Effect of individualised nutritional intervention on the postpartum nutritional status of patients with gestational diabetes mellitus and the growth and development of their offspring: a quasi-experimental study

Abstract

This study explored the effect of individualised nutritional intervention on the postpartum nutritional status of patients with the growth and development of their offspring. This study included pregnant women with gestational diabetes mellitus (GDM) at Hangzhou Women’s Hospital in 2019. At 42 days after childbirth, the HbA1c (95% CI: 0.44-0.56%, p < 0.001), the FPG (95% CI: 0.01–0.26 mmol/L, p < 0.05), 2HPG (95% CI: −0.01–0.73 mmol/L, p < 0.05) and TCH (95% CI: −0.34–0.00 mmol/L, p < 0.05) level of the control group were 0.14, 0.36, and 0.17 mmol/L higher than in the intervention group. There were no differences in TG and HGB between the two groups (all p > 0.05). There were significant differences in the number of macrosomia and neonatal weight between the two groups (both p < 0.05). Differences in WHZ after childbirth were not statistically significant between the two groups (all p > 0.05). Individualised nutritional intervention could improve blood glucose levels 42 days after childbirth and reduce macrosomia incidence in pregnant women with GDM.

Impact statement

• What is already known on this subject? Individualised nutrition intervention can improve blood glucose status and complications during pregnancy, thus improving pregnancy outcomes.

• What the results of this study add? Individual nutrition intervention improved the blood glucose and nutritional status of patients at 42 days postpartum, but there was no difference in the growth and development indicators of their offspring at 0–24 months.

• What the implications are of these findings for clinical practice and/or further research? Improve nutritional intervention programs for gestational diabetes, improve blood glucose during pregnancy and postpartum, to improve pregnancy outcomes and reduce the occurrence of type 2 diabetes and other metabolic diseases; Extend the monitoring range of the growth and development of the offspring of gestational diabetes, find the problems and timely carry out the nutritional intervention, to improve the development of the offspring.

Keywords:

• Gestational diabetes mellitus
• growth and development
• individualisation
• nutritional intervention
• nutritional status
• offspring

Introduction

Gestational diabetes mellitus (GDM) is the abnormal glucose metabolism first identified during pregnancy. GDM occurs in about 1%-14% of all pregnancies (American Diabetes A 2014), and its prevalence is increasing, probably due to the increased prevalence of obesity, sedentary lifestyle, and older maternal age (American Diabetes A 2021; Feig et al. 2018). In addition, a familial history of type 2 diabetes mellitus (T2DM) or a personal history of impaired glucose metabolism are also risk factors for GDM. The management of GDM includes pharmacological therapy (insulin, metformin, and glyburide) (American Diabetes A 2021; Feig et al. 2018) and lifestyle management (diet and exercise) (American Diabetes A 2021).

With the opening of the two-child policy, continuous improvement of living standards, and an increase in maternal reproductive age, the incidence of GDM has been significantly increasing in recent years in China (Sheikh and Sheikh 2020). GDM increases the risk of postpartum T2DM, obesity, hypertension, and metabolic syndrome and has adverse effects on the offspring (Farahvar et al. 2019; Johns et al. 2018). The postpartum risk of T2DM in patients with GDM is nearly 10 times that of healthy ones (Vounzoulaki et al. 2020). Furthermore, GDM increases the incidence of foetal macrosomia, growth restriction, premature delivery, malformations, and neonatal hypoglycaemia and increases the risk of obesity in childhood, adolescence, and adulthood for their offspring (Qiao and Leng 2020).

Timely individualised nutritional interventions for patients with GDM can provide a basis for lowering their long-term risk of developing T2DM and improving the growth and development of their offspring. Indeed, dietary interventions may lower infant birth weight, reduce the risk of macrosomia, and the need for maternal antidiabetic medication in women with GDM (Yamamoto et al. 2018). The DASH diet is associated with a lower risk of caesarean section than the control diet, but specific dietary interventions might not affect most perinatal and postpartum outcomes in women with GDM (Han et al. 2017). A low-carbohydrate diet may not reduce the need for insulin in women with GDM (Moreno-Castilla et al. 2013). Importantly, a Cochrane review highlighted the lack of evidence to determine the optimal diet for women with GDM (Han et al. 2013). Hence, it is possible that a fixed nutritional intervention in patients with GDM might not be appropriate for all patients and that an individualised approach could have better prospects. A study showed that individualised nutrition therapy in women with GDM could reduce pregnancy complications and improve pregnancy outcomes (Shi et al. 2016). A study is underway to prevent GDM using individualised nutritional intervention (Zhang et al. 2021). Still, studies on the postpartum status and the offspring are mostly lacking.

