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Association between Perinatal Outcomes and Maternal Risk Factors: Age, Body Mass Index and Cigarette Smoking

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Abstract
Objective: To analyse the association between maternal risk factors such as age, body mass index (BMI) and cigarette smoking, with perinatal outcomes. Materials and Methods: This a retrospective analysis from prospectively collected data performed at Hospital Universitario de Torrejón (Madrid, Spain), between September 2017 and December 2019. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination at 11+0 to 13+6 weeks' gestation were invited to participate. Association between preeclampsia, preterm birth, gestational diabetes mellitus (GDM), small for gestational age (SGA) or fetal growth restricted (FGR) neonates and type of delivery according to maternal age, BMI, and cigarrete smoking was studied. Logistic mixed models were used to analyze the data. Results: 1921 patients were included in the analysis. Women of 40 years old had a significantly higher risk of having GDM (Odds Ratio (OR) 1.61, 95% Confidence Interval (CI) 1.08 to 2.36), and SGA neonates (OR 1.54, 95% CI 1.00 to 2.37). Women with a BMI <18 had an increased rate of birth SGA and FGR neonates (OR 3.28, 95% CI 1.51 to 7.05, and OR 3.73, 95% CI 1.54 to 8.37, respectively), whereas women with a BMI ≥35 had a higher risk of GDM (OR 3.10, 95% CI 1.95 to 4.89). Smoking increased the risk of having SGA and FGR neonates (OR 1.83, 95% CI 1.36 to 2.46, and OR 1.91, 95% CI 1.29 to 2.78). Conclusions: Advanced maternal age, low or high BMI, and smoking status are significant risk factors for pregnancy complications. Both clinicians and society should concentrate their efforts on addressing these factors to enhance reproductive health.
Keywords: 
Subject: Medicine and Pharmacology  -   Obstetrics and Gynaecology

1. Introduction

Maternal characteristics such as age, body mass index (BMI) or cigarette smoking are important risk factors for pregnancy complications. Multiple studies have shown the association between these factors and adverse perinatal outcomes [1,2,3,4,5].
Many countries have reported a decline in birth rates, although the proportion of births in older women have increased [6,7]. Advanced maternal age (AMA), defined as pregnant women of 40 years and older, could be responsible for a substantial proportion of the increased rate of low birth weight (LBW) <2500g, small for gestational age (SGA) and preterm delivery observed in the past decades [8,9,10]. There are also other complications that have been described in advanced aged mothers such as preeclampsia (PE) and gestational diabetes mellitus (GDM) [5,11,12,13]. A 2019 meta-analysis that studied the adverse perinatal outcomes related to advanced maternal age, included 10 studies, and concluded that women aged between 35-40 and older were more likely to present overweight, GDM and gestational hypertension. Additionally, they were at a higher risk for adverse perinatal outcome such as preterm delivery or low birth weight babies [5]. It has also been reported that AMA mothers, are more likely than younger women to experience labor dystocia [14] and cesarean delivery [10,11,15,16,17].
Obesity prevalence is increasing worldwide [18,19]. Maternal obesity carries significant risks and is likely to be associated with adverse perinatal outcomes such as GMD, gestational hypertension, PE or large-for-gestational-age fetuses (LGA), and this risks appear to increase along with the severity of the condition [2,19,20,21]. Due to these obesity-related maternal disorders, obesity might increases the risk of medically indicated preterm birth, but whether obesity increases the risk for spontaneous preterm birth is still unknown [22]. At last, obesity, has also been described as a risk factor for both, programmed and intrapartum cesarean section [23,24]. The basis of many of these complications is likely to be related to the altered metabolic state associated with morbid obesity [25,26].
Despite the current obesity epidemic, maternal underweight remains a common but less studied condition also with potential adverse perinatal outcomes [27]. Low maternal BMI at the beginning of the pregnancy, has been associated with preterm labor, LBW, SGA, fetal growth restriction (FGR), and cesarean section; these risks increasing with the severity of the condition [3,22,27,28,29,30].
Smoking during pregnancy not only affects women’s own health, but may also be associated with adverse perinatal and offspring outcomes, like preterm birth, LBW, SGA and FGR [1,4,31] with a dose-dependent increase in risks [1]. Paradoxically, smoking during pregnancy has been associated with a reduced risk of preeclampsia [32,33].
In this study we aimed to analyse the association between these three maternal risk factors, age, BMI and smoking, and adverse perinatal outcomes.

