Abstract
Problem
Prior results on the association between serum uric acid (UA) levels in the early trimester and the risk of small for gestational age (SGA) remain unclear. This study evaluated the association of maternal first-, second-, and third-trimester UA levels with the risk of SGA infants.
Method of study
A total of 23, 194 singleton mothers from the International Peace Maternity and Child Health Hospital between January 2014 and January 2017 were included. Maternal UA levels were measured at 12.1 ± 1.08th (UA1) and 32.2 ± 1.03th (UA2) gestational weeks. △UA was calculated as the difference between UA2 and UA1. Logistic regression and restricted cubic spline (RCS) were performed to evaluate the association between maternal UA and △UA during pregnancy and SGA. Receiver operating characteristic (ROC) analysis was employed to assess the serum uric acid prediction value.
Results
Women in the higher quartiles of UA1 had a significantly higher risk of SGA. A clear increased risk for SGA was observed with higher quartiles for UA2 (p for trend <0.05). An approximately “J-shaped” relationship was observed between UA2 and △UA, and the risk of SGA was observed. When compared with those with a lower level of UA in the first trimester, those with a higher level of UA1, the more increase in the later UA levels were associated with a higher risk of SGA [adjusted odds ratio (aOR) = 1.67, 95% CI:1.37–2.05]. The ROC curve areas were 0.525 for UA1, 0.582 for UA2 and 0.576 for △UA.
Conclusions
The findings suggested that non-preeclamptic and non-hypertensive women who experienced early pregnancy with high UA levels had an elevated risk of SGA. Moreover, a high maternal UA level in the earlier trimester may be an early predictor of SGA.
Introduction
According to the theory of the origin of the development of health and disease, maternal metabolism and the intrauterine environment influence the growth and development of the fetus and, hence, its health in adulthood [Citation1, Citation2]. Maternal uric acid (UA) level is one of the most critical maternal metabolic variables in fetal growth and development. Uric acid is an effective antioxidant at physiological levels [Citation3]. However, when uric acid levels in the blood plasma exceed healthy levels, oxidative damage spreads. Furthermore, an increase in uric acid, which is chronic rather than acute, is a risk factor for multiple diseases, as it promotes inflammation and endothelial dysfunction [Citation4, Citation5]. Maternal serum UA flows via the placenta to adjust to maternal-fetal physiology and levels of UA change throughout pregnancy. Maternal serum UA levels typically decrease in early pregnancy but can significantly increase throughout the third trimester, implying that these metabolic alterations are critical for fetal growth [Citation6–8]. Previous research has indicated that high plasma UA levels may enhance the risk of preeclampsia and gestational hypertension [Citation9–11]. Nonetheless, there is insufficient research on whether maternal hyperuricemia can predict poor delivery outcomes [Citation12, Citation13].
The global prevalence of small for gestational age (SGA) among neonates has increased steadily in recent years to 9.7% [Citation14], with a higher incidence in low- and middle-income countries [Citation15, Citation16]. Compared to appropriate for gestational age (AGA) neonates, SGA newborns have a higher risk of perinatal, childhood, and adulthood problems [Citation17–26]. Therefore, understanding the impact of maternal UA levels on fetal development is necessary to optimize birth outcomes and subsequently reduce the occurrence of many diseases after childhood.
Most previous studies have focused on the effect of abnormally high levels of UA in maternal serum during the second and third trimesters of pregnancy on the outcome of delivery, and it has been shown that abnormally high serum UA levels in the third trimester of pregnancy could induce SGA [Citation12, Citation27]. However, the correlation between first-trimester UA levels and SGA risk requires further evaluation. Consequently, the purpose of our study was to clarify the relationship between UA levels in the first trimester of pregnancy and risk of SGA. Our findings might aid in the early trimester prediction of SGA births.
