Abstract
Introduction
It is estimated that 30%–50% of spontaneous preterm deliveries (PTDs) are caused by infectious states. Acute‐phase responders (APRs) accompany chronic as well as acute inflammatory states. In addition, hyperlipidemia is considered as a precursor of inflammation and oxidative stress. This study aimed to examine the associations between maternal serum levels of lipids and APRs in the 1st trimester of pregnancy and pregnancy outcomes.
Methods
A prospective cohort study was conducted among multipara individuals with singleton pregnancies. Recruitment took place between 11 and 13 gestational weeks. The exposures were serum levels of the following APRs: C‐ reactive protein, albumin, triglyceride, total cholesterol, low density lipoprotein calculated (LDL‐c), and high density lipoprotein. The outcomes were gestational diabetes mellitus, preeclampsia, gestational age at delivery, PTD (< 37 gestational weeks + 0 days), low birthweight, and macrosomia. Participants with the lowest or highest levels for each APR were identified based on clinical cut points. Multivariable analyses were performed to evaluate the independent association between the APRs and the studied outcomes, including linear models for gestational age at delivery or birthweight, and logistic models for PTD and other outcomes.
Result
A total of 201 participants were included in the study. Mean birthweight was higher among individuals with high total cholesterol (3392.710 ± 426 g vs. 3220.782 ± 448 g, p value = 0.01). Similarly, mean birthweight was higher among individuals with high LDL‐c (3414.520 ± 407 g vs. 3237.324 ± 453 g, p value = 0.018). In multivariable linear models for the association between total cholesterol or LDL‐c and birthweight, which adjusted for newborn sex, gestational age at delivery, gestational diabetes mellitus or pregestational diabetes mellitus, and maternal body mass index (BMI), an increase in one unit of total cholesterol was significantly related with higher birthweight (2.796 g per unit, 95% CI = 0.682–3.956), and an increase in one unit of LDL‐c was significantly related with higher birthweight (2.463 g per unit, 95% CI = 0.152–4.773). No associations were found between early pregnancy APRs and PTD, gestational age at delivery, and the other outcomes of interest.
Conclusions
Higher birthweight was found among individuals with high early pregnancy total cholesterol and LDL‐c levels, even after controlling for maternal BMI. Early monitoring of maternal lipid profile may serve as a useful predictor of excessive fetal growth, providing a potential tool for identifying pregnancies and individuals at higher risk for this complication, and which could benefit from closer observation. Levels of APRs were not associated with other pregnancy outcomes of interest.
Keywords: acute‐phase responders, birthweight, lipid profile, maternal C‐reactive protein, maternal LDL, maternal total cholesterol, preterm delivery
1. INTRODUCTION
Acute‐phase responders (APRs), despite its name, accompany chronic as well as acute inflammatory states. It is associated with a wide variety of disorders, including infection, trauma, infraction, inflammatory arthritis, other systemic autoimmune and inflammatory diseases, and various neoplasms [1]. An acute‐phase protein has been defined as one that increases (positive acute‐phase proteins, such as C‐ reactive protein, i.e., CRP) or decreases (negative acute‐phase proteins, such as albumin) plasma concentration by at least 25% during inflammatory disorders [2]. Metabolic changes are also considered as acute‐phase responders, such as hepatic lipogenesis and lipolysis in adipose tissue, which increase in response to inflammatory states. These changes are reflected by lipid profile, including cholesterol and triglyceride [2, 3]. In addition, hyperlipidemia is considered as a precursor of inflammation and oxidative stress [4].
Typical human pregnancy is accompanied by significant inflammatory changes. One notable change is an increase in white blood cell count [5], likely driven by elevated cortisol levels and possibly other factors. However, the alterations in other inflammatory markers, such as CRP, are less well defined. While CRP levels do not show a clear correlation with gestational age, median CRP levels tend to be slightly higher in pregnant individuals compared to nonpregnant ones [6], and elevated CRP levels have already been observed as early as the 4th gestational week [7].
Preterm delivery (PTD), defined as a birth that occurs before 37 gestational weeks, is the leading cause of perinatal morbidity and mortality in developed countries [8]. Individuals with a previous PTD have approximately 4–6 times greater risk of having another PTD in a subsequent pregnancy as compared to individuals without such history [9]. It is estimated that 30%–50% of spontaneous PTDs are caused by an infectious state. One of the common theories is that in response to an infection during pregnancy cytokines levels increase, leading to the production of prostaglandin. Thus, it can ultimately lead to an induction of uterine contractions, cervical dilation, and spontaneous PTD [10].
Many studies have focused on the analysis of infectious biomarkers in maternal blood such as CRP, a stable positive APR which is a sensitive marker of inflammation [11]. Elevated concentrations of CRP in maternal serum during early pregnancy were associated with PTD [12, 13, 14, 15] and preeclampsia [16]. However, data regarding the association between PTD and other APRs, such as albumin, in maternal blood during early pregnancy are limited. The association between maternal lipid profile during early pregnancy and pregnancy outcomes is controversial. Elevated maternal lipid profiles in early pregnancy have been associated with PTD [17], preeclampsia [18], gestational diabetes mellitus [19], higher birthweight, and increased risks of macrosomia [18, 20].
