Independent Risk Factor For Macrosomia Research Articles

FETO-MATERNAL COMLICATIONS IN PREGANCY WITH METABOLIC SYNDROME -A HOSPITAL BASED STUDY

Background: Being first identified by Reaven as a syndrome in 1988,[14] metabolic syndrome (MetS) is a series of metabolic disorders, including abdominal obesity, impaired glucose tolerance and insulin metabolism, hypertension, and dyslipidaemia. [1,8,9] Prepregnancy metabolic changes are not only the determinant of complications during pregnancy, after pregnancy, during postpartum life, but the reasons for inappropriate perinatal outcomes. Overweight women before pregnancy increases the risk of pregnancy complications obesity turns out to be an independent risk factor for macrosomia,cesarean section,pregnancy-induced hypertension, preterm delivery, intrauterine growth restriction, congenital malformation, intrauterine foetal death,etc. [2,7]The emergence of MetS characteristics during pregnancy may also harm the fetus.[4]This study was conducted to study the maternal and foetal outcome in pregnant women with Metabolic Syndrome. Methods: Metabolic syndrome was diagnosed by utilizing the pregnancy adaptation of MeS criteria of NCEPATP III laboratory and clinical criteria. Cases were followed throughout pregnancy till delivery and maternal and foetal complications were recorded. Results: Sample size was 100patients less than 20 weeks of gestation with metabolic syndrome and 100 controls which were normal pregnant patients less than 20 weeks of gestation without any comorbidities. The mean age was 29-32 years. The mean gestational age was 13-16 weeks. LSCS was significantly higher in cases than controls. APH, Pregnancy Induced Hypertension, Pre-eclampsia, Gestational Diabetes Mellitus was significantly higher in cases as compared to controls. Preterm, IUGR, Macrosomia was significantly higher in cases as compared to controls. Conclusion: Metabolic syndrome and its associated complications in pregnancy have a strong effect on the maternal and the fetal well-being. A careful history and examination,proper and timely investigations and proper monitoring and follow up with required medication and optimal control of all the parameters will result in better maternal and foetal outcome.

Maternal isolated hypothyroxinemia in the first trimester is not associated with adverse pregnancy outcomes, except for macrosomia: a prospective cohort study in China.

Insufficient thyroid hormone levels during pregnancy, especially in the first trimester, adversely affect maternal and fetal health. However, the impact of isolated hypothyroxinemia (IH) on adverse pregnancy outcomes remains controversial. Therefore, this study aimed to investigate the association between IH during the first trimester and adverse pregnancy outcomes. This prospective cohort study included 1236 pregnant women. Thyroid-stimulating hormone and free thyroxine levels were measured before 13 weeks of gestation. Logistic regression analysis and the Cochran-Armitage trend test were used to assess the association between IH in the first trimester and adverse pregnancy outcomes. IH during the first trimester was associated with an increased risk of macrosomia. After adjusting for confounding factors, including age, body mass index, parity, abnormal pregnancy history, fasting blood glucose, and total cholesterol, multivariate logistic regression analysis showed that IH in the first trimester remained an independent risk factor for macrosomia. In addition, the risk of macrosomia increased with IH severity. However, no significant relationship was found between IH during the first trimester and gestational diabetes mellitus, hypertensive disorders of pregnancy, spontaneous abortion, premature rupture of membranes, placental abruption, oligohydramnios, premature delivery, fetal distress, or low birth weight. IH during the first trimester did not increase the risk of adverse pregnancy outcomes, except for macrosomia.

Establishment of a nomogram model to predict the risk of macrosomia in patients with gestational diabetes mellitus

Background To establish and verify a nomogram model that can predict the risk of macrosomia in patients with gestational diabetes mellitus (GDM). Methods Data of patients with GDM who delivered their babies in Shanxi Bethune Hospital between November 2020 and February 2022 were analyzed. Multifactor logistic regression analysis was used to screen the independent risk factors for macrosomia. The model was constructed by R software. The area under the receiver operating characteristic curve (AUC) and goodness-of-fit analysis were used to evaluate its efficiency and accuracy. The clinical application value was evaluated using the decision curve analysis (DCA). Results A total of 991 patients with GDM were enrolled for modeling. Multigravida, pre-pregnancy body mass index, family history of hypertension, abdominal circumference, and biparietal diameter were independent risk factors for macrosomia, and the prediction model was established. The AUC in the training and test set were 0.93 (0.89–0.97) and 0.90 (0.84–0.96), respectively, and the difference was not statistically significant. The DCA suggested that the model has a high clinical application value. Conclusion The nomogram model for predicting macrosomia in patients with GDM was established. The model has certain accuracy and is expected to be a quantitative tool to guide clinical decision of delivery timing, individualized labor monitoring, and delivery mode.

