetal cardiac abnormalities in elderly parturients.

Methods

A retrospective study was conducted on 108 elderly (≥ 35 years old) pregnant women suspected of having fetal heart abnormalities (high-risk cohort). They were divided into a normal group (42 cases) and an abnormal group (66 cases). APOC1, LMNA, and TPM4 with significant differences were selected from 10 cardiac related proteins in blood samples collected at 18–24 weeks of pregnancy. A combined prediction model was constructed using logistic regression.

Results

In the abnormal group, APOC1 level was increased, but LMNA and TPM4 levels were decreased compared with the normal group (both P<0.05). The level of APOC1 was elevated, but LMNA and TPM4 levels were declined with the severity of cardiac abnormalities,(P<0.05). After adjusting for folate supplementation history, APOC1 (OR=1.85), LMNA (OR=0.52), and TPM4 (OR=0.41) were independent predictors of fetal cardiac abnormalities (all P<0.001). The AUC of the joint model for predicting fetal cardiac abnormalities was 0.906, with a sensitivity of 98.49% and a specificity of 76.19%, all of which were superior to individual indicator (P<0.05).

Conclusion

The combined detection of APOC1, LMNA, and TPM4 has high predictive value for fetal cardiac abnormalities in elderly mothers, but external validation is needed to confirm clinical applicability.

Introduction

Congenital Heart Disease (CHD) refers to the structural or functional abnormalities of the heart and large blood vessels during embryonic development, and is one of the most common birth defects worldwide. According to statistics, over 1.3 million newborns worldwide suffer from CHD each year, with approximately 15% of cases potentially leading to death due to severe heart malformations.^1,2 With the development of social economy and changes in fertility concepts, the proportion of elderly parturients (referring to mothers aged ≥ 35) has been increasing year by year. Research has shown that the incidence of CHD in fetuses of elderly parturients reaches 83.56 million, and has become an important independent risk factor for fetal cardiac structural and functional abnormalities.^3,4 Elderly parturients (≥ 35 years old) is an independent risk factor for fetal cardiac abnormalities. The pathological and physiological mechanisms may differ from those of younger parturients, including aging of oocytes, decreased placental function, changes in metabolic profile, and chronic low-grade inflammatory state, etc.^5,6 However, However, there is currently very little study on serum markers specifically targeting the special high-risk group of the elderly. The existing literature mostly adjusts for the elderly as a confounding factor rather than conducting in-depth analysis as an independent research subject.

Although prenatal screening currently mainly relies on fetal echocardiography, its operator dependence is strong, and the detection rate of early small lesions is limited. Additionally, elderly pregnant women often increase the difficulty of examination due to factors such as abdominal wall fat thickening and fetal position.⁷ In recent years, non-invasive screening strategies based on maternal serum biomarkers have gradually gained attention. Luo et al⁸ found that serological indicators in the second trimester of pregnancy could be used to predict fetal sex chromosome abnormalities, and Liu et al⁹ reported that maternal apolipoprotein levels in the second trimester were associated with adverse pregnancy outcomes. Cardiac development involves multiple key molecular pathways, among which lipid metabolism provides energy and a membrane structural foundation for cardiomyocytes, nuclear structural proteins maintain the stability of the cell nucleus and regulate gene expression, and myocardial function proteins directly participate in the assembly of the cardiac contraction apparatus^10–12. APOC1, as a crucial lipid metabolism regulatory protein, its abnormal levels may affect the energy supply and membrane structure construction required for embryonic heart development.¹⁰ LMNA is a core protein that maintains the stability of the cell nucleus structure and is involved in regulating cell differentiation and migration, being crucial for the normal development of cardiac progenitor cells.¹¹ TPM4 is an important component of the sarcomere and directly affects the contraction function of cardiomyocytes and the assembly of myofibrils.¹² The above three pathways respectively form the core network that influences cardiac morphogenesis at the levels of metabolism, cellular structure and contraction function. However, there is currently a lack of multi-dimensional combined assessment studies targeting these three pathways.

Although some scholars have conducted preliminary studies on the functions of APOC1, LMNA and TPM4 in cardiovascular diseases, there is still a significant research gap regarding the direct association between these three indicators and fetal cardiac structural abnormalities. The research on APOC1 in prenatal diagnosis mostly focuses on lipid metabolism and pregnancy complications. The study by Pecks et al¹³ only analyzed the relationship between apolipoprotein profiles in umbilical cord blood and intrauterine growth restriction, and did not involve the association between maternal serum APOC1 and fetal cardiac structure. Secondly, regarding LMNA, Mukherjee et al¹⁴ found that the methylation status of LMNA was associated with congenital heart disease. However, his study was based on DNA epigenetics and had not yet been translated into widely available maternal serum protein testing in clinical practice. Furthermore, research on TPM4 in fetal heart malformations is even more scarce. Scholars such as Liu et al¹⁵ mentioned the value of “serum protein measurement” when exploring the combination of ultrasound and serum protein detection, but did not clarify the expression characteristics of TPM4 and its correlation with the type and severity of cardiac malformations. At present, there is no systematic evaluation of the correlation between maternal serum APOC1, LMNA, and TPM4 levels and fetal cardiac structural abnormalities in the high-risk group of elderly parturients with fetal cardiac abnormalities, and there is a lack of clinical efficacy verification of their combined detection in prenatal diagnosis.

