Abstract
Background
Preterm birth (PTB) is a global epidemic, defined as delivery before 37 weeks of gestation, and is an important risk factor for neonatal death, morbidity and abnormal childhood development. Premature birth is currently regarded as a complex disease influenced by multiple factors. Common risk factors include nutritional deficiency during pregnancy, maternal obesity, environmental exposure, infection and inflammation, among which maternal nutrition during pregnancy is an important modifiable factor.
Objective
To assess the relationship between maternal dietary calcium, phosphorus intake, and calcium supplement use before and during pregnancy was associated with the risk of PTB in offspring.
Method
This study was a nested case–control study conducted based on a large cohort study. And included pregnant women who were registered at the Perinatal Medicine Center of Gansu Provincial Maternal and Child Health Hospital from March 2018 to March 2019 and whose birth outcomes could be followed up. One-on-one dietary interviews were conducted during pregnancy, and a database was established based on the overall dietary intake levels for subsequent statistical analysis. PTB was defined as the outcome variable, while the intake levels of different substances during pregnancy were set as independent variables. Unconditional logistic regression models estimated the association between nutrient intake and the risk of PTB. Calculating the odds ratio (OR) and its 95% confidence interval (CI) to analyze the impact of different substance intake levels on PTB. Additionally, a restricted cubic spline (RCS) model with multivariable adjustment was applied to excess the non-linear association between dietary magnesium and calcium intake was associated with the risk of PTB.
Result
A total of 8897 pregnant women were included in the study, with 880 assigned to the case group and 8017 to the control group. Multivariate logistic regression analysis showed that low phosphorus intake in the second trimester was associated with an increased risk of PTB (OR = 1.297, CI: 1.020–1.649, P = 0.0341). Furthermore, similar results also exist for non-use of calcium supplements during the third trimester and low calcium intake preconception and during pregnancy. In addition, calcium, phosphorus and calcium supplements have a synergistic effect was associated with the risk of PTB.
Conclusion
During the second and third trimesters of pregnancy, the intake of phosphorus and the use of calcium supplements should be increased. Additionally, to prevent premature birth, the intake of calcium should be increased preconception and during pregnancy. Furthermore, this might lead to the optimization of public health policies or the formulation of guidelines for prenatal nutrition.
Supplementary Information
The online version contains supplementary material available at 10.1186/s41043-025-01211-8.
Keywords: Calcium, Phosphorus, Calcium supplement, Intake, Pregnancy, Preterm birth
Introduction
According to the standards set by the World Health Organization (WHO), preterm birth (PTB) refers to the situation where delivery occurs before 37 weeks of pregnancy [1]. PTB is an important factor influencing the adverse prognosis and mortality of newborns [2]. Although the number of global cases of PTB and deaths among newborns has shown a downward trend from 1990 to 2019, PTB remains the second leading direct cause of death among children under 5 years old [3]. In 2022, approximately 1 million newborns died, a figure similar like that of ten years ago [4]. The PTB rate in China is 5%, and it varies by region, with 5.3% in the eastern region, 4.6% in the central region, and 3.8% in the western region [5]. The nutritional status of a pregnant woman during pregnancy is an important modifiable factor, which is related to the mother’s health and the growth and development of the offspring [6]. Therefore, we hypothesized that lower calcium and phosphorus intake and lack of supplementation increase PTB risk through metabolic and uterine contractility pathways.
Calcium plays a key role in all cellular functions, among which calcium ions are particularly important for the coordination of excitation–contraction coupling [7]. Transmembrane calcium flux, as a core regulatory factor of intracellular calcium homeostasis, can trigger a series of downstream events, including the regulation of the interaction between myosin and actin by myosin light chain kinase, ultimately inducing muscle contraction [8, 9]. Phosphorus is the second most abundant mineral in the human body, accounting for approximately 1% of body weight, and is widely distributed both inside and outside cells. It is involved in energy storage and the formation of Adenosine Triphosphate (ATP) phosphate bonds. It can also influence organ functions such as kidney excretion and immune response by buffering blood, regulating gene transcription, activating enzyme catalysis, and mediating signal transduction [10]. Studies have shown that changes in Ca2+ signaling within the myometrium are related to uterine contractions [11]. Calcium and phosphorus are crucial to the physiological processes of life, and maintaining their homeostasis is indispensable for survival [12].
Studies have shown that abnormal states of key nutrients during pregnancy are associated with adverse pregnancy outcomes, but the relevant data is limited and the results vary. The pathogenesis of PTB remains unclear, although the identified risk factors for preterm birth include maternal demographic characteristics, pregnancy history, infection, inflammation, uterine contractions, birth defects, race, etc. [13, 14]. However, there are still few studies on the relationship between nutrient intake during pregnancy and preterm birth. Therefore, based on the cohort data of Gansu Provincial Maternal and Child Health Hospital from 2018 to 2019, this study aims to clarify the impact of dietary calcium and phosphorus intake of pregnant women preconception, in the first, second, and third trimesters on PTB. Starting from public health, maternal and child health care, to provide a scientific basis for preventing and reducing the occurrence of PTB.
Materials and methods
Study population
This study was a nested case–control study conducted based on a large cohort study, which was conducted at the largest maternal and child health hospital in Lanzhou City, Gansu Province from January 2018 to June 2019. The eligible participants were pregnant women aged between 18 and 48 years old, without a history of mental illness, who received regular prenatal care and provided written informed consent. Participants excluded those who did not complete the questionnaire survey, multiple births, stillbirths, birth defects, and those who lost their reproductive outcomes. The study included a total of 8,897 eligible women, among whom 880 were PTB infants and 8,017 had non-preterm birth (non-PTB) infants. The on-site survey included basic sociodemographic characteristics (e.g., age, educational attainment, average monthly household per capita income), preconception information (e.g., weight, smoking and drinking habits, personal and family medical history), and lifestyle factors (e.g., preconception body mass index, prenatal nutritional intake). The study protocol was approved by the Institutional Review Board of the Maternal and Child Health Hospital of Gansu Province [2018 (029)], and all participants provided written informed consent [15].