Relationship between the Central and Peripheral Thyroid Sensitivity Indices and Fetal Macrosomia: A Cohort Study of Euthyroid Pregnant Women in China

(1) Background: To explore the correlation between central and peripheral thyroid sensitivity indices and macrosomia in euthyroid pregnant women and to provide clinical basis for the prevention and treatment of macrosomia. (2) Methods: This study is a prospective study. A total of 1176 euthyroid women in early pregnancy in the obstetrics department of Peking University International Hospital from December 2017 to March 2019 were enrolled. The women were divided into two groups, namely the macrosomia and non-macrosomia groups, according to birth weight. (3) Results: The level of free triiodothyronine (FT3), thyroid-stimulating hormone (TSH), thyroid feedback quantile-based index (TFQI), thyrotropin-T4 resistance index (TT4RI), thyroid-stimulating hormone index (TSHI), and free triiodothyronine/free thyroxine (FT3/FT4) in the macrosomia group was higher than that in the non-macrosomia group (p < 0.05). The multivariate logistic regression model showed that FT3, TFQI, TT4RI, TSHI, and FT3/FT4 were independent risk factors for macrosomia in early pregnancy after adjusting for age, body mass index, parity, blood pressure, blood glucose, and blood lipid levels (p < 0.05, respectively). (4) Conclusions: TFQI, TT4RI, TSHI, and FT3/FT4 are independent risk factors for fetal macrosomia in early pregnancy in euthyroid women.

Same disease - different effect: maternal diabetes impact on birth weight stratified by fetal sex.

Male-sex is an independent risk factor for adverse perinatal outcomes. One example is gestational diabetes mellitus (GDM), which is associated with large gestational age neonates. It was previously described that fetal glucose metabolism is affected by fetal sex. To examine whether the birth weight of neonates is affected differently by GDM according to fetal sex. A retrospective normalized cohort analysis, using the open database of 2017 Natality Data from the National Vital Statistics System in the US. We compared the delta in neonatal birth weight, according to fetal sex, between pregnancies with or without GDM. Linear regression was used to take into consideration the effect of multiple confounders. For evaluation whether fetal sex is an independent risk factor for macrosomia (> 4000 and > 4500g) following pregnancies complicated by GDM we used multivariate logistic regression. A significant relationship was found between the sex of the neonate and the delta in birth weight associated with GDM (P-value < 0.0001). The average weight gain in neonates to GDM pregnancies was 71g in females, and 56g in males. The prevalence of macrosomia above 4000g and 4500g that was attributed to GDM was higher in female-sex neonates compared to male-sex neonates (P < 0.05). According to our study results, female sex is associated with higher fetal weight gain in pregnancies complicated by GDM. Moreover, macrosomia's rate (> 4000g and > 4500g) attributed to GDM raised in a more significant manner in female-sex neonates.

Correlation of glycosylated hemoglobin and macrosomia in infants of diabetic mothers

