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. 2025 Nov 18;43(2):194–204. doi: 10.1111/phn.70045

Prepregnancy Overweight and Obesity in Kuwait Birth Cohort Study

Abdullah Al‐Taiar 1,✉, Ali H Ziyab 2, Eelaf A Husain 3, Maryam M Y Mohammad 3, Maryem A Shamsah 3, Noor Salah AlAli 4, Reem Sharaf‐Alddin 5, Majeda S Hammoud 2
PMCID: PMC12968484  PMID: 41250812

ABSTRACT

Objectives

This study aimed to estimate the prevalence of prepregnancy obesity and overweight, and to identify a set of sociodemographic factors that could guide weight management interventions for women prior to conception.

Design, Sample, and Measurements

Pregnant women (N = 989) were recruited in the Kuwait Birth Cohort Study during the period June 2017 to February 2020. Prepregnancy weight and height were self‐reported, while current weight and height were measured in a standardized procedure. Data on sociodemographic factors were collected through face‐to‐face interviews conducted by a trained data collector. Multinomial logistic regression was used to investigate the association between sociodemographic factors and prepregnancy obesity and overweight.

Results

Among 989 pregnant women (89.26% of the cohort), the prevalence of prepregnancy obesity and overweight was 28.82% (95% CI: 26.08%–31.72%) and 37.92% (95% CI: 34.94%–40.99%), respectively. Only 24 pregnant women (2.43%; 95% CI: 1.63%–3.60%) were underweight prior to pregnancy. Notably, 17.67% of obese and 56.03% of overweight women perceived their weight as normal.

Conclusion

Obesity and overweight prior to pregnancy are extremely high in Kuwait, with more than two‐thirds of women affected. Public health interventions targeting obesity in all women, especially those of reproductive age, should be implemented along with lifestyle counseling to optimize prepregnancy weight.

Keywords: Kuwait, prepregnancy BMI, prepregnancy obesity, prepregnancy overweight, self‐perception of body weight

1. Background

Prepregnancy obesity has been linked to various adverse pregnancy and birth outcomes including gestational diabetes (Doherty et al. 2006; Vinturache et al. 2014), pregnancy‐induced hypertension (Doherty et al. 2006; Vinturache et al. 2014), delivery by cesarean section (Doherty et al. 2006; Wahabi et al. 2021), preterm birth (Ke et al. 2023; Su et al. 2019), stillbirth (Chu et al. 2007; Flenady et al. 2011), congenital anomalies (Rasmussen et al. 2008; Stothard et al. 2009), postpartum hemorrhage (Doherty et al. 2006), macrosomia/large‐for‐gestational‐age infants (Su et al. 2019; Wahabi et al. 2021), anxiety during pregnancy, and postpartum depression (Catalano and Shankar 2017; Dachew et al. 2021). The long‐term consequences of prepregnancy obesity in offspring include childhood obesity (Catalano and Shankar 2017; Heslehurst et al. 2019), earlier age of menarche in females (Zhou et al. 2022), compromised neurodevelopmental outcomes (Sanchez et al. 2018), emotional/behavioral problems (Dachew et al. 2024; Sanchez et al. 2018), autism spectrum disorder (Dodds et al. 2011; Sanchez et al. 2018), attention deficit hyperactivity disorder/child hyperactivity‐intention symptoms (Dow et al. 2023; Rodriguez et al. 2008), and cerebral palsy (Hu et al. 2023). Conversely, prepregnancy underweight has been linked to premature birth, low birth weight, and small‐for‐gestational‐age infants (Han et al. 2011; Nakanishi et al. 2022).