Therefore, this study aimed to explore the effect of individualised nutritional intervention on the postpartum nutritional status of patients with GDM and the growth and development of their offspring. The results could help improve the management of mothers with GMD and their children.

Methods

Study design and participants

This quasi-experimental study included 229 pregnant women diagnosed with GDM who gave birth at the Hangzhou Women’s Hospital obstetrics clinic in June and July 2019. Approval was granted by the Ethics Committee of Hangzhou Women’s Hospital (2021K11-02). Informed consent was obtained from all individual participants included in the study.

The inclusion criteria were 1) diagnosed with GDM according to the diagnostic criteria recommended by the American Diabetes Association (ADA) guidelines (American Diabetes A 2021; Su and Yang 2019) and confirmed using a 75-g oral glucose tolerance test (OGTT), and 2) willing to improve blood glucose through diet. The exclusion criteria were 1) twin pregnancy, 2) pregnant women whose gestational age was less than 37 weeks, 3) pregnant women with T2DM, 4) pregnancy complicated by heart, liver, kidney, or other underlying diseases, or 5) threatened preterm labour or miscarriage, placenta previa, and other high-risk pregnancies.

GDM was diagnosed using the ADA diagnostic criteria for GDM (American Diabetes A 2021; Su and Yang 2019). During the 75-g OGTT, fasting and 1- and 2-h blood glucose levels higher than 5.1, 10.0, and 8.5 mmol/L, respectively, indicated GDM.

Randomisation and blinding

Since it was a quasi-experimental study, no randomisation or blinding was performed. The participants were divided into the control group (recruited in June 2019) and the intervention group (recruited in July 2019).

Intervention

The intervention group did not receive insulin treatment. A dietary survey was made using a 24-h dietary recall. The nutritional evaluation included pre-pregnancy body mass index (BMI), physical activity, weight gain during pregnancy, gestational week, and B-ultrasound for foetal size. The total daily energy was calculated. Individualised nutrition education was performed to inform the participants of the principles of nutritional treatment and intake, and it was quantified with food models. Food glycaemic index (GI) value tables and exchange lists were used for food selection. Individualised weekly recipes and postpartum recipes were developed. Standards of blood glucose control during pregnancy were provided (Chen et al. 2019), and blood glucose was measured every 1–2 weeks. The participants were encouraged to exercise regularly and appropriately. Brochures about exercise during pregnancy were distributed. If there was no special discomfort, exercising was insisted on. The participants could take a 30-min walk after half an hour of rest after each meal. A nutrition outpatient return visit was performed based on the time of the obstetric examinations. The participants were encouraged to attend nutrition outpatient visits and an OGTT 42 days after delivery. Then, an individualised nutritional intervention was provided. If, after the nutritional intervention, the outpatient monitoring showed that the blood glucose did not meet the target twice, the participants were referred to the internal medicine outpatient for insulin treatment.

The purpose of the nutritional intervention was to control the blood sugar of the diabetic pregnant women within the normal range, ensure a sufficient nutritional intake for the pregnant women and foetuses, and reduce the occurrence of maternal and infant complications (Obstetrics Group of Chinese Society of Obstetrics and Gynecology, Gestational Diabetes Mellitus Cooperative Group of Chinese Society of Perinatal Medicine 2014). The principles of the nutritional intervention were as follows. (1) According to the body mass index (BMI) and weight gain during pregnancy, the total daily energy intake was guided to develop an individualised and rational dietary plan aiming at ≥1600 kcal/d in the first trimester of pregnancy and 1800–2200 kcal/d in the second and third trimesters. (2) Energy supply ratio of each nutrient: ≥175 g carbohydrates (50%-60% of total energy), ≥70 g protein, limited intake of saturated fatty acids (≤7% of total energy), and 25–30 g/d of dietary fibre. (3) It was recommended to have three regular meals and 2-3 extra meals. The energy of breakfast, lunch, and evening meals should be controlled at 10%–15%, 30%, and 30% of the total daily energy intake, respectively, and the energy of each extra meal could account for 5%–10%. (4) The intake of vitamins and minerals was ensured, paying attention to iron, folate, calcium, vitamin D, and iodine. (5) Foods with a low glycemic index (GI) were preferred. (6) According to pre-pregnancy BMI, the weight gain target during pregnancy was set, and the range and speed of weight gain were monitored. (7) The dietary intake was adjusted according to the patient’s weight gain, foetal growth, blood glucose, and urine ketone body examination, and dietary intake was appropriately increased in the late pregnancy.