Objective: Maternal diabetes mellitus has a significant impact on the neonatal outcome. Birth weight is related to the degree of glycemic control. Poor glycemic control in the mother causes somatic overgrowth and results in macrosomia. Maternal obesity is an independent risk factor for macrosomia. The study objective was to evaluate the correlation between glycosylated hemoglobin (HbA1c) level in diabetic mothers and macrosomia in their neonates. Methods: A cross-sectional observational study was done on diabetic mothers and their neonates. Maternal body mass index, weight gain, duration of diabetes and type of treatment were recorded. Maternal HbA1c levels were measured within one hour of delivery and birth order, mode of delivery, gestational age and birth weight were recorded. The discrete variables were analyzed by chi-square test and continuous variables by independent t-test. Receiver operating characteristic (ROC) analysis was used to determine the optimum cut-off for predicting macrosomia with HbA1c. A p-value of &lt;0.05 was considered significant. Results: A total of 228 diabetic mothers and their neonates were studied and 18 neonates were macrosomic. Diabetes was pregestational in 56.6% of mothers and gestational in 43.4% of mothers. There was a significant difference in BMI, weight gain, and HbA1c in mothers of macrosomic and non-macrosomic neonates. There was no significant difference in mean values of maternal age, duration of diabetes and gestational age in the two groups. Conclusion: This study suggests that diabetic women with elevated HbA1c&gt;5.6% measured after delivery were likely to have macrosomic neonates.

Air pollution exposure and the risk of macrosomia: Identifying specific susceptible months

Birth weight is an important indicator of future growth and development for newborns. Few studies investigated the potential effects of air pollutants on macrosomia and their susceptible windows. We included 38,971 singleton full-term births from Beijing HaiDian Maternal and Child Health Hospital between 2014 and 2018, and assessed the associations of air pollutants exposure during preconception and pregnancy with macrosomia as well as the corresponding susceptible windows. The concentrations of air pollutants (PM2.5, PM10, SO2, NO2, CO and O3) for participants were calculated by the data from the nearest monitoring stations. Distributed lag models (DLM) incorporating logistic regression models were used to estimate the associations between air pollutants exposure during the 3 months before conception and pregnancy period and the risk of macrosomia, identifying susceptible windows of air pollutants. Weighted quantile sum (WQS) regression was applied to estimate the joint effect of air pollutants. A 10 μg/m3 increase in PM2.5 exposure from 3rd to 8th gestational month was positively associated with the risk of macrosomia, with the strongest effect in the 6th month (OR = 1.010, 95 % CI: 1.002–1.019). For a 10 μg/m3 increase in SO2, the windows of significant exposure were from the 1st preconception month to the 3rd gestational month, with the strongest effect in the 2nd month (OR = 1.030, 95 % CI: 1.010–1.049). We also observed the significant positive associations were in the 5th–8th gestational months for PM10, the 8th–9th gestational months for NO2 and the 3rd-7th gestational months for CO respectively. WQS regression also indicated a positive association between co-exposure to air pollutants and macrosomia. Our results suggest air pollution exposure is associated with increased risk of macrosomia. The windows of exposure for susceptibility to the risk of macrosomia vary between air pollutants. The susceptible exposure windows were middle and late pregnancy for PM, CO and NO2, while for SO2, early pregnancy is the window of vulnerability. Our findings provide the evidence that air pollution exposure is an independent risk factor for macrosomia and a basis for targeted environment policy.

A predictive model of macrosomic birth based upon real-world clinical data from pregnant women

BackgroundFetal macrosomia is associated with an increased risk of several maternal and newborn complications. Antenatal predication of fetal macrosomia remains challenging. We aimed to develop a nomogram model for the prediction of macrosomia using real-world clinical data to improve the sensitivity and specificity of macrosomia prediction.MethodsIn the present study, we performed a retrospective, observational study based on 13,403 medical records of pregnant women who delivered singleton infants at a tertiary hospital in Shanghai from 1 January 2018 through 31 December 2019. We split the original dataset into a training set (n = 9382) and a validation set (n = 4021) at a 7:3 ratio to generate and validate our model. The candidate variables, including maternal characteristics, laboratory tests, and sonographic parameters were compared between the two groups. A univariate and multivariate logistic regression was carried out to explore the independent risk factors for macrosomia in pregnant women. Thus, the regression model was adopted to establish a nomogram to predict the risk of macrosomia. Nomogram performance was determined by discrimination and calibration metrics. All the statistical analysis was analyzed using R software.ResultsWe compared the differences between the macrosomic and non-macrosomic groups within the training set and found 16 independent risk factors for macrosomia (P < 0.05), including biparietal diameter (BPD), head circumference (HC), femur length (FL), amniotic fluid index (AFI) at the last prenatal examination, pre-pregnancy body mass index (BMI), and triglycerides (TG). Values for the areas under the curve (AUC) for the nomogram model were 0.917 (95% CI, 0.908–0.927) and 0.910 (95% CI, 0.894–0.927) in the training set and validation set, respectively. The internal and external validation of the nomogram demonstrated favorable calibration as well as discriminatory capability of the model.ConclusionsOur model has precise discrimination and calibration capabilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women.