Multiple biologically plausible mechanisms link prepregnancy obesity to adverse pregnancy and birth outcomes. The most frequently cited pathways include increased insulin resistance and subsequent hyperinsulinemia, systemic inflammation, and oxidative stress — all of which may contribute to early placental and fetal dysfunction (Catalano and Shankar 2017; van der Burg et al. 2016), potentially leading to preterm delivery, stillbirth, or other adverse outcomes. Furthermore, the effects of maternal obesity on offspring health may be mediated by epigenetic modifications, consistent with the Developmental Origins of Health and Disease (DOHaD) hypothesis (Barker 2007; Heindel and Vandenberg 2015).

Although prepregnancy obesity is widely recognized as a key modifiable risk factor for adverse pregnancy and birth outcomes, its optimal management to reduce these risks remains uncertain. Management strategies include lifestyle interventions, pharmacotherapy, or bariatric surgery. Diet and physical activity interventions have not consistently reduced the risk of gestational diabetes or large‐for‐gestational‐age infants (Poston et al. 2017; Poston et al. 2015). However, recent evidence suggests these interventions may lower the risk of pregnancy‐induced hypertension and preeclampsia (Schenkelaars et al. 2021). The role of bariatric surgery in improving pregnancy outcomes remains debated. Although bariatric surgery can reduce the risk of gestational diabetes, gestational hypertension, large‐for‐gestational‐age infants, and cesarean delivery, it may also increase the likelihood of small‐for‐gestational‐age infants and preterm birth (Kwong et al. 2018; Yang et al. 2021). A recent meta‐analysis of 20 studies involving 40,108 women found that Roux‐en‐Y gastric bypass, compared to controls, reduced the risk of large‐for‐gestational‐age infants, gestational hypertension/preeclampsia, and gestational diabetes but increased the risk of small‐for‐gestational‐age infants and maternal anemia (Mustafa et al. 2023). In contrast, evidence on the effects of sleeve gastrectomy on pregnancy and birth outcomes remains limited (Mustafa et al. 2023).

In recent decades, the prevalence of prepregnancy overweight and obesity has increased in various regions, such as the United States (Kim et al. 2007; Wang et al. 2021), Ireland (Reynolds et al. 2019), Sweden (Chaparro et al. 2015), and Iceland (Jonsdottir et al. 2024), as well as many low‐, middle‐, and high‐income countries (Black et al. 2013; Chen et al. 2018). A recent estimate from the United States indicates that the prevalence of prepregnancy obesity is around 29% (Driscoll and Gregory 2020). In Arab states in the Gulf region, the prevalence of obesity in the general population has been increasing for decades (Al‐Quwaidhi et al. 2014; Al‐Taiar, Alqaoud, Ziyab et al. 2021). Although numerous studies have assessed the prevalence of obesity in children and adults in these settings, data specifically focusing on prepregnancy obesity and its associated factors are scarce. Identifying sociodemographic characteristics associated with prepregnancy obesity is essential for targeting high‐risk women before conception, thereby mitigating adverse pregnancy and birth outcomes, as well as potential long‐term effects on offspring (Boudet‐Berquier et al. 2017; Taha et al. 2022). The relative importance of these sociodemographic characteristics varies across different settings, with no consensus reached on the factors that can be used to identify women with a high risk of prepregnancy obesity. This study aimed to estimate the prevalence of prepregnancy obesity and investigate the associated sociodemographic factors among participants in the Kuwait Birth Cohort Study.

2. Methods

2.1. Study Participants and Study Design

This study is a secondary data analysis of the Kuwait Birth Cohort Study, in which pregnant women were recruited during antenatal visits in their second or third trimester. Details of the study have been published previously (Al‐Sabah et al. 2024; Al‐Taiar et al. 2025). Ethical approval for this study was obtained from the Research Ethics Committee at the Ministry of Health, Kuwait (Ref: project 173/2014; date: February 14, 2017), as well as from the Institutional Review Board at Old Dominion University (Ref: 1517949). Prior to enrollment, written informed consent was obtained from each participant in the study.

