Predictors of fetal macrosomia in Iringa, Tanzania: a case-control study

Abstract

Introduction: It is challenging to predict fetal macrosomia before delivery. This study aimed at assessing predictors of fetal macrosomia at Iringa Regional Referral Hospital in Tanzania from June to December 2020. 

Method: An unmatched case-control study with 216 participants of whom 72 were cases - women who delivered babies weighing ≥ 4000g - and 144 were controls - women who delivered babies weighing 2500g to 3499g. The purposive sampling technique described below was employed to recruit both controls and cases. SPSS version 25 software program was used for data entry and analysis.

Results: Of the 216 participants, 116 (53.7%) were aged between 25 – 34 years (standard deviation  6.0). The majority of 132 (61.1%) delivered at a gestational age of 37 – 39 weeks + 6 days of whom 17 (23.6%) were cases and 115 (79.86%) were controls. The predictors of fetal macrosomia were advanced gestation age (AOR=8.10, 95% CI 3.66-17.91, p=<0.0001) and diabetes mellitus during pregnancy (AOR =14.94, 95% CI 1.60 -39.91, p= 0.0178).

Conclusion: Women with higher gestational age and gestational diabetes mellitus are at an increased risk of delivering a baby with macrosomia at Iringa Regional Referral Hospital. An early plan  for the mode of delivery, such as labour induction, will aid  the prevention of advanced gestational age. Ensuring a healthy diet and physical exercises in our communities will help to reduce diabetes mellitus and hence fetal macrosomia. 

Keywords: Macrosomia, Predictors, Tanzania

Introduction

Fetal macrosomia is widely defined as a birth weight of ≥ 4000g or birth weight greater than the 90th percentile at birth.^[1] The worldwide prevalence of fetal macrosomia is 0.5 % to 15 % of all pregnancies.^[2] The prevalence in developed countries ranges from 15 – 20 % due to excess nutritional intake, obesity, and diabetes mellitus.^[1] In Africa, the prevalence ranges from 2% to 9 % and the reasons include multiparity and variations in maternal weight gain during pregnancy.^[1,3–5]

The predictors of fetal macrosomia studied to date include a previous history of delivering a macrosomic baby, multiparity, male fetus, uncontrolled diabetes mellitus, gestational diabetes, gestational age, maternal weight gain during pregnancy, parental height, post-term pregnancy, and ethnicity.^[2,6] Fetal macrosomia has been linked to adverse maternal and fetal outcomes such as postpartum haemorrhage, the leading cause of maternal mortality, and neonatal death.^[3,7] The gold standard for diagnosing fetal macrosomia is magnetic resonant imaging (MRI) which is rarely available at many facilities in developing countries.^[6] Clinical examination and obstetric ultrasound  are less accurate compared to MRI.^[6,8] This study aimed to assess the predictors of fetal macrosomia in Iringa, Tanzania. 

Method

A hospital-based unmatched case-control study was designed involving term pregnant women with a singleton birth at the Department of Obstetrics and Gynaecology at Iringa Regional Referral Hospital from June to December 2020. A total of 216 women were included  of whom the 72 who delivered babies weighing ≥4000g were considered as cases and the 144 women who delivered babies weighing 2500g to 3499g were taken as controls. The reasons for selecting these weight groups is:

Cases (macrosomic group) - women who delivered babies of birthweight of 4000g and above.  This  agrees with the globally standard accepted definition of fetal macrosomia. 

Controls (normal weight babies) - women who delivered babies of 2500g to 3499g, the cut-off lower limit was 2500g because below 2500g is  a ‘low birth weight’ and in Tanzania, 3500g and above are considered as big babies /macrosomia. 

We used a modified World Health Organization criteria to diagnose diabetes mellitus during pregnancy. A 2-hour 75g oral glucose tolerance test (OGTT) was used. Study participants who were known to be diabetic were not included in the test. Participants were required to fast for a minimum of 8 hours before the test, the fasting blood glucose was obtained. After 2 hours of taking the oral glucose, blood glucose was  measured. All women with a fasting plasma glucose level of ≥7.0mmol/l (126mg/dl) or a 2–hour plasma glucose of ≥11.1mmol/l (200mg/dl) were considered as women with diabetes during pregnancy

Data were analyzed using SPSS, version 25.0 (IBM Inc., Chicago). Ethical clearance was obtained from the University of Dodoma Directorate of Research and Publication and informed consent was obtained from the participants. 