2. Materials and Methods

2.1. Study Design and Population

This a retrospective analysis from prospectively collected data derived from a cohort study conducted to screen for preterm PE in the routine population [34]. All pregnant women with singleton pregnancies and non-malformed live fetuses attending their routine ultrasound examination at 11+0 to 13+6 weeks’ gestation at Hospital Universitario de Torrejón (Madrid, Spain) between September 2017 and December 2019 were invited to participate. Association between PE, preterm birth, GDM, SGA or FGR neonates and type of delivery according to BMI, maternal age, and smoking status at the beginning of the pregnancy was studied. This study was approved by the local Research Ethics Committee and all women provided written consent form.
During the 11+0 to 13+6 weeks hospital visit, patient characteristics and medical history were recorded in a clinical database (ViewPoint® software, GE Healthcare; Munich, Germany) including maternal age, race (White, Black, South Asian, East Asian, or Mixed), method of conception (natural or using assisted reproductive technology defined as in vitro fertilization or use of ovulation drugs), smoking during pregnancy, weight, height (BMI was calculated as Kg/m2), medical and obstetric history. The obstetric history included parity (parous or nulliparous if no previous pregnancies at ≥24 weeks of gestation), and for parous women, previous PE, and gestational age at delivery of previous baby.

2.2. Pregnancy Outcomes

Participants were followed up according to the clinical protocols, and any pregnancy complication, as well as delivery data, were recorded by reviewing hospital/regional records or contacting delivering hospitals or the women’s general medical practitioners/midwives.
PE was diagnosed according to the American College of Obstetricians and Gynecologists [35]. GDM was diagnosed by a sequential model (O’Sullivan test and, if positive 100mg Oral Glucose Tolerance Test (OGTT) according to the Diabetes in Pregnancy Spanish Group (Grupo Español de Diabetes y embarazo, GEDE) [36]. Preterm birth was defined as delivery before 37 weeks of gestation. Neonatal weight was assessed within the first 24 hours of life and converted to centiles using The Fetal Medicine Foundation charts [37]. SGA was diagnosed when birth weight was <10th centile, and FGR when birth weight was <3rd.

2.3. Statistical Analysis

Descriptive data were expressed as the median and interquartile range (IQR) and in proportions (absolute and relative frequencies). We studied the association of preterm birth, PE, GDM, fetal growth disorders (birth weight percentiles below the 10th, the 3rd, and above the 95th) and type of delivery with first, maternal age (40 or more years compared to the group of less than 40), second, BMI (35 or more and less than 18 compared to the group between 18 and 35) and third, smoking status. For each variable of interest, we adjusted a multiple logistic regression model. Adjusted odds Ratio (aORs), their 95% confidence intervals (CIs) and p-values were computed. The level of significance was set at 0.05. All analyses were carried out with the statistical software R in its version 4.3.0 [38] and the packages Table 1 [39] and sjPlot [40].

3. Results

3.1. Study Population and Pregnancy Outcomes

1921 patients were included in this analysis. Maternal characteristics according to risk factors are described in Table 1. Table 2 shows pregnancy outcomes according to maternal risk factors.

3.2. Risk Factors for Pregnancy Complications

3.2.1. Maternal Age

There were 145 pregnant women of 40 years old or more at the beginning of the pregnancy. After adjusting for possible confounders, this group of women showed a significantly higher risk of having GDM (aOR 1.61, 95% CI 1.08 to 2.36, p = 0.018), and SGA neonates (aOR 1.54, 95% CI 1.00 to 2.37, p = 0.049). However, no association was detected between maternal age ≥ 40 and preterm birth, mode of delivery, PE, or FGR (Table 3 and A1).