The aim of the current study was to examine the associations between lipid profile and APRs’ maternal serum levels in the 1st trimester of pregnancy—and pregnancy outcomes.
2. MATERIALS AND METHODS
A prospective cohort study was conducted among pregnant multipara individuals with a singleton, aged 18 years or older, with or without a history of PTD (Figure 1). Nulligravid individuals were excluded, as without pregnancy history it is not possible to classify them as high risk or normal risk for PTD. Following initial screening for eligibility, all eligible individuals were offered to participate in the study, and written consents were provided. The recruitment period was between August 2022 and July 2023. Recruitment took place during the nuchal translucency test during 11–13 gestational weeks, performed at the ultrasound clinic. Participants completed a detailed questionnaire and had their blood collected for serum APR levels. Blood samples were obtained at the time of recruitment. The pregnancy outcomes and complications were summarized and compared between participants based on their level of APRs.
FIGURE 1.
Study population flow diagram.
The exposures were serum levels of the following APRs: CRP (mg/dL), albumin (gr/dL), triglyceride (mg/dL), total cholesterol (mg/dL), low density lipoprotein calculated (LDL‐c, mg%), and high density lipoprotein (HDL, mg/dL). The APRs were divided into binomial groups, high versus low, based on the following: CRP, total cholesterol, and triglyceride; the exposure group is above the normal value, which were CRP > 0.5 mg/dL, total cholesterol > 200 mg/dL, and triglycerides > 150 mg/dL. In the variable LDL‐c, the exposure is the highest quarter (> 75%); LDL‐c > 110 mg%. In the variables, HDL and albumin, the exposure is the lowest quarter (< 25%); HDL < 60 mg/dL and albumin < 4 g/dL. The outcomes were gestational diabetes mellitus (GDM), preeclampsia, gestational age at delivery, PTD (> 20 and < 37 gestational weeks), birthweight, low birthweight (< 2500 g), and macrosomia (> 4000 g). Individuals who have experienced abortion or termination of pregnancy after 20 gestational weeks were not included in the analyses of the outcomes. The background characteristics were participant age, gravidity, participant body mass index (BMI), smoking in the past, and previous PTD. Background characteristics were available from questionnaires and summaries of medical records.
CRP, albumin, triglyceride, total cholesterol, LDL‐c, and HDL levels were tested for normal distribution using mean, mode, and standard deviation, as well as skewness and kurtosis. Participants with lowest or highest levels were identified for each of these APRs, and a dichotomous variable was created for each. First, the associations between the background characteristics mentioned and the APRs were tested (i.e., CRP, albumin, triglyceride, total cholesterol, LDL‐c, and HDL). This was done by comparing the dichotomous groups of CRP, albumin, triglyceride, total cholesterol, LDL‐c, and HDL categories (for instance, between categories of BMI, participant age, comorbidities, etc.). This was done using Chi square or Fisher tests. The association between the APRs and continuous variables, such as participant age, was studied using t‐test. Second, the associations between the outcomes (i.e., PTD, birthweight, gestational age at delivery, etc.) and the APRs were tested. This was done using Chi square or Fisher tests (for PTD, low birthweight, macrosomia, etc.) or t‐test (for birthweight, gestational age at delivery, based on the normal distribution of these variables). Lastly, multivariable analyses were performed to evaluate the independent associations between CRP, albumin, triglyceride, total cholesterol, LDL‐c, HDL, and the outcomes studied, including linear models for gestational age at delivery or birthweight, and logistic models for PTD and macrosomia.
3. RESULTS
This study included 201 pregnant participants. Mean gestational age at blood collection was 12 weeks + 3.92 days. Mean age of participants was 32.40 ± 4.41 years, 95.5% (189) were married, 13.5% (27) were smokers or smoked in the past, and 17.8% (35) were obese. The incidence of PTD was 5.7% (11), of which 64% (7) were spontaneous and the rest were initiated by the health care provider due to maternal or fetal issues. The incidence of GDM was 6.9% (14), of preeclampsia was 0.5% (1), of LBW was 4.3% (8), and of macrosomia was 5.7% (11). Mean gestational age at delivery was 38 ± 3.66 weeks. Blood was collected for measurements of serum APRs, including CRP, cholesterol, triglyceride, LDL‐c, HDL, and albumin. Two‐thirds of the participants (66%, n = 130) had high level of CRP (> 0.5), 35% (n = 70) had high level of total cholesterol (> 200), 32% (n = 64) had high level of triglycerides (> 150), 26% (n = 49) had high level of LDL‐c (> 110), 27% (n = 54) had low level HDL (< 60), and 39% (n = 77) had low level of albumin (< 4).