The food exchange lists referred to food classification into four categories (eight subcategories) according to source and nature. Same-type food contained similar amounts of protein, fat, carbohydrates, and calories per weight, and the amount of calories (90 kcal) provided by different foods was the same. The patient’s daily intake of energy was quantified according to the number of servings, and the number of servings of the meals was subdivided by grains and potatoes, vegetables, fruits, meat and eggs, milk, oils, and nuts into the food exchange lists, and the number of servings was provided to the patient. At the same time, the food model was used in the clinic to inform the patient of one serving of a certain type of food (90 kcal).

Postpartum dietary intake of patients with gestational diabetes was calculated according to the pre-pregnancy body mass index and milk production, aiming at 2000–2300 kcal (Obstetrics Group of Chinese Society of Obstetrics and Gynecology Gestational Diabetes Mellitus Cooperative Group of Chinese Society of Perinatal Medicine, Chinese Association of Maternal and Child Health Care Professional Committee of Gestational Diabetes Mellitus et al. 2022). The recommended amount of food per day was as follows: (1) cereal 250–300 g, including potato 75 g, whole grains, and beans ≥1/3; (2) vegetables 500 g, of which green leafy vegetables and coloured vegetables >2/3, fruit 200–400 g; (3) fish, poultry, eggs, and meat (including liver) daily total of 220 g, 400–500 mL milk, 25 g beans, 10 g nuts, 25 g cooking oil, and salt ≤6 g; (4) to ensure the supply of vitamin A, it was recommended to eat liver 1 to 2 times a week, for a total of 85 g of liver or 40 g of chicken liver. The above principles were communicated to patients by food exchange lists.

The control group received routine check-ups. They were not followed to receive a nutritional intervention at the nutrition outpatient clinic. They did not receive insulin treatment and underwent an OGTT 42 days after delivery.

Outcomes

The primary outcomes were the blood glucose indices and HbA1c at 42 days after childbirth. The secondary outcomes were the postpartum nutritional indices (FPG, 2HPG, TG, TCH, HGB) at 42 days after childbirth, the neonatal indices (number of neonatal hypoglycaemia events, the number of neonates undergoing interventions for hypoglycaemia, the number of macrosomia), the weight-for-length/height Z-scores (WHZs) at 1, 3, 6, 8, 12, 18, and 24 months after childbirth.

According to the 2014 ADA criteria, the postpartum blood glucose evaluation criteria were FPG <5.6 mmol/L, 2HPG <7.8 mmol/L, and HbA1c <5.7%, and the participants were evaluated as normal when they met the criteria for FPG and 2HPG at the same time. In 2007, the Guidelines for the Prevention and Treatment of Dyslipidaemia in Adults in China proposed that normal lipid values were TG <1.7 mmol/L and TCH <5.2 mmol/L in postpartum women (Expert Committee on Diagnosis and Treatment Pathways of Pre-pregnancy Obesity in Chinese Women 2019). The diagnostic criteria from the World Health Organisation (WHO) in 1972 considered that anaemia could be diagnosed if HGB is <120 g/L in sea-level areas (World Health Organization 2001). The diagnostic criteria for hypoglycaemia were a whole blood glucose level of <2.2 mmol/L, but clinical intervention is required when the blood glucose is <2.6 mmol/L (Ma 2020).

After birth, the neonates were weighed in grams (g) and measured in cm. Low birth weight was defined as <2500 g, and macrosomia was defined as ≥4000 g. The weight-height Z-score (WHZ) was calculated using the 2006 WHO growth standard value in the Health Industry Standards of People’s Republic of China WS423-2013 (National Health Commission of People’s Republic of China 2013).