3.2.2. Body Mass Index

29 women had a BMI <18 at the beginning of pregnancy. These women showed an increased rate of birth SGA (aOR 3.28, 95% CI 1.51 to 7.05, p = 0.002) and FGR neonates (aOR 3.73, 95% CI 1.54 to 8.37, p = 0.002). No significant differences were found in either group in the incidence of preterm delivery, mode of delivery or PE (Tables 3 and A2).
There were 90 women with a BMI ≥35. This group had a higher risk of GDM (aOR 3.10, 95% CI 1.95 to 4.89, p <0.001) (Table 3). When using BMI as a continuous variable, the risk of GDM increases with increasing BMI (aOR 1.11, 95% CI 1.08 to 1.13, p <0.001) (Table A3). Besides, there was an association between BMI ≥35 and fetal birth weight >95th centile (aOR 3.50, 95% CI 1.37 to 8.91, p = 0.009) (Tables 3 and A3). The risk of cesarean section was also increased (aOR 2.12, 95% CI 1.25 to 3.54).

3.1.3. Smoking

There were 274 smokers at the beginning of the pregnancy. Pregnant smokers were at a higher risk of having SGA (aOR 1.83, 95% CI 1.36 to 2.46) and FGR (aOR 1.91, 95% CI 1.29 to 2.78) fetuses (Tables 3 and A4). Smoking was not associated with, preterm birth, mode of delivery, PE, or GDM.

4. Discussion

4.1. Main Findings of the Study

This study showed that, first, AMA and obesity are significant risk factors for GDM and second, advanced maternal age, BMI <18 and smoking at the beginning of the pregnancy are risk factors for developing SGA and FGR fetus.

4.2. Comparison with Previous Studies

Similar to previous studies, we identified AMA, body mass index or cigarette smoking as important maternal risk factors that must be consider while planning pregnancy care [1,2,3,4,5].
Women are postponing childbearing to their late 30s, and beyond 40 all around the world, but particularly in high-income countries [6,7]. In our cohort 7.5% of pregnant women were 40 years old or more at the beginning of the pregnancy. Consistent with prior studies, our research confirms a higher incidence of GDM in older women [5,10,11,12]. This observation aligns with the well-stablished trend of decreased pancreatic β-cell function and insulin sensitivity with age [41,42]. As Cnattingius et al. and Khalil et al. [8,11] described in their studies, we also found an association between AMA and the increased risk of low birth weight. However, the underlying mechanism behind this association remains undetermined. Khalil et al. [11] carried out a retrospective study that included 76158 singleton pregnancies. They concluded that not only AMA is a risk factor for GDM and SGA, but also for preeclampsia and cesarean section. In contrast, we found no evidence to establish an association between AMA and PE, nor with mode of delivery, although this might be due to our smaller sample size. Consistent with our findings, they also demonstrated no significant association between AMA and preterm delivery [11]. However, Pinheiro et al. [5], described in their meta-analysis and increased risk of preterm birth with increased maternal age. This inconsistency among the results, could be explained by differences in the definition of preterm delivery, differentiation between spontaneous or iatrogenic preterm labour and baseline characteristics of the populations.
Obesity is a chronic disease whose prevalence is increasing worldwide, and is a major contributor to poor health and adverse perinatal outcomes [2,20,21,22,43]. In Spain 10-15% of women in reproductive age are obese and around 20-29% are overweight [44]. As previously described [2,20,21,45,46,47,48], we found that high BMI associates a higher risk of GDM and LGA babies, although the latter was not found statistically significant after adjusting for other confounders. The associations of maternal adiposity with LGA infants might be explained by fetal over-nutrition, since an increased placental transfer of nutrients to the fetus might lead to an increased synthesis of insulin and insulin-like growth factors, both of which are growth-promoting hormones [49].
On the other hand, around 3.5% of the women in Spain are underweight, being more prevalent (between 5-10%) in women at reproductive age [44]. However, it remains a much less studied condition than obesity. In our sample, 1.6% of women had a BMI <18.5, which is lower than reported. Consistent with the existing literature, we have found that maternal pre-pregnancy underweight was associated with an increased risk of LBW [3,22,27,28,29,30].
Interestingly, unlike most previous published studies [2,3,20,22,27,28,45], we have not found an association between extreme BMI in either side and PE, preterm birth nor mode of delivery. These negative results could be related to a smaller than expected proportion of this conditions in our study.
Finally, smoking is a known risk factor for adverse perinatal outcomes including LBW, SGA and FGR [4,31], which is consistent with our results. The mechanisms that could explain why maternal smoking may affect intrauterine growth and birth weight, include vasoconstriction caused by nicotine (by inducing maternal catecholamine release), increased carboxyhaemoglobin levels in umbilical arteries which results in fetal hypoxia [50,51] or a decreased concentration of leptin [52]. On the other hand, we didn’t find any association between smoking and PE, which was also reported on a recent meta-analysis and systematic review [32,33]. In our study no association was found between smoking and mode of delivery; however, Li et al. performed a retrospective cohort study with 20477 (14,6%) women who smoked during pregnancy and 119396 controls, that revealed that women who smoked were more likely to have a caesarean section for non-reassuring fetal status (adjusted odds ratio (OR) 1.16, 95% CI 1.07 to 1.26, p < 0.001) [53]. Contrary to previous studies [1,54,55] we did not found either an association with preterm birth. Liu et al. [55] found that maternal smoking during either the first or second trimester of pregnancy was associated with an increased risk of preterm birth. These differences could be explained by the much smaller sample size of our study, as well as by differences in the maternal characteristics of the populations or in the number of cigarettes smoked per day that may contribute as confounders.