Sample size estimation
As illustrated in the sample size calculation formula (Fig. 1), The key parameters of this study were predefined based on previous literature and pilot data: two-sided significance level (α) = 0.05, type II error rate (β) = 0.1 (corresponding to 90% statistical power), exposure rate of calcium deficiency (control group, p0 ≈ 0.35; case group, p1 ≈ 0.50), and exposure rate of phosphorus deficiency (control group, p0 ≈ 0.51; case group, p1 ≈ 0.63).The effect size is reflected by the expected odds ratios (ORs) derived from the above exposure rates: OR = 1.83 for calcium deficiency and OR = 1.62 for phosphorus deficiency. Using these parameters and the adopted formula, the minimum sample size required for both the case group and the control group was determined to be 356 cases each, resulting in a total minimum sample size of 712 cases.
Fig. 1.
Sample estimation formula
Dietary surveys, phosphorus, calcium and calcium supplements daily intake
Daily surveys and dietary phosphorus, calcium and calcium supplements were collected through a face-to-face semi-quantitative food frequency questionnaire (FFQ). The effectiveness of FFQ has been verified by the team members in the early stage [6]; [15–17]. The survey covered sociological characteristics, disease history, physiological fertility, and dietary intake. Dietary status was assessed using a 24-h dietary recall survey conducted by trained medical staff. Participants reported the types and quantities of all foods consumed over three consecutive days, covering 12 main categories: cereals, oils and fats, vegetables, fruits, poultry, livestock meat and its products, eggs, aquatic products, beans and soy products, milk and milk products, bacteria and algae, snacks and drinks, as well as 59 other common food items. After obtaining the informed consent from the patients, four dietary and nutritional surveys were conducted on all the research subjects. The first survey was carried out during the 3-month period preconception when they came to the hospital for preconception consultation and health check-ups. The other three surveys were conducted during the early pregnancy (≤ 13 weeks of gestation), the middle pregnancy (14—27 weeks of gestation), and the late pregnancy (≥ 28 weeks of gestation) when they came to the hospital for prenatal check-ups. After completing all dietary surveys, the daily intake of dietary vitamins and trace elements of each pregnant woman during different pregnancies was calculated following the second edition of the Chinese Food Composition Table 2009 [18]. Daily vitamin and trace element intakes in different pregnancies were divided into groups based on the recommended nutrient intake (RNI) of Chinese residents [19] for statistical analysis. The RNI is the amount of a nutrient that is enough to ensure that the needs of 97.5% of the population are met. Data on pregnancy-related complications and birth outcomes were extracted from medical records.
Preterm birth
Preterm birth (PTB) was defined as a live birth occurring before the completion of 37 weeks of gestation, and such cases were included in the case group for analysis. Non-preterm birth (non-PTB), defined as delivery at or beyond 37 weeks of gestation (full-term infants), was included in the control group for analysis. Deliveries were further stratified into three categories: < 32 weeks, ≦ 32 < 34 weeks, and ≦ 34 < 37 weeks.
Body mass index (BMI) and gestational weight gain
Preconception weight was self-reported during the first prenatal care visit. BMI was calculated as weight (kg) divided by the square of height (m2), and then subcategorized in accordance with the Working Group of Obesity in China as follows: underweight (BMI < 18.5 kg/m2), normal weight (18.5 kg/m2 ≤ BMI < 24 kg/m2), overweight (24 kg/m2 ≤ BMI < 28 kg/m2), and obesity (BMI ≥ 28 kg/m2). Gestational weight gain in kg was calculated by subtracting preconception weight from maternal weight at delivery, which was categorized based on the US Institute of Medicine (IOM) Gestational Weight Gain Guidelines 2009 [20].
Covariates
In this study, the following covariates were included in the adjustment for confounding factors to reduce confounding bias, including: maternal age (maternal age < 23/23 ≤ maternal age ≤ 30/maternal age > 30), preconception BMI, multivitamin intake (Yes/No), weight gain during pregnancy (kg), total energy intake (kcal/d), maternal education level (education level ≤ middle school/middle school < education level ≤ community college/education level ≥ college), family’s average monthly income (Renminbi: RMB) (income ≤ 2000/2000 < income ≤ 5000/income > 5000), maternal employment (during pregnancy/no/no during pregnancy), smoking (passive smoking, active smoking) (passive smoking exposure was operationally defined as inhaling secondhand smoke from others’ tobacco products for a minimum of 15 min daily) (Yes/No), drink during pregnancy (Yes/No), gestational diabetes mellitus (GDM) (Yes/No), gestational hypertension (Yes/No), childbearing history (primipara/multipara), abortion history (Yes/No), history of premature birth (Yes/No).
Statistical analysis
In our previous research, we made some descriptions regarding the method section [6].The chi-square test or Fisher’s exact test was used to compare the differences in selected characteristics between the preterm group and the non-PTB group. For measurement data, the independent sample t-test was used for variables with a normal distribution, and the Wilcoxon rank sum test was used for variables with non-normal distribution. Unconditional logistic regression was used to calculate the odds ratio (OR) and its 95% confidence interval (CI) for the associations between dietary phosphorus, calcium intake, and calcium supplements and the risk of preterm birth and its clinical subtypes, and to adjust for confounding factors (maternal weight gain during pregnancy, maternal body mass index, total energy intake, family’s average monthly income, maternal education level, smoking, employment, multivitamin supplementation, gestational hypertension, preterm birth history, and childbearing history). SPSS (IBM SPSS Statistics for Windows, Version 27.0. Armonk, NY: IBM) was used to determine the cut off values of phosphorus and calcium in distinct gestational stages to assess the dose–response relationship. All measurement values were reported with their CI; Perform the P trend test (P for trend) using the quartiles of calcium and phosphorus statistical significance was set at P < 0.05. A multivariable-adjusted RCS model (with 3 nodes determined by Bayesian and Akaike information criteria) was used to explore potential non-linear associations and adjust for the above confounding factors. The multiplicative interaction parameters (OR = OR11/(OR01 × OR10) and their CI [21] were calculated. The missing values of RERI and AP were 0, and the missing value of S was 1. Analysis was performed using SAS 9.4 (SAS Institute, Cary, North Carolina, USA); the RCS model was executed in R 4.4.2 (packages "Hmisc", "rms", "survival").