Statistical analysis

The statistical analysis was performed using SSPS 16.0 (IBM Corp., Armonk, NY). Continuous variables were given as means ± standard deviations or median values with the interquartile range depending on the normality of the variables. Categorical variables were presented as percentages. Comparisons for continuous data were performed using Student t-test or one-way ANOVA. Categorical variables were compared using the chi-square test or Fisher exact test. The comparison of blood glucose levels and nutritional status between the two groups at 42 days after childbirth was tested using one-way covariance analysis. Two-sided values p < 0.05 were considered statistically significant.

Results

Between June and July 2019, 400 pregnant women with GDM diagnosed were screened for inclusion, and, of these, 171 pregnant women have excluded primarily due to the number of visits to the nutrition clinic <6 times and patients who did not have the child physical examination. Of the 229 participants who were enrolled in the study, 129 participants were allocated to the nutritional intervention group and 100 participants to the control group. The age, gravidity, parity, and BMI were no significant differences between the two groups (all p > 0.05). Prenatal FPG, 2HPG, HbA1c, TCH, TG, and HGB levels between the two groups also had no differences (all p > 0.05) (Table 1).

Table 1. General characteristics and prenatal conditions between the two groups.

Group	Nutritional intervention group (n = 129)	Control group (n = 100)	p
Age (years)	31.8 ± 4.1	31.9 ± 4.2	0.892
Gravidity	2.2 ± 1.2	2.4 ± 1.3	0.195
Parity	1.4 ± 0.5	1.5 ± 0.5	0.396
BMI (kg/m^2)	22.3 ± 3.1	22.0 ± 2.83	0.252
FPG (mmol/L)	4.71 ± 0.57	4.57 ± 0.57	0.068
2HPG (mmol/L)	8.41 ± 1.50	8.63 ± 1.18	0.231
HbA1c (%)	5.01 ± 0.42	4.98 ± 0.35	0.633
TG (mmol/L)	3.13 ± 1.26	2.87 ± 1.28	0.127
TCH (mmol/L)	6.39 ± 1.44	6.06 ± 1.36	0.081
HGB (g/L)	115.74 ± 9.10	117.47 ± 9.89	0.170

BMI: body mass index; FPG: fasting plasma glucose; 2HPG: 2-hour postprandial glucose; HbA1c: glycated haemoglobin; TG: triglycerides; TCH: total cholesterol; HGB: haemoglobin.

The results showed that after adjusting prenatal HbA1c, FPG, 2HPG, TG, TCH, and HGB levels, the HbA1c level of the control group at 42 days was 0.50 higher than that of the individualised nutritional intervention group (95% CI: 0.44–0.56, p < 0.01). The FPG level of the control group at 42 days was 0.14 mmol/L higher than that of the individualised nutritional intervention group (95% CI: 0.01–0.26 mmol/L, p < 0.05). The 2HPG level of the control group at 42 days was 0.36 mmol/L higher than that of the individualised nutritional intervention group (95% CI: −0.01-0.73 mmol/L, p < 0.05). The TCH level of the control group at 42 days was 0.17 mmol/L higher than that of the individualised nutritional intervention group (95% CI: −0.34–0.00 mmol/L, p < 0.05. But there was no difference in TG and HGB between the two groups at 42 days (all p > 0.05) (Table 2).

Table 2. Comparison of blood glucose levels and nutritional status between the two groups at 42 days after childbirth.

Group	Nutritional intervention group (n = 129)	Control group (n = 100)	Mean difference	95% CI	p
FPG (mmol/L)	4.92 ± 0.38	5.06 ± 0.55	0.14	0.01–0.26	0.032
2HPG (mmol/L)	6.55 ± 1.31	8.92 ± 1.51	0.36	−0.01–0.73	0.047
HbA1c (%)	5.20 ± 0.30	5.71 ± 0.63	0.50	0.44–0.56	<0.001
TG (mmol/L)	1.33 ± 0.40	1.47 ± 0.45	0.12	-0.03–0.28	0.123
TCH (mmol/L)	5.19 ± 0.70	5.33 ± 0.54	0.17	-0.34–0.00	0.046
HGB (g/L)	132.41 ± 8.28	130.75 ± 9.89	1.74	-4.12–0.64	0.152

FPG: fasting plasma glucose; 2HPG: 2-hour postprandial glucose; HbA1c: glycated haemoglobin; TG: triglycerides; TCH: total cholesterol; HGB: haemoglobin.