Influence of overweight and obesity on perinatal outcomes in assisted reproduction: a retrospective cohort study

BackgroundIn spontaneous pregnancies, maternal weight and gestational diabetes are independent risk factors for macrosomia and large-for-gestational-age newborns. Furthermore, maternal body mass index (BMI) of ≥25 kg/m2 is associated with worse neonatal vitality, classified as an Apgar score of < 7 at the fifth minute of life. However, few studies have evaluated the influence of BMI on perinatal outcomes in pregnancies resulting from assisted reproduction. Therefore, this study aimed to analyze whether the perinatal outcomes of assisted reproduction are influenced by BMI.MethodsThis was a retrospective cohort study performed at a reproductive medicine center. Patients undergoing assisted reproduction (2013–2020) were divided into three groups according to their BMI (kg/m2): group 1, < 25; group 2, 25–29.9, and group 3, ≥30. In total, 1753 in vitro fertilization embryo transfer cycles were analyzed. Data were expressed as mean ± standard deviation or frequency (%). The analysis of variance and chi-square test were performed for comparison. To determine the participants and number of cycles for these analyses, generalized estimating equations were used, considering p < 0.05.ResultsIn groups 1, 2, and 3, the rates of live birth were 33.5, 32.3, and 29.9% (p = 0.668); preeclampsia were 2.9, 6.1, and 6.3% (p = 0.268); small-for-gestational-age newborns were 23, 23.2, and 21.7% (p = 0.965); macrosomia were 1.9, 0.9, and 2.7% (p = 0.708); Apgar score > 7 at the fifth minute were 97.6, 98.2, and 100% (p = 0.616); and preterm birth were 29.6, 30.1, and 35.1% (p = 0.970), respectively.ConclusionsIn conclusion, although the three groups had similar perinatal outcomes in this study, the study population was too small for conclusive results. The higher the BMI, the lower the chances of clinically relevant LBR and the higher the chances of premature labor and preeclampsia.

Evaluation of the factors affecting newborn weight

Purpose: There is a trend that causes an increase in newborn weights and macrosomy rates in many countries around the world. The aim of our study is to compare the mean newborn weights in 2012 and 2016, to investigate whether there is a tendency to increase in the mean newborn weight and macrosomia rates, and to evaluate the effects of neonatal gender, mode of birth, maternal age and the season of birth on newborn weight.&#x0D; Materials and Methods: The data of a total of 1573 newborns 2012 (n:687) and 2016 (n=886) between 37-40 weeks of gestation were evaluated retrospectively. The data of the newborns' birth weight, gender, gestationel age, maternal age, mode of birth and season of birth were evaluated statistically.&#x0D; Results: It was found that there was an increase in the rate of macrosomia (birth weight ≥4000 g) from 4.9% in 2012 to 8.1% in 2016. There is a weak positive correlation between maternal age and newborn weight for both years. There was a significant increase in terms of birth weight between babies delivered by cesarean section and babies delivered by normal route 2016. It was observed that the season of birth had no effect on birth weight.&#x0D; Conclusion: There was an increase macrosomia rates in 2016. It was observed that maternal age and neonatal gender affected the weight of the newborn, but the seasons have no effect on neonatal weight. Maternal age, sex of newborn and gestational age were considered as independent risk factors for macrosomia.