In this study, we aimed to explore the clinical value of serum APOC1, LMNA, and TPM4 in predicting fetal cardiac abnormalities in prenatal diagnosis of elderly parturients, aiming to provide accurate screening basis for clinical practice.

Materials and Methods

Research Objects and Grouping

This study retrospectively included 136 elderly (delivery age ≥ 35 years) singleton parturients who underwent prenatal examinations in our department during January 2022 to March 2025 as the research subjects. After screening based on inclusion and exclusion criteria, 108 pregnant women were ultimately included. All selected pregnant women were suspected of having fetal cardiac structural abnormalities due to ultrasound findings or high-risk factors such as advanced age, abnormal serological screening, etc. The patient inclusion process was shown in Figure 1.

Figure 1.

Process diagram for patient inclusion.

Grouping criteria: Using pregnancy outcomes as the gold standard, all included pregnant women were tracked and followed up. Based on the diagnosis of cardiac structure confirmed by neonatal echocardiography or pathological anatomical examination after induced abortion, all 108 pregnant women were ultimately divided into the following two groups: Normal fetal heart group (n=42): Pregnant women with no congenital abnormalities in fetal heart structure. Fetal heart abnormality group (n=66): Pregnant women with one or more congenital heart structural abnormalities in their fetuses.

Inclusion criteria: (1) Pregnant women aged ≥ 35 years old; (2) Natural conception, singleton pregnancy; (3) Those completed standardized fetal systematic ultrasound examination (including fetal cardiac ultrasound screening) and serological indicators (APOC1, LMNA, TPM4) testing involved in this study at our hospital between 18–24 weeks of pregnancy; (4) Those with complete follow-up data on pregnancy outcomes (cardiac ultrasound reports within 42 days after birth of live births, or pathological diagnosis reports of induced fetuses); (5) Those with complete clinical medical records; (6) Pregnant women signed the informed consent.

Exclusion criteria: (1) Pregnant women had a clear history of acute infection with pathogens such as cytomegalovirus, Toxoplasma gondii, rubella virus, and herpes simplex virus (TORCH) during pregnancy, or had used potentially teratogenic drugs for a long time; (2) Pregnant women with uncontrolled infectious diseases, blood system diseases, autoimmune diseases, severe liver and kidney dysfunction or other serious chronic diseases; (3) Due to factors such as fetal position, oligohydramnios, or excessive abdominal wall thickness in pregnant women, the quality of mid pregnancy ultrasound images could not meet diagnostic requirements; (4) Severe congenital abnormalities in the fetus with confirmed chromosomal abnormalities (such as trisomy 21, trisomy 18, etc.) or other systems (such as the nervous system, digestive system); (5) Pregnant women with incomplete clinical data or lost to follow-up.

Sample size estimation basis: the method of Hanley-McNeil¹⁶ was referred, assuming the combined detection AUC to be 0.85, and the ratio of the abnormal group to the normal group to be approximately 1.5:1, with α set at 0.05 and the test power 1 - β set at 0.80, the estimated minimum sample size required was 90 cases. This study actually included 108 cases, which met the minimum sample size requirement.

Serological Indicator Detection and Data Collection

Serum Sample Collection and Processing

All study subjects had 4 mL of elbow vein blood collected by professional nurses on an empty stomach in the morning between 18–24 weeks of pregnancy. The blood sample was placed in a non-anticoagulant vacuum blood collection tube and left to solidify at room temperature for 30 minutes. Subsequently, the sample was centrifuged at a speed of 4500 r/min for 15 minutes under the condition of a centrifuge radius of 15 cm. The upper serum layer was carefully aspirated, transferred into sterile EP tubes, and immediately stored in a −80°C ultra-low temperature freezer for testing. The sample should be thaw uniformly before testing to avoid repeated freezing and thawing.

Serological Indicator Detection

This study detected 10 serum proteins related to cardiac development and function, including apolipoprotein C1 (APOC1), laminin A (LMNA), tropomyosin 4 (TPM4), N-terminal proBNP (NT-proBNP), cardiac troponin I (cTnI), apolipoprotein B (APOB), apolipoprotein A1 (APOA1), homocysteine (Hcy), growth differentiation factor 15 (GDF15), and soluble growth stimulating gene 2 protein (sST2). All indicators were quantitatively detected using ELISA.