Results
Basic characteristics of the study population
A total of 8,897 eligible females participated in the study, including 880 PTB infants and 8,017 non-PTB infants. As showed in the basic characteristics of the study population (Table 1), It could be seen that compared with the non-preterm birth non-PTB group, the maternal age in the PTB group was older, the preconception BMI was higher, the intake of multivitamins was lower, the weight gain during pregnancy was less, the total energy intake was less, the maternal education level was lower, the average monthly income of the family was less, there was more pregnancy work, gestational diabetes, gestational hypertension, history of premature birth, and history of childbirth among primiparas. In addition, no differences were found between the two groups in smoking (passive smoking, active smoking), drinking during pregnancy, and abortion history.
Table 1.
Basic characteristics of the study population
| Characteristics | PTB (n = 880) |
Non—PTB (n = 8017) |
P-value |
|---|---|---|---|
| Maternal age | < 0.0001 | ||
| Maternal age < 23 | 110(12.50) | 504(6.29) | |
| 23 ≤ Maternal age ≤ 30 | 471(53.52) | 5308(66.21) | |
| Maternal age > 30 | 299(33.98) | 2205(27.5) | |
| Preconception BMI | 20.96 ± 2.93 | 20.65 ± 2.70 | 0.0013 |
| Multivitamin intake | 0.0014 | ||
| yes | 831(94.43) | 7318(91.28) | |
| no | 49(5.57) | 699(8.72) | |
| Weight gain during pregnancy (kg) | 14.09 ± 6.12 | 17.32 ± 5.32 | < 0.0001 |
| Total energy intake (kcal/d) | 1544.2 ± 576.9 | 1704.2 ± 1808.8 | 0.0091 |
| Maternal education level | < 0.0001 | ||
| education level ≤ middle school | 333(37.84) | 1577(19.67) | |
| middle school < education level ≤ Community college | 340(38.64) | 3215(40.10) | |
| education level ≥ college | 207(23.52) | 3225(40.23) | |
| Family’s average monthly income (RMB) | < 0.0001 | ||
| income ≤ 2000 | 340(38.64) | 1945(24.26) | |
| 2000 < income ≤ 5000 | 476(54.09) | 5171(64.50) | |
| income > 5000 | 64(7.27) | 901(11.24) | |
| Maternal employ | < 0.0001 | ||
| during pregnancy | 381(43.30) | 4432(55.28) | |
| no | 357(40.56) | 2384(29.74) | |
| no during pregnancy | 142(16.14) | 1201(14.98) | |
| Smoking (passivesmoke,activesmoke) | 0.0051 | ||
| yes | 209(23.75) | 1584(19.76) | |
| no | 671(76.25) | 6433(80.24) | |
| Drink during pregnancy | 0.1893 | ||
| yes | 3(0.34) | 12(0.15) | |
| no | 877(99.66) | 8805(99.85) | |
| Gestational diabetes | 0.0270 | ||
| yes | 15(1.70) | 74(0.92) | |
| no | 865(98.30) | 7943(99.08) | |
| GDM | < 0.0001 | ||
| yes | 142(16.14) | 291(3.63) | |
| no | 738(83.86) | 7726(96.37) | |
| Childbearing history | < 0.0001 | ||
| primipara | 549(62.39) | 5983(74.63) | |
| multipara | 331(37.61) | 2043(25.37) | |
| Abortion history | 0.3313 | ||
| yes | 1098(13.70) | 131(14.89) | |
| no | 6919(86.30) | 749(85.11) | |
| History of premature birth | < 0.0001 | ||
| yes | 62(7.05) | 107(1.33) | |
| no | 818(92.95) | 7910(98.67) |
BMI: body mass index
RMB: nenminbi
GDM: gestational diabetes mellitus
PTB: preterm birth
Non-PTB: non-preterm birth
Associations of maternal dietary phosphorus intake with the risk of PTB
As showed in associations of maternal dietary phosphorus intake with the risk of PTB (Table 2), the dose—response relationship between dietary phosphorus intake and the probability of PTB is presented. During the second trimester of pregnancy, compared with the high phosphorus intake group, The low intake group was associated with an increased the risk of PTB (OR = 1.297, CI: 1.020–1.649, P = 0.0341). When the intake continued to decrease, the risk of PTB was 1.248 times that of the high intake group, and the trend test showed a significant effect (P < 0.0001). (Fig. 2) RCS models of PTB risk associated with phosphorus intake preconception (A), at the first trimester (B), at the second trimester (C), and at the third trimester (D), illustrated the RCS curve of dietary phosphorus intake distinct gestational stages PTB risk. The RCS curve showed a nonlinear association where PTB risk decreased up to mid-level dietary calcium intake (A: 837.681—1559.017 mg/d; B: 1089.858—1846.085 mg/d; C: 1125.329—1676.022 mg/d; D: 1137.529—3397.308 mg/d) and then stabilized.
Table 2.