There were significant differences in the number of macrosomia and neonatal weight between the two groups (both p < 0.01), while there were no significant differences in the number of neonatal hypoglycaemia events, the number of neonates undergoing interventions for hypoglycaemia, and neonatal length (all p > 0.05). The differences in WHZ at 1, 3, 6, 8, 12, 18, and 24 months after childbirth were not statistically significant between the two groups (all p > 0.05) (Table 3).

Table 3. Comparison of the neonatal status, growth, and development of offspring between the two groups.

Group	Nutritional intervention group (n = 129)	Control group (n = 100)	p
Number of neonatal hypoglycaemia n, (%)	2 (1.56)	1 (1.00)	0.716
Number of neonates undergoing glucose interventions n, (%)	8 (6.20)	10 (10.00)	0.289
Number of macrosomia n, (%)	3 (2.32)	15 (15.00)	<0.001
Neonatal weight (g)	3349.77 ± 360.88	3505.50 ± 488.16	0.006
Neonatal length (cm)	50.01 ± 0.39	50.03 ± 0.70	0.844
30-day WHZ	0.10 ± 0.02	0.16 ± 0.02	0.067
3-month WHZ	0.33 ± 0.05	0.36 ± 0.12	0.179
6-month WHZ	0.34 ± 0.10	0.35 ± 0.10	0.979
8-month WHZ	0.27 ± 0.08	0.30 ± 0.06	0.823
1-year WHZ	0.35 ± 0.10	0.42 ± 0.12	0.641
18-month WHZ	0.30 ± 0.05	0.29 ± 0.08	0.071
2-year WHZ	0.15 ± 0.04	0.26 ± 0.03	0.475

WHZ: weight–height Z-score.

Discussion

This quasi-experimental study explored the effect of individualised nutritional intervention on the postpartum nutritional status of patients with GDM and the growth and development of their offspring. The results suggest that individualised nutritional intervention could effectively improve their blood glucose levels 42 days after delivery in a patient with GDM. Individualised nutritional intervention can reduce macrosomia incidence in pregnant women with GDM, thereby improving pregnancy outcomes. There was no impact of the nutritional intervention on the physical development of the offspring. Still, the intervention could be used to prevent diabetic complications after delivery. Indeed, (Su and Yang 2019) suggested that patients with GDM should undergo an OGTT 4–12 weeks after childbirth, and timely intervention should be given to those with impaired glucose tolerance. A systematic review and meta-analysis by (Juan and Yang 2020) indicated that the postpartum screening rate should be increased, and T2DM should be prevented in GDM pregnant women through diet, lifestyle, and other interventions. In the present study, all patients underwent OGTT 6 weeks after delivery, according to those recommendations in China.

The different types of hyperglycaemia during pregnancy are classified as follows: (1) pregestational diabetes mellitus (PGDM), type 1 diabetes combined with pregnancy or type 2 diabetes combined with pregnancy; (2) pre-diabetes: impaired fasting blood glucose and impaired glucose tolerance; (3) GDM, which develops during pregnancy (American Diabetes A 2014). After nutrition management and exercise guidance, people with good blood glucose control can be defined as A1 type GDM; if it is necessary to add hypoglycaemic drugs to control the blood glucose, then GDM is defined as A2 type GDM (Corrado et al. 2014). All patients with GDM in this study were A1-type GDM. It is recommended that all women with GDM undergo postpartum OGTT test to measure fasting and 2-h blood glucose levels after a glucose challenge and have a follow-up at least once every 3 years. In 2020, an article by Vounzoulaki et al. 2020 in the British Medical Journal pointed out that pregnant women with GDM have a nearly 10 times higher risk of postpartum type 2 diabetes than healthy women. Still, the long-term follow-up of patients with GDM after individualised nutrition intervention needs further study. The investigations should also be performed in patients with PGDM or pre-diabetes.