Methods
Study population and data collection
This study recruited consecutive pregnant women who gave birth at the International Peace Maternity and Child Health Hospital between January 2014 and January 2017 through a retrospective observational cohort design. Single birth, complete medical records and laboratory examinations, live births, and the absence of birth defects were considered eligibility criteria for participation in the study. Pregnant women who had multiple pregnancies, took alcohol or illicit drugs during pregnancy, had major pre-pregnancy diseases such as diabetes mellitus, active gout, nephritis, chronic kidney and liver disease, chronic hypertension and other cardiovascular disorders, intrauterine infections during pregnancy, or preeclamptic or hypertension patients were excluded. Each pregnant woman was registered at about 12 weeks of gestation for prenatal examination and blood examination, and routine prenatal examination was performed again at 28–34 weeks of gestation, that is, at least two blood examinations during pregnancy. All data were collected during hospital visits and from medical records.
Sample size was calculated to assume a 3–10% prevalence of SGA among our pregnant population. We estimated a 6% predicted difference in clinically meaningful adverse outcomes between with and without assuming a 3–10% frequency of neonates SGA outcome within our total population as per similar studies [Citation27]. Based on an α of 0.01 and a β of 0.8, our calculated sample size per comparison group must be 759–2340 to allow for two-sided analyses.
Covariate
Sociodemographic factors such as the mother’s age at delivery (<35, ≥35), ethnicity (Han, others), education level (high school and below, college and above), parity (1, >1), maternal pre-pregnancy body mass index (BMI) (<25, ≥25), gestational diabetes mellitus (GDM) (no, yes), creatinine (in continuous), gestational weight gain (GWG) (<14, ≥14), delivery mode (vaginal delivery, cesarean section), and the child’s sex (male, female) were included in this analysis, and sociodemographic information was extracted from a questionnaire during their first perinatal health care and medical records.
Exposure
Fasting blood tests were conducted on all participants, including uric acid and creatinine, at the laboratory of the International Peace Maternity and Child Health Care Hospital, and were taken twice during hospital visits. Each pregnant woman was recorded for a prenatal examination and blood examination at about the 12th week of gestation, and a regular prenatal examination was performed again at the 28th–34th week of xgestation, for a total of at least two blood examinations during pregnancy. Serum uric acid was measured using a diagnostic kit supplied by Beckman Coulter (catalogue no. 66300). Serum uric acid levels were determined by spectrophotometry using the uricase-peroxidase method with a lower detection limit of 1 mg/dl. The coefficient of variance was 5.0%, and the interassay variability was <10%. Serum creatinine was measured using the sarcosine oxidase method, with a lower detection limit of 0.88 μmol/L. Maternal UA levels were measured at 12.1 ± 1.08th (UA1) and 32.2 ± 1.03th (UA2) gestational weeks. △UA was calculated as the difference between UA2 and UA1. All uric acid and creatinine results were derived from medical and outpatient records.
Outcome
The birth characteristics of the newborn were collected at the time of birth, including the baby’s gender, length at birth, weight at birth, and gestational age. Grams and centimeters were used to measure the weight and length of the newborns. The gestational age was determined by combining the last menstrual period with ultrasound examinations during the first trimester of pregnancy. A newborn was defined as SGA if its birth weight was below the 10th percentile for gestational age and as LGA if its birth weight was above the 90th percentile for gestational age. AGA was defined as birth weight that fell between the 10th and 90th percentiles for gestational age [Citation28]. AGA and LGA infants are considered non-SGA.
Statistical analysis
Continuous variables are presented as means (standard deviations), and categorical variables are presented as frequencies (percentages). Comparisons between the SGA and non-SGA groups were performed using either Pearson’s chi-square test (for categorical variables) or Student’s t-test (for continuous variables). UA levels were categorized into quartiles, with the lowest quartile serving as the reference group. Logistic regression models were used to evaluate the association between maternal UA and △UA during pregnancy and SGA. △UA is divided by the median, which is statistically robust and less sensitive to extreme values. Stratified analysis was further performed using median UA1, to determine whether the risk of SGA increased if women had higher △UA levels. Multivariable adjustment models were implemented, with adjustments for the covariates mentioned above. Logistic regression models with restricted cubic splines were used to address the potential nonlinearity of the association between maternal UA and △UA levels during pregnancy and SGA. In order to investigate possible non-linear relationships between UA levels and the odds of SGA, restricted cubic splines (RCS) were used in logistic regression. Area under the curve (AUC) and test characteristics were determined and plotted on a ROC curve. All statistical analyses were performed using R, version 3.5.3 (The R Foundation for Statistical Computer, www.r-project. org). A 2-sided p-value of <0.05 was recognized as being statistically significant.