2.2. Data Collection on Prepregnancy Obesity

In this study, the outcome variable was prepregnancy body mass index (BMI), calculated as weight in kilograms divided by the square of height in meters (kg/m2). Although BMI is widely used to define obesity across population groups, it is not a precise measure of fat accumulation, as it can be influenced by muscle mass and bone density, especially in athletes. Despite not directly measuring body fat, BMI is considered a reliable proxy due to its simplicity, low cost, and practicality compared to direct methods, which require specialized and expensive equipment. The extent to which BMI reflects body fat varies by age, sex, and race (Flegal et al. 2009; Gallagher et al. 1996; Jeong et al. 2023; Meeuwsen et al. 2010; Wagner and Heyward 2000; Wang et al. 1994), with reported correlations ranging from 0.70 to 0.85 (Akindele et al. 2016; Grier et al. 2015; Misra et al. 2019; Ranasinghe et al. 2013; Wang et al. 1994).

Since pregnancy significantly alters women's weight due to the fetus, placenta, and fluid retention, self‐reported prepregnancy weight and height were used, as in many other cohort studies (Boudet‐Berquier et al. 2017; Gaillard et al. 2013; Haugen et al. 2014). Current weight and height were measured in a standardized manner using a sliding weight column scale after removal of shoes and heavy clothing. Self‐reported height was closely matched to measured height, demonstrating a high level of agreement (Pearson correlation r = 0.97; p < 0.001). BMI was categorized according to the WHO classification (WHO 2000), including underweight (BMI <18.5 kg/m2), normal weight (BMI 18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), and obese (BMI ≥30 kg/m2).

2.3. Sociodemographic and Other Factors

Sociodemographic and lifestyle data were obtained through in‐person interviews conducted by a trained data collector with pregnant women. The data collection form was developed and pilot tested on 30 pregnant women who were not included in the main study. Age was computed by subtracting the date of birth from the date of recruitment in the study. Data on age of menarche were obtained through a retrospective method using questions that have been used in our setting (Al‐Awadhi et al. 2013; Al‐Taiar, Al‐Sabah, et al. 2022). Although data pertaining to dietary habits and physical activity during pregnancy were collected, the reference period for this data collection was during pregnancy. Therefore, these factors cannot be considered as risk factors for prepregnancy obesity.

2.4. Statistical Methods

The data were entered in a specially designed database using EpiData software and analyzed by STATA Release 17. The prevalence of underweight, overweight, and obesity, along with their 95% confidence intervals (95% CI), were computed. The association between each categorical variable and BMI categories was assessed using either Chi‐square or Fisher's exact test. We used multinomial logistic regression to investigate factors associated with pre‐pregnancy obesity and overweight. In this analysis, the BMI categories, including normal weight (used as reference), overweight, and obesity, were considered as the outcome variable, with underweight omitted due to the fact that only 24 women fell into the underweight category (BMI <18.5 kg/m2). Additionally, morbidly obese women (n = 24) were grouped with the obese category and collectively referred to as obese. Factors that showed an association with prepregnancy overweight/obesity at a 20% level of significance in univariable analysis were considered in the multivariable model. Throughout the analysis, variables with p < 0.05 were deemed to be statistically significant.

3. Results

Out of 1108 pregnant women, 989 (89.26%) were able to recall their weight and height before pregnancy. The mean (SD) age of the study group was 31.39 (5.22) years, with the majority being non‐Kuwaiti citizens. The mean (SD) of prepregnancy BMI, height, and weight were 27.56 (5.16) kg/m2, 161.29 (6.10) cm, 71.91 (14.61) kg, respectively. The prevalence of obesity was 28.82% (95% CI: 26.08%–31.72%), while the prevalence of overweight was 37.92% (95% CI: 34.94%–40.99%). Only 24 pregnant women (2.43%; 95% CI: 1.63%–3.60%) were underweight before pregnancy, while another 24 (2.43%) participants were morbidly obese (BMI >40 kg/m2).