Establishment of a nomogram model to predict macrosomia in pregnant women with gestational diabetes mellitus

AimTo establish a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus in China.MethodsWe retrospectively collected the medical records of 783 pregnant women with gestational diabetes who underwent prenatal examinations and delivered at the Affiliated Hospital of Qingdao University from October 2019 to October 2020. The pregnant women were randomly divided into two groups in a 4:1 ratio to generate and validate the model. The independent risk factors for macrosomia in pregnant women with gestational diabetes mellitus were analyzed by multivariate logistic regression, and the nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus was established and verified by R software.ResultsLogistic regression analysis showed that prepregnancy body mass index, weight gain during pregnancy, fasting plasma glucose, triglycerides, biparietal diameter and amniotic fluid index were independent risk factors for macrosomia (P < 0.05). The areas under the ROC curve for internal and external validation of the model were 0.813 (95 % confidence interval 0.754–0.862) and 0.903 (95 % confidence interval 0.588–0.967), respectively. The calibration curve was a straight line with a slope close to 1.ConclusionsIn this study, we constructed a nomogram model to predict the risk of macrosomia in pregnant women with gestational diabetes mellitus. The model has good discrimination and calibration abilities, which can help clinical healthcare staff accurately predict macrosomia in pregnant women with gestational diabetes mellitus.

Gestational Weight Gain as an Independent Risk Factor for Macrosomia in Women with Intermediate State Gestational Blood Glucose

Objective: Macrosomia is closely associated with gestational diabetes mellitus (GDM) but its relationship with maternal intermediate state gestational blood glucose (ISGBG; normal fasting blood glucose and 7.8 mmol/L <1 hour blood glucose [BG] <10 mmol/L or 6.7 mmol/L <2 hour BG <8.5 mmol/L) is unclear. Here, we analyzed the clinical characteristics and pregnancy outcomes and explored risk factors for macrosomia in women with ISGBG. Methods: A total of 847 women with normal glucose tolerance gestation, 330 with ISGBG, and 99 with GDM were included. Maternal and fetal clinical data were collected and 3-point BG following oral glucose tolerance test, fasting insulin, glycated hemoglobin, and blood lipids profile were measured. Results: The incidence rate of macrosomia among the neonates of women with ISGBG was as high as 10.9%. In the ISGBG group, prepregnancy body mass index (BMI), gestational weight gain (GWG) and the proportion of women with excessive GWG (eGWG) were significantly higher in women with macrosomia compared with those who delivered a normal weight neonate. In women with ISGBG, neonate weight was positively correlated with maternal prepregnancy weight (r = 0.183, P<.01), prepregnancy BMI (r = 0.135, P<.01), and GWG (r = 0.255, P<.01), and negatively correlated with high-density lipoprotein cholesterol (r = -0.172, P<.01). Nonetheless, only eGWG was an independent risk factor (odds ratio = 3.18, 95% confidence interval = 1.26 to 7.88, P<.05) for macrosomia. The risk of macrosomia in pregnant women with prepregnancy BMI <25 kg/m2 or BMI ≥25 kg/m2 and eGWG was 3.39 and 3.27 times, respectively. Conclusion: The incidence rate of macrosomia is increased in women with ISGBG and eGWG is the strongest independent risk factor. In order to reduce the risk for macrosomia, timely lifestyle intervention to promote appropriate weight gain during pregnancy deserves evaluation. Abbreviations: AUC = area under the curve; BG = blood glucose; 1 hour BG = 1 hour blood glucose after OGTT; 2 hour BG = 2 hour blood glucose after OGTT; BMI = body mass index; CI = confidence interval; eGWG = excessive gestational weight gain; FBG = fasting blood glucose; FINS = fasting insulin; GDM = gestational diabetes mellitus; HbA1c = glycated hemoglobin; HDL-C = high-density lipoprotein cholesterol; HOMA-IR = homeostasis model assessment of insulin resistance index; ISGBG = intermediate state gestation blood glucose; LDL-C = low-density lipoprotein cholesterol; Ln = natural logarithm; MLBW = mature low birth weight; NGTG = normal glucose tolerance gestation; OGTT = oral glucose tolerance test; OR = odds ratio; SD = standard deviation.

Pre-pregnancy obesity, gestational diabetes or gestational weight gain: Which is the strongest predictor of pregnancy outcomes?

