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Research Note
Pregnancy in overweight women – the impact of some biological and social determinants
expand article infoVidin Kirkov, Valentina Petkova, Dobriana Sidjimova, Lilia Koleva, Emilia Naseva, Milen Dimitrov
‡ Medical University of Sofia, Sofia, Bulgaria
Open Access

Abstract

Obesity is one of the risk factors for the development of socially significant diseases from the group of chronic non-communicable diseases, and obesity in women of reproductive age and in particular in pregnant women is an extremely important medico-social problem affecting both the mother and her child, and indeed all of modern society. In this article, the influence of some biological and social determinants influencing this medico-social problem, leading to changes in the trends of medico-demographic health indicators serving to assess public health, will be examined and analyzed.

Keywords

obesity, pregnant women, biological and social determinants

Introduction

Obesity in pregnant women is an especially important medical and social phenomenon, as it can lead to the emergence of serious, socially significant diseases, impacting both the pregnant woman and her child. It can also lead to changes in the medical and demographical health indicators used to assess the trends in public health. The treatments for those diseases are costly and can directly affect the health, social and economic conditions of society at large.

Overweightness and obesity among women in their childbearing years correlates with a series of general somatic and reproductive diseases, which can lead to a decline in overall fertility. Adipose tissue is a peripheral synthesis location for a number of hormones. It also actively participates in the activity of the vessels and in the formation of the body’s immune response, which is why an excess of fat can lead to metabolic, hormonal, vascular and inflammatory disturbances. Women who are overweight or obese have a significantly lower rate of pregnancy by normal means, as well as a lower rate of successful infertility treatment, compared to their counterparts whose body mass is normal.

Obesity is a condition, which occurs due to an excess accumulation of adipose tissue in the human body. Depending on the severity of this accumulation, the condition can be identified as either overweightness or obesity. Obesity is a result of an energy balance disturbance – the ratio between the energy value of the food consumed and the energy expended by a person – i.e. people gain weight when they consume more calories than they burn. This disease is not only an aesthetic concern, but also a health issue. It can be a reason for developing a number of diseases: cardiovascular (atherosclerosis, arterial hypertension, Ischaemic heart disease, stroke), Type 2 diabetes mellitus, malignant neoplasms, gout, joint and respiratory diseases. etc. (https://www.rzi-vt.bg/zatl.htm (RZI VT undated))

In accordance with the WHO’s classification, overweightness and obesity are defined based on the Body Mass Index (BMI). which is calculated dividing the weight in kilograms by the square of height in meters. The formula for BMI is m/h2, m being the mass in kilograms and h, the height in meters. Class 1 obesity occurs in case the BMI = 30 to 35, Class 2 - BMI = 35 to 40, Class 3 - BMI = 40.

The following definition from 1997, published in 2000, is used to define body mass. (Technical report series 894: World Health Organization 2000):

  • BMI below 18.5 kg/m 2 – underweight
  • BMI of 18.5–24.9 kg/m 2 – normal weight
  • BMI of 25–29.9 kg/m 2 – overweight
  • BMI of 30.0–39.9 kg/m 2 – obesity
  • BMI of over 40.0 kg/m 2 – severe or morbid obesity
  • BMI of over 35.0 kg/m 2, if combined with one or more co-morbid conditions, is also classified as morbid obesity.

According to some pieces of scientific research, the spread of obesity among pregnant women in different countries can vary. In the USA, overweightness and obesity are found in 18.5–38.3% of the pregnant women studied, in the UK – among 39.5–44.5%, in Australia – 18.5–42%, in Italy – around 33%, in Finland – 36.5%, in China – 1.8%. (Timoshina 2010).

Obesity is the most common lipid genesis disturbance, which affects 21–28% women who are in their reproductive years, and there is a trend towards a constant expansion of the disease. According to obstetrician-gynaecologists, obesity occurs in 15.5–26.9% of pregnancies. (krasotaimedicina.ru 2020).

According to the classification of obesity during pregnancy, 4 basic levels are distinguished corresponding to the proportion of excess weight and the pregnant woman’s complication risks.

Level I – body weight exceeds the norm with 10–29%. Pregnancy complications observed in 25–40% of women.

Level II – body weight exceeds the norm with 30–49%. Pregnancy complications observed in 70–80% of women.