4.3. Clinical Implications

National efforts should prioritize raising awareness of modifiable risk factors before pregnancy, including maintaining healthy weight and promoting pregnancies at optimal maternal ages. Although AMA and increased BMI are not modifiable once gestation occurs, perinatal outcomes can still be improved by an early detection of pregnancy complications such as GDM and SGA.
On the other hand, smoking is a modifiable risk factor. Women of reproductive age or those who are pregnant and smoke should be strongly encouraged and supported to quit smoking before conception or during the early stages of the pregnancy. Antenatal clinics should incorporate smoking cessation interventions, with heavy smokers receiving personalized counseling and follow-up tailored to their specific risks.

4.4. Strengths and Limitations

The main strength of this study relies on the fact of being a prospective unselected cohort from a non-referral center, which is likely representative of the general population in our city.
However, its observational nature is a primary limitation, preventing the establishment of definite associations. Additionally, the limited number of cases for extreme ranges in all variables or for adverse perinatal outcomes may have hindered the identification of significant predictors.

5. Conclusions

Advanced maternal age, low or high BMI, and smoking status are significant risk factors for pregnancy complications. Both, clinicians and society, should concentrate their efforts on addressing these factors to enhance reproductive health.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org. Appendix Supplementary Table A1. Association between maternal age >40 and preeclampsia, preterm delivery (>37 w), Gestational Diabetes, Fetal growth and mode of delivery. Appendix Supplementary Table A2. Association between BMI <18 and preeclampsia, preterm delivery (>37 w), Gestational Diabetes, Fetal growth and mode of delivery. Appendix Supplementary Table A3. Association between BMI >40 and preeclampsia, preterm delivery (>37 w), Gestational Diabetes, Fetal growth and mode of delivery. Appendix Supplementary Table A4. Association between smoking and preeclampsia, preterm delivery (>37 w), Gestational Diabetes, Fetal growth and mode of delivery.

Author Contributions

Conceptualization: PP, RMA, IFB, MMG. Data curation: PP, IFB, MMG. Formal analysis: PP, IFB, RMA, VR, MMG. Investigation: PP, RMA, IFB, CGF, CA, VP, EF, MMG, BS. Methodology: IFB, MMG, BS. Project administration: MMG, BS. Supervision: MMG, BS. Validation: PP, RMA, IFB, VR, MMG, BS. Writing—original draft: PP, RMA, IFB, MMG, BS. Writing—review & editing: PP, RMA, IFB, VR, MMG, BS. Visualization: CGF, CA, VP, EF. Statistics: VR.

Funding

Gil MM was awarded a Leonardo grant from the BBVA Foundation to conduct this study. This study was also supported by a grant from Instituto de Salud Carlos III (ISCIII) PI18/01749. iMaterna Foundation (Registry No: 2148) provided smaller grants to support the study and General Electrics (GE Medical Systems, Zipf, Austria) via Health Net Connections (HNC, Spain) provided the software (ViewPoint®) to collect the data. None of these bodies had any involvement in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Informed Consent Statement

The study was approved by each one of the Local Research Ethics Committees at the participant centers. All women gave written informed consent.