Figure 1 shows the awareness of women about their body weight status compared to their actual body weight. Among those with prepregnancy obesity, the majority recognized their weight as higher than normal, while around one‐fifth perceived their weight to be normal. Similarly, among those who were actually overweight before conception, 43.43% described their weight as higher than normal, while more than half believed their weight was normal. Approximately 16% of pregnant women reported using a method to control weight before pregnancy, with the majority citing diet; however, 25 pregnant women reported undergoing bariatric surgery.

FIGURE 1.

FIGURE 1

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Perceived body weight compared to the actual body weight prior to pregnancy in Kuwait Birth Cohort Study. [Colour figure can be viewed at wileyonlinelibrary.com]

Prevalence of healthy weight, overweight, and obesity according to various socio‐demographic factors is shown in Table 1. In univariable analysis, age, husband's education, and women's employment status were significantly associated with prepregnancy obesity and overweight. Table 2 shows the distribution of pre‐pregnancy overweight or obesity by reproductive factors, as well as active and passive smoking. Age at first marriage, age at first pregnancy, number of children, time since last delivery, receiving treatment to aid conception, and history of abortion or miscarriage were all found to be significantly associated with prepregnancy overweight and obesity.

TABLE 1.

Distribution of pre‐pregnancy overweight and obesity by socio‐demographic factors in Kuwait Birth Cohort Study.

Characteristics N a Healthy weight Overweight Obesity p b
Nationality n (%)
Kuwaiti 233 75 (32.19) 84 (36.05) 74 (31.76) 0.557
Non‐Kuwait 732 230 (31.42) 291 (39.75) 211 (28.83)
Age (year)
<25 years 93 48 (51.61) 29 (31.18) 16 (17.20) <0.001
25–29.99 years 296 113 (38.18) 108 (36.49) 75 (25.34)
30–34.99 years 319 87 (27.27) 143 (44.83) 89 (27.90)
35+ years 256 57 (22.27) 94 (36.72) 105 (41.02)
Mother's education
Elementary school or less 17 3 (17.65) 4 (23.53) 10 (58.82) 0.069
Secondary (high school) 217 67 (30.88) 78 (35.94) 72 (33.18)
University & above 730 235 (32.19) 292 (40.00) 203 (27.81)
Husband's education
Elementary school or less 20 3 (15.00) 6 (30.00) 11 (55.00) 0.023
Secondary (high school) 235 74 (31.49) 80 (34.04) 81 (34.47)
University & above 709 227 (32.02) 289 (40.76) 193 (27.22)
Husband's monthly Income (KD)
Less than 500 223 64 (28.70) 87 (39.01) 72 (32.29) 0.292
500–1000 272 80 (29.41) 102 (37.50) 90 (33.09)
More than 1000 148 50 (33.78) 56 (37.84) 42 (28.38)
Prefer not to tell 311 109 (35.05) 126 (40.51) 76 (24.44)
Mother employment
Housewife 495 157 (31.72) 193 (38.99) 145 (29.29) 0.007
Paid employment 449 134 (29.84) 176 (39.20) 139 (30.96)
Others 20 14 (70.00) 5 (25.00) 1 (5.00)
Mother's monthly income (Kuwaiti Dinar)
No specific income 512 168 (32.81) 199 (38.87) 145 (28.32) 0.679
Less than 500 142 37 (26.06) 59 (41.55) 46 (32.39)
500–1000 130 41 (31.54) 55 (42.31) 34 (26.15)
More than 1000 72 25 (34.72) 22 (30.56) 25 (34.72)
Prefer not to tell 106 33 (31.13) 40 (37.74) 33 (31.13)
Type of housing
Rented apartment 755 232 (30.73 297 (39.34) 226 (29.93) 0.513
Rented house 31 7 (22.58) 16 (51.61) 8 (25.81)
Owned apartment 25 9 (36.00) 9 (36.00) 7 (28.00)
Owned house 153 57 (37.25) 52 (33.99) 44 (28.76)
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a

Twenty‐four pregnant women were excluded because they were underweight.

b

p values were generated by Chi‐square or Fisher's Exact test as appropriate.