Level III – body weight exceeds the norm with 50–99%. Pregnancy complications observed in 97–99% of women.

Level VI – body weight exceeds the norm with 100%. Pregnancy complications observed in all women.

The signs of obesity in pregnant women are expressed with clinical symptoms, which are directly dependant on the obesity level. In the initial phase, pregnant women complain of fatigue, excessive perspiration, shortness of breath, and constipation. Externally, fat accumulation can be observed in the thighs, abdomen, chest, shoulders, neck and chin. Level III and IV obesity can lead to the formation of fat deposits, shortness of breath even at rest, limited mobility and swelling. Spinal and joint pain occurs. (krasotaimedicina.ru 2020).

Pregnancy in overweight women is typically associated with reproductive problems, which requires that obstetrician-gynaecologist physicians follow such pregnancies with increased attention from as early as the 6th gestational week, and assign them to the group at increased risk of adverse outcomes.

The health problems faced by women with obesity emerge not only during the pregnancy but also significantly earlier (Frederick et al. 2006). Reproductive pathologies are observed in cases polycystic ovarian syndrome, which often lead to infertility (Seligman et al. 2006).

According to Guelinckx et al. (2008) a pregnant woman’s diet is essential for the woman herself, as well as for the fetus, and consequently the health of the child. Numerous pieces of scientific research have established a direct relationship between overweightness in pregnant women and the children born to them tending towards obesity, as well as the discovery of serious health problems in the child’s early years.

The goal of this article is to investigate and analyze the impact of biological and social determinants of obesity in pregnant women and to assess the risk of obesity in pregnancy and childbirth.

In order to achieve the goal of the study, the following tasks were set:

  1. To research and analyse the age characteristics of the patients by group;
  2. To research and analyse of the educational status of the patients by group;
  3. To research and analyse the BMI of the participants by group;

To realise the outlined tasks the following scientific methods will be applied: documentary method, comparative analysis, mathematical and statistical methods.

Statistical methods

  1. Descriptive statistics methods
  1. Categorical variables are presented as absolute and relative frequencies
  2. Quantitative variables are presented as median and range (minimum and maximum) as they do not have a normal distribution. For comprehensiveness, and in order to facilitate the general public’s understanding, they have been supplemented with mean (x) and standard deviation (sd).
  3. Appropriate graphic representations have been used – a boxplot for the quantitative variables and various types of bar chart for the categorical variables.
  1. Analytical statistics methods
  1. The shape of the distribution is assessed with the Kolmogorov-Smirnov and Shapiro-Wilk tests
  2. Chi-square analysis was applied in order to search for a relationship between categorical variables
  3. The Mann-Whitney U test was used to compare means of two independent samples
  4. The Kruskal-Wallis test was used for comparing the means of k independent samples
  5. The Wilcoxon test was used for comparing the means of two related samples.

SPSS, version 22, was used for the statistical analysis. Bar charts were plotted with MS Excel 2010.

4 groups were formed:

  • pregnant women from Sofia
  • pregnant women from Plovdiv
  • pregnant women from Kardzhali
  • pregnant women treated for infertility.

Each was divided into two sub-groups – Cases (overweight and BMI = 25.1–30.0) and Controls (with BMI below 18.6–24.4).

The patients included were the following:

Sub-group of pregnant women from Sofia – 196 Cases and 182 Controls from the Dr. Liliya Dimitrova Medical Center – Sofia and the Geya Med Medical Center – Sofia

Sub-group of pregnant women from Plovdiv – 25 Cases and 25 Controls from the Medicus Alpha Private Clinic

Sub-group of pregnant women from Kardzhali – 25 Cases and 25 Controls from the Avicenna Medical Center – Kardzhali;

Sub-group of pregnant women from Sofia – 25 Cases and 25 Controls from the Dr. Liliya Dimitrova Medical Center – Sofia and the Geya Med Medical Center – Sofia

The outcome of pregnancy and delivery is determined based on the spontaneity of its initiation – normal or by caesarean section. The influence of obesity as a factor on perineal trauma has been studied.

Results and discussion

Age characteristics of the groups

Age characteristics of the patients

The age of the patients during the pregnancy is one of the indicators included in the research. The mean of the patient’s age is 29.8, while the median is 30, Fig. 1.

Figure 1. 

Age distribution in the four groups studied.

Figure 2. 

Patient distribution according to education level and group.