Data Availability Statement

The data presented in this study are available on request from the corresponding author and conditioned to approval from the relevant Research Ethics Committees due to data protection regulations.

Acknowledgments

The authors are grateful to all participants and their attending obstetricians, nurses, midwives, and technicians for facilitating the performance of this study. This study is part of the Ph.D. Thesis of Dr Raquel Martin-Alonso for Universidad Francisco de Vitoria. The data included in this study is part of the PREVAL-DECREASE study. M.M.G. was awarded a Leonardo grant from the BBVA Foundation to conduct this study, which was also supported by a grant from Instituto de Salud Carlos III (ISCIII) PI18/01749 and iMaterna Foundation (No: 2148).

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Maternal characteristics of the study population according to risk factors.
Table 1. Maternal characteristics of the study population according to risk factors.
Overall (n =1921) Maternal Age Body mass index Smoker
Less than 40 (n=1776) 40 or more (n=145) < 18 (n=29) 18 to <35 (n=1802) ≥ 35 No Yes
(n=90) (n=1647) (n=274)
Maternal age in years, 33.6 33.1 41.2 29.8 33.7 32.8 33.8 32.3
median (IQR) (30.0, 36.6) (29.7, 35.9) (40.5, 42.2) (24.5, 33.9) (30.1, 36.6) (30, 36) (30.3, 36.6) (29.0, 36.0)
Body mass index in Kg/m2, median (IQR) 24.0 23.9 24.9 17.2 23.9 38.0 24.0 24.4
(21.7, 27.5) (21.6, 27.5) (22.2, 27.5) (16.87, 17.5) (21.7, 27) (36.4, 40.3) (21.7, 27.5) (21.8, 27.6)
Smoker, n (%) 274 (14.3%) 259 (14.6%) 15 (10.3%) 6 (20.7%) 254 (14.1%) 14 (15.6%) 0 274 (100%)
Race, n (%)
 White 1873 (97.5%) 1735 (97.7%) 138 (95.2%) 29 (100%) 1756 (97.4%) 88(97.8%) 1601 (97.2%) 272 (99.3%)
 Black 30 (1.6%) 24 (1.4%) 6 (4.1%) 0 28 (1.6%) 2 (2.2%) 28 (1.7%) 2 (0.7%)
 East Asian 9 (0.5%) 8 (0.5%) 1 (0.7%) 0 9 (0.5%) 0 9 (0.5%) 0
 Mixed 5 (0.3%) 5 (0.3%) 0 0 5 (0.3%) 0 5 (0.3%) 0
 South Asian 4 (0.2%) 4 (0.2%) 0 0 4 (0.2%) 0 4 (0.2%) 0
Nulliparity n (%) 847 (44.1%) 796 (44.8%) 51 (35.2%) 14(48.3%) 802 (44.5%) 31(34.4%) 713 (43.3%) 134 (48.9%)
Conception n (%)
 Spontaneous 1798 (93.6%) 1692 (95.3%) 106 (73.1%) 29 (100%) 1683(93.4%) 86 (95.6%) 1539 (93.4%) 259 (94.5%)
 Assisted reproductive techniques 123(6.4%) 84 (4.7%) 39 (26.9%) 0 119 (6.6%) 4 (4.4%) 108 (6.6%) 15 (5.5%)
Chronic hypertension n (%) 28 (1.5%) 22 (1.2%) 6 (4.1%) 0 22(1.2%) 6(6.7%) 25 (1.5%) 3 (1.1%)
Diabetes Mellitus n (%)
 Type 1 10 (0.5%) 9 (0.5%) 1 (0.7%) 0 9 (0.5%) 1 (1.1%) 10 (0.6%) 0
 Type 2 5 (0.3%) 4 (0.2%) 1 (0.7%) 0 5 (0.3%) 0 4 (0.2%) 1 (0.4%)