Results

There were 79 (54.9%) male babies and 65 (45.1%) female babies. The mean age of the 216 mothers was 28.5 years (standard deviation 6.0 years); 116 (53.7%) of participants were aged between 25 – 34 years of  whom 44 (61.1%) were cases and 72 (5%) were controls.  Seventy-eight (33.8%) were primiparous of  whom 15 (20.83%) were cases and 58 (40.28) were controls. Table 1.

Table 1. Social demographic characteristics of the participants (N=216)

The predictors of fetal macrosomia

This study found that, among the maternal factors, gestation age ≥40 weeks (p-value = < 0.0001), a previous history of macrosomic baby (p-value = < 0.0001), diabetes mellitus in pregnancy, higher parity (p-value = <0.0001) and higher maternal age (p-value =0.0028) were predictors of fetal macrosomia on cross tabulation as shown in Table 2. 

Table 2. Predictors of fetal macrosomia (N=216)

p-value with * indicates p-value calculated by the Fisher exact test

In this study, women with advanced gestation age (≥40 weeks) were  eight times more likely to give birth to a macrosomic baby compared to women who delivered at a gestation age of 37 to 39 weeks (AOR=8.10, 95% CI=3.66-17.91, p=<0.0001). Women with diabetes mellitus were 14 times more likely to give birth to a macrosomic baby compared to women without diabetes mellitus (AOR=14.94, 95% CI=1.60-39.91, p=0.0178) as shown in Table 3. 

Table 3. Logistic regression of factors associated with fetal macrosomia (N=216)

Discussion

In Taiwan and Indonesia, fetal macrosomia has been associated with maternal overweight and obesity.^[10,11] This is contrary to the finding in this study in which body mass index did not predict fetal macrosomia. Although not statistically significant, excessive weight gain before and during pregnancy has been linked to decreasing insulin sensitivity which facilitates the transfer of glucose via the placenta to the growing fetus hence leading to increasing fetal size.^[11,12] These two studies were cohort studies conducted for a longer duration and with a larger sample size compared to our study. This may explain why BMI was significantly associated in their study and not in ours.

The predictors of fetal macrosomia in Malaysia included increasing maternal age, increasing BMI, increasing weight gain during pregnancy, higher parity, and diabetes mellitus during pregnancy.^[14] In this study advanced maternal age, maternal  BMI, maternal weight gain during pregnancy, and higher parity were not associated with fetal macrosomia although diabetes mellitus during pregnancy was strongly associated. Ours was a case-control study with only 216 participants while in Malaysia it was a cross-sectional study with 2332 participants,  a difference  that may explain why findings differ. Diabetes mellitus during pregnancy leads to increasing fetal adiposity hence an increase in fetal weight leading into macrosomia.^[14]

Advanced gestation age was the commonest predictor of fetal macrosomia in this study and is similar to findings in Sydney in which it was estimated that a fetus gains 176.5g per week; this  may explain why advanced gestation age predisposes a woman to deliver a macrosomic baby.^[15]

In Tanzania and Ethiopia, the predictors of fetal macrosomia were a maternal weight above 80kg, multiparity, diabetes mellitus during pregnancy, and maternal pre-pregnancy BMI and pregnancy weight gain respectively while maternal age and previous history of delivering a macrosomic baby were found in both studies.^[5,9]  These findings are at variance with those in our study where only higher gestation age and gestational diabetes mellitus during pregnancy were significantly associated with fetal macrosomia. 

Efforts to prevent diabetes mellitus which may result in fetal macrosomia should be one of the community interventions. Diabetes mellitus can be prevented by first ensuring a healthy balanced diet in our communities  and also by educating families on physical exercises. It is best for women with advanced gestation age who are at risk of fetal macrosomia to seek care early and the mode of delivery determined. Early labour induction or a possible Caesarean Section play an important role in preventing dangerous obstetric outcomes of fetal macrosomia. 

Conclusion

Women with higher gestational age and gestational diabetes mellitus are at an increased risk of delivering a baby with macrosomia at Iringa Regional Referral Hospital. An early plan  for the mode of delivery, such as labour induction, will aid  the prevention of advanced gestational age. Ensuring a healthy diet and physical exercises in our communities will help to reduce diabetes mellitus and hence fetal macrosomia.

Acknowledgement: We thank Dr Alfred Laison Mwakalebela and Dr. Malangalila, Scholastica Mathew of Iringa Regional Referral Hospital for supporting this study as well as all study participants.