Figure 3. 

Patient distribution according to education level and patient type.

Figure 4. 

Patient distribution according to education level and the timing of the birth.

Considered as a whole, Cases and Controls had similar mean ages (p > 0.05), Table 1.

Table 1.

The mean age of the patients in the obese group and the control group.

Cases Controls
X sd median min max x sd median min max
30.2 6.7 31 17 49 29.4 6.1 29 15 49

We compared both sub-groups of patients who delivered before term versus those who delivered at and after term by age. The ones who gave birth before term have a mean age of 32.6, and the ones who did after term – 29.1. The difference proved to be statistically significant (p < 0.001).

Age distribution of the Sofia group

The age distribution in the two sub-groups has mean values shown in Table 2. The mean age of patients in both sub-groups is 30. For the group with obesity, the mean age is 30.6, while in the control group it is 29.4.

Table 2.

Mean age of patients according to delivery term.

before term at/after term
X sd median min max x sd median min max
32.6 5.6 33 17 47 29.1 6.4 29 15 49
Table 3.

Mean age of patients in the group with obesity and the control sub-group.

x sd median min max
Cases 30.6 6.9 31 17 48
Controls 29.4 6.0 29 15 47
total 30.0 6.5 30 15 48

No significant difference in the median age of the two sub-groups was demonstrated (p > 0.05).

Age distribution in the Infertility group

The age distribution in the two sub-groups has mean values shown in Table 4. The mean age of patients in both groups is 35.1. For the group with obesity, the mean age is 35, while in the control group it is 35.1.

Table 4.

The mean age of the patients in the obese sub-group and the control group.

x sd median min max
Cases 35.0 4.3 34 29 49
Controls 35.3 4.3 34 29 49
total 35.1 4.3 34 29 49

No significant difference in the median age of the two groups was demonstrated (p > 0.05).

Age distribution of the Plovdiv group

The age distribution in the two sub-groups has mean values shown in Table 5. The mean age of patients in both sub-groups is 26.9. For the sub-group with obesity, the mean age is 27, while in the control group it is 26.8.

Table 5.

Mean age of patients in the group with obesity and the control sub-group.

x Sd median min max
Cases 27.0 4.4 27 18 37
Controls 26.8 4.9 27 17 38
total 26.9 4.6 27 17 38

No significant difference in the median age of the two groups was demonstrated (p > 0.05).

Age distribution of the Kardzhali group

The age distribution in the two sub-groups has mean values shown in Table 6. The mean age of patients in both sub-groups is 25.9. For the sub-group with obesity, the mean age is 25.8, while in the control group it is 26.1.

Table 6.

Mean age of patients in the group with obesity and the control sub-group.

x Sd median min max
Cases 25.8 4.8 26 18 34
Controls 26.1 4.5 27 17 35
total 25.9 4.6 26 17 35
Table 7.

Frequency of different education levels.

Sofia infertility Plovdiv Kardzhali
n % n % n % n %
Lower 23 6.3% 6 12.0% 13 26.0% 16 32.0%
secondary 112 30.8% 30 60.0% 21 42.0% 10 20.0%
Higher 229 62.9% 14 28.0% 16 32.0% 24 48.0%
Table 8.

Frequency of different education levels according patient type and group.

lower secondary higher p
Sofia Cases n 5 53 124 0.010
% 2.7% 29.1% 68.1%
Controls n 18 59 105
% 9.9% 32.4% 57.7%
infertility Cases n 3 17 5 0.433
% 12.0% 68.0% 20.0%
Controls n 3 13 9
% 12.0% 52.0% 36.0%
Plovdiv Cases n 4 11 10 0.226
% 16.0% 44.0% 40.0%
Controls n 9 10 6
% 36.0% 40.0% 24.0%
Kardzhali Cases n 8 5 12 0.999
% 32.0% 20.0% 48.0%
Controls n 8 5 12
% 32.0% 20.0% 48.0%
Table 9.

Frequency of different education levels according patient type.

Cases and Controls
Cases Controls
n % n %
lower 20 7.8% 38 14.8%
secondary 86 33.5% 87 33.9%
higher 151 58.8% 132 51.4%
Table 10.

Mean BMI, minimum and maximum height, standard deviation of pregnant patients according to term of delivery.

before term at/after term
x sd median min max x sd median min max
32.4 7.5 33.4 17.4 56.6 30.4 8.0 28.9 16.8 57.2
Table 11.