The APOC1, LMNA, and TPM4 ELISA kits were purchased from Shanghai Aoruisai Biotechnology Co., Ltd. (item number: APOC1 was ORSE268H, LMNA was ORSE2145H, and TPM4 was ORSE3775H). The ELISA kits for the remaining indicators were purchased from Wuhan Huamei Bioengineering Co., Ltd. (item numbers were CSB-E07941h, CSB-E08430h, CSB-E09608h, CSB-E07946h, CSB-E08901h, CSB-E13390h, CSB-E17014h in turn). The testing process was strictly operated according to the instructions of each reagent kit. The main steps were listed as follows: the serum sample to be tested were diluted according to the instructions and added to a microplate pre coated with antibodies, incubating at 37°C for 60 minutes. Then, the plate was washed for 5 times. Subsequently, the detection antibody labeled with horseradish peroxidase was added and incubated at 37°C for 30 minutes. Then, the plate was washed for 5 times. Subsequently, the substrate solution of tetramethylbenzidine (TMB) was added and incubated in the dark for 15 minutes. Finally, the termination solution was added. A Bio-Rad 680 ELISA reader was immediately used to read the absorbance (OD value) at a wavelength of 450 nm. The concentrations of each indicator were calculated based on the standard curve. All samples were set with double wells, and the average was taken as the final result.

Clinical Data Collection

By reviewing the hospital’s electronic medical record system, the following baseline and clinical information of two groups of pregnant women were collected and recorded: age, gestational age, pre pregnancy body mass index (BMI), educational level, previous pregnancy history (number of pregnancies, number of miscarriages), family history of congenital heart disease (referring to first or second degree relatives with a history of congenital heart disease), regular supplementation of folic acid in early pregnancy (≤ 12 weeks), and comorbidities of gestational hypertension or thyroid dysfunction.

Construction of the Combined Prediction Model

This study first conducted a comparison between two groups for 10 cardiac-related serum proteins (APOC1, LMNA, TPM4, NT-proBNP, cTnI, APOB, APOA1, Hcy, GDF15, sST2) (using independent sample t-test or Mann–Whitney U-test). Only APOC1, LMNA, and TPM4 showed statistically significant differences between the normal and abnormal fetal heart groups (P < 0.05), while the other 7 indicators did not show significant differences (P > 0.05). Therefore, the above three types of proteins were incorporated into the subsequent combined prediction model. A binary Logistic regression analysis (using the Enter method) was employed. The fetal heart abnormality was set as the dependent variable (with values: 0 = normal, 1 = abnormal), and the measured values of APOC1, LMNA, and TPM4 were used as independent variables to construct a combined prediction model. The predicted probability (P) for each patient was calculated. Using this predicted probability as the joint detection indicator, the ROC curve was drawn and the AUC, sensitivity, and specificity were calculated.

Classification and Severity Assessment of Fetal Cardiac Abnormalities

Diagnosis and classification of abnormal types: All cases included in the “fetal heart abnormality group” were diagnosed based on the specific type of cardiac malformation detected by neonatal echocardiography within 42 days after birth or pathological anatomical examination of the fetus after induced labor. Abnormal types included atrial septal defect, ventricular septal defect, tetralogy of Fallot, patent ductus arteriosus, and other complex malformations (such as transposition of the great arteries, pulmonary artery occlusion, etc).

Severity grading: This study referred to The expert consensus on commonly used standard sections and technical requirements for prenatal screening and diagnosis of fetal heart ultrasound⁵ and Quan et al³‘s research method on severity stratification of congenital heart disease in fetuse to grade the severity of fetal cardiac abnormalities. All the ultrasound images were evaluated by two senior pediatric echocardiologists who were unaware of the serological test results. The severity was classified into three levels based on the anatomical complexity of the malformation, the expected impact on hemodynamics, and the urgency of interventions required after birth. Mild abnormality: There was a simple and isolated defect (such as small atrial septal defect, ventricular septal defect), which was expected to have little impact on circulatory function and might self-heal or only required simple intervention. Moderate abnormality: There were complex single or combined deformities (such as moderate or above defects, patent ductus arteriosus) that were expected to have a clear impact on circulatory function and required surgical or interventional treatment during childhood. Severe abnormalities: There were complex and critical deformities (such as Tetralogy of Fallot, Monoventricular, etc.) that posed a serious threat to circulatory function from birth and required major surgical intervention in the neonatal or early stages of infancy.