Associations of maternal dietary phosphorus intake with the risk of PTB
| Dietary phosphorus intake(mg/d) |
Non—PTB (8017) |
PTB (< 37 weeks) (880) |
ORa(CI) | P-value | ORb(CI) | P-value | < 32 weeks | 32 ≤ weeks < 34 | 34 ≤ weeks < 37 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | ORb(CI) | P-value | n | ORb(CI) | P-value | n | ORb(CI) | P-value | ||||||||
| Preconception | ≥ 862.6922 | 3925 | 327 | Ref | Ref | 59 | Ref | 58 | Ref | 210 | Ref | |||||
| < 862.6922 | 4092 | 553 |
1.622 (1.405–1.897) |
< 0.0001 |
1.018 (0.821–1.264) |
0.8683 | 103 |
0.748 (0.456–1.225) |
0.2484 | 112 |
1.199 (0.758–1.895) |
0.4383 | 338 |
1.059 (0.811–1,381) |
0.6751 | |
|
Per interquartile decrease |
1.261 (1.184–1.344) |
0.981 ((0.870–1.106) |
0.838 (0.638–1.088) |
0.970 (0.748–1.259) |
1.039 (0.897–1.203) |
|||||||||||
| P for trend | < 0.0001 | 0.7513 | 0.1800 | 0.8207 | 0.6084 | |||||||||||
| First trimester | ≥ 1037.5869 | 4879 | 399 | Ref | Ref | 66 | Ref | 75 | Ref | 258 | Ref | |||||
| < 1037.5869 | 3138 | 481 |
1.874 (1.629–2.156) |
< 0.0001 |
1.128 (0.892–1.426) |
0.3141 | 96 |
0.968 (0.579–1.619) |
0.9024 | 95 |
0.991 (0.600–1.637) |
0.9731 | 290 |
1.219 (0.914–1.626) |
0.1785 | |
|
Per interquartile decrease |
1.347 (1.263–1.436) |
1.085 (0.956–1.232) |
1.098 (0.829–1.455) |
1.066 (0.811–1.400) |
1.098 (0.941–1.282) |
|||||||||||
| P for trend | < 0.0001 | 0.2049 | 0.5133 | 0.6478 | 0.2353 | |||||||||||
| Second trimester | ≥ 1137.2697 | 4438 | 343 | Ref | Ref | 59 | Ref | 57 | Ref | 227 | Ref | |||||
| < 1137.2697 | 3579 | 537 |
1.941 (1.684–2.239) |
< 0.0001 |
1.297 (1.020–1.649) |
0.0341 | 103 |
0.915 (0.535–1.566) |
0.7455 | 113 |
1.516 (0.896–2.564) |
0.1212 | 321 |
1.382 (1.031–1.852) |
0.0304 | |
|
Per interquartile decrease |
1.338 (1.255–1.427) |
1.012 (0.887–1.154) |
0.904 (0.675–1.211) |
1.113 (0.836–1.482) |
1.023 (0.872–1.202) |
|||||||||||
| P for trend | < 0.0001 | 0.8636 | 0.4998 | 0.4619 | 0.7774 | |||||||||||
| Third trimester | ≥ 1073.9491 | 4970 | 397 | Ref | Ref | 57 | Ref | 67 | Ref | 273 | Ref | |||||
| < 1073.9491 | 3047 | 483 |
1.984 (1.725–2.283) |
< 0.0001 |
1.159 (0.910–1.475) |
0.2319 | 105 |
1.135 (0.651–1.979) |
0.6545 | 103 |
1.571 (0.935–2.640) |
0.8783 | 275 |
1.047 (0.779–1.406) |
0.7605 | |
|
Per interquartile decrease |
1.384 (1.297–1.476) |
1.248 (1.134–1.373) |
1.094 (0.933–1.283) |
1.055 (0.794–1.402) |
1.094 (0.933–1.283) |
|||||||||||
| P for trend | < 0.0001 | < 0.0001 | 0.3566 | 0.7117 | 0.2673 | |||||||||||
PTB: preterm birth
Non-PTB: non-preterm birth
Ref: reference group
OR: odds ratio
CI: 95% confidence interval
ORa: univariate analyses
ORb: adjusted for Maternal age, Preconception BMI, Multivitamin intake, Weight gain during pregnancy (kg), Total energy intake (kcal/d), Maternal education level, Family’s average monthly income (RMB), Maternal employ, Smoking (passivesmoke,activesmoke), GDM, Gestational hypertension, Childbearing history, History of premature birth, Dietary calcium intake and Supplement calcium
Fig. 2.
RCS models of PTB risk associated with preconception phosphorus intake (A), at the first trimester (B), atthe second trimester (C), and at the third trimester (D)
Associations of maternal dietary calcium intake with the risk of PTB
As showed in associations of maternal dietary calcium intake with the risk of PTB (Table 3), in the preconception, first trimester, second trimester, and third trimester periods, a dose—response relationship was observed between dietary calcium intake and the probability of PTB. The results showed that compared with the high intake group, The low intake group was associated with an increased the risk of PTB in the third trimester (OR = 1.497, CI: 1.207—1.856, P = 0.0002). When the intake continued to decrease, the risk of PTB was 1.238 times that of the high intake group, and the trend test showed a significant effect (P < 0.0001). This risk still existed after grouping by gestational stage. Similar results were observed in distinct gestational stages. (Fig. 3) RCS models of PTB risk associated with calcium intake preconception (A), at the first trimester (B), at the second trimester (C), and at the third trimester (D), illustrated the RCS curve of dietary calcium intake distinct gestational stages PTB risk. The RCS curve showed a nonlinear association where PTB risk decreased up to mid-level dietary calcium intake (A: 413.797—1840.256 mg/d; B: 588.460—1256.442 mg/d; C: 606.187—1175.636 mg/d; D: 603.536—1924.791 mg/d) and then stabilized.
Table 3.