In this study, OGTT was performed for GDM women 42 days after childbirth. The results showed that the glycaemic control indexes were all improved in the intervention group, as supported by Shi et al. 2016. Blood lipids show a downward trend after childbirth, returning to normal 4–6 weeks after childbirth Xiao et al. 2020. In the present study, at 42 days after childbirth, there was a statistically significant difference in TCH between the two groups (p < 0.05), suggesting that dietary and lifestyle interventions were effective. There was no difference in HGB levels between the two groups, indicating that the general condition of the two groups was similar.

GDM is a high-risk factor for macrosomia (Kc et al. 2015; Yang et al. 2020). Sustained high blood glucose during pregnancy stimulates the foetus to produce insulin, activates the amino acid transfer system, and promotes protein and fat synthesis and growth of the foetus, leading to macrosomia and an increase in the rate of caesarean section (Kc et al. 2015). In the present study, the number of macrosomia and neonatal weight in the nutritional intervention group was lower than in the control group, and the difference was statistically significant (p < 0.01). The above findings suggest that individualised nutritional intervention could reduce the number of macrosomia and effectively control neonatal weight, which is consistent with the findings by Zeng et al. (Zeng and Zhang 2019). Indeed, Zeng and Zhang 2019 showed that nutritional intervention could increase the natural delivery rate, reduce the number of macrosomia, and thereby lower the incidence of related complications, supporting the present study.

Chronic intrauterine foetal hyperglycaemia caused by poor maternal blood glucose control is a common cause of transient hyperinsulinemic hypoglycaemia in neonates (Desoye and Nolan 2016; Liu et al. 2020). In the present study, the number of foetal hypoglycaemia events (1.6% vs. 1.0%) and the number of neonates receiving hypoglycaemia interventions (6.2% vs. 10.0%) were similar between the two groups (p > 0.05), which might be related to the sample size. It will have to be examined in future studies.

Lawlor et al. (2011) showed that the mean BMI of male offspring with GDM mothers in adulthood is higher than that of their elder brothers born from the same mothers before developing GDM. Crume et al. (2011) found that from birth to 26 months, there was no statistically significant difference in BMI between the offspring of mothers with GDM and those of mothers without GDM, but from 27 months to 13 years, the two groups had significant differences in BMI, especially between 10- and 13-years old. Hou and Yang (2019) found that the overall change trend of the offspring growth indices of GDM patients at 0–36 months old is the same as that of offspring of normal mothers. In the present study, the offspring in the two groups were followed from birth to 24 months. The findings showed that the differences in WHZ between the two groups were not statistically significant. Further research will be performed to explore whether there is a difference after 24 months of birth.

Inositol is a substance found naturally in cantaloupe, citrus fruits, and many fibre-rich foods (such as beans, brown rice, corn, and wheat bran) and can be used as a dietary supplement to improve insulin sensitivity (Chan et al. 2021; Crawford et al. 2015). Inositol was not specifically studied in the present study, but given its possible role in GDM management, future studies could be performed to examine the impact of an inositol-rich diet on GDM.

This study had some limitations. It was a quasi-experimental study without randomisation or blinding. The patients were enrolled in two different periods, but the two periods were June and July 2019, and it is unlikely that there were significant changes in clinical practice and GDM management over 2 months. No power analysis was performed, and it is unknown whether the study was adequately powered. Finally, the blood glucose follow-up was only 42 days, and it is unknown whether such intervention can prevent the onset of T2DM.

In conclusion, individualised nutritional intervention can effectively improve blood glucose levels at 42 days after childbirth and reduce macrosomia incidence in pregnant women with GDM, thereby improving pregnancy outcomes.

Ethics approval and consent to participate

This work has been carried out in accordance with the Declaration of Helsinki (2000) of the World Medical Association. Approval was granted by the Ethics Committee of Hangzhou Women’s Hospital (2021K11-02). Informed consent was obtained from all individual participants included in the study.

Author contributions

JT contributed to data collection and drafted the manuscript; LLH contributed to data analysis; XQ contributed to data collection and data interpretation; XHW helped to draft the manuscript; All authors read and approved the final manuscript.

Acknowledgments

Not applicable.

Disclosure statement

The authors declare no Conflict of Interest for this article.

Data availability statement

All data generated or analysed during this study are included in this published article.

Additional information

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.