Results
Based on the defined eligibility criteria, 23194 pregnant women aged 18–49 years old were included in the original cohort (). Of the 23194 pregnant women, 783 delivered SGA. In the present study, the incidence of SGA was 3.37%. The maternal characteristics of SGA and non-SGA neonates varied significantly with regard to the mother’s age at delivery, parity, maternal pre-pregnancy BMI, and delivery mode (). The interval weeks for △UA were approximately 19 weeks of gestation, there were no significant differences of sampling time and sampling interval between the two groups ().
Figure 1. Flow chart of the study.
Table 1. Maternal and child characteristics.
When comparing UA levels at different gestational weeks and △UA between SGA and non-SGA groups, a consistently higher UA level was observed in the SGA group than in the non-SGA group, and the UA level of mothers delivering SGA infants increased faster than that of the control group ().
Table 2. Distribution of UA concentrations in different SGA groups.
The associations of UA concentrations (in quartiles) with risk for SGA in crude and adjusted analyses are shown in . Maternal UA levels in the higher quartiles were positively associated with SGA risk. After adjusting for all covariates, the women in the higher quartiles of UA1 had a significantly increased risk of SGA by 33% for Q2 [adjusted odds ratio (aOR) = 1.33, 95% CI:1.07–1.66], 36% for Q3 (aOR = 1.36, 95% CI:1.09–1.69), and 28% for Q4 (aOR = 1.28, 95% CI:1.02–1.60), when compared with the lowest quartile (reference). There was a clear increased risk for SGA with increasing quartiles for UA2 (all p for trend within categories <0.05), and the results of multivariable logistic regression indicated that the adjustment for potential confounders did not substantially affect the UA2 associations (adjusted OR (95% CIs): Q1 = reference; Q2 = 1.29 (1.02, 1.64); Q3 = 1.34 (1.09, 1.79); Q4 = 2.42 (1.88, 3.13).
Table 3. Odds ratios and 95% Confidence Intervals for SGA with the quartiles of UA levels.
When further stratified by the median of UA1, a higher increase in UA level was associated with an increased risk of SGA, with slightly higher odds observed in the higher UA1 level (above median) group (aOR = 1.67, 95% CI:1.37–2.05), when compared with the lower UA1 level (below median) group (aOR = 1.54, 95% CI:1.23–1.94) ().
Table 4. Odds ratios and 95% Confidence Intervals for SGA with the △UA stratified by UA1 levels.
As shown in , approximately “J-shaped” relationships between UA2, △UA, and SGA risk were observed after adjusting for all covariates.
Figure 2. Restricted cubic spline plots of the association between concentration of UA1 (A), UA2 (B), △UA (C) and SGA. The associations were adjusted for mother’s age at delivery, mother’s race, educational level, parity, pre-pregnancy BMI, GDM, creatinine, GWG, delivery mode, child gender, the gestational ages at the time of the blood tests were adjusted for models of UAI and UA2, interval weeks between two tests was adjusted for model of △UA. The baseline UA1 was additionally adjusted in models for UA2.
The ROC curves of UA1, UA2, △UA, maternal age at delivery, pre-pregnancy BMI, GWG, and the combination prediction model were generated based on the logistic regression analysis, and the area under the curve (AUC) was computed (Figure S1). The results demonstrate that the AUC of the combined prediction model is 0.636, which is higher than the AUCs of UA1, UA2, △UA, maternal age at delivery, pre-pregnancy BMI, and GWG (0.525, 0.582, 0.576, 0.474, 0.622, and 0.500, respectively) (Table S1).
Discussion
According to this retrospective case-control study, mothers with elevated UA levels in the first, second, or third trimester were associated with a higher risk of SGA among non-hypertensive and non-preeclamptic pregnant Chinese pregnant women. Specifically, higher risks of SGA were associated with higher first- and second-trimester UA levels and a larger UA increase.
According to this study, a higher risk of SGA is associated with high levels of maternal UA. Several population-based studies have examined the relationship between UA and unfavorable outcomes in both mothers and children. The findings they came to, though, were contradictory. In a POUCH (pregnancy outcomes and community health study) study cohort of 3019 American women, it was discovered that maternal UA levels between the 16th and 27th week of pregnancy were nonlinearly related to SGA in women without renal illness [Citation12]. Higher maternal levels of UA and urea nitrogen were high-risk factors for the development of SGA in a retrospective cohort study of healthy Chinese pregnant women, regardless of whether they were in the second (16–18 weeks) or third (28–30 weeks) trimester [Citation29]. Another cohort study of 404 Iranian women with normal blood pressure found a correlation between the risk of SGA and high maternal UA levels in the third trimester [Citation13]. A case-control study of 120 normotensive Japanese women in late pregnancy and an observational cohort study of 11,579 Chinese women in their third trimester revealed similar results [Citation27, Citation30]. However, increased levels of UA during the first trimester of pregnancy (less than 15 weeks) were not linked to SGA in a prospective analysis of 1,541 pregnant women [Citation31]. An Ulm SPATZ Health Study of 885 pregnant women indicated that elevated UA levels were not associated with SGA [Citation32]. Since most previous studies have examined UA in the third trimester, we investigated pregnant women in China in a relatively large cohort based on these findings and observed that SGA is more likely to occur in pregnant women with higher UA levels in the first, second, or third trimester.
The uric acid level tends to initially drop and then rise throughout typical pregnancy. Maternal UA levels drastically decreased during the first eight weeks of pregnancy. These levels remained the same until approximately 24 weeks of pregnancy, when maternal UA levels quickly increased to levels observed before conception [Citation33, Citation34]. It is possible that factors such as study design, sample size, the length of time a biomarker was evaluated, diagnostic standards, or other confounding factors contributed to the heterogeneity observed in earlier studies.
There was a consistently higher UA level among mothers who gave birth to SGA infants than among those who gave birth to non-SGA infants. Furthermore, UA levels increased more rapidly in mothers who gave birth to SGA infants than in the controls. In many countries, the prevalence of low birth weight and SGA has increased over the past four decades [Citation35, Citation36]. The current prevalence of SGA in this population is 9.7%. The prevalence of SGA varies in many countries, ranging from 6.9% to 16% [Citation37]. Two-thirds of SGA cases in Asia [Citation38]. Approximately 6.5% of SGA births occur in China, ranking fifth globally [Citation17]. Amid such a major epidemic tendency, it is critical to lower the incidence of SGA in China. Prior studies have mostly concentrated on how maternal UA affects newborn birth weight or SGA in the middle or late stages of pregnancy [Citation12, Citation13, Citation27, Citation30]. And a competing risk model was previously created for predicting small-for-gestational-age neonates from biophysical markers at 19 to 24 weeks of gestation. This model probably takes into account variables like gestational age and other pertinent factors that compete to influence the risk of SGA [Citation39]. Our study specifically explores the relationship between maternal serum uric acid and SGA risk, the FMF's competing risk model provides a broader prediction framework that takes into account multiple biophysical markers and competing outcomes to estimate the risk of SGA neonates during a specific gestational period. Integrating the insights from the FMF model into our study could potentially enhance the understanding of how maternal serum uric acid levels interact with other factors in predicting SGA outcomes. To the best of our knowledge, this is the first study to specifically include maternal UA levels in the first trimester of pregnancy as a risk factor for the delivery of SGA babies before physiological changes in blood UA concentrations during pregnancy. This outcome may serve as a guide for regulating maternal UA levels in the first trimester and lowering the occurrence of SGA.