Associations of maternal dietary calcium intake with the risk of PTB
| Dietary calcium intake(mg/d) | Non—PTB (8017) |
PTB (< 37 weeks) (880) |
ORa(CI) | P-value | ORb(CI) | P-value | < 32 weeks | 32 ≤ weeks < 34 | 34 ≤ weeks < 37 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | ORb(CI) | P-value | n | ORb(CI) | P-value | n | ORb(CI) | P-value | ||||||||
| Preconception | ≥ 359.2753 | 5171 | 438 | Ref | Ref | 74 | Ref | 84 | Ref | 280 | Ref | |||||
| < 359.2753 | 2846 | 442 |
1.834 (1.594–2.109) |
< 0.0001 |
1.311 (1.071–1.606) |
0.0086 | 88 |
1.560 (0.984–2.473) |
0.0588 | 86 |
1.075 (0.707–1.635) |
0.7358 | 268 |
1.295 (1.009–1.664) |
0.0426 | |
|
Per interquartile decrease |
1.306 (1.225–1.393) |
1.169 (1.047–1.306) |
1.224 (0.959–1.563) |
1.168 (0.921–1.579) |
1.129 (0.985–1.293) |
|||||||||||
| P for trend | < 0.0001 | 0.0056 | 0.1045 | 0.1997 | 0.0807 | |||||||||||
| First trimester | ≥ 529.9334 | 5209 | 427 | Ref | Ref | 69 | Ref | 79 | Ref | 279 | Ref | |||||
| < 529.9334 | 2808 | 453 |
1.968 (1.711–2.264) |
< 0.0001 |
1.346 (1.088–1.664) |
0.0061 | 93 |
1.620 (1.016–2.584) |
0.0428 | 91 |
1.471 (0.939–2.306) |
0.0922 | 269 |
1.249 (0.961–1.622) |
0.0959 | |
|
Per interquartile decrease |
1.337 (1.253–1.425) |
1.065 (0.954–1.190) |
1.314 (1.026–1.682) |
1.193 (0.940–1.515) |
0.969 (0.845–1.111) |
|||||||||||
| P for trend | < 0.0001 | 0.2622 | 0.0302 | 0.1463 | 0.6471 | |||||||||||
| Second trimester | ≥ 550.5764 | 5204 | 422 | Ref | Ref | 69 | Ref | 70 | Ref | 283 | Ref | |||||
| < 550.5764 | 2813 | 458 |
2.088 (1.746–2.309) |
< 0.0001 |
1.296 (1.055–1.592) |
0.0135 | 93 |
1.689 (1.065–2.687) |
0.0059 | 100 |
1.652 (1.063–2.568) |
0.0257 | 265 |
0.881 (0.729–1.065) |
0.1918 | |
|
Per interquartile decrease |
1.363 (1.278–1.454) |
1.124 (1.006–1.257) |
1.286 (1.003–1.648) |
1.081 (0.846–1.382) |
1.093 (0.953–1.253) |
|||||||||||
| P for trend | < 0.0001 | 0.0394 | 0.0475 | 0.5328 | 0.2047 | |||||||||||
| Third trimester | ≥ 529.4478 | 5498 | 437 | Ref | Ref | 59 | Ref | 78 | Ref | 300 | Ref | |||||
| < 529.4478 | 2519 | 443 |
2.213 (1.923–2.456) |
< 0.0001 |
1.497 (1.207–1.856) |
0.0002 | 103 |
2.697 (1.655–4.397) |
< 0.0001 | 92 |
1.479 (0.947–2.310) |
0.0823 | 248 |
1.332 (1.023–1.734) |
0.0335 | |
|
Per interquartile decrease |
1.411 (1.322–1.506) |
1.238 (1.139–1.346) |
1.481 (1.141–1.924) |
1.164 (0.912–1.487) |
1.086 (0.947–1.245) |
|||||||||||
| P for trend | < 0.0001 | < 0.0001 | 0.0032 | 0.224 | 0.2363 | |||||||||||
PTB: preterm birth
Non-PTB: non-preterm birth
Ref: reference group
OR: odds ratio
CI: 95% confidence interval
ORa: univariate analyses
ORb: adjusted for Maternal age, Preconception BMI, Multivitamin intake, Weight gain during pregnancy (kg), Total energy intake (kcal/d), Maternal education level, Family’s average monthly income (RMB), Maternal employ, Smoking (passivesmoke,activesmoke), GDM, Gestational hypertension, Childbearing history, History of premature birth, Dietary phosphorus intake Supplement calcium
Fig. 3.
RCS models of PTB risk associated with preconception calcium intake (A), at the first trimester (B), at thesecond trimester (C), and at the third trimester (D)
Associations of maternal calcium supplements with the risk of PTB
As showed in associations of maternal calcium supplements with the risk of PTB (Table 4), the relationship between dietary calcium supplements and the probability of PTB was presented. In the third trimester, compared with the group that used calcium supplements, non-use calcium supplements were associated with an increased the risk of PTB (OR = 1.306, CI: 1.113—1.534, P = 0.0011). After grouping by gestational age, in the < 32 weeks group, associated with the PTB risk was (OR = 3.031, CI: 1.932—4.754, P < 0.0001).
Table 4.