AimsBoth obesity and gestational diabetes (GDM) are risk factors for adverse pregnancy outcomes. The aim of our study is to ascertain the independent role of prepregnancy BMI (pp-BMI), gestational weight gain (GWG), and GDM on pregnancy outcomes. MethodsWe analyzed data of 1198 pregnant women, who underwent selective screening for GDM. Data on pregnancy outcomes was collected from hospital discharge records. ResultsCesarean section (CS) was comparable in GDM and NGT women. Prevalence of fetal macrosomia was 5.9%, with no difference between GDM and normal glucose tolerance (NGT), neonatal hyperbilirubinemia were more frequent in newborns of GDM women (63.3% vs. 52.2%; p < 0.01). Offspring of women with excessive GWG weighed more than those of women with regular GWG (3405 ± 510 g vs. 3287 ± 524 g; p < 0.01). On a logistic regression analysis, GWG was an independent risk factor for macrosomia (OR 1.08, 95% CI 1.02–1.13; p < 0.01) and delivery at a gestational age <37 weeks (OR 0.29, 95% CI 0.16–0.53; p < 0.0001). GDM and pp-BMI were not independent risk factors for adverse outcomes in this cohort. ConclusionsGWG rather than GDM is associated with adverse pregnancy outcomes. These findings call for an early education and implementation of a healthy lifestyle in women planning a pregnancy.

Correlation between plasma ferritin level and gestational diabetes mellitus and its impact on fetal macrosomia.

Aims/IntroductionTo explore the relationship between plasma iron levels and gestational diabetes mellitus, as well as its impact on macrosomia.Materials and MethodsWe retrospectively compared ferritin level and other characteristics between pregnant women with gestational diabetes mellitus (GDM) and pregnant women without GDM. The correlation between the levels of plasma ferritin, glucose and hemoglobin was explored. Meanwhile, we assessed the risk factors of macrosomia. Furthermore, we explored the relationship between ferritin level and the incidence of macrosomia.ResultsA total of 793 pregnant women were enrolled in the present study, of which 92 pregnant women had GDM and 701 pregnant women were healthy. Meanwhile, 51 pregnant women gave birth to infants with macrosomia and another 742 women had normal infants. Compared with non‐GDM women, pregnant women with GDM were older, with higher pre‐pregnancy body mass index, plasma ferritin, fasting plasma glucose, 1‐h postprandial glucose, 2‐h plasma glucose and hemoglobin. In addition, our results showed a significant positive correlation between the levels of ferritin and fasting plasma glucose when ferritin levels were >70 ng/mL. Our results also showed that pre‐pregnancy overweight or obesity, a high concentration of ferritin, as well as abnormal levels of fasting plasma glucose, 1‐h plasma glucose and 2 h plasma glucose were risk factors for macrosomia. Furthermore, as the level of ferritin increased, so did the incidence of macrosomia.ConclusionsThe current study provides evidence that pregnant women with high levels of ferritin might be prone to GDM. In addition, a high level of ferritin might be an independent risk factor for macrosomia. Therefore, the negative effect of iron supplementation in non‐anemic pregnant women might be noteworthy.

Effect of Iodine Nutrition on Pregnancy Outcomes in an Iodine-Sufficient Area in China.

Many studies focused on the association between thyroid disease and pregnancy outcomes. The present study explored the effect of iodine nutrition during the first trimester on pregnancy outcomes. One thousand five hundred sixty-nine pregnant, euthyroid women at ≤12weeks of gestation in an iodine-sufficient area in China were recruited. According to the World Health Organization (WHO) criteria for iodine nutrition during pregnancy, participants were divided into four groups: adequate iodine (median urinary iodine concentration (UIC), 150-249μg/L), mild deficiency (UIC, 100-150μg/L), moderate and severe deficiency (UIC, <100μg/L), and more than adequate and excessive (UIC, ≥250μg/L) groups. Pregnancy outcomes, including abortion, gestational hypertension, pre-eclampsia, gestational diabetes mellitus (GDM), placenta previa, placental abruption, preterm labor, low birth weight infants, macrosomia, breech presentation, and cord entanglement, were obtained during follow-up. The results showed that there was no significant difference in general characteristics, including age, body mass index, abdominal circumference, systolic blood pressure, diastolic blood pressure, heart rate, smoking rate, and drinking rate, among the four groups. In the more than adequate and excessive group, thyroid-stimulating hormone (TSH) was greater and free thyroxine (FT4) was lower than any other groups but still within normal range. The thyroglobulin (Tg) level was greater in the moderate and severe deficiency group. The incidence of GDM was significantly greater in women with mild iodine deficiency than in women with adequate iodine nutriture (18.38 vs. 13.70%, p<0.05). Compared with the adequate group, incidence of macrosomia was significantly greater in the more than adequate and excessive group (12.42 vs. 9.79%, p<0.05). Mild iodine deficiency was an independent risk factor for GDM (odds ratio=1.566, 95% confidence interval=1.060-2.313, p=0.024); more than adequate and excessive iodine was an independent risk factor for macrosomia (OR=1.917, CI=1.128-3.256, p=0.016). In summary, during 1st trimester, both mild iodine deficiency and excessive iodine intake had adverse impacts on pregnancy outcomes in an iodine-sufficient area.