Mean BMI, minimum and maximum height, standard deviation of pregnant patients in the two sub-groups.

x sd median min max
Cases 38.39 2.12 35.4 18.29 28.62
Controls 23.53 6.06 25.9 27.15 57.15
Table 12.

Mean BMI, minimum and maximum height, standard deviation of pregnant patients in the two sub-groups.

x sd median min max
Cases 38.8 5.2 39.8 24.4 48.3
Controls 21.2 2.2 21.6 16.8 24.3
total 30.0 9.7 24.3 16.8 48.3
Table 13.

Mean BMI, minimum and maximum height, standard deviation of pregnant patients in the two sub-groups.

x sd median min max
Cases 39.4 3.9 39.3 32.6 45.7
Controls 20.3 1.4 20.3 17.0 22.6
total 29.8 10.1 27.6 17.0 45.7
Table 14.

Mean BMI, minimum and maximum height, standard deviation of pregnant patients in the two sub-groups.

x sd median min max
Cases 39.6 3.6 39.1 34.5 49.0
Controls 20.2 2.1 20.3 16.8 25.0
total 29.9 10.2 29.8 16.8 49.0

No significant difference in the median age of the two groups was demonstrated (p > 0.05).

Discussion

Considered as a whole, Cases and Controls had similar mean ages (p > 0.05). This shows that, when it comes to this indicator, there is no difference between Cases and Controls.

Comparison of the four groups shows that the difference between them is significant (p < 0.001), and a pairwise comparison shows that the average (median) age is significantly different between Sofia and the infertility group (p < 0.001), between the infertility group and the patients from Plovdiv (p < 0.001), as well as between the Sofia group and the one from Kardzhali (p < 0.001). No significant difference in the mean age between the participants from Plovdiv and Kardzhali is found (p > 0.05). The observed difference indicates a trend towards later pregnancies in large cities, which is most likely related to professional plans and financial stability. This is consistent with the global trend for pregnancy at older ages in countries with high economic development (Sauer 2015; Wu et al. 2019; Attali and Yogev 2021).

This is also the reason for the later age and problems with conception in patients with infertility.

When comparing the two patient sub-groups with respect to the timing of the delivery in relation to age, a significant difference was demonstrated. This aligns with results obtained by other researchers (Slack et al. 2019) and shows that obesity is a risk factor for complications and worse pregnancy outcomes.

Education

The biggest portion of patients have a higher education diploma (53.6%); a third of them (32.8%) have completed their secondary education; 11.0% have an education level lower than secondary; and 2.7% haven’t picked an answer.

In the separate groups the number of higher education graduates varies – from 62.9% in Sofia, through 48% in Kardzhali, to 32% in Plovdiv and 28% among the patients with infertility. Respectively, the secondary education graduates vary from 60% among the patients with fertility, through 42% in Plovdiv, 30.8% in Sofia and 20% in Kardzhali. 32% of the patients from Kardzhali do not have a secondary education diploma. This is true of 26% of the Plovdiv patients, 12% of the patients with infertility and 6.3% of the Sofia patients. The differences are significant, p < 0.001.

In Sofia, the education level distribution between Cases and Controls proves to be significantly different (p = 0.010). Higher education graduates are more common among Cases and those with an education level lower than secondary are more common among Controls.

No significant difference in education level was found between Cases and Controls in the other groups.

Looked as a whole, Cases and Controls differ significantly in their education level (p = 0.032): more often, Cases have completed higher education, while Controls more often have an education level lower than secondary.

A significant correlation is found between education and the timing of the birth (p = 0.015), but this is more likely attributable to the age difference of the two groups (education is strongly correlated with age) rather than an apparent dependence.

Discussion

Cases and Controls are significantly different in terms of education level: more often, Cases have completed higher education, while Controls more often have an education level lower than secondary. This indicates that Cases are most often patients with insufficient physical activity in line with the nature of work-office activities, which are associated with intellectual activity and a lack of movement.

No such difference was established in the patients with infertility. However, this is due to the small number of patients and the diversity of the group.

BMI

The mean BMI of all patients was found to be 30.8 kg/m 2

A comparison of the four groups shows that there is no significant difference between them (p > 0.05), and a pairwise comparison shows that there is also no significant difference.

A significant difference (p = 0.010) is found when considering the patients in relation to the delivery timing. Patients who delivered before term had, on average, a higher BMI (32.4) compared to those who delivered at or after term (30.4).

Mean BMI values of patients in the Sofia group

The mean BMI values for the first sub-group are 38.39, and 23.53 for the second. On comparison, a statistical difference between the two sub-groups in this indicator becomes apparent (p < 0.001).

Mean BMI values of patients in the infertility group

The mean BMI values for the first sub-group are 38.8, and 21.2 for the second. On comparison, a statistical difference between the two sub-groups in this indicator becomes apparent (p < 0.001).

Mean BMI values of patients in the Plovdiv group

The mean BMI values for the first sub-group are 39.4, and 20.3 for the second. On comparison, a statistical difference between the two sub-groups in this indicator becomes apparent (p < 0.001).

Mean BMI values of patients in the Kardzhali group

The mean BMI values for the first sub-group are 39.6, and 20.2 for the second. On comparison, a statistical difference between the two sub-groups in this indicator becomes apparent (p < 0.001).

Discussion

The body mass index is a medical and biological indicator for defining normal weight. It is harder to interpret in pregnant patients. To reduce the subjective influence of the pregnancy, patients were selected in the first trimester. Then, the initial values are closest to the real ones. Nonetheless, BMI remains a much more accurate indicator in comparison with patients’ weight. No significant difference for this indicator emerges when four groups are compared. Naturally, considered as a whole, the two sub-groups were significantly different in mean BMI levels (p < 0.001). However, these differences are the same across the four main groups.

The comparison of BMI and pregnancy outcome is of interest. Here again, the dependency is maintained, as with weight. A significant difference (p = 0.010) is found when considering the patients in relation to the delivery timing. Patients who gave birth before term had, on average, a higher BMI than those who gave birth at term or after term. Our data are consistent with most data from worldwide studies (Vinturache et al. 2017; Su et al. 2020).

Conclusions

When the data from the four groups is analysed on the “age characteristic of all patients” indicator, a difference can be found, indicating a trend towards later pregnancy in large cities. This is most likely related to the role of the woman in public life and her plans for career development and financial stability. This is most likely the reason for the later age and conception problems of patients with infertility. When the data for the aforementioned indicators was collected in both their sub-group of patients who gave birth before term and that of the ones who gave birth on or after term, it can be observed that the ones from the first sub-group were 32.6, while the others were 29.1. The difference proved to be statistically significant (p < 0.001).

When it comes to the “education level” indicator analysed, a significant difference between Cases and Controls is established – more often, Cases have completed higher education, while Controls more often have an education level lower than secondary. This indicates that Cases are most often patients with insufficient physical activity in line with the nature of work-office activities, which are associated with intellectual activity and a lack of movement.

From the analysis of the BMI (Body Mass Index) data, it is observed that there was a significant difference (p = 0.010) when considering the patients in relation to the delivery timing. Patients who gave birth before term had, on average, a higher BMI than those who gave birth at term or after term.

Based on the results of the empirical study and meta-analyses, this article identifies the importance of biological and social determinants of obesity studied as risk factors leading to pathological conditions of pregnancy and childbirth. The health authorities should take measures to prevent and reduce the risk factors discussed, with regard to the trends in the basic medical and demographical indicators, which are, namely, declining birth rates, increasing life expectancy, and an ageing population, which has a regressive age structure and a negative rate of natural increase, given the medical and social aspects of infant mortality.

Additional information

Conflict of interest

The authors have declared that no competing interests exist.

Ethical statements

The authors declared that no clinical trials were used in the present study.

The authors declared that no experiments on humans or human tissues were performed for the present study.

The authors declared that no informed consent was obtained from the humans, donors or donors’ representatives participating in the study.

The authors declared that no experiments on animals were performed for the present study.

The authors declared that no commercially available immortalised human and animal cell lines were used in the present study.

Funding

No funding was reported.

Author contributions

All authors have contributed equally.

Author ORCIDs

Valentina Petkova https://orcid.org/0000-0002-6938-1054

Emilia Naseva https://orcid.org/0000-0002-1282-8441

Milen Dimitrov https://orcid.org/0000-0002-7744-0384

Data availability

All of the data that support the findings of this study are available in the main text.

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