Associations of maternal calcium supplements with the risk of PTB
| Calcium supplements | Non—PTB (8017) |
PTB (< 37 weeks) (880) |
ORa(CI) | P-value | ORb(CI) | P-value | < 32 weeks | 32 ≤ weeks < 34 | 34 ≤ weeks < 37 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | ORb(CI) | P-value | n | ORb(CI) | P-value | n | ORb(CI) | P-value | ||||||||
| Preconception | Users | 242 | 27 | Ref | Ref | 4 | Ref | 4 | Ref | 19 | Ref | |||||
|
Non useres |
7775 | 853 |
0.983 (0.657–1.473) |
0.9346 |
0.938 (0.615–1.430) |
0.7661 | 158 |
1.098 (0.397–3.039) |
0.8567 | 166 |
1.078 (0.390–2.980) |
0.8842 | 529 |
0.858 (0.526–1.402) |
0.5414 | |
| First trimester | Users | 356 | 33 | Ref | Ref | 4 | Ref | 7 | Ref | 22 | Ref | |||||
|
Non useres |
7661 | 847 |
1.193 (0.829–1.716) |
0.3423 |
1.124 (0.770–1.641) |
0.5433 | 158 |
1.646 (0.597–4.541) |
0.3359 | 163 |
0.989 (0.452–2.162) |
0.9773 | 526 |
1.065 (0.677–1.674) |
0.7866 | |
| Second trimester | Users | 2421 | 297 | Ref | Ref | 58 | Ref | 58 | Ref | 181 | Ref | |||||
|
Non useres |
5596 | 583 |
0.849 (0.733–0.984) |
0.0300 |
0.860 (0.737–1.003) |
0.0550 | 104 |
0.768 (0.551–1.070) |
0.1189 | 112 |
0.800 (0.576–1.111) |
0.1828 | 367 |
1.123 (0.874–1.444) |
0.3640 | |
| Third trimester | Users | 2958 | 247 | Ref | Ref | 23 | Ref | 50 | Ref | 174 | Ref | |||||
|
Non useres |
5059 | 633 |
1.498 (1.284–1.747) |
< 0.0001 |
1.306 (1.113–1.534) |
0.0011 | 139 |
3.031 (1.932–4.754) |
< 0.0001 | 120 |
1.173 (0.833–1.651) |
0.3617 | 374 |
1.110 (0.916–1.345) |
0.2856 | |
PTB: preterm birth
Non-PTB: non-preterm birth
Ref: reference group
OR: odds ratio
CI: 95% confidence interval
ORa: univariate analyses
ORb: adjusted for Maternal age, Preconception BMI, Multivitamin intake, Weight gain during pregnancy (kg), Total energy intake (kcal/d), Maternal education level, Family’s average monthly income (RMB), Maternal employ, Smoking (passivesmoke,activesmoke), GDM, Gestational hypertension, Childbearing history, History of premature birth, Dietary calcium intake and dietary phosphorus intake
Interaction effects of maternal dietary phosphorus and calcium intake on the risk of PTB
As showed in interaction effects of maternal dietary phosphorus and calcium intake on the risk of PTB (Table 5), in distinct gestational stages, with high dietary calcium and phosphorus as the control group, after adjustment, low dietary calcium and phosphorus was associated with an increased the risk of PTB, with the most pronounced association observed in the third trimester (OR = 1.214, CI: 1.119—1.316). Furthermore, a multiplicative interaction existed between dietary calcium and phosphorus, and this interaction remained most evident in the third trimester (OR = 1.213, CI: 1.126—1.307, P < 0.0001).
Table 5.
Interaction effects of maternal dietary phosphorus and calcium intake on the risk of PTB
| Maternal dietary intake | Non - PTB | PTB | ORa(CI) | ORb(CI) | |
|---|---|---|---|---|---|
| Preconception | High calcium and high phosphorus | 3806 | 306 | Ref | Ref |
| High calcium and low phosphorus | 1365 | 132 | 1.203(0.972–1.489.972.489) | 1.063(0.804–1.407.804.407) | |
| Low calcium and high phosphorus | 119 | 21 | 1.482(1.167–1.882.167.882) | 1.227(0.911–1.653.911.653) | |
| Low calcium and low phosphorus | 2727 | 421 | 1.243(1.180–1.309.180.309) | 1.155(1.073–1.244.073.244) | |
| Multiplicative interaction: ORb(CI)=1.146(1.069–1.228.069.228) P=0.0001 | |||||
| First trimester | High calcium and high phosphorus | 4497 | 356 | Ref | Ref |
| High calcium and low phosphorus | 712 | 71 | 1.260(0.965–1.645.965.645) | 0.952(0.669–1.356.669.356) | |
| Low calcium and high phosphorus | 382 | 43 | 1.192(1.009–1.409.009.409) | 1.078(0.888–1.309.888.309) | |
| Low calcium and low phosphorus | 2426 | 410 | 1.288(1.225–1.354.225.354) | 1.154(1.106–1.247.106.247) | |
| Multiplicative interaction: ORb(CI)=1.158(1.078–1.245.078.245) P<0.0001 | |||||
| Second trimester | High calcium and high phosphorus | 4199 | 319 | Ref | Ref |
| High calcium and low phosphorus | 1005 | 103 | 1.349(1.069–1.703.069.703) | 1.132(0.811–1.581.811.581) | |
| Low calcium and high phosphorus | 239 | 24 | 1.150(0.925–1.429.925.429) | 1.031(0.800–1.329.800.329) | |
| Low calcium and low phosphorus | 2574 | 434 | 1.304(1.240–1.372.240.372) | 1.160(1.066–1.263.066.263) | |
| Multiplicative interaction: ORb(CI)=1.164(1.079–1.255.079.255) P<0.0001 | |||||
| Third trimester | High calcium and high phosphorus | 4679 | 359 | Ref | Ref |
| High calcium and low phosphorus | 819 | 78 | 1.241(0.961–1.603.961.603) | 1.103(0.787–1.547.787.547) | |
| Low calcium and high phosphorus | 291 | 38 | 1.305(1.093–1.558.093.558) | 1.148(0.928–1.419.928.419) | |
| Low calcium and low phosphorus | 2228 | 405 | 1.333(1.268–1.402.268.402) | 1.214(1.119–1.316.119.316) | |
| Multiplicative interaction: ORb(CI)=1.213(1.126–1.307.126.307) P<0.0001 | |||||
PTB: preterm birth
Non-PTB: non-preterm birth
Ref: reference group
OR: odds ratio
CI: 95% confidence interval
P: P- value
ORa univariate analyses
ORb adjusted for Maternal age, Preconception BMI, Multivitamin intake, Weight gain during pregnancy (kg), Total energy intake (kcal/d), Maternal education level, Family’s average monthly income (RMB), Maternal employ, Smoking (passivesmoke,activesmoke), GDM, Gestational hypertension, Childbearing history, History of premature birth and calcium supplements
Interaction effects of maternal dietary phosphorus and calcium intake, and calcium supplements intake on the risk of PTB
As showed in interaction effects of maternal dietary phosphorus and calcium intake, and calcium supplements intake on the risk of PTB (Table 6), with those who have high dietary calcium and phosphorus intake and use calcium supplements as the control group, during the third trimester, low dietary calcium and phosphorus intake and non-use of calcium supplements was associated with an increased the risk of PTB (OR = 1.112, CI: 1.057—1.169). A multiplicative interaction existed between dietary calcium and phosphorus and calcium supplements, and this interaction remained most evident in the third trimester (OR = 1.263, CI: 1.169—1.364, P < 0.0001).
Table 6.