The mechanism by which hyperuricemia affects fetal growth and development in pregnant women remains unclear. Recent research has revealed that high UA levels not only impair placental trophoblastic invasion and integration into endothelial monolayers, which results in placental hypoperfusion and malfunction, but also disrupt fetal growth and development by inducing placental inflammation [Citation40–42]. It has been demonstrated that UA causes inflammation in the placenta by triggering the production of COX-2 and MCP-1 and activating NF-kB, AP-1, and NLRP3, consequently, the development and growth of the fetus are affected [Citation43–45]. Due to the necessity of nutrients passing through the syncytio-vascular layer to reach the fetal circulation, the placenta is essential for fetal development and growth. Consequently, placental alterations in mothers who give birth to SGA infants may be caused by high maternal UA levels.
This study had several advantages and limitations. This study focused on the relationship between UA levels and SGA risk in pregnant women in the first or second trimester of gestation, which is a novel perspective. The study had a large sample size and high participation rate. UA levels were measured at two time points, and a fast-induced blood sample was obtained without affecting the UA levels. While the data on UA levels throughout pregnancy are not available, we only have discrete measurements at two time points, this could be a significant limitation of the study. Without complete longitudinal data, it may not be possible to examine trends or changes in UA levels over time, which could be essential for understanding the relationship with SGA occurrence. Additionally, the specific timing of the blood draws within these windows was not available for our analysis. This lack of information regarding the exact timing of the blood draws could introduce an element of uncertainty in our findings. Variations in uric acid levels during different timing could potentially confound the observed associations between uric acid levels and the occurrence of SGA neonates. The study cohort consisted of women who received regular prenatal care and planned to give birth at a Shanghai hospital, limiting the generalizability of the results to the general population and requiring further studies in other populations. Furthermore, the rate of SGA < 10th percentile is low (3.37%) when compare to other studies, which may due to this study might have involved a specific in Shanghai city of the Chinese population that is not fully representative of the entire population. Improved socioeconomic conditions and access to better healthcare facilities of Shanghai could lead to better fetal growth and development. For example, urban term infants who generally had better socioeconomic conditions had higher birth weights than rural term infants [Citation46, Citation47].
In addition, the clinical significance of the proposed reference values in this study is unclear, and further studies are needed to address this issue.
Conclusions
In general, non-preeclamptic and nonhypertensive women who experienced early pregnancy with high UA levels had an elevated risk of SGA. Health practitioners and obstetricians can better understand the value and practicality of managing UA levels in early pregnancy to lower the likelihood of SGA and its associated difficulties by knowing that high levels of UA in early pregnancy may be an early predictor of SGA.
Ethics approval and consent to participate
The study was performed in accordance with the relevant national guidelines and local regulations. The protocol for this study was approved by the Medical Ethical Committee of the International Peace Maternity and Child Health Hospital (No. GKLW2016-21), School of Medicine, Shanghai Jiao Tong University, and the Chinese Clinical Trial Registry (registration number: ChiCTR1900027447). According to World Medical Association standards, the study was conducted following the declaration of HELSINKI.
Author contributions
ZL and HL conceived and designed the study. YW, XH, and ZT collected the data and assisted with the study design. HL contributed to the statistical analysis and wrote the manuscript. YW and QZ provided statistical advice and assisted with the data analysis. ZL and HL have reviewed and edited the manuscript. All the authors have read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
| Abbreviations | ||
| SGA | = | small for gestational age |
| UA | = | uric acid |
| AGA | = | appropriate for gestational age |
| LGA | = | large for gestational age |
| BMI | = | body mass index |
| GDM | = | gestational diabetes mellitus |
| GWG | = | gestational weight gain |
| COX-2 | = | cyclooxygenase-2 |
| MCP-1 | = | monocyte chemoattractant protein-1 |
| NF-κB | = | nuclear factor kappa-B |
| AP-1 | = | activator protein 1 |
| NLRP3 | = | nod-like receptor protein 3 |
| OR | = | odds ratio |
| SD | = | standard deviation |
| 95% Cis | = | 95% confidence interval. |
Supplemental Material
Download MS Word (181.6 KB)Acknowledgements
We thank all participants for their support and help in this study and all clinical staff at the International Peace Maternity and Child Health Care Hospital for their support and contribution.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Additional information
Funding
References
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