TABLE 2.

Distribution of pre‐pregnancy overweight and obesity by reproductive factors as well as active and passive smoking in univariable analysis.

Characteristics N a Healthy weight Overweight Obesity p b
Age of menarche (year)
8– 131 33 (25.19) 51 (38.93) 47 (35.88) 0.118
12– 268 80 (29.85) 111 (41.42) 77 (28.73)
13– 278 83 (29.86) 111 (39.93) 84 (30.22)
14– 275 105 (38.18) 97 (35.27) 73 (26.55)
Age at first marriage
Lower tertile <22 years 279 91 (32.62) 90 (32.26) 98 (35.13) 0.046
Second tertile >22 to <25 years 317 82 (25.87) 138 (43.53) 97 (30.60)
Third tertile 25 years or more 367 105 (28.61) 146 (39.78) 116 (31.61)
Age at first pregnancy
Lower tertile <23 years 268 98 (36.57) 83 (30.97) 87 (32.46) 0.002
Second tertile >23 to <27 years 366 88 (24.04) 166 (45.36) 112 (30.60)
Third tertile 27 years or more 324 97 (29.94) 123 (37.96) 104 (32.10)
Number of children
Zero 266 115 (43.23) 99 (37.22) 52 (19.55) <0.001
1–2 children 494 149 (30.16) 203 (41.09) 142 (28.74)
3–4 children 161 35 (21.74) 57 (35.40) 69 (42.86)
5 or more children 44 6 (13.64) 16 (36.36) 22 (50.00)
Time since the last delivery (months)
≤18 months 95 30 (31.58) 37 (38.95) 28 (29.47) <0.001
19–24 months 63 13 (20.63) 30 (47.62) 20 (31.75)
25 months or above 502 136 (27.09) 196 (39.04) 170 (33.86)
No previous pregnancy/delivery 305 126 (41.31) 112 (36.72) 67 (21.97)
Wanted to get pregnant
No 296 95 (32.09) 111 (37.50) 90 (30.41) 0.837
Yes 666 209 (31.38) 263 (39.49) 194 (29.13)
Became pregnant while using contraception
No 886 282 (31.83) 345 (38.94) 259 (29.23) 0.473
Yes 72 18 (25.00) 30 (41.67) 24 (33.33)
Receiving treatment to aid conception
No 867 280 (32.30) 342 (39.45) 245 (28.26) 0.029
Yes 97 25 (25.77) 32 (32.99) 40 (41.24)
Ever had an abortion or miscarriage
No 594 212 (35.69) 226 (38.05) 156 (26.26) 0.001
Yes 371 93 (25.07) 149 (40.16) 129 (34.77)
Smoking cigarettes/shisha in the last 9 months
No 911 287 (31.50) 355 (38.97) 269 (29.53) 0.950
Yes 54 18 (33.33) 20 (37.04) 16 (29.63)
Passive smoking at home
No 603 187 (31.01) 235 (38.97) 181 (30.02) 0.856
Yes 362 118 (32.60) 140 (38.67) 104 (28.73)
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a

Twenty‐four pregnant women were excluded because they were underweight.

b

p values were generated by Chi‐square or Fisher's Exact test as appropriate.

Table 3 shows factors associated with pre‐pregnancy overweight and obesity in a multivariable model. Strong collinearity was found between age at first marriage and age at first pregnancy; therefore, only the latter was included in the model. In this analysis, the variables significantly associated with prepregnancy overweight or obesity were age at recruitment, age of menarche, total number of children, and receiving treatment to aid conception. Older women (aged 30 years or above) were more likely to have prepregnancy overweight or obesity compared to younger women (global Wald test p = 0.038). Age remained statistically significant (p = 005) when treated as a continuous variable. Conversely, a higher age of menarche was inversely associated with prepregnancy overweight and obesity (global Wald test p = 0.031). Despite strong collinearity with the age of pregnant women, the total number of children was retained in the model.