Interaction effects of maternal dietary phosphorus and calcium intake, and calcium supplements intake on the risk of PTB
| Maternal dietary intake | Calcium supplements | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Users | Non-Users | ||||||||
| Non—PTB | PTB | ORa(CI) | ORb(CI) | Non—PTB | PTB | ORa(CI) | ORb(CI) | ||
| Preconception | High calcium and high phosphorus | 135 | 9 | Ref | Ref | 3671 | 297 |
1.102 (0.782–1.552) |
1.245 (0.833–1.861) |
| High calcium and low phosphorus | 39 | 5 |
1.923 (0.609–6.072) |
1.109 (0.111–11.130) |
1326 | 127 |
1.128 (0.894–1.424) |
1.116 (0.824–1.513) |
|
| Low calcium and high phosphorus | 6 | 1 |
1.257 (0.722–2.191) |
1.991 (0.714–5.554) |
113 | 20 |
1.177 (1.025–1.350) |
1.094 (0.885–1.354) |
|
| Low calcium and low phosphorus | 46 | 7 |
1.179 (0.957–1.453) |
1.092 (0.760–1.568) |
2681 | 414 |
1.127 (1.023–1.243) |
1.105 (0.984–1.240) |
|
| Multiplicative interaction: ORb(CI) = 1.138(1.106–1.219) P = 0.0002 | |||||||||
| First trimester | High calcium and high phosphorus | 220 | 15 | Ref | Ref | 4277 | 341 |
1.081 (0.828–1.413) |
1.075 (0.800–1.446) |
| High calcium and low phosphorus | 29 | 2 |
1.012 (0.220–4.650) |
1.507 (0.164–13.894) |
683 | 69 |
1.140 (0.940–1.383) |
1.067 (0.863–1.417) |
|
| Low calcium and high phosphorus | 17 | 1 |
0.964 (0.572–1.622) |
/ | 365 | 42 |
1.091 (0.985–1.208) |
1.068 (0.946–1.206) |
|
| Low calcium and low phosphorus | 79 | 12 |
1.174 (1.000–1.378) |
1.121 (0.831–1.512) |
2347 | 398 |
1.139 (1.055–1.229) |
1.092 (0.995–1.197) |
|
| Multiplicative interaction: ORb(CI) = 1.160(1.080–1.246) P < 0.0001 | |||||||||
| Second trimester | High calcium and high phosphorus | 1247 | 103 | Ref | Ref | 2952 | 216 |
0.941 (0.833–1.063) |
0.942 (0.814–1.091) |
| High calcium and low phosphorus | 316 | 31 |
1.188 (0.780–1.808) |
0.776 (0.432–1.393) |
689 | 72 |
1.082 (0.974–1.201) |
0.952 (0.814–1.113) |
|
| Low calcium and high phosphorus | 72 | 7 |
1.042 (0.852–1.273) |
1.011 (0.801–1.276) |
167 | 17 |
1.035 (0.947–1.133) |
0.990 (0.897–1.113) |
|
| Low calcium and low phosphorus | 731 | 145 |
1.192 (1.129–1.257) |
1.078 (0.988–1.175) |
1843 | 289 |
1.096 (1.060–1.133) |
1.020 (0.967–1.076) |
|
| Multiplicative interaction: ORb(CI) = 1.137(1.052–1.228) P = 0.0012 | |||||||||
| Third trimester | High calcium and high phosphorus | 1716 | 113 | Ref | Ref | 2963 | 246 |
1.123 (1.001–1.260) |
1.051 (0.915–1.207) |
| High calcium and low phosphorus | 297 | 22 |
1.125 (0.701–1.805) |
1.025 (0.560–1.875) |
522 | 56 |
1.177 (1.052–1.316) |
1.031 (0.883–1.205) |
|
| Low calcium and high phosphorus | 101 | 11 |
1.134 (0.964–1.334) |
1.111 (0.923–1.338) |
190 | 27 |
1.137 (1.055–1.224) |
1.042 (0.947–1.147) |
|
| Low calcium and low phosphorus | 743 | 87 |
1.122 (1.058–1.190) |
1.034 (0.945–1.130) |
1485 | 318 |
1.183 (1.146–1.222) |
1.112 (1.057–1.169) |
|
| Multiplicative interaction: ORb(CI) = 1.263(1.169–1.364) P < 0.0001 | |||||||||
PTB: preterm birth
Non-PTB: non-preterm birth
Ref: reference group
OR: odds ratio
CI: 95% confidence interval
P: P- value
ORa: univariate analyses
ORb: adjusted for Maternal age, Preconception BMI, Multivitamin intake, Weight gain during pregnancy (kg), Total energy intake (kcal/d), Maternal education level, Family’s average monthly income (RMB), Maternal employ, Smoking (passivesmoke,activesmoke), GDM, Gestational hypertension, Childbearing history, History of premature birth
Discussion
In this study, it was found that insufficient intake of phosphorus and calcium during pregnancy, as well as the absence of calcium supplementation, was associated with an increased the risk of PTB. when PTB risk decreased up to mid-level dietary phosphorus and calcium intake and then stabilized.
The research found that patients with preterm labor had deficiencies of calcium and phosphorus in their bodies [22]. Another study also pointed out that PTB infants failed to receive timely phosphorus supplementation, as approximately 80% of phosphorus accumulates from the 24th week of pregnancy to full-term delivery [23]. Our research was similar like their conclusions. During the second trimester, low dietary phosphorus intake in pregnant women was associated with a 1.297-fold increased risk of PTB, compared with high phosphorus intake (OR = 1.297, CI: 1.020—1.649, P = 0.0341). However, there are also studies suggesting that phosphorus intake has no relation to PTB [24]. Insufficient phosphorus intake can induce hypophosphatemia induced depletion syndrome, leading to hypercalcemia, hypercalciuria, hypophosphatemia, and rickets [25]. In the pregnancy stage, insufficient calcium and phosphorus intake during the perinatal period can lead to metabolic bone disease (MBD), characterized by reduced bone mass, changes in bone mineralization, and increased risk of fractures, and infants with PTB have a higher risk of MBD [26]. Studies have found that patients with threatened PTB show deficiencies in serum phosphorus, calcium, and magnesium, which may be related to premature decline of uterine contractions [22]. Furthermore, the research found that there is a linear correlation between increasing dietary phosphorus intake and the risk of type 2 diabetes in women [27]. The tolerable upper intake level (UL) of phosphorus varies among existing studies. One study suggests that the UL of phosphorus is 3.5 g/d, while another study considers that the UL of phosphorus for women aged 14—70 is 3000—4000 mg/d [28, 29].