References

1. Dennedy MC, Dunne F. Macrosomia: Defining the problem worldwide. Lancet 2013;381(9865):435–436.https://doi.org/10.1016/S0140-6736(12)62090-X
2. Black MH LJ, Sacks DA, Xiang AH. The relative contribution of prepregnancy overweight and obesity, gestational weight gain, and IADPSG-defined gestational diabetes mellitus to fetal overgrowth. Diabetes Care 2013;36:56–62 .https://doi.org/10.2337/dc12-0741
3. Pagani G, Palai N, Zatti S, Fratelli N, Prefumo F, and Frusca T. Fetal weight estimation in gestational diabetic pregnancies: Comparison between conventional and three-dimensional fractional thigh volume methods using gestation-adjusted projection. Ultrasound Obstet. Gynecol.2014; 43(1):72–76.https://doi.org/10.1002/uog.12458
4. Akindele RN, Audu LI, Mokuolu OA. Macrosomic births in Abuja: A case-control study of predisposing factors and early neonatal outcome. Niger J Clin Pract 2017;20 (3):320-7. https://doi.org/10.4103/1119-3077.196060
5. Said AS, Manji KP. Risk factors and outcomes of fetal macrosomia in a tertiary center in Tanzania: a case-control study. BMC Pregnancy Childbirth 2016;16 (1):1-8.https://doi.org/10.1186/s12884-016-1044-3
6. Maher MHK, Soltani R, Zeinalzadeh AH, and Pourasghar S. Complications and risk factors of neonatal macrosomia: A case-control study. Iran. J. Neonatal 2018.https://doi.org/10.22038/ijn.2018.10488
7. Bandika VL, Were FN, Simiyu ED, and Oyatsi DP. Hypoglycemia and hypocalcemia as determinants of admission birth weight criteria for term stable low-risk macrosomic neonates. Afr. Health Sci. 2014;14(3):510–516. https://doi.org/10.4314/ahs.v14i3.3
8. Kadji C, Cannie MM, Resta S, Guez D, Abi-Khalil F, De Angelis R, Jani JC. Magnetic resonance imaging for prenatal estimation of birthweight in pregnancy: a review of available data, techniques, and future perspectives. Am J Obstet Gynecol. 2019;220(5):428-439. https://doi.org/10.1016/j.ajog.2018.12.031
9. Tela FG, Bezabih AM, Adhanu AK, Tekola KB. Fetal macrosomia and its associated factors among singleton live births in private clinics in Mekelle city, Tigray, Ethiopia. BMC Pregnancy Childbirth 2019;19(1):1-6. https://doi.org/10.1186/s12884-019-2379
10. Hung TH and Hsieh TT. Pregestational Body Mass Index, Gestational Weight Gain, and Risks for Adverse Pregnancy Outcomes Among Taiwanese Women: a Retrospective Cohort Study. Taiwanese Journal of Obstetrics & Gynecology 2016.55 (4):575-81. https://doi.org/10.1016/j.tjog.2016.06.016
11. Soltani H, Lipoeto NL, Fair FJ, Kilner K, and Yusrawati Y. Pre-pregnancy body mass index and gestational weight gain and their effects on pregnancy and birth outcomes: A cohort study in West Sumatra, Indonesia. BMC WomensHealth 2017;17(1):1–12.https://doi.org/10.1186/s12905-017-0455-2
12. Pongcharoen T, Gowachirapant S, Wecharak P, Sangket N, Winichagoon P. Pre-pregnancy body mass index and gestational weight gain in Thai pregnant women as risks for low birth weight and macrosomia. Asia Pac J Clin Nutr. 2016;25(4):810-817.https://doi.org/10.6133/apjcn.092015.41
13. Vickers MH. Developmental programming and transgenerational transmission of obesity. Ann. Nutr. Metab.2014;64(1):26 – 34. https://doi.org/10.1159/000360506
14. Yadav H and Lee N. Factors influencing macrosomia in pregnant women in a tertiary care hospital in Malaysia. J. Obstet. Gynaecol. Res. 2014;40(2):439-444.https://doi.org/10.1111/jog.12209
15. Mongelli M., Benzie R., and Condous G. Average fetal weekly weight gain: A novel measure of fetal growth velocity. Journal of Maternal-Fetal and Neonatal Medicine. 2016;29(4):676–679. https://doi.org/10.3109/14767058.2015.1015981 

More from the November 2021 issue