Table 1 presents the associations between background characteristics and the APRs. Participants’ BMI was significantly higher among individuals with CRP > 0.5 (22.03 ± 3.4 vs. 26.09 ± 5.23, p value < 0.001, among individuals with low and high CRP, respectively), as well as among individuals with total cholesterol > 200 (23.90 ± 4.81 vs. 25.88 ± 5.34, p value = 0.009, among individuals with low and high cholesterol, respectively), or high triglycerides (> 150) (23.72 ± 4.69 vs. 26.48 ± 5.39, p value < 0.001, among individuals with low and high triglyceride, respectively), or high LDL‐c (> 110) (24.22 ± 4.87 vs. 26.03 ± 5.42, p value = 0.031, among individuals with low and high LDL‐c, respectively), or low (< 60) HDL (24.05 ± 4.68 vs. 25.93 ± 5.77, p value = 0.036, among individuals with high and low HDL, respectively), or among individuals with low (< 4) albumin (23.82 ± 4.36 vs. 25.99 ± 5.83, p value = 0.006, among individuals with high and low albumin, respectively). Similar findings were found while addressing BMI categories: Obesity (BMI > 30) was more common among individuals with high CRP, high triglyceride, or low albumin. Smoking in the past was found to be significantly related with triglyceride and HDL, so that individuals with high triglycerides were more likely to have smoked (9% vs. 21.9%, among individuals with low and high triglyceride, respectively, p value = 0.012), as well as individuals with low HDL (9.1% vs. 22.2%, among the high and low HDLs, respectively, p value = 0.012). Individuals with low versus high HDL were older (32.81 ± 4.36 years vs. 31.32 ± 4.41 years old, p value = 0.041).
TABLE 1.
The associations between background characteristics and the APRs.
|
Overall cohort n = 201 |
CRP ≤ 0.5 n = 66, 34% |
CRP > 0.5 n = 130, 66% |
p value |
Triglycerides ≤ 150 n = 137, 68% |
Triglycerides > 150 n = 64, 32% |
p value |
Albumin ≥ 4 n = 121, 61% |
Albumin < 4 n = 77, 39% |
p value | |
|---|---|---|---|---|---|---|---|---|---|---|
| Participant age—categorical | ||||||||||
| <28 (n, %) | 48, 24% | 18, 27.3% | 31, 24% | 0.443 | 35, 26.1% | 13, 20.3% | 0.642 | 36, 30.5% | 12, 15.6% | 0.061 |
| 28–35 (n, %) | 103, 52% | 36, 54.5% | 64, 49.6% | 69, 51.5% | 34, 53.1% | 56, 47.5% | 45, 58.4% | |||
| >35 (n, %) | 47, 24% | 12, 18.2% | 34, 26.4% | 30, 22.4% | 17, 26.6% | 26, 22.0% | 20, 26.0% | |||
| Participant age—continuous (years, mean ± SD) | 32.40 ± 4.41 | 31.64 ± 4.72 | 32.71 ± 4.27 | 0.113 | 32.12 ± 4.458 | 32.98 ± 4.268 | 0.210 | 31.96 ± 4.57 | 33.03 ± 4.14 | 0.108 |
| Gravidity | ||||||||||
| 2–3 (n, %) | 174, 87% | 61, 92.4% | 109, 84% | 0.191 | 120, 87.6% | 54, 85.7% | 0.790 | 106, 87.6% | 65, 85.5% | 0.861 |
| >4 (n, %) | 26, 13% | 5, 7.6% | 21, 16% | 17, 12.4% | 9, 14.3% | 15, 12.4% | 11, 14.5% | |||
| BMI—categorical | ||||||||||
| <19 (n, %) | 20, 10.2% | 12, 18.2% | 7, 5.5% | <0.001 | 17, 12.7% | 3, 4.8% | 0.017 | 12, 10.2% | 7, 9.2% | 0.005 |
| 19–24.99 (n, %) | 101, 51.3% | 40, 60.6% | 58, 45.3% | 75, 56.0% | 26, 41.3% | 66, 55.9% | 33, 43.4% | |||
| 25–29.99 (n, %) | 41, 20.8% | 12, 18.2% | 30, 23.4% | 23, 17.2% | 18, 28.6% | 28, 23.7% | 13, 17.1% | |||
| >30 (n, %) | 35, 17.8% | 2, 3% | 33, 25.8% | 19, 14.2% | 16, 25.4% | 12, 10.2% | 23, 30.3% | |||
| BMI—continuous (mean ± SD) | 24.6 ± 5.08 | 22.03 ± 3.43 | 26.09 ± 5.23 | <0.001 | 23.72 ± 4.69 | 26.48 ± 5.39 | <0.001 | 23.82 ± 4.36 | 25.99 ± 5.83 | 0.006 |
| Smoking (n, %) | 27, 13.5% | 8, 12.1% | 19, 14.7% | 0.618 | 12, 9.0% | 14, 21.9% | 0.012 | 15, 12.7% | 11, 14.3% | 0.752 |
| Previous PTD (n, %) | 29, 14.4% | 6, 8.5% | 19, 13.6% | 0.249 | 20, 13.9% | 9, 12.9% | 0.836 | 11, 8.7% | 14, 16.7% | 0.078 |
|
Total cholesterol ≤ 200 n = 128, 65% |
Total cholesterol > 200 n = 70, 35% |
p value |
LDL‐c ≤ 110, n = 139, 74% |
LDL‐c > 110, n = 49, 26% |
p value |
HDL ≥ 60 n = 146, 73% |
HDL < 60 n = 54, 27% |
p value | |
|---|---|---|---|---|---|---|---|---|---|
| Participant age—categorical | |||||||||
| <28 (n, %) | 35, 27.3% | 13, 18.6% | 0.379 | 36, 25.9% | 9, 18.4% | 0.561 | 28, 19.6% | 20, 37.0% | 0.031 |
| 28–35 (n, %) | 64, 50.0% | 39, 55.7% | 71, 51.1% | 27, 55.1% | 77, 53.8% | 25, 46.3% | |||
| >35 (n, %) | 29, 22.7% | 18, 25.7% | 32, 23% | 13, 26.5% | 38, 26.6% | 9, 16.7% | |||
| Participant age—continuous (years, mean ± SD) | 32.07 ± 4.602 | 33.01 ± 3.979 | 0.156 | 32.12 ± 4.59 | 33.13 ± 3.89 | 0.181 | 32.81 ± 4.36 | 31.32 ± 4.41 | 0.041 |
| Gravidity | |||||||||
| 2–3 (n, %) | 113, 88.3% | 61, 84.7% | 0.450 | 122, 89.1% | 42, 82.4% | 0.221 | 124, 84.9% | 49, 91.5% | 0.298 |
| >4 (n, %) | 15, 11.7% | 11, 15.3% | 15, 10.9% | 9, 17.6% | 22, 15.1% | 4, 7.5% | |||
| BMI—categorical | |||||||||
| <19 (n, %) | 16, 12.6% | 4, 5.7% | 0.112 | 16, 11.6% | 3, 6.1% | 0.285 | 13, 9.1% | 7, 13.2% | 0.108 |
| 19–24.99 (n, %) | 69, 54.3% | 32, 45.7% | 71, 51.4% | 23, 46.9% | 80, 55.9% | 21, 39.6% | |||
| 25–29.99 (n, %) | 24, 18.9% | 17, 24.3% | 30, 21.7% | 10, 20.4% | 30, 21.0% | 11, 20.8% | |||
| >30 (n, %) | 18, 14.2% | 17, 24.3% | 21, 15.2% | 13, 26.5% | 20, 14.0% | 14, 26.4% | |||
| BMI—continuous (mean ± SD) | 23.90 ± 4.81 | 25.88 ± 5.34 | 0.009 | 24.22 ± 4.87 | 26.03 ± 5.42 | 0.031 | 24.05 ± 4.68 | 25.93 ± 5.77 | 0.036 |
| Smoking (n, %) | 18, 14.1% | 8, 11.4% | 0.600 | 21, 15.1% | 4, 8.2% | 0.218 | 13, 9.1% | 12, 22.2% | 0.014 |
| Previous PTD (n, %) | 21, 15% | 8, 11.4% | 0.394 | 20, 13.3% | 7, 13.2% | 0.982 | 18, 11.8 | 10, 16.7% | 0.341 |
Abbreviations: BMI, body mass index; CRP, C reactive protein; HDL, high density lipoprotein; LDL‐c, low density lipoprotein calculated; PTD, preterm delivery.
Table 2 presents the association between pregnancy outcomes and the measured APRs. Birthweight was found to be significantly higher among individuals with high total cholesterol (3392.710 ± 426 vs. 3220.782 ± 448 g, than among individuals with high and normal total cholesterol, respectively, p value = 0.01, Figure 2). Similarly, birthweight was found to be significantly higher among individuals with high LDL‐c (3414.520 ± 407 g vs. 3237.324 ± 453 g, than among individuals with high and normal LDL‐c, respectively, p value = 0.018, Figure 2). No other associations were found between the APRs and other birth outcomes of interest, including gestational diabetes, PTD, gestational age at delivery, low birthweight, and macrosomia.
TABLE 2.
The associations between pregnancy outcomes and the APRs.
|
CRP ≤ 0.5 n = 66, 34% |
CRP > 0.5 n = 130, 66% |
p value |
Triglycerides ≤ 150 n = 137, 68% |
Triglycerides > 150 n = 64, 32% |
p value |
Albumin ≥ 4 n = 121, 61% |
Albumin < 4 n = 77, 39% |
p value | |
|---|---|---|---|---|---|---|---|---|---|
| GDM (n, %) | 2, 2.9% | 12, 8.9% | 0.109 | 8, 5.8% | 6, 8.8% | 0.409 | 7, 5.6% | 7, 8.8% | 0.392 |
| Preeclampsia (n, %) | 0, – | 1, 0.8% | 0.657 | 1, 0.7% | 0, – | 0.667 | 0, – | 1, 1.3% | 0.394 |
| Gestational age at delivery (days, mean ± SD) | 267.28 ± 35.9 | 268.85 ± 25.7 | 0.725 | 268.242 ± 29.34 | 268.478 ± 29.48 | 0.958 | 267.25 ± 32.77 | 269.96 ± 23.90 | 0.531 |
| PTD (n, %) | 2, 3.1% | 9, 7.1% | 0.262 | 8, 6.3% | 3, 4.6% | 0.643 | 6, 5.3% | 5, 6.6% | 0.704 |
| Spontaneous PTD (n, %) | 2, 3.1% | 5, 4.1% | 1.000 | 4, 3.2% | 3, 4.6% | 0.693 | 3, 2.7% | 4, 5.3% | 0.442 |
| Birthweight (g, mean ± SD) | 3220.75 ± 403 | 3317.51 ± 467 | 0.16 | 3277.398 ± 457.5 | 3291.800 ± 428.7 | 0.833 | 3296.39 ± 433.26 | 3276.05 ± 464.76 | 0.758 |
| Low birthweight (< 2500) (n, %) | 2, 3.3% | 5, 4% | 0.801 | 6, 4.9% | 2, 3.1% | 0.561 | 3, 2.7% | 4, 5.4% | 0.345 |
| Macrosomia (> 4000) (n, %) | 3, 4.7% | 8, 6.3% | 0.753 | 6, 4.7% | 5, 7.7% | 0.512 | 6, 5.3% | 5, 6.6% | 0.757 |