APS and/or SLE n (%) 22 (1.1%) 20 (1.1%) 1 (1.4%) 0 22 (1.2%) 0 22 (1.3%) 0
Previous preeclampsia n(%) 55 (2.9%) 52 (2.9%) 3 (2.1%) 1 (3.2%) 49 (2.7%) 5 (5.6%) 49 (3.0%) 6 (2.2%)
Previous neonate’s birth weight <10th percentile n(%) 259 (13.5%) 234 (13.2%) 25 (17.2%) 5 (16.1%) 245(13.6%) 9(10%) 213 (12.9%) 46 (16.8%)
Previous neonate’s birth weight <3rd percentile n(%) 127 (6.6%) 112 (6.3%) 15 (10.3%) 4 (12.9%) 120 (6.7%) 3(3.3%) 102 (6.2%) 25 (9.1%)
Previous Gestational Diabetes n (%) 67 (3.5%) 57 (3.2%) 10 (6.9%) 0 57(3.2%) 10(11.1%) 56 (3.4%) 11 (4.0%)
Previous preterm birth n(%) 83 (4.3%) 74 (4.2%) 9 (6.2%) 1 (3.4%) 80 (4.4%) 2 (2.2%) 69 (4.2%) 14 (5.1%)
Previous fetal weight > 95% percentile n (%) 34 (3.2%) 32 (3.3%) 2 (2.1%) 0 (0%) 29 (2.9%) 5 (8.5%) 29 (3.1%) 5 (3.6%)
Results are expressed as median (interquartile rage, IQR) and n and percentage (%) as required; APS: Antiphospholipid Syndrome; SLE: Systemic lupus-erithematosus.
Table 2. Pregnancy outcomes according to maternal risk factors.
Table 2. Pregnancy outcomes according to maternal risk factors.
Overall (n=1921) Maternal Age Body mass index Smoker
Less than 40 (n=1776) 40 or more (n=145) < 18 (n=29) 18 to 35 (n=1802) ≥ 35 (n=90) No (n=1647) Yes (n=274)
Neonatal outcome, n (%)
 Live birth 1916 (99.7%) 1771 (99.7%) 145 (100%) 29 (100%) 1797 (99.7%) 90 (100%) 1643 (99.8%) 273 (99.6%)
 Neonatal death 2 (0.1%) 2 (0.1%) 0 0 2 (0.1%) 0 2 (0.1%) 0
 Stillbirth 3 (0.2%) 3 (0.2%) 0 0 3 (0.2%) 0 2 (0.1%) 1 (0.4%)
Gestational age at birth in weeks, median (IQR) 39.0 39.0 39.0 39.0 39.0 39.0 39.0 39.0
(38.0, 40.0) (38.0, 40.0) (38.0, 40.0) (38.0, 40.0) (38.0, 40.0) (38.0, 40.0) (38.0, 40.0) (38.0, 40.0)
Preterm birth n (%) 118 (6.1%) 108 (6.1%) 10 (6.9%) 4 (13.8%) 108 (6.0%) 6 (6.7%) 104 (6.3%) 14 (5.1%)
Labour onset n (%)
 Spontaneous 967 (50.3%) 899 (50.6%) 68 (46.9%) 11 (37.9%) 927 (51.4%) 29 (32.2%) 840 (51.0%) 127 (46.4%)
 Induced 817 (42.5%) 753 (42.4%) 64 (44.1%) 14(48.3%) 749 (41.6%) 54 (60.0%) 684 (41.5%) 133 (48.5%)
 No labour 137 (7.1%) 124 (7.0%) 13 (9.0%) 4 (13.8%) 126 (7%) 7 (7.8%) 123 (7.5%) 14 (5.1%)
Mode of delivery n (%)
 Elective cesarean section 93 (4.8%) 84 (4.7%) 9 (6.2%) 3 (10.3%) 84 (4.7%) 6 (6.7%) 83 (5.0%) 10 (3.6%)
 Emergency cesarean section 288 (15.0%) 261 (14.7%) 27 (18.6%) 1 (3.4%) 265 (14.7%) 22 (24.4%) 244 (14.8%) 44 (16.1%)
 Instrumental 326 (17.0%) 304 (17.1%) 22 (15.2%) 4 (13.8%) 311 (17.3%) 11 (12.2%) 278 (16.9%) 48 (17.5%)
 Vaginal 1214 (63.2%) 1127 (63.5%) 87 (60.0%) 21 (72.4%) 1142 (63.4%) 51 (56.7%) 1042 (63.3%) 172 (62.8%)
Birth weight in grams, 3200 3200 3200 3030 3200 3500 3210 3080