Based on the current research background, the conclusions regarding calcium intake and PTB remained inconsistent. Some studies had shown that insufficient calcium intake during pregnancy increased the risk of PTB, while supplementing calcium during pregnancy could effectively reduce the risk of preterm birth in the offspring [30–32]. Our conclusion was similar like these studies. For example, low calcium intake in the third trimester was associated with a 1.497-fold higher risk of PTB compared with high calcium intake (OR = 1.497, CI: 1.207—1.856, P = 0.0002). Some studies had found that taking calcium supplements before 37 weeks of pregnancy did not reduce the risk of PTB. On the contrary, taking vitamin D and calcium supplements simultaneously might even increase the risk of PTB [33, 34]. Furthermore, some studies have indicated that dietary calcium intake higher than the median might not only elevate the risk of myocardial infarction but also increase the incidence of ureteral stones associated with hypercalcemia [35, 36]. Some studies had suggested that pregnant women should consume 1.5 to 2 g of calcium per day before delivery [37]. A study conducted in Northwest China had shown that the intake of trace elements among pregnant women was lower than the recommended levels in Chinese guidelines and the standards proposed by the WHO [38]. Calcium was the most important mineral in the human body and served as the main component of bones and teeth. It participated in numerous physiological processes within the body. Specifically, calcium was crucial for nerve impulse conduction, blood clotting, muscle contraction, regulation of ion transport across cell membranes, glycogen breakdown and gluconeogenesis, as well as the activity of many enzymes [39, 40]. Calcium supplementation could alleviate hypertension and excessive uterine muscle contraction, as it was well known that calcium could inhibit the renin-angiotensin system and reduce the contraction of vascular smooth muscle cells [41, 42]. Furthermore, studies had shown that leg cramps occurred in pregnant women during the third trimester, which might be caused by calcium deficiency [43]. Some research had also found that calcium supplementation increased the risks of myocardial infarction, stroke, and type 2 diabetes [44].
The main subjects of the study were women from the urban area of Lanzhou. Their dietary nutrient intake showed no significant difference from that of women in other western cities in China. Therefore, the subjects of this study are generally representative and have reference value for women in other Chinese cities. Maternal calcium and phosphorus intake during pregnancy, as well as the rational use of calcium supplements, might have had a significant impact on pregnancy outcomes and long-term health in low-income and middle-income regions. This could have led to the optimization of their public health policies or prenatal nutrition guidelines. In the future, we will further verify this conclusion based on serum calcium and phosphorus levels to minimize the occurrence of adverse pregnancy outcomes to the greatest extent.
This study was based on a large cohort study with complete follow-up. A comprehensive and systematic assessment was conducted to evaluate the potential effects of phosphorus and calcium levels, as well as the use of calcium supplements, during different stages of pregnancy association with the risk of PTB. Additionally, the interaction among the three was analyzed. However, it is necessary to acknowledge that this study had some limitations. First, as this study adopted a case–control design, causal inference was inherently limited. Second, the use of a food frequency questionnaire (FFQ) may have introduced recall bias. Moreover, despite adjusting for multiple known confounders, residual or unmeasured confounding remained a potential limitation of this study.
Conclusions
Our research indicates that insufficient phosphorus intake during the second trimester, the absence of calcium supplementation during the third trimester, and insufficient calcium intake throughout the entire pregnancy increase the risk of PTB. We also discovered a synergistic effect among calcium, phosphorus, and calcium, phosphorus, and calcium supplements. These findings suggest that it is necessary to pay attention to appropriately increasing dietary calcium, phosphorus, and calcium supplements intake during pregnancy to reduce the risk of PTB among pregnant women in Lanzhou City, Gansu Province.
Supplementary Information
Acknowledgements
The authors thank all the study personnel from the Gansu Provincial Maternity and Child-care Hospital (Gansu Provincial Central Hospital) for their exceptional efforts in study subject recruitment.
Author contributions
B.M. conceptualized the research. Z.W. conducted the data analysis and completed the manuscript. L.C. checked the data multiple times. L.Y. strictly reviewed and revised the manuscript. Y.L., Q.T., C.Y., H.F. and Y.F. supervised the research and provided comprehensive guidance. All authors reviewed and approved the final version of the manuscript.
Funding
This work was supported in part by Gansu Provincial Health Commission Research Project (No.GSWSKY2024-14; No.GSWSKY2024-49), and the Traditional Chinese Medicine Management Project of Gansu Province (No.GZKG-2024–43).
Data availability
The datasets generated and/or analyzed during the current study are not made public as they contain private information of the women. However, they can be obtained from the corresponding author upon reasonable request.
Declarations
Ethics approval and consent to participate
Informed consent was obtained from study participants. This research approved by Medical Ethics Committee of Gansu Provincial Maternity and Child-care Hospital [2018] The Ethics Committee of Gansu Provincial Maternity and Child-care Hospital No.(29). All the participants in the study have fully understood the research content, potential risks and benefits, and have voluntarily agreed to take part in the study.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Zifu Wang and Lei Cao are authors contributed equally to this work.
Contributor Information
Yue Fang, Email: 451459730@qq.com.
Baohong Mao, Email: Baohong.Mao@gszy.edu.cn.
Liping Yang, Email: liping.yang@gszy.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets generated and/or analyzed during the current study are not made public as they contain private information of the women. However, they can be obtained from the corresponding author upon reasonable request.