Severe maternal insulin resistance in pregnancy: An independent predictor of fetal macrosomia

Objective: Macrosomia is associated with increased maternal and fetal complications in obstetric practice. Gestational diabetes mellitus (GDM), maternal obesity, insulin resistance (IR), and other variables such as maternal age and gestational age at delivery may influence neonatal birth weight. It is not clear if a severe degree of IR in pregnancy is an independent risk factor for macrosomia. We therefore investigated the association between IR and macrosomia independent of GDM and other confounding factors. Materials and Methods: We measured the insulin sensitivity index (Matsuda index) in 118 pregnant women during a 75-g oral glucose tolerance test at 24-32 weeks of gestation. The birth weights of their neonates were measured at delivery. Multiple logistic regression was use to assess the association between IR and macrosomia after controlling for confounders GDM and other confounding factors. Results: Twenty-four women (20.3%) were classified under IR, 20 women (16.9%) and 62 women (52.5%) had GDM and obesity, respectively. Eleven women (9.3%) had macrosomic babies. Although the fasting insulin and 2-h insulin were higher in women with macrosomic babies compared to the normal weight babies, the observed difference was not significant (P > 0.05). The Matsuda index was significantly lower among women with macrosomic babies. Severe IR (odds ratio [OR] [95% confidence interval (CI)] = 9.3 [2.4-35.1]) and GDM (OR [95% CI] = 12.7 [3.3-49.2]) were significantly associated with macrosomia. After adjusting for the confounding variables, IR remained significantly associated with macrosomia (adjusted OR [95% CI] = 10.0 [1.6-64.4]). Conclusion: IR is an independent risk factor for macrosomia, and its assessment during pregnancy should form a basis for categorizing women at risk of macrosomia.

Weight gain in pregnancy, maternal age and gestational age in relation to fetal macrosomia.

To investigate the possible risk factors related to macrosomia. Pregnant women and their newborns (n = 1041) were recruited from a cohort study in Maternal and Child Care Center of Hefei from January 2011 to July 2012. Questionnaires were applied to collect the demographic data besides the medical records. Detailed health records of the entire pregnancy were obtained using retrospective study. Meanwhile the data of neonatal outcomes was prospectively tracked. Associations between exposure risk factors and macrosomia were analyzed using Pearson's chi squared test. Logistic regression models were used to assess the independent association between these potential predictors and macrosomia. The incidence of macrosomia of this cohort was 11.24% of which male: female = 2.55:1. Male incidence (8.07%) of macrosomia was higher than female (3.17%), p < 0.001. Body mass index (BMI) before pregnancy (pre-BMI), maternal height, parity were not independently associated with macrosomia; multiple logistic regression analysis indicated that macrosomia was mainly independently associated with weight gain in pregnancy (OR=1.14, 95% CI [1.10-1.19]), maternal age (OR = 1.09, 95% CI [1.03-1.15]) and gestational age (OR = 1.62, 95% CI [1.31-1.99]), respectively. Our findings indicate that weight gain in pregnancy, maternal age and gestational age should be considered as independent risk factors for macrosomia.

Is gestational diabetes mellitus an independent risk factor for macrosomia: a meta-analysis?