TABLE 3.

Factors associated with pre‐pregnancy overweight and obesity in multivariable model. a

Characteristics N b Overweight, RRR (95% CI) p Obesity RRR (95% CI) p
Age (year)
<25 years 93 1 (reference) 1 (reference)
25–29.99 years 296 1.35 (0.70–2.60) 0.366 1.70 (0.80–3.61) 0.170
30–34.99 years 319 2.26 (1.08–4.72) 0.030 1.89 (0.82–4.36) 0.135
35+ years 256 2.33 (1.01–5.33) 0.046 2.97 (1.19–7.40) 0.019
Age of menarche (year)
8– 131 1 (reference) 1 (reference)
12– 268 0.91 (0.52–1.57) 0.727 0.73 (0.41–1.30) 0.287
13– 278 0.80 (0.46–1.38) 0.422 0.67 (0.38–1.19) 0.172
14– 275 0.53 (0.31–0.91) 0.021 0.41 (0.23–0.73) 0.002
Number of children
Zero 266 1 (reference) 1 (reference)
1– 2 children 494 0.89 (0.40–1.98) 0.775 1.93 (0.84–4.47) 0.121
3–4 children 161 1.02 (0.40–2.65) 0.960 3.33 (1.25–8.87) 0.016
5 or more children 44 1.41 (0.38–5.25) 0.611 4.02 (1.07–15.06) 0.039
Treatment to help get pregnant
No 867 1 (reference) 1 (reference)
Yes 97 1.13 (0.63–2.03) 0.679 2.26 (1.26–4.04) 0.006
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a

Model included mother education, husband's education, mother employment, age at pregnancy, time since the last delivery, and ever having an abortion or miscarriage.

b

Twenty‐four pregnant women were excluded because they were underweight.

4. Discussion

Despite the significant short‐term and long‐term health implications associated with prepregnancy obesity, there remains a dearth of data on this matter within Arab states in the Gulf region. This study sought to address this gap by estimating the prevalence of prepregnancy obesity and overweight, which were found to be high. Additionally, we identified several sociodemographic factors that can help in the identification of women at risk of prepregnancy obesity to be targeted by weight management interventions from preconception and through their life‐course. These factors include age, age of menarche, and seeking treatment to aid conception.

We found the prevalence of prepregnancy obesity and overweight to be 29% and 38%, respectively, which is similar to that reported from other countries such as the United States (29% obesity) (Driscoll and Gregory 2020), Saudi Arabia (34.8% obesity and 33.3% overweight) (Wahabi et al. 2021), Qatar (29.9% obesity and 32.2% overweight) (Olukade et al. 2024), or the United Arab Emirates (59.4% with BMI ≥25 kg/m2) (Hashim et al. 2019). Additionally, our estimates align with the overall figures for obesity and overweight among adult women in Kuwait (28% obesity and 33% overweight in women aged 18–29 years, and 43% obesity and 36.5% overweight in women aged 30–44) (Ministry of Health 2015). Conversely, the prevalence of prepregnancy obesity and overweight in Kuwait appears to exceed that reported in other settings, including China (1.9% obesity and 12.6% overweight) (Sun et al. 2020) (9.37% obesity and 12.07% overweight) (Su et al. 2019), Netherlands (8.8% obesity and 19.2% overweight) (Gaillard et al. 2013), France (10.2% obesity and 18.0% overweight) (Boudet‐Berquier et al. 2017), the United Arab Emirates (6.1% obesity and 25.7% overweight) (Taha et al. 2022), Canada (10.6% obese and 23.6% overweight) (Vinturache et al. 2014), Norway (8.8% obesity) (Haugen et al. