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The association between macrosomia and glucose, lipids and hormones levels in maternal and cord serum: a case-control study

BMC Pregnancy and Childbirth volume 24, Article number: 599 (2024) Cite this article

Abstract

Background

The formation of macrosomia is associated with excessive nutrition and/or unable to regulate effectively. This case-control study aims to explore the relationship between macrosomia and glucose, lipids and hormones levels in maternal and cord serum.

Methods

In the case-control study, 78 pairs of mothers and newborns were recruited who received care at one hospital of Hebei, China between 2016 and 2019. According to the birth weight (BW) of newborns, participants were divided into macrosomia group (BW ≥ 4000 g, n = 39) and control group (BW between 2500 g and 3999 g, n = 39). Maternal vein blood and cord vein blood were collected and assayed. All data were compared between the two groups. Unconditional logistics regression analysis was used to test the relationship between macrosomia and glucose, lipids and hormones in maternal and cord serum.

Results

In maternal and cord serum, the levels of leptin, leptin/adiponectin ratio (LAR), glucose and triglyceride (TG) in macrosomia group were higher than those in control group, and the levels of high-density lipoprotein cholesterol (HDL-C) were lower. The percentage of maternal glucose and lipids transfer to cord blood did not differ between the two groups. High levels of TG in maternal serum were positively correlated with macrosomia, and high levels of LAR, TG and glucose in cord serum were positively correlated with macrosomia.

Conclusion

In conclusion, the results of the current study, suggest that the nutrients and metabolism-related hormones in maternal and umbilical cord are closely related to macrosomia. During pregnancy, the nutritional status of pregnant women should be paid attention to and to obtain a good birth outcome.

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Introduction

Macrosomia is defined as an infant birth weight ≥ 4000 g [1]. Based on the definition, the incidence of macrosomia in China is increasing as the improvement of people’s living standards and the change of fertility policy [2,3,4,5]. Macrosomia will increase the risk of shoulder dystocia, labor injury, postpartum hemorrhage and perinatal death [6, 7], and it is associated with long-term health problems, there is an increased risk of metabolic diseases such as overweight, obesity, diabetes and affect the mental health of offspring [8,9,10]. The etiology of macrosomia can be divided into non-modifiable factors (i.e. genes, fetal sex, parity, and maternal age, etc.) and modifiable factors (i.e. maternal nutritional intake, pre-pregnancy body mass index (BMI), metabolic parameters, gestational weight gain (GWG), physical activity level, smoking, etc.) [11,12,13,14,15,16]. The physiological and pathological mechanism of macrosomia is related to the excessive supply of nutrients to the fetus by the mother and/or the inability of the fetus to effectively or efficiently regulate the metabolism of nutrients [17], the substances involved are glucose, lipids, amino acids, metabolism-related hormones and so on.

For the intrauterine development of the fetus, nutrients from the mother’s blood are the basis and nutrients from the cord blood are the direct source. Parts of current studies about the relationship between macrosomia and glucose and lipids in maternal blood are consistent, they found that maternal glucose and triglyceride (TG) levels are positively correlated with birth weight (BW), and high levels of glucose and TG will increase the risk of macrosomia [18,19,20,21]. Tamer et al. [22]. reported that maternal total cholesterol (TC) were positively correlated with BW, and Xi et al. [20]. and Misra et al. [21] found that low levels of high density lipoprotein cholesterol (HDL-C) during pregnancy could be regarded as high-risk indicators for macrosomia. Most of the studies on the correlation between macrosomia and metabolism related hormones in maternal serum have found that maternal insulin, leptin and adiponectin are not correlated with BW [23,24,25,26,27], while some studies reported different results [28,29,30]. Limited studies existed that combined maternal blood macronutrients with metabolism-related hormones and analyze the relationship with BW.

Findings on the relationship between BW and cord blood glucose and lipid levels vary widely [31,32,33,34,35,36]. In the studies about the correlation between macrosomia and metabolism related hormones in cord serum, Ahmad et al. ‘s [37] study showed that insulin in cord serum was positively correlated with BW. Tsai et al. found that adiponectin in cord serum was positively correlated with BW [38,39,40], differing from the findings of Wang et al. and Donatella et al. [30, 41]. The studies of Wiznitzer et al. [42] and Shaarawy et al. [43]. showed that leptin in cord blood was positively correlated with BW and was an independent risk factor for macrosomia. Whereas there are very few studies on the comprehensive analysis of the association of BW and cord blood glucose, lipids and their metabolism-related hormones.

The results of current studies vary greatly, and studies that analyze both maternal blood and cord blood and combine glucose, lipids and hormones to jointly analyze the association with BW are rare. Therefore, based on the possible causes of macrosomia and the current research status, this study combined maternal serum and cord serum, glucose, lipids and metabolism-related hormones to analyze the relationship between macrosomia and these substances. We hypothesized that glucose, TG and TC in maternal and cord serum may were positively correlated with macrosomia, HDL-C and metabolism-related hormones may were negatively correlated with macrosomia.

Methods

Study participants

This case-control study enrolled 78 pairs of pregnant women and their newborn from a prospective cohort about maternal and child nutrition and health in China which carried out in Wuqiang, China [44]. A sample size of 33 in each group was calculated to detect a 1.24 mmol/L difference of TG concentrations in maternal serum and provide 90% power at a significance level of p < 0.05 based on a previous study by Xu et al. [45]. To account for potential dropouts (those who not enough blood was taken), the sample size was increased by 15%, with 39 participants in each group. So we recruited 39 macrosomia newborns (BW ≥ 4000 g) and 39 normal birth weight newborns (BW between 2500 g and 3999 g) and their mothers from this cohort. These pregnant women gave birth between December 2016 to November 2019 in Wuqiang County Hospital, Hebei Province, China. Inclusion criteria of pregnant women: (1) aged 18 to 45 years old, (2) gestational week > 37, (3) singleton pregnancy. Exclusion criteria of pregnant women: (1) foreign nationality, (2) having infectious disease, (3) with history of habitual abortion, (4) with history of diabetes, hypertension and in current pregnancy.

Data collection

The information including maternal age, height, weight before pregnancy (self-reported), GWG (pre-delivery weight minus pre-pregnancy weight), gestational age at delivery, mode of delivery, gender, and BW were extracted from the hospital’s medical record information system. Then pre-pregnancy BMI was calculated. Pre-pregnancy BMI and GWG were classified according to standards established in China [46, 47]. During the antepartum period, the medical staff were required to collect elbow vein blood from the pregnant women. After the fetus were delivered and the umbilical cord were cut, cord vein blood was extracted. All blood samples were centrifuged at 3500 r/min for 15 min, and the serum was taken and stored at -80℃ until detection. All samples were analyzed for the levels of glucose, TG, TC, HDL-C, LDL-C, leptin, adiponectin and insulin.

Blood sample analyses

The concentrations of glucose, TG, TC, HDL-C and LDL-C were analyzed respectively by hexokinase method, GPO-N-(3-sulfopropyl)-3-methoxy-5-methylaniline (HMMPS) method, cholesterol oxidase and HMMPS method, antibody blocking method and selective protection method with automatic biochemical analyzer (HITACHI 7600 series, Hitachi Limited, Japan), using the kits Wako 998-18301, Wako 999-32991, Wako 999-33391, Wako 999–09001 and Wako 999-39891 respectively.

The concentration of insulin was analyzed by electrochemical luminescence (Cobas 6000 e601, Roche, Switzerland) using kit (Roche 12017547122), calibrating solution (Roche 12017504122) and multi-label substance control (Roche 05341787190). The concentration of adiponectin and leptin were analyzed by enzyme-linked immunosorbent assay (ELISA) using kits (R&D systems DRP300 and abcam 108879).

The detection deviation of high concentration quality control products (HQC) and the low concentration quality control products (LQC) of glucose and lipids were less than 15%, The detection deviation of HQC and LQC of insulin were less than 10%, the detection deviation within and between plates of leptin and adiponectin were less than 20%.

Statistical analyses

All statistical analyses were performed using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). Continuous variables were presented as mean and standard deviations (SD) or median (interquartile range), and categorical variables were expressed as frequency (percentage). Shapiro-Wilk test was used to analyze the normality of quantitative data. Normally distributed data were presented as mean ± SD, while non-normally distributed data were described as median (interquartile range). Student’s t-test or Mann-Whitney U test were used to analyze the differences between the two groups depending on whether it’s normal distribution. In order to better explain the correlation between different levels of indexes in serum and macrosomia, these indexes were classified into three categories according to the quartile [48]. With the lowest quartile as the reference group, univariate and unconditional multivariate logistics regression analysis were used to test the association between macrosomia and glucose, lipids and hormones associated with their metabolism in maternal and cord serum. All results with a p-value < 0.05 were considered statistically significant.

Results

Maternal and neonatal data

Baseline characteristics are presented in Table 1. The proportion of overweight and obesity before pregnancy and newborn BW, length and placenta weight in the macrosomia group were higher than those in the control group (p < 0.05). There were no significant differences between the two groups in maternal age, the proportion of excessive GWG, parity, the mode of delivery, and newborn gender (p > 0.05). The mode of conception for all pregnant women were spontaneous and all pregnant women were multiparas. According to the criteria [46], the participants did not have inadequate GWG, so it was not shown in the table.

Table 1 Characteristics of study populations [Mean ± SD or n (%)]
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Comparative analysis of glucose, lipids and hormones levels in maternal and cord serum between the two groups

The levels of leptin, LAR, glucose and TG of maternal serum in macrosomia group were higher than those in control group (p < 0.05), while the level of HDL-C was lower (p < 0.05). The levels of leptin, LAR, glucose and TG of cord serum in macrosomia group were higher than those in control group (p < 0.05), while the level of HDL-C was lower (p < 0.05). There was no significant difference in other indicators between the two groups (p > 0.05). The percentage of maternal glucose and lipids transfer to cord blood between the two groups showed no difference (p > 0.05) (Table 2).

Table 2 Comparison of glucose, lipids and hormones levels in maternal serum between the two groups (Mean ± SD or median and IQR)
Full size table

Relationship between macrosomia and glucose, lipids and hormones in maternal and cord serum

The results of univariate logistic regression analysis showed that high levels of TG in maternal serum were positively correlated with macrosomia (p < 0.05) (Table 3), high levels of glucose, TG and medium-high level of LAR in cord serum were positively correlated with macrosomia (p < 0.05), and high levels of HDL-C were negatively correlated with macrosomia (p < 0.05) (Table 4).

Table 3 Univariate analysis of the association between macrosomia and glucose, lipids and hormones in maternal serum
Full size table
Table 4 Univariate analysis of the association between macrosomia and glucose, lipids and hormones in cord serum
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The results of multivariate analysis showed that after adjusting for confounding factors, namely pre-pregnancy BMI and gestational week, compared with low levels, high levels of TG in maternal serum increased the risk of macrosomia by 6.14 times (OR = 6.14, 95%CI: 1.13 to 33.49), high levels of glucose in cord serum increased the risk of macrosomia by 9.82 times (OR = 9.82, 95%CI: 1.69 to 57.16), and high levels of TG increased the risk of macrosomia by 4.77 times (OR = 4.77, 95%CI: 1.74 to 30.82), medium-high levels of LAR increased the risk of macrosomia by 6.60 times (OR = 6.60, 95%CI: 1.10 to 39.71)(Table 5).

Table 5 Multivariate analysis of the association between macrosomia and glucose, lipids and hormones in maternal and cord serum
Full size table

Discussion

The objective of this study was to analyze the relationship between macrosomia and glucose, lipids and three kinds of metabolism related hormones in maternal and cord serum. The results showed that the levels of leptin, LAR, glucose and TG in maternal and cord serum of macrosomia group were higher than those of control group, and the levels of HDL-C were lower. High levels of LAR, TG and glucose in cord serum were positively correlated with macrosomia, and high levels of TG in maternal serum were positively correlated with macrosomia.

Relationship between macrosomia and glucose in maternal and cord serum

The correlation between macrosomia and glucose in maternal and cord serum can be explained by Pedersen hypothesis [49]. When the mother has high blood glucose, the glucose transferred to the fetal circulation will increase, this will stimulate the fetal to secrete too much insulin, resulting in hyperinsulinemia. The combined effect of hyperglycemia (the main raw material involved in anabolism) and hyperinsulinemia (the main hormone regulating anabolism) increases fetal fat and protein synthesis and storage, thereby leading to the development of macrosomia. However, in previous studies, glucose in cord serum had no or negative correlation with macrosomia. These studies have explained that it is the result of the regulation of substances such as insulin or C-peptide [31, 32]. But in this study, the level of insulin in macrosomia group was not higher, so the regulation of blood glucose may not effective, resulting in the increase of blood glucose level in the macrosomia group.

Relationship between macrosomia and lipids in maternal and cord serum

The TG levels in maternal and cord serum of the macrosomia group were higher, and TG in maternal and cord serum were both positively correlated with macrosomia in this study (in cord serum, P = 0.054), which were consistent with the theory of Pablo et al. [50]. , who believed that hyperlipidemia was another risk factor for macrosomia independent of hyperglycemia. Our results are consistent with previous studies [20, 21, 33]. The physiological mechanism is that TG in maternal blood is difficult to pass through the placenta, but TG can be hydrolyzed to free fatty acids by lipoprotein lipase and endothelial lipase on microvillar membrane, and the placenta produces lipolitic hormones in the third trimester of pregnancy [51]. Fatty acids cross the placenta through simple diffusion, so the maternal-fetal transfer of fatty acids is driven by the concentration gradient. Although there are fatty acid transporters on the plasma membrane, the current study found that their role is still to promote the transport of fatty acids along the concentration gradient [52]. Therefore, although TG in maternal blood cannot directly pass through the placenta, the fatty acid supply to the fetus increases, then fetal lipid synthesis increases, fat accumulation, resulting in the occurrence of macrosomia. In addition, the level of HDL-C in maternal and cord serum of the control group is higher, and the main physiological effect of HDL-C is to reduce the cholesterol in the surrounding tissues, so the increase of HDL-C in control group is conducive to reducing the accumulation of cholesterol in the cell of the fetus, reducing the risk of cardiovascular disease in the future [53]. However, the association between high levels of HDL-C in maternal and cord serum and macrosomia were not significant in multivariate analysis, and a larger sample size may be needed to explore the association between them.

Relationship between macrosomia and hormones in maternal and cord serum

The leptin levels in maternal and cord serum of macrosomia group were higher than those of control group, there was no difference in adiponectin between the two groups, and high LAR level was positively associated with macrosomia group (P = 0.055). These are consistent with some previous studies [41,42,43]. Leptin is an obesity gene encoding product mainly produced by white adipose tissue. The secretion of leptin is mainly affected by the amount of fat, and within a certain range, the more fat, the higher the circulating leptin concentration [54]. Compared with normal birth weight infants, the fat of macrosomia is more, then the level of leptin synthesized and secreted is also increased. Although the physiological role of leptin is to increase energy consumption and reduce body weight, but the increased leptin does not play the due role of fat reduction, probably because macrosomia already has leptin resistance, which needs to be confirmed by further research [55]. LAR is a comprehensive indicator of the effects of adiponectin and leptin, and most current studies have used it as an indicator to assess the risk of atherosclerosis in obese people, so the risk of cardiovascular disease in macrosomia may be higher than that of normal birth weight infants [56]. In addition, since leptin, adiponectin and insulin are all macromolecular substances, theoretically, substances with molecular weight greater than 500 Da will not pass through the placenta, so they cannot be directly transmitted between maternal and fetal circulation [30], so insulin, adiponectin and leptin in cord blood and maternal blood are produced by their own pancreatic islets and adipose tissue respectively. According to the results of this study, Cord blood leptin can reflect the nutritional status and fat accumulation of newborns to a certain extent.

Limitations, future directions and conclusion

Due to the interaction of physiological effects of these indicators, some results may be different from previous studies with single indicator. Among the basic characteristics of the study subjects, no information was collected about pre-eclampsia incidence and risk for pulmonary embolism. This study is a cross-sectional study, and it is difficult to determine the causal relationship between these indicators and macrosomia and this study did not collect dietary data of pregnant women that could affect these indicators and the association. In the future, the dietary data and nutritional indicators of mothers in the first, second and third trimesters can be combined to analyze the relationship between nutrition and macrosomia during the whole pregnancy, so as to predict and prevent the occurrence of macrosomia. In addition, the confidence interval of OR value in the results of multi-factor analysis is relatively wide, which may be related to the insufficient sample size of this study, so the sample size should be expanded for further research in the future.

Conclusion

We found that the levels of leptin, LAR, glucose and TG in maternal and cord serum of macrosomia group were higher than those of control group, and the levels of HDL-C were lower. High levels of TG in maternal serum were positively correlated with macrosomia, while high levels of LAR, TG and glucose in cord serum were positively correlated with macrosomia. Although further studies are needed to establish their causal relationship, our findings suggests that we should pay more attention to maternal nutrition and intrauterine environment, which are related to macrosomia. Our findings also provide a scientific basis for the early prevention of chronic diseases that threaten adult health.

Data availability

The datasets generated or analyzed during the current study are not publicly available due to the data management requirements of our institution, but are available from the corresponding author on reasonable request.

Abbreviations

AGA:

Appropriate for gestational age

BMI:

Body mass index

BW:

Birth weight

CI:

Confidence interval

GWG:

Gestational weight gain

HDL-C:

High density lipoprotein cholesterol

LDL-C:

Low density lipoprotein cholesterol

LGA:

Large for gestational age

TC:

Total cholesterol

TG:

Triglyceride

References

  1. Harvey L, van Elburg R, van der Beek E. Macrosomia and large for gestational age in Asia: one size does not fit all. J Obstet Gynaecol Res. 2021;47:1929–45.

    Article  PubMed  CAS  Google Scholar 

  2. Lu Y, Zhang J, Lu X, Xi W, Li Z. Secular trends of macrosomia in southeast China, 1994–2005. BMC Public Health. 2011;11:818.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Li G, Kong L, Li Z, Zhang L, Fan L, Zou L, et al. Prevalence of macrosomia and its risk factors in China: a multicentre survey based on birth data involving 101,723 singleton term infants. Paediatr Perinat Epidemiol. 2014;28:345–50.

    Article  PubMed  Google Scholar 

  4. Wang D, Hong Y, Zhu L, Wang X, Lv Q, Zhou Q, et al. Risk factors and outcomes of macrosomia in China: a multicentric survey based on birth data. J Mater. 2017;30(5):623–7.

    Google Scholar 

  5. Yu D, Zhai F, Zhao L, Liu A, Yu W, Jia F, et al. Incidence of fetal macrosimia and influencing factors in China in 2006. Chin J Child Health Care. 2008;16(01):11–3.

    Google Scholar 

  6. Pillai S, Cheyney M, Everson CL, Bovbjerg ML. Fetal macrosomia in home and birth center births in the United States: maternal, fetal, and newborn outcomes. Birth (Berkeley Calif). 2020;47(4):409–17.

    Article  PubMed  Google Scholar 

  7. Turkmen S, Johansson S, Dahmoun M. Foetal macrosomia and foetal-maternal outcomes at birth. J Pregnancy. 2018;2018:4790136.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Schellong K, Schulz S, Harder T, Plagemann A. Birth weight and long-term overweight risk: systematic review and a meta-analysis including 643,902 persons from 66 studies and 26 countries globally. PLoS ONE. 2012;7(10):e47776.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Collier A, Abraham EC, Armstrong J, Godwin J, Monteath K, Lindsay R. Reported prevalence of gestational diabetes in Scotland: the relationship with obesity, age, socioeconomic status, smoking and macrosomia, and how many are we missing? J Diabetes Invest. 2017;8(2):161–7.

    Article  CAS  Google Scholar 

  10. Van Lieshout RJ, Savoy CD, Ferro MA, Krzeczkowski JE, Colman I. Macrosomia and psychiatric risk in adolescence. Eur Child Adolesc Psychiatry. 2020;29(11):1537–45.

    Article  PubMed  Google Scholar 

  11. Johnston LB, Clark AJ, Savage MO. Genetic factors contributing to birth weight. Archives of disease in childhood. Fetal Neonatal Ed. 2002;86(1):F2–3.

    CAS  Google Scholar 

  12. Spellacy WN, Miller S, Winegar A, Peterson PQ. Macrosomia–maternal characteristics and infant complications. Obstet Gynecol. 1985;66(2):158–61.

    PubMed  CAS  Google Scholar 

  13. Jolly MC, Sebire NJ, Harris JP, Regan L, Robinson S. Risk factors for macrosomia and its clinical consequences: a study of 350,311 pregnancies. Eur J Obstet Gynecol Reprod Biol. 2003;111(1):9–14.

    Article  PubMed  Google Scholar 

  14. Moore VM, Davies MJ, Willson KJ, Worsley A, Robinson JS. Dietary composition of pregnant women is related to size of the baby at birth. J Nutr. 2004;134(7):1820–6.

    Article  PubMed  CAS  Google Scholar 

  15. Boulet SL, Alexander GR, Salihu HM, Pass M. Macrosomic births in the United States: determinants, outcomes, and proposed grades of risk. Am J Obstet Gynecol. 2003;188(5):1372–8.

    Article  PubMed  Google Scholar 

  16. Alderman BW, Zhao H, Holt VL, Watts DH, Beresford SA. Maternal physical activity in pregnancy and infant size for gestational age. Ann Epidemiol. 1998;8(8):513–9.

    Article  PubMed  CAS  Google Scholar 

  17. Bai L, Lu Q, Han Z, Wang J, Wu H, Gao C, et al. Analysis of related factors of macrosomia. Hebei Med J. 2017;39(23):3599–601.

    Google Scholar 

  18. Guo F, Liu Y, Ding Z, Zhang Y, Zhang C, Fan J. Observations of the effects of maternal fasting plasma glucose changes in early pregnancy on fetal growth profiles and birth outcomes. Front Endocrinol. 2021;12:666194.

    Article  Google Scholar 

  19. Akpan JO. A comparison of maternal and cord blood glucose levels in diabetic and non-diabetic nigerians in relation to birth weight and maternal body mass index. Acta Diabetol Lat. 1989;26(2):95–102.

    Article  PubMed  CAS  Google Scholar 

  20. Xi F, Chen H, Chen Q, Chen D, Chen Y, Sagnelli M, et al. Second-trimester and third-trimester maternal lipid profiles significantly correlated to LGA and macrosomia. Arch Gynecol Obstet. 2021;304(4):885–94.

    Article  PubMed  CAS  Google Scholar 

  21. Misra VK, Trudeau S, Perni U. Maternal serum lipids during pregnancy and infant birth weight: the influence of prepregnancy BMI. Obes (Silver Spring Md). 2011;19(7):1476–81.

    Article  CAS  Google Scholar 

  22. Abdel-Hamid TA, AbdelLatif D, Ahmed E, Abdel-Rasheed M. Relation between maternal and neonatal serum lipid profile and their impact on birth weight. Am J Perinatol. 2022;39(10):1112–6.

    Article  PubMed  Google Scholar 

  23. Ökdemir D, Hatipoğlu N, Kurtoğlu S, Siraz ÜG, Akar HH, Muhtaroğlu S, et al. The role of irisin, insulin and leptin in maternal and fetal interaction. J Clin Res Pediatr Endocrinol. 2018;10(4):307–15.

    PubMed  PubMed Central  Google Scholar 

  24. Stefaniak M, Dmoch-Gajzlerska E, Mazurkiewicz B, Gajzlerska-Majewska W. Maternal serum and cord blood leptin concentrations at delivery. PLoS ONE. 2019;14(11):e0224863.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Özbörü Aşkan Ö, Bozaykut A, Sezer RG, Güran T, Bereket A. Effect of maternal factors and fetomaternal glucose homeostasis on birth weight and postnatal growth. J Clin Res Pediatr Endocrinol. 2015;7(3):168–74.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Mazurek D, Bronkowska M. Maternal anthropometric factors and circulating adipokines as predictors of birth weight and length. Int J Environ Res Public Health. 2020;17(13).

  27. Chełchowska M, Gajewska J, Maciejewski TM, Mazur J, Ołtarzewski M, Ambroszkiewicz J. Associations between maternal and fetal levels of total adiponectin, high molecular weight adiponectin, selected somatomedins, and birth weight of infants of smoking and non-smoking mothers. Int J Environ Res Public Health. 2020;17(13).

  28. Ozdemir U, Gulturk S, Aker A, Guvenal T, Imir G, Erselcan T. Correlation between birth weight, leptin, zinc and copper levels in maternal and cord blood. J Physiol Biochem. 2007;63(2):121–8.

    Article  PubMed  CAS  Google Scholar 

  29. Ahlsson F, Diderholm B, Ewald U, Jonsson B, Forslund A, Stridsberg M, et al. Adipokines and their relation to maternal energy substrate production, insulin resistance and fetal size. Eur J Obstet Gynecol Reprod Biol. 2013;168(1):26–9.

    Article  PubMed  CAS  Google Scholar 

  30. Wang J, Shang LX, Dong X, Wang X, Wu N, Wang SH, et al. Relationship of adiponectin and resistin levels in umbilical serum, maternal serum and placenta with neonatal birth weight. Aust N Z J Obstet Gynaecol. 2010;50(5):432–8.

    Article  PubMed  Google Scholar 

  31. Özdemir ZC, Akşit MA. The association of ghrelin, leptin, and insulin levels in umbilical cord blood with fetal anthropometric measurements and glucose levels at birth. J Mater. 2020;33(9):1486–91.

    Google Scholar 

  32. Lee IL, Barr ELM, Longmore D, Barzi F, Brown ADH, Connors C, et al. Cord blood metabolic markers are strong mediators of the effect of maternal adiposity on fetal growth in pregnancies across the glucose tolerance spectrum: the PANDORA study. Diabetologia. 2020;63(3):497–507.

    Article  PubMed  CAS  Google Scholar 

  33. Kelishadi R, Badiee Z, Adeli K. Cord blood lipid profile and associated factors: baseline data of a birth cohort study. Paediatr Perinat Epidemiol. 2007;21(6):518–24.

    Article  PubMed  Google Scholar 

  34. Sheikhahmadi S, Kazemian M, Afjeh S. Association of umbilical cord lipid profile with gestational age and birth weight in newborns in Mahdieh Hospital in 2017. Sci J Kurdistan Univ Med Sci. 2018;23(5):88–95.

    Google Scholar 

  35. Nayak CD, Agarwal V, Nayak DM. Correlation of cord blood lipid heterogeneity in neonates with their anthropometry at birth. Indian J Clin Biochemistry: IJCB. 2013;28(2):152–7.

    Article  CAS  Google Scholar 

  36. Elizabeth KE, Krishnan V, Vijayakumar T. Umbilical cord blood nutrients in low birth weight babies in relation to birth weight & gestational age. Indian J Med Res. 2008;128(2):128–33.

    PubMed  CAS  Google Scholar 

  37. Ahmad A, Mysore Srikantiah R, Yadav C, Agarwal A, Ajay Manjrekar P, Hegde A. Cord blood insulin levels: it’s correlation with gender, birth weight and placental weight in term newborns. Indian J Clin Biochemistry: IJCB. 2016;31(4):458–62.

    Article  CAS  Google Scholar 

  38. Tsai PJ, Yu CH, Hsu SP, Lee YH, Chiou CH, Hsu YW, et al. Cord plasma concentrations of adiponectin and leptin in healthy term neonates: positive correlation with birthweight and neonatal adiposity. Clin Endocrinol. 2004;61(1):88–93.

    Article  CAS  Google Scholar 

  39. Sivan E, Mazaki-Tovi S, Pariente C, Efraty Y, Schiff E, Hemi R, et al. Adiponectin in human cord blood: relation to fetal birth weight and gender. J Clin Endocrinol Metab. 2003;88(12):5656–60.

    Article  PubMed  CAS  Google Scholar 

  40. Kotani Y, Yokota I, Kitamura S, Matsuda J, Naito E, Kuroda Y. Plasma adiponectin levels in newborns are higher than those in adults and positively correlated with birth weight. Clin Endocrinol. 2004;61(4):418–23.

    Article  CAS  Google Scholar 

  41. Cortelazzi D, Corbetta S, Ronzoni S, Pelle F, Marconi A, Cozzi V, et al. Maternal and foetal resistin and adiponectin concentrations in normal and complicated pregnancies. Clin Endocrinol. 2007;66(3):447–53.

    Article  CAS  Google Scholar 

  42. Wiznitzer A, Furman B, Zuili I, Shany S, Reece EA, Mazor M. Cord leptin level and fetal macrosomia. Obstet Gynecol. 2000;96(5 Pt 1):707–13.

    PubMed  CAS  Google Scholar 

  43. Shaarawy M, el-Mallah SY. Leptin and gestational weight gain: relation of maternal and cord blood leptin to birth weight. J Soc Gynecol Investig. 1999;6(2):70–3.

    Article  PubMed  CAS  Google Scholar 

  44. Wang J, Duan Y, Yang J, Li J, Li F, Zhou P, et al. Cohort profile: the Taicang and Wuqiang mother-child cohort study (TAWS) in China. BMJ open. 2022;12(5):e060868.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Xu J, Ge F, Cao J, Kong X, Song J. Analysis of maternal blood lipid levels in the third trimester of pregnancy and the risk factors of macrosomia. J Med Inform. 2021;34(16):74–6.

    Google Scholar 

  46. China NHCo. Standard of recommendation for weight gain during pregnancy period. Biomed Environ Sci: BES. 2022;35(10):875–7.

    Google Scholar 

  47. Force GoCOT. Body mass index reference norm for screening overweight and obesity in Chinese children and adolescents. Chin J Epidemiol. 2004;25(02):10–5.

    Google Scholar 

  48. Hou RL, Jin WY, Chen XY, Jin Y, Wang XM, Shao J, et al. Cord blood C-peptide, insulin, HbA1c, and lipids levels in small- and large-for-gestational-age newborns. Med Sci Monitor: Int Med J Experimental Clin Res. 2014;20:2097–105.

    Article  Google Scholar 

  49. Catalano PM, Hauguel-De Mouzon S. Is it time to revisit the Pedersen hypothesis in the face of the obesity epidemic? Am J Obstet Gynecol. 2011;204(6):479–87.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Olmos P, Martelo G, Reimer V, Rigotti A, Busso D, Belmar C, et al. Nutrients other than glucose might explain fetal overgrowth in gestational diabetic pregnancies. Rev Med Chil. 2013;141(11):1441–8.

    Article  PubMed  CAS  Google Scholar 

  51. Lager S, Powell TL. Regulation of nutrient transport across the placenta. J Pregnancy. 2012;2012:179827.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Glatz JF, van der Vusse GJ. Cellular fatty acid-binding proteins: their function and physiological significance. Prog Lipid Res. 1996;35(3):243–82.

    Article  PubMed  CAS  Google Scholar 

  53. Campbell S, Genest J. HDL-C: clinical equipoise and vascular endothelial function. Expert Rev Cardiovasc Ther. 2013;11(3):343–53.

    Article  PubMed  CAS  Google Scholar 

  54. Izquierdo AG, Crujeiras AB, Casanueva FF, Carreira MC. Leptin, obesity, and leptin resistance: where are we 25 years later? Nutrients. 2019;11(11).

  55. Myers MG Jr., Leibel RL, Seeley RJ, Schwartz MW. Obesity and leptin resistance: distinguishing cause from effect. Trends Endocrinol Metab. 2010;21(11):643–51.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Frithioff-Bøjsøe C, Lund MAV, Lausten-Thomsen U, Hedley PL, Pedersen O, Christiansen M, et al. Leptin, adiponectin, and their ratio as markers of insulin resistance and cardiometabolic risk in childhood obesity. Pediatr Diabetes. 2020;21(2):194–202.

    Article  PubMed  Google Scholar 

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Acknowledgements

We thank all of the participants in the study.

Funding

This research was funded by the Program for Healthcare Reform from the Chinese National Health and Family Planning Commission (A prospective maternal and child nutrition and health cohort in China).

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Authors and Affiliations

  1. National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China

    Xinxin Xing, Yifan Duan, Jie Wang, Zhenyu Yang, Qingqing Man & Jianqiang Lai

Authors
  1. Xinxin Xing
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  2. Yifan Duan
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  3. Jie Wang
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  4. Zhenyu Yang
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  5. Qingqing Man
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  6. Jianqiang Lai
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Contributions

XX and JL designed the research study. YD and JW performed the research. XX analyzed the data, ZY and QM contributed essential reagents or tools.  XX wrote the paper. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Jianqiang Lai.

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Ethics approval and consent to participate

This project has been approved by Ethics Committee of Institute of Nutrition and Health, Chinese Center for Disease Control and Prevention (No. 2016-014). All women in the study had signed informed consent.

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Not applicable.

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The authors declare no competing interests.

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Xing, X., Duan, Y., Wang, J. et al. The association between macrosomia and glucose, lipids and hormones levels in maternal and cord serum: a case-control study. BMC Pregnancy Childbirth 24, 599 (2024). https://doi.org/10.1186/s12884-024-06740-4

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  • DOI: https://doi.org/10.1186/s12884-024-06740-4

Keywords

BMC Pregnancy and Childbirth

ISSN: 1471-2393

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