Statistical Analysis

Statistical analysis was conducted using SPSS 26.0 software. The measurement data were first subjected to normality test (Shapiro–Wilk test) and homogeneity of variance test (Levene test). Measurement data that conformed to a normal distribution were represented as () and independent sample t-test was used between the two groups. Measurement data that did not conform to a normal distribution were represented by the median (interquartile range) [M (Q1, Q3)], and Mann–Whitney U-test was used for inter group comparison. The enumeration data were represented as [cases (%)], and the comparison between groups was conducted using the chi square test. For comparisons between multiple groups (such as different types and severity levels), if the data followed a normal distribution and homogeneity of variance, one-way ANOVA was used. The pairwise comparison between groups should be tested using LSD method according to the needs; For enumeration data that did not follow a normal distribution, Kruskal Wallis H-test was used, and Mann–Whitney U-test was used for pairwise comparisons between groups with adjusted test levels. To control the influence of potential confounding factors, the following strategies were adopted: (1) In the univariate analysis, baseline variables with P < 0.05 (regular supplementation of folic acid during early pregnancy) were regarded as potential confounding factors. (2) The above variables and three serum indicators were simultaneously included in the multivariate Logistic regression model (using the Enter method) to evaluate the independent predictive effects of each indicator. (3) The Hosmer-Lemeshow test was used to evaluate the goodness of fit of the model, and the AUC was used to evaluate the discrimination ability of the model. The predictive value of serum APOC1, LMNA, and TPM4 for fetal cardiac abnormalities was analyzed using receiver operating characteristic (ROC) curves. The difference was statistically significant with P<0.05.

Results

General Information Analysis of Two Groups of Pregnant Women

There was no statistically significant difference between the two groups of pregnant women in terms of age, gestational age, pre pregnancy BMI, number of previous pregnancies, number of miscarriages, coexisting thyroid dysfunction, educational level, and gestational hypertension (P>0.05). The proportion of regular folic acid supplementation in the group with fetal heart abnormalities during early pregnancy (24.24%) was significantly lower than that in the group with normal fetal heart (47.62%, P<0.05, Table 1).

Table 1.

General Information Analysis of Two Groups of Pregnant Women [Cases (%),(X±s)]

Groups	Normal Fetal Heart Group (n=42)	Fetal Heart Abnormality Group (n=66)	χ2/t	P
Age (year)	39.75±3.24	40.42±5.27	0.739	0.462
Gestational age (weeks)	22.35±1.26	22.68±1.31	1.295	0.198
Pre pregnancy BMI (kg/m2)	23.94±2.80	23.65±3.17	1.186	0.238
Number of previous pregnancies (times)	1.89±0.76	1.95±0.81	0.384	0.702
Number of miscarriages (times)	0.52±0.31	0.58±0.35	0.907	0.366
Coexisting thyroid dysfunction	38	58	0.175	0.675
Educational level			0.438	0.803
Primary school and below	5(11.90)	7(10.61)
Middle school/high school	17(40.48)	31(46.97)
College degree or above	20(47.62)	28(42.42)
Early supplementation of folic acid	20(47.62)	16(24.24)	6.312	0.012
Hypertension	6(14.29)	11(16.67)	0.108	0.740

Analysis of Serum Indicators in Two Groups of Pregnant Women

Ten serum proteins related to cardiac development and function were selected for preliminary testing, including APOC1, LMNA, TPM4, NT-proBNP, cTnI, APOB, APOA1, Hcy, GDF15, and sST2.

In this study cohort, the pregnant women with fetal heart abnormalities had significantly higher serum APOC1 level and lower serum LMNA and TPM4 levels than those in the fetal heart normal group (P<0.05). The differences in the remaining 7 indicators between the two groups were not statistically significant (P>0.05, Table 2). Due to the limited sample size, the above differences need to be verified in an independent cohort.

Table 2.

Analysis of Serum Indicators in Two Groups of Pregnant Women (X±s), ng/mL

Indicators	Normal Fetal Heart Group (n=42)	Fetal Heart Abnormality Group (n=66)	t value	P value
APOC1	2.35±0.42	3.89±0.65	13.637	<0.001
LMNA	2.03±0.35	1.21±0.28	13.446	<0.001
TPM4	1.56±0.26	0.87±0.19	15.909	<0.001
NT-proBNP	125.33±42.76	131.63±48.23	0.691	0.491
cTnI	0.032±0.011	0.035±0.014	1.176	0.242
APOB	0.89±0.21	0.93±0.25	0.861	0.391
APOA1	1.42±0.28	1.38±0.31	0.678	0.499
Hcy	8.63±2.14	9.02±2.53	1.417	0.159
GDF15	452.36±127.74	481.85±145.20	1.077	0.284
sST2	18.61±5.42	19.36±6.14	0.647	0.519

Comparison of Serum Indicator Levels for Different Types of Fetal Cardiac Abnormalities

The levels of serum APOC1, LMNA, and TPM4 in patients with different types of fetal heart abnormalities were compared, and the differences were statistically significant (APOC1: F=8.763, P=0.001; LMNA: F=6.542, P=0.003; TPM4: F=7.895, P=0.002). Further pairwise comparison (LSD method) showed that the patients with Tetralogy of Fallot had significantly higher serum APOC1 level (4.52 ± 0.71 ng/mL) and lower serum LMNA (0.98 ± 0.21 ng/mL) and TPM4 (0.65 ± 0.15 ng/mL) levels than the groups with atrial septal defect, ventricular septal defect, patent ductus arteriosus, and other malformations (all P<0.05). Besides, patients with atrial septal defect had markedly lower serum APOC1 level (3.21 ± 0.58 ng/mL) and higher serum LMNA (1.45 ± 0.26 ng/mL) and TPM4 (1.02 ± 0.18 ng/mL) levels than patients with tetralogy of Fallot, ventricular septal defect, patent ductus arteriosus, and other malformations (all P<0.05, Table 3).

Table 3.

Comparison of Serum APOC1, LMNA, and TPM4 Levels in Different Types of Fetal Cardiac Abnormalities (X±s), ng/mL

Types of Fetal Cardiac Abnormalities	Cases	APOC1	LMNA	TPM4
Atrial septal defect	16	3.21±0.58	1.45±0.26	1.02±0.18
Ventricular septal defect	21	3.75±0.62 a	1.28±0.24a	0.91±0.17 a
Tetralogy of Fallot	11	4.52±0.71 abcd	0.98±0.21 ab	0.65±0.15 abc
Patent ductus arteriosus	10	3.56±0.59 a	1.32±0.25 a	0.95±0.16 a
Other deformities	8	3.98±0.64 ab	1.15±0.23ab	0.82±0.14ab
F value		8.763	6.542	7.895
P value		0.001	0.003	0.002

Note: ^aP<0.05 indicated that compared to the group with atrial septal defect; ^bP<0.05 represented that compared with the ventricular septal defect group; ^cP<0.05 represented that compared with the group with patent ductus arteriosus and ^dP<0.05 represented that compared with the other malformations.

Comparison of Serum APOC1, LMNA, and TPM4 Levels in Different Severity Levels of Fetal Cardiac Abnormalities

According to one-way analysis of variance and LSD test, the overall differences in serum APOC1, LMNA, and TPM4 levels among mild, moderate, and severe fetal cardiac abnormalities groups were statistically significant (P<0.05). Compared with the mild abnormality group, the serum APOC1 levels in the moderate and severe abnormality groups were significantly increased, while the levels of LMNA and TPM4 were significantly decreased (P<0.05). Compared with the moderate abnormal group, the severe abnormal group showed a further significant increase in serum APOC1 levels and a further significant decrease in LMNA and TPM4 levels (P<0.05, Table 4).

Table 4.

Comparison of Serum APOC1, LMNA, and TPM4 Levels in Different Severity Levels of Fetal Cardiac Abnormalities (X±s), ng/mL

Types of Fetal Cardiac Abnormalities	Cases	APOC1	LMNA	TPM4
Mild abnormality	28	3.25±0.56	1.42±0.27	1.05±0.18
Moderate abnormality	23	3.98±0.61 a	1.15±0.23 a	0.89±0.16 a
Severe abnormality	15	4.82±0.73 ab	0.86±0.19 ab	0.62±0.14 ab
F value		32.102	27.253	33.258
P value		<0.001	<0.001	<0.001

Note: ^aP<0.05 compared to the mild abnormality group, ^bP<0.05 compared to the severe abnormality group.

Correlation Analysis Between Serum APOC1, LMNA, and TPM4

Pearson correlation analysis was used to explore the correlation between serum APOC1, LMNA, and TPM3. The results showed that APOC1 was significantly negatively correlated with LMNA (r=−0.425, P<0.001) and negatively correlated with TPM4 (r=−431, P<0.001). LMNA was significantly positively correlated with TPM4 (r=0.558, P<0.001). The pairwise correlations between each indicator had statistical significance, showing that these three indicators might synergistically participate in the regulation of fetal heart development (Figure 2).

Figure 2.

Correlation analysis between serum APOC1, LMNA, and TPM4. (A) Correlation between serum APOC1 and LMNA; (B) Correlation between serum APOC1 and TPM4; (C) Correlation between serum LMNA and TPM.

The Predictive Value of Serum APOC1, LMNA, and TPM4 for Fetal Cardiac Abnormal Structure

Based on the binary Logistic regression model (Enter method), with fetal cardiac abnormalities as the dependent variable (0 = normal, 1 = abnormal), and the measured values of APOC1, LMNA, and TPM4 as independent variables, the combined prediction probability (P) for each patient was calculated. This prediction probability was used as the combined detection indicator, and the ROC curve was drawn.

The ROC curve analysis results showed that the AUC (95% CI) of serum APOC1, LMNA, and TPM4 for diagnosing fetal cardiac abnormalities separately were 0.824 (0.749–0.900), 0.767 (0.675–0.860), and 0.862 (0.789–0.934), respectively. The AUC of the combined detection of the three indicators (95% CI) was 0.906 (0.847–0.965). According to the Z-test comparison, the AUC of combined detection was higher than that of APOC1 (Z=2.315, P=0.021), LMNA (Z=3.892, P<0.001), and TPM4 (Z=2.103, P=0.035) detected separately. Specific data could be found in Table 5 and Figure 3. In this study cohort, the predictive accuracy of this model was superior to that of any single indicator detection. Given the retrospective design and the lack of external validation, further testing of the model’s predictive accuracy is needed in a prospective multi-center study.

Table 5.

The Predictive Value of Serum APOC1, LMNA, and TPM4 for Fetal Cardiac Abnormal Structure

Indicators	AUC	Sensitivity	Specificity	Yoden Index	Cut-off value	P	95% CI
APOC1	0.824	62.12	90.48	0.526	2.57 ng/mL	<0.05	0.749–0.900
LMNA	0.767	83.33	61.91	0.452	1.93 ng/mL	<0.05	0.675–0.860
TPM4	0.862	86.36	76.19	0.626	1.40 ng/mL	<0.05	0.789–0.934
Combined detection	0.906	98.49	76.19	0.747	——	<0.05	0.847–0.965

Figure 3.

ROC curves for predicting fetal cardiac abnormal structures using serum APOC1, LMNA, and TPM4.

Multivariate Logistic Regression Analysis

The fetal cardiac abnormalities were regarded as the dependent variable, variables with P<0.05 in univariate analysis, as well as serum APOC1, LMNA, and TPM4, were simultaneously included in a multivariate logistic regression model (Enter method). The results showed that after adjusting for the history of folic acid supplementation, APOC1 (OR = 1.85, 95% CI: 1.32–2.59, P < 0.001), LMNA (OR = 0.52, 95% CI: 0.38–0.71, P < 0.001), and TPM4 (OR = 0.41, 95% CI: 0.28–0.60, P < 0.001) remained independent predictors of fetal cardiac abnormalities. The Hosmer-Lemeshow goodness-of-fit test indicated that the model was well-fitted (χ² = 6.230, P = 0.621). The AUC of this multivariate model was 0.921 (95% CI: 0.863–0.979), and there was no statistically significant difference (Z = 0.876, P = 0.381) compared with the AUC of the combined prediction model (which only contained three serum indicators) (0.906), indicating that the predictive value of the three serum indicators was not significantly affected by the aforementioned confounding factors (Table 6, Figures 4 and 5).

Table 6.

Multivariate Logistic Regression Analysis of Factors Influencing Fetal Cardiac Abnormalities

Indicators	β	SE	Wald χ2	P	OR	95% CI
APOC1 (ng/mL)	0.615	0.172	12.780	<0.001	1.850	1.320–2.590
LMNA (ng/mL)	−0.654	0.158	17.130	<0.001	0.520	0.380–0.710
TPM4 (ng/mL)	−0.892	0.196	20.710	<0.001	0.410	0.280–0.600
Regular supplementation of folic acid (yes or no)	−0.857	0.342	6.280	0.012	0.424	0.217–0.830
Constant	−2.341	0.526	19.800	–	–	–

Figure 4.

ROC curve of the multivariate Logistic regression model. The fetal cardiac abnormalities were regarded as the dependent variable. The variables with P<0.05 by univariate analysis, and serum APOC1, LMNA, and TPM4, were simultaneously included in a multivariate logistic regression model (Enter method). The AUC of this multivariate model was 0.921 (95% CI: 0.863–0.979).

Figure 5.

Calibration curve of the multivariate Logistic regression model.

Discussion

This study included 108 elderly (≥ 35 years old) pregnant women with suspected fetal heart abnormalities (high-risk cohort) in a retrospective single-center design, and that the serum levels of APOC1 in pregnant women with fetal heart abnormalities were higher than those in the normal group, while the levels of LMNA and TPM4 were lower than those in the normal group. Further analysis showed that there were characteristic differences in the expression of three indicators among different types of heart malformations: patients with Tetralogy of Fallot had the highest APOC1 level and the lowest LMNA and TPM4 levels, while patients with atrial septal defect showed the opposite pattern. After adjusting for the history of folic acid supplementation using multivariate Logistic regression, APOC1 (OR = 1.85), LMNA (OR = 0.52), and TPM4 (OR = 0.41) remained as independent predictors (all P < 0.001). The diagnostic efficacy assessment showed that the AUC of the combined detection of the three indicators was 0.906, with a sensitivity of 98.49%. Its diagnostic value was superior to that of any single indicator detection in this study cohort. The above results indicated that the combined detection of serum APOC1, LMNA and TPM4 may assist in ultrasound examinations, providing certain laboratory references for the early identification and risk stratification of fetal cardiac structural abnormalities in elderly pregnant women.

As a key apolipoprotein regulating lipid metabolism, the high expression of APOC1 in the fetal heart abnormality group is closely related to the impact of lipid metabolism disorders on embryonic heart development.⁵ Previous studies have confirmed¹⁷ that abnormally elevated levels of APOC1 disrupt normal lipid metabolism homeostasis. Lipids are not only the main components of cell membranes, but also precursors for steroid hormone synthesis, which are crucial for embryonic cell proliferation, differentiation, and signal transduction. As a high-energy consuming organ, the development of the heart heavily relies on normal lipid supply and metabolism. Overexpression of APOC1 may lead to an imbalance of specific lipid substances required for embryonic heart development by affecting lipid transport. The inflammatory response mediated by APOC1 may also affect the migration and differentiation of cardiac progenitor cells, ultimately leading to abnormal cardiac morphology.¹⁸ AlHageh et al found that APOC1 gene mutations were associated with multiple coronary artery diseases, suggesting that functional abnormalities of APOC1 might affect cardiac structure formation through genetic or epigenetic pathways.¹⁹ Liu Q et al found a correlation between maternal apolipoprotein levels in mid pregnancy and the risk of preterm birth, suggesting that the apolipoprotein family may play a role in maintaining pregnancy.⁷ The results of this study showed that in elderly pregnant women with suspected fetal heart abnormalities, the serum APOC1 level in the fetal heart abnormality group was significantly higher than that in the normal group. Further analysis revealed characteristic differences in APOC1 expression among different types of cardiac malformations: patients with Tetralogy of Fallot had the highest APOC1 levels, while those with atrial septal defect had the lowest levels, and the APOC1 levels gradually increased as the severity of cardiac abnormalities increased. In this study cohort, the AUC of APOC1 alone for predicting fetal cardiac abnormalities was 0.824. The above results preliminarily suggested that APOC1 may be involved in fetal heart development abnormalities through the lipid metabolism disorder pathway, and its elevated level is associated with the complexity of cardiac malformations to some extent. However, it should be noted that this study cohort is a high-risk population, and this result still needs to be verified in independent samples.

As a core component of type A nuclear fiber layer protein, low expression of LMNA in the fetal heart abnormality group reflects the impact of nuclear fiber layer dysfunction on embryonic development. Laminin regulates key biological processes such as cell invasion and migration by mediating material exchange and signal transduction between cytoplasm and nucleus. Insufficient expression of LMNA during embryonic stage can lead to disordered proliferation and differentiation of cardiac progenitor cells, which in turn can cause structural abnormalities.^20,21 Mukherjee et al’s research provided support from an epigenetic perspective, as they found an association between the DNA methylation status of LMNA genes and congenital heart disease.¹⁴ Chen et al found that a decrease in maternal serum LMNA levels was associated with an increased risk of adverse pregnancy outcomes, suggesting that it can serve as a prenatal predictive biomarker.²² The results of this study further indicated that in elderly pregnant women with suspected fetal cardiac abnormalities, the serum LMNA levels in the fetal cardiac abnormality group were significantly lower. This finding suggested that LMNA may serve as a candidate serological indicator for assessing the severity of fetal cardiac abnormalities, but further validation in independent cohorts is required.

As a sarcomere skeleton protein, TPM4 directly affects the establishment of myocardial contraction mechanism by regulating myosin function. Abnormal expression of TPM4 during embryonic development can lead to impaired assembly of myocardial structures, ultimately resulting in cardiac malformations.^23,24 Dube DK et al²⁵ confirmed that TPM4α was the main sarcomere tropomyosin subtype in chicken hearts from embryonic to adult stages, highlighting its fundamental role in heart development. In recent years, mutations in the TPM gene (including TPM1) have been clearly associated with familial cardiomyopathy, such as left ventricular hypoplasia and dilated cardiomyopathy. The studies by Zaklyazminskaya EV²⁶ and Barry ME et al²⁷ further elucidated how TPM mutations led to cardiomyopathy by damaging the structural and functional characteristics of the myocardium. Rouland et al’s study suggests that TPM4 has certain diagnostic value for fetal cardiac malformations (AUC=0.882).²⁸ This study obtained similar results in the elderly cohort (AUC = 0.862), and found significant differences in TPM4 among different types of cardiac abnormalities. The TPM4 level was highest in patients with atrial septal defect and lowest in patients with tetralogy of Fallot, and was negatively correlated with the severity of the abnormality (r=−0.547). The mechanism might be that insufficient expression of TPM4 directly affected the sarcomere assembly and functional maturation of fetal cardiomyocytes. The normal morphology of the heart depended on the mechanical force generated by the rhythmic and coordinated contraction activity of myocardial cells. The decrease in TPM4 levels might lead to poor development or weakened function of myocardial cell contractile devices, thereby interfering with the biomechanical signals required for key morphological processes such as cardiac septum, valve formation, and large vessel docking.^29,30 The above results suggested that TPM4 can not only be used for early screening, but also may provide a reference for the preliminary diagnosis of malformations. However, it should be noted that the sample size of this study was limited, and the results of subgroup analysis for different types of malformations should be interpreted with caution.

In the combined prediction model constructed of this study, the AUC of the combined detection of serum APOC1, LMNA and TPM4 was 0.906, and its diagnostic efficacy was higher than that of individual detection of each indicator in this study cohort, which is consistent with the research trend of other multi-marker combined strategies.^31,32 This study is the first to systematically evaluate the combined serum protein testing value of three key pathways - metabolism, nuclear structural stability, and sarcomere function - in the specific high-risk population of advanced maternal age, providing a novel perspective for understanding the multidimensional molecular regulation of fetal cardiac development. Secondly, the high sensitivity of the combined model (98.49%) indicates that it can serve as an effective supplementary tool for ultrasound screening. Especially for cases where the ultrasound image quality is poor due to factors such as thickened abdominal wall fat and abnormal fetal position in elderly pregnant women, the serological combined testing may help reduce missed diagnoses and may assist in prioritizing the identification of cases in high-risk populations that require further targeted fetal echocardiography examinations. However, this hypothesis requires validation in prospective studies. Furthermore, the combined model demonstrated gradient variations across different types and severities of cardiac malformations, suggesting that it can not only be used for binary classification screening of “presence/absence” of abnormalities, but also potentially provide reference information for risk stratification of malformation types and severities. These findings have provided a new laboratory approach for prenatal screening of fetal heart abnormalities in elderly pregnant women. Of course, due to the limitations of the single-center retrospective design and the lack of external validation, the actual clinical efficacy of this combined model needs to be further tested in independent prospective cohorts. However, the combined model in this study was constructed based on single-center retrospective data and has not undergone external validation. Its universality in different populations and medical centers needs to be confirmed. Therefore, this model is currently only used as an exploratory analysis result and should not be directly generalized to routine clinical screening.

This study has the following limitations: (1) It is a single-center retrospective design, which may introduce selection bias, and the sample size is relatively small (66 cases in the abnormal group), which may affect the statistical power of subgroup analysis and the stability of the model. (2) The research subjects were pre-selected high-risk individuals (all suspected of having fetal cardiac abnormalities due to ultrasound findings or high-risk factors), and the results of this study cannot be generalized to the general population of pregnant women. (3) The combined prediction model has not undergone external validation. Its diagnostic threshold and prediction accuracy need to be re-evaluated in an independent cohort. (4) Although multivariate regression was used to correct for known confounding factors, there may still be unmeasured residual confounding factors (such as nutritional status, environmental exposure, socioeconomic status, etc). (5) Due to retrospective design limitations, there may be some bias in the temporal relationship between serum sample collection and ultrasound evaluation. In the future, multi-center and large-sample prospective studies should be conducted, and external validation cohorts should be included to further verify the diagnostic thresholds and clinical applicability of the three indicators.

In summary, this study found that elevated serum APOC1 levels, decreased levels of LMNA and TPM4 in elderly pregnant women were statistically associated with fetal cardiac abnormalities. The combined detection of these three indicators showed a higher predictive efficacy in the study cohort. The tri-sequential detection protocol may serve as an auxiliary tool for ultrasound screening. However, due to the limitations of the retrospective design, single-center, small sample size, and lack of external validation, the clinical applicability of this model still needs to be re-evaluated in an independent external cohort. In the existing prenatal screening process, this combined model can serve as a preliminary risk stratification tool before ultrasound examinations to identify high-risk and elderly pregnant women who require further targeted fetal echocardiography. However, the specific intervention timing, threshold setting, and integration strategy with other screening indicators (such as serological screening and ultrasound soft indicators) still need to be clarified through prospective studies.

Funding Statement

There is no funding to report.

Data Sharing Statement

All data generated or analysed during this study are included in this published article.

Ethics Approval and Consent Statements

This study had been approved by the Henan Provincial People’s Hospital (2025LS057). Written informed consent was obtained from all individual participants included in the study. All procedures performed in studies involving human participants were in accordance with the standards upheld with those of the 1964 Helsinki Declaration and its later amendments for ethical research involving human subjects.

Consent to Participate

Written Informed consent was obtained from every human participant in the study and the patients participating in the study all agree to publish the research results.

Disclosure

The authors declare that they have no competing interests in this work.