| Total cholesterol ≤ 200 n = 128, 65% | Total cholesterol > 200 n = 70, 35% | p value | LDL‐c ≤ 110, n = 139, 74% | LDL‐c > 110, n = 49, 26% | p value | HDL ≥ 60 n = 146, 73% | HDL < 60 n = 54, 27% | p value | |
|---|---|---|---|---|---|---|---|---|---|
| GDM (n, %) | 8, 5.9% | 6, 8.3% | 0.511 | 8, 5.6% | 4, 8.2% | 0.521 | 8, 5.4% | 6, 10.3% | 0.205 |
| Preeclampsia (n, %) | 1, 0.8% | 0, – | 0.647 | 1, 0.7% | 0, – | 0.745 | 0, – | 1, 1.8% | 0.285 |
| Gestational age at delivery (days, mean ± SD) | 267.01 ± 31.64 | 270.69 ± 24.60 | 0.397 | 270.028 ± 23.72 | 270.286 ± 25.14 | 0.949 | 268.72 ± 28.67 | 267.39 ± 31.32 | 0.774 |
| PTD (n, %) | 7, 5.7% | 4, 5.7% | 0.995 | 8, 5.8% | 2, 4.2% | 0.666 | 8, 5.8% | 3, 5.5% | 0.917 |
| Spontaneous PTD (n, %) | 4, 3.3% | 3, 4.3% | 0.707 | 5, 3.7% | 2, 4.2% | 0.886 | 5, 3.7% | 2, 3.7% | 1.00 |
| Birthweight (g, mean ± SD) | 3220.782 ± 448 | 3392.710 ± 426 | 0.01 | 3237.324 ± 453 | 3414.520 ± 407 | 0.018 | 3305.83 ± 430.46 | 3231.82 ± 485.59 | 0.301 |
| Low birthweight (< 2500) (n, %) | 7, 5.9% | 1, 1.4% | 0.262 | 7, 5.2% | 1, 2.1% | 0.326 | 4, 3.0% | 4, 7.5% | 0.165 |
| Macrosomia (> 4000) (n, %) | 4, 3.2% | 7, 10.1% | 0.057 | 5, 3.6% | 5, 10.4% | 0.07 | 9, 6.6% | 2, 3.6% | 0.732 |
Abbreviations: CRP, C reactive protein; GDM, gestational diabetes mellitus; HDL, high density lipoprotein; LDL‐c, low density lipoprotein calculated; PTD, preterm delivery.
FIGURE 2.
The associations between early pregnancy maternal total cholesterol or low density lipoprotein calculated (LDL‐c) levels and birthweight. Results of t‐tests comparing mean birthweight among individuals with high versus low total cholesterol and LDL‐c.
The multivariable linear model for the association between total cholesterol and birthweight, while adjusting for newborn sex, gestational age at delivery, gestational diabetes mellitus or pregestational diabetes mellitus, and maternal BMI, found that an increase in one unit of total cholesterol was significantly related with higher birthweight (2.796 g per unit, 95% CI = 0.682–3.956). Corresponding results are presented in Table 3. The multivariable linear model for the association between LDL‐c and birthweight, while adjusting for newborn sex, gestational age at delivery, gestational diabetes mellitus or pregestational diabetes mellitus, and maternal BMI, found that an increase in one unit of LDL‐c was significantly related with higher birthweight (2.463 g per unit, 95% CI = 0.152–4.773). Corresponding results are presented in Table 4.
TABLE 3.
The multivariable linear model for the association between total cholesterol and birthweight.
|
Unstandardized Coefficients B |
95% confidence Interval for B | |||
|---|---|---|---|---|
| p value | Lower bound | Upper Bound | ||
| Total cholesterol | 2.319 | 0.006 | 0.682 | 3.956 |
| Maternal BMI | −0.17 | 0.997 | −10.688 | 10.654 |
| Newborn sex | 164.044 | 0.004 | 53.652 | 274.436 |
| Gestational age at delivery | 26.047 | <0.001 | 20.074 | 32.019 |
| GDM or pregestational DM | −61.434 | 0.555 | −266.627 | 143.758 |
Abbreviations: BMI, body mass index; GDM, gestational diabetes mellitus.
TABLE 4.
The multivariable linear model for the association between LDL‐c and birthweight.
|
Unstandardized Coefficients B |
95% confidence Interval for B | |||
|---|---|---|---|---|
| p value | Lower bound | Upper Bound | ||
| LDL‐c | 2.463 | 0.037 | 0.152 | 4.773 |
| Maternal BMI | 2.116 | 0.706 | −8.490 | 13.173 |
| Newborn sex | 169.649 | 0.003 | 56.890 | 282.407 |
| Gestational age at delivery | 26.126 | <0.001 | 20.019 | 32.234 |
| GDM or pregestational DM | −73.223 | 0.519 | −296.897 | 150.450 |
Abbreviations: BMI, body mass index; GDM, gestational diabetes mellitus; LDL‐c, low density lipoprotein calculated.