The aim of our meta-analysis was to explore whether gestational diabetes mellitus (GDM) is an independent risk factor for macrosomia or not. Three databases were systematically reviewed and reference lists of relevant articles were checked. Meta-analysis of published epidemiological studies (cohort and case-control studies) comparing whether GDM was associated with macrosomia. Calculations of pooled estimates were conducted in random-effect models. Heterogeneity was tested by using Chi square test and I (2) statistics. Publication bias was estimated from Egger's test (linear regression method) and Begg's test (rank correlation method). Twelve studies met the inclusion criteria, including five cohort studies and seven case-control studies. The meta-analysis showed that GDM was associated with macrosomia independent of other risk factors. The adjusted odds ratio was 1.71, 95% CI (1.52, 1.94) in random-effect model, stratified analyses showed no differences regarding different study design, quality grade, definition of macrosomia, location of study and number of confounding factors adjusted for. There was no indication of a publication bias either from the result of Egger's test or Begg's test. Our findings indicate that GDM should be considered as an independent risk factor for newborn macrosomia. To adequately evaluate the clinical evolution of GDM need to be carefully assessed and monitored.

Increased mRNA levels of apolipoprotein M and apolipoprotein AI in the placental tissues with fetal macrosomia.

The present study examined mRNA levels of apolipoprotein M (apoM) and apolipoprotein AI (apoAI) in the term placental tissues obtained from 37 women with normal birth weight neonates and from 37 women with macrosomic neonates (birth body weight ≥4,000 g), and further discussed possible clinical significance of these observations. The mRNA levels of apoM and apoAI in the placental tissues were determined by the real time RT-PCR, which demonstrated that both apoM and apoAI mRNA levels were significantly higher in the placentas from macrosomia than those from normal birth. Moreover, we analyzed the overexpressions of apoM and apoAI with the clinical data. Meanwhile we examined several known risk factors of macrosomia including the mRNA levels of insulin-like growth factor I (IGF-I), IGF-II, insulin-like growth factor I receptor (IGF-IR) and IGF-IIR. It demonstrated that apoM expression was significantly positively correlated to the placental weight, fetal birth weight, pregestational body mass index (BMI), weight gain during pregnancy, maternal weight, maternal BMI and the mRNA levels of IGF-IR as well as IGF-IIR. The apoAI mRNA level was statistically significantly correlated to the placental weight, fetal birth weight, IGF-I and IGF-IR mRNA levels. Binary logistic regression analysis suggested that both apoM and apoAI mRNA may considered as independent risk factors for macrosomia. The clinical significance needs further investigation.

The role of gestational diabetes, pre-pregnancy body mass index and gestational weight gain on the risk of newborn macrosomia: results from a prospective multicentre study

BackgroundIt is crucial to identify in large population samples the most important determinants of excessive fetal growth. The aim of the study was to evaluate the independent role of pre-pregnancy body mass index (BMI), gestational weight gain and gestational diabetes on the risk of macrosomia.MethodsA prospective study collected data on mode of delivery and maternal/neonatal outcomes in eleven Hospitals in Italy. Multiple pregnancies and preterm deliveries were excluded. The sample included 14109 women with complete records. Associations between exposure variables and newborn macrosomia were analyzed using Pearson’s chi squared test. Multiple logistic regression models were built to assess the independent association between potential predictors and macrosomia.ResultsMaternal obesity (adjusted OR 1.7, 95% CI 1.4-2.2), excessive gestational weight gain (adjusted OR 1.9, 95% CI 1.6-2.2) and diabetes (adjusted OR 2.1, 95% CI 1.5-3.0 for gestational; adjusted OR 3.0, 95% CI 1.2-7.6 for pre-gestational) resulted to be independent predictors of macrosomia, when adjusted for other recognized risk factors. Since no significant interaction was found between pre-gestational BMI and gestational weight gain, excessive weight gain should be considered an independent risk factor for macrosomia. In the sub-group of women affected by gestational or pre-gestational diabetes, pre-gestational BMI was not significantly associated to macrosomia, while excessive pregnancy weight gain, maternal height and gestational age at delivery were significantly associated. In this sub-population, pregnancy weight gain less than recommended was not significantly associated to a reduction in macrosomia.ConclusionsOur findings indicate that maternal obesity, gestational weight gain excess and diabetes should be considered as independent risk factors for newborn macrosomia. To adequately evaluate the clinical evolution of pregnancy all three variables need to be carefully assessed and monitored.