median (IQR) (2900, 3500) (2910, 3500) (2790, 3510) (2600, 3220) (2900, 3500) (3220, 3770) (2920, 3520) (2760, 3360)
Birth weight percentile 31.5 31.7 29.8 15.9 31.5 61.9 33.7 20.5
Median (IQR) (12.6, 59.0) (12.9, 58.8) (9.75, 59.8) (2.85, 45.6) (12.6, 58.8) (30.2, 79.1) (14.0, 61.0) (6.72, 42.1)
Birth weight <10th percentile, n (%) 413 (21.5%) 375 (21.1%) 38 (26.2%) 14 (48.3%) 385 (21.4%) 14 (15.6%) 326 (19.8%) 87 (31.8%)
Birth weight <3rd percentile, n (%) 186 (9.7%) 170 (9.6%) 16 (11.0%) 9 (31.0%) 174 (9.7%) 3 (3.3%) 143 (8.7%) 43 (15.7%)
Birth weight >95th percentile, n (%) 38 (2.0%) 34 (1.9%) 4 (2.8%) 0 (0%) 31 (1.7%) 7 (7.8%) 37 (2.2%) 1 (0.4%)
Developed preeclampsia n(%) 82 (4.3%) 69 (3.9%) 13 (9.0%) 1 (3.4%) 73(4.1%) 8 (8.9%) 73 (4.4%) 9 (3.3%)
Developed pregnancy hypertension n (%) 43 (2.2%) 38 (2.1%) 5 (3.4%) 0 36 (2.0%) 7 (7.8%) 36 (2.2%) 7 (2.6%)
Developed gestational diabetes n (%) 455 (23.7%) 406 (22.9%) 49 (33.8%) 3 (10.3%) 408 (22.6%) 44 (48.9%) 386 (23.4%) 69 (25.2%)
Results are expressed as median (interquartile rage, IQR) and n and percentage (%) as required.
Table 3. Summary results from multiple logistic regression analyses.
Table 3. Summary results from multiple logistic regression analyses.
Pregnancy complication Maternal Age ≥ 40 (n=145) BMI <18 (n=29) BMI≥35 (n=90) Smoking (n=274)
aOR (95% CI) p aOR (95% CI) p aOR (95% CI) p aOR (95% CI) p
Preterm delivery 0.99 (0.45 to 1.96) 0.970 2.67 (0.77 to 7.13) 0.077 1.01 (0.38 to 2.26) 0.978 0.78 (0.42 to 1.36) 0.415
Cesareansection 1.13 (0.70 to 1.79) 0.612 0.71 (0.20 to 1.92) 0.539 2.12 (1.25 to 3.54) 0.005 0.97 (0.68 to 1.36) 0.851
Vaginal delivery 0.87 (0.57 to 1.32) 0.504 1.35 (0.60 to 3.27) 0.478 0.63 (0.38 to 1.03) 0.064 1.04 (0.78 to 1.40) 0.786
Birth weight<10th percentile 1.54 (1.00 to 2.37) 0.049 3.28 (1.51 to 7.05) 0.002 0.73 (0.40 to 1.34) 0.308 1.83 (1.36 to 2.46) <0.001
Birth weight <3rd percentile 1.19 (0.63 to 2.11) 0.569 3.73 (1.54 to 8.37) 0.002 0.31 (0.10 to 1.02) 0.055 1.91 (1.29 to 2.78) 0.001
Birth weight >95th percentile 1.31 (0.44 to 3.89) 0.623 1.19 (0.07 to 18.84) 0.908 3.50 (1.37 to 8.91) 0.009 0.15 (0.01 to 0.70) 0.061
Preeclampsia 2.00 (0.91 to 4.11) 0.070 0.97 (0.05 to 4.85) 0.977 1.94 (0.76 to 4.31) 0.129 0.79 (0.35 to 1.56) 0.522
Gestational diabetes mellitus 1.61 (1.08 to 2.36) 0.018 0.32 (0.05 to 1.07) 0.118 3.10 (1.95 to 4.89) <0.001 1.06 (0.76 to 1.45) 0.745
BMI: Body Mass Index; aOR: adjusted Odds Ratio (the complete models are provided in the supplemental material); CI: Confidence Index; p: p-value. Highlighted in bold p<0.05.
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