4. DISCUSSION
In this prospective cohort study, higher birthweight was found among participants with high early pregnancy total cholesterol and LDL‐c levels, even after controlling for maternal BMI. No associations were found between the APRs and PTD, gestational age at delivery, and other pregnancy outcomes of interest.
Inflammation and infection within the fetoplacental unit have been suggested as a mechanism involved in spontaneous PTD. Throughout a healthy pregnancy, the immune system must carefully balance the need to avoid rejecting the fetus while also activating innate and adaptive immune responses to protect against infections. These immune responses can be systemic, involving factors and cells present in the bloodstream, or local, involving components surrounding the developing maternal‐fetal unit. Research in animal models has shown that certain cytokines in the amniotic fluid, such as IL‐22 and IL‐6, are associated with spontaneous PTD. In a typical pregnancy, labor begins with a transition from a resting to a contracting state, which may be influenced by a change from anti‐inflammatory to pro‐inflammatory signaling pathways [10]. Thus, it has been suggested that biomarkers of inflammation state in maternal bloodstream can present systemic immune response, and their measurement may predict a higher risk for spontaneous PTD.
Cholesterol plays an essential role in the formation of cell membranes, which are crucial for fetal growth. Additionally, it is involved in the synthesis of various steroid hormones that regulate pregnancy and fetal development [21]. Starting from the 12th gestational week, lipid levels, including cholesterol, triglycerides, and HDL levels, gradually rise, particularly during the second and third trimesters [22, 23]. This adjustment in lipid metabolism reflects physiological adaptation in maternal body, shifting from glucose metabolism to lipid metabolism to support fetal development [24]. An excessive increase in total cholesterol and maternal LDL concentrations is associated with endothelial dysfunction in the umbilical vein and placental vasculature. These alterations are accompanied by impaired nitric oxide (NO) signaling and are correlated with increased oxidative stress and inflammatory markers in maternal circulation, cord blood, and placental tissue [25, 26]. Furthermore, maternal hyperlipidemia was found to be related to gestational diabetes mellitus, which is a significant risk factor for macrosomia [19, 27].
Most studies that have examined the impact of maternal lipid profile on pregnancy outcomes focus on the second and third trimesters. The association between early pregnancy maternal lipid profile and excess birthweight has also been found by Zhu et al. [20]. As in Zhu et al., in this current study, increased early pregnancy total cholesterol and LDL‐c levels were associated with higher birthweight. While Zhu et al. also found an association between triglyceride and birthweight, in this current study no such association was found. The variability in findings may be attributable to differences in the characteristics of the study populations. Further studies which collect cord blood samples to examine the relationship between maternal and fetal lipid levels and fetal growth, are recommended.
While studies suggested that elevated concentrations of CRP in maternal serum during early pregnancy are associated with increased risk of PTD [12, 13, 14, 15], the current and other studies [28, 29] have not found such an association. The differences may be due to CRP measurements in different stages of pregnancy, for instance, the median was 16.3 gestational weeks in Hvilsom et al. [12] or up to 20 gestational weeks in Lohsoonthorn et al. [14], as opposed to 11–13 gestational weeks in current study. It is also possible that other matrices should be considered for measuring maternal CRPs, such as amniotic fluid, which has shown to have higher concentration CRP among individuals who delivered preterm as compared to individuals who delivered at term [28, 29]. Previous studies which measured APRs in amniotic fluids suggest that inflammation at the maternal‐fetal interface, rather than systemic inflammation, play a major role in the etiology of spontaneous PTD.
Data regarding the association between PTD and other APRs, such as lipids, in maternal blood during early pregnancy is limited and controversial. High total cholesterol or triglycerides at ≤15 gestational weeks have been associated with an increased risk for PTD [17], however, no associations between hyperlipidemia and spontaneous PTD were found in other studies [18, 30]. As in Odunsi et al. [30] and in Vrijkotte et al. [18], in this current study, no association was found between maternal lipid levels and PTD. While this may suggest maternal hyperlipidemia does not play a significant part in spontaneous PTD mechanism, further studies, at possible later stages of pregnancy, are recommended to clarify the possible association.
This study has several strengths. In this prospective cohort study, blood was collected for study purposes in healthy pregnant individuals preforming a nuchal translucency at the ultrasound clinic, therefore representation bias is unlikely. Moreover, collection of blood for APRs was performed during the first trimester between 11 and 13 gestational weeks. During this period the results can be used to identify pregnancies and individuals at risk for pregnancy complications.
Several study limitations need to be addressed. The results of this research may not be generalizable to all parturient and needs to be evaluated for other ethnic groups. The study enrolls individuals during 11–13 gestational weeks and therefore, was not able to study the association between APRs serum levels and early pregnancy loss. This may also introduce a “survival bias,” since it is possible that individuals with very high or low levels of APRs may have experienced early pregnancy loss, leaving only individuals with relatively normal levels of APRs in the research. The possibility of beta‐error regarding the association between CRP and PTD cannot be ruled out. Lastly, the results of this study cannot be generalized to nulligravid individuals, since these people may be a selective group with unique characteristics.
5. CONCLUSIONS
Further studies should examine the relationship between early pregnancy maternal lipid profile and birthweight. Early monitoring of maternal lipid profile may serve as a useful predictor of excessive fetal growth, providing a potential tool for identifying pregnancies and individuals at higher risk for this complication, and which could benefit from closer observation.
Future research should examine the association between adverse pregnancy outcomes such as spontaneous PTD, and APRs at later pregnancy stages and in additional matrices, such as amniotic fluid, to enable specific intervention strategies to lower the risk and reduce offspring mortality and morbidity.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflicts of interest.
ACKNOWLEDGMENTS
This study was supported by the Israel Science Foundation (grant#1435/20, PI: Tamar Wainstock).
This is the SMFM Pregnancy Meeting™ manuscript. Abstracts numbers: 1897433 and 1897467.
REFERENCES
- 1. Mantovani, A. , and Garlanda C.. 2023. “Humoral Innate Immunity and Acute‐Phase Proteins.” New England Journal of Medicine 388(5): 439. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Cem, G. , and Irving K.. 1999. “Acute‐Phase Proteins and Other Systemic Responses to Inflammation.” New England Journal of Medicine 340(6): 448–54. [DOI] [PubMed] [Google Scholar]
- 3. Nathan, C. . 2022. “Nonresolving Inflammation Redux.” Immunity 55(4): 592. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Ridker, P. M. . 2012. “Hyperlipidemia as an Instigator of Inflammation: Inaugurating New Approaches to Vascular Prevention.” Journal of the American Heart Association 1(1): 3–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Rebelo, I. , Carvalho‐Guerra F., Pereira‐Leite L., and Quintanilha A.. 1995. “Lactoferrin as a Sensitive Blood Marker of Neutrophil Activation in Normal Pregnancies.” European Journal of Obstetrics, Gynecology, and Reproductive Biology 62(2): 189–94. [DOI] [PubMed] [Google Scholar]
- 6. Belo, L. , Santos‐Silva A., Rocha S., Caslake M., Cooney J., Pereira‐Leite L., Quintanilha A., and Rebelo I.. 2005. “Fluctuations in C‐Reactive Protein Concentration and Neutrophil Activation during Normal Human Pregnancy.” European Journal of Obstetrics, Gynecology, and Reproductive Biology 123(1): 46. [DOI] [PubMed] [Google Scholar]
- 7. Sacks, G. P. 2004. “Maternal C‐Reactive Protein Levels Are Raised at 4 Weeks Gestation.” Human Reproduction 19(4): 1025. [DOI] [PubMed] [Google Scholar]
- 8. Wainstock, T. , Sergienko R., and Sheiner E.. 2021. “Can We Predict Preterm Delivery Based on the Previous Pregnancy?.” Joint Consultative Machinery 10(7): 1517. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Ferrero, D. M. , Larson J., Jacobsson B. O., Di Renzo G. C., Norman J. E., Martin J. N., Simpson L., et al. 2016. “Cross‐Country Individual Participant Analysis of 4.1 Million Singleton Births in 5 Countries with Very High Human Development Index Confirms Known Associations but Provides No Biologic Explanation for 2/3 of All Preterm Births.” Public Library of Science ONE 11(9): e0162506. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Gilman‐Sachs, A. , Dambaeva S., Salazar Garcia M. D., Hussein Y., Kwak‐Kim J., and Beaman K.. 2018. “Inflammation Induced Preterm Labor and Birth.” Journal of Reproductive Immunology 129: 53. [DOI] [PubMed] [Google Scholar]
- 11. Kluft, C. , and De Maat M. P. M.. 2002. “Sensitive Markers of Inflammation Make It Possible to Study the Chronic Process: The Rise of Interest in Low Levels of C‐Reactive Protein.” Vascular Pharmacology 39(3): 99–104 [DOI] [PubMed] [Google Scholar]
- 12. Hvilsom, G. B. , Thorsen P., Jeune B., and Bakketeig L. S.. 2002. “C‐Reactive Protein: A Serological Marker for Preterm Delivery?.” Acta Obstetricia Et Gynecologica Scandinavica 81(5): 424. [DOI] [PubMed] [Google Scholar]
- 13. Pitiphat, W. , Gillman M. W., Joshipura K. J., Williams P. L., Douglass C. W., and Rich‐Edwards J. W.. 2005. “Plasma C‐Reactive Protein in Early Pregnancy and Preterm Delivery.” American Journal of Epidemiology 162(11): 1108–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Lohsoonthorn, V. , Qiu C., and Williams M. A.. 2007. “Maternal Serum C‐Reactive Protein Concentrations in Early Pregnancy and Subsequent Risk of Preterm Delivery.” Clinical Biochemistry 40(5–6): 330–5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Huang, S. , Tian J., Liu C., Long Y.u., Cao D., Wei L., Mo Z., et al. 2020. “Elevated C‐Reactive Protein and Complement C3 Levels Are Associated with Preterm Birth: A Nested Case–Control Study in Chinese Women.” BioMed Central Pregnancy and Childbirth 20(1): 131. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16. Thilaganathan, B. , Wormald B., Zanardini C., Sheldon J., Ralph E., and Papageorghiou A. T.. 2010. “Early‐Pregnancy Multiple Serum Markers and Second‐Trimester Uterine Artery Doppler in Predicting Preeclampsia.” Obstetrics and Gynecology. 115(6): 1233–8. [DOI] [PubMed] [Google Scholar]
- 17. Catov, J. M. , Bodnar L. M., Kip K. E., Hubel C., Ness R. B., Harger G., and Roberts J. M.. 2007. “Early Pregnancy Lipid Concentrations and Spontaneous Preterm Birth.” American Journal of Obstetrics and Gynecology 197(6): 610. [DOI] [PubMed] [Google Scholar]
- 18. Vrijkotte, T. G. M. , Krukziener N., Hutten B. A., Vollebregt K. C., Van Eijsden M., and Twickler M. B.. 2012. “Maternal Lipid Profile During Early Pregnancy and Pregnancy Complications and Outcomes: The ABCD Study.” Journal of Clinical Endocrinology & Metabolism 97(11): 3917. [DOI] [PubMed] [Google Scholar]
- 19. Wu, Y. , Zhang C., Mol B. W., Kawai A., Li C., Chen L., Wang Y., et al. 2021. “Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning.” Journal of Clinical Endocrinology & Metabolism 106(3): e1191. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20. Zhu, S.‐M. , Zhang H.‐Q., Li C., Zhang C., Yu J.‐L., Wu Y.‐T., and Huang H.‐F.. 2022. “Maternal Lipid Profile during Early Pregnancy and Birth Weight: A Retrospective Study.” Frontiers in Endocrinology 13: 951871. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21. Schwartz, N. , Quant H. S., Sammel M. D., and Parry S.. 2015. “Macrosomia Has Its Roots in Early Placental Development.” Placenta 35(9): 684. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Bartels, Ä. , Egan N., Broadhurst D. I., Khashan A. S., Joyce C., Stapleton M., O'Mullane J., and O'Donoghue K.. 2012. “Maternal Serum Cholesterol Levels Are Elevated from the 1st Trimester of Pregnancy: A Cross‐Sectional Study.” Journal of Obstetrics and Gynaecology 32(8): 747. [DOI] [PubMed] [Google Scholar]
- 23. Jin, W.‐Y. , Lin S.‐L., Hou R.‐L., Chen X.‐Y., Han T., Jin Y., Zhao Z.‐Y., et al. 2016. “Associations between Maternal Lipid Profile and Pregnancy Complications and Perinatal Outcomes: A Population‐Based Study from China.” BioMed Central Pregnancy and Childbirth 16(1): 60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24. Hajar Sharami, S. , Abbasi Ranjbar Z., Alizadeh F., and Kazemnejad E.. 2019. “The Relationship of Hyperlipidemia with Maternal and Neonatal Outcomes in Pregnancy: A Cross‐Sectional Study.” International Journal of Research Methodology 17(10): 739–48. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Fuenzalida, B. , Sobrevia B., Cantin C., Carvajal L., Salsoso R., Gutiérrez J., Leiva A., et al. 2018. “Maternal Supraphysiological Hypercholesterolemia Associates with Endothelial Dysfunction of the Placental Microvasculature.” Scientific Reports 8(1): 7690. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Contreras‐Duarte, S. , Cantin C., Farias M., and Leiva A.. 2021. “High Total Cholesterol and Triglycerides Levels Increase Arginases Metabolism, Impairing Nitric Oxide Signaling and Worsening Fetoplacental Endothelial Dysfunction in Gestational Diabetes Mellitus Pregnancies.” Biochimica et Biophysica Acta (BBA)—Molecular Basis of Disease 1867(12): 166216. [DOI] [PubMed] [Google Scholar]
- 27. Sánchez‐Vera, I. , Bonet B., Viana M., Quintanar A., Martín M. D., Blanco P., Donnay S., and Albi M.. 2007. “Changes in Plasma Lipids and Increased Low‐Density Lipoprotein Susceptibility to Oxidation in Pregnancies Complicated by Gestational Diabetes: Consequences of Obesity.” Metabolism 56(11): 1527. [DOI] [PubMed] [Google Scholar]
- 28. Ghezzi, F. , Franchi M., Raio L., Di Naro E., Bossi G., D'Eril G. V. M., and Bolis P.. 2002. “Elevated Amniotic Fluid C‐Reactive Protein at the Time of Genetic Amniocentesis Is a Marker for Preterm Delivery.” American Journal of Obstetrics and Gynecology 186(2): 268. [DOI] [PubMed] [Google Scholar]
- 29. Wei, S. , Fraser W., and Luo Z.. 2010. “Inflammatory Cytokines and Spontaneous Preterm Birth in Asymptomatic Women: A Systematic Review.” Obstetrics and Gynecology 116(2 Pt 1):393–401. [DOI] [PubMed] [Google Scholar]
- 30. Ottun, A. T. , Odunsi M. A., Jinadu F. O., Olumodeji A. M., Tijani F., Olalere H. F., and Adewunmi A. A.. 2022. “Maternal Hyperlipidemia and Spontaneous Preterm Delivery: A Multi‐Centre Cohort Study.” Journal of Maternal‐Fetal & Neonatal Medicine 35(25): 8530–5. [DOI] [PubMed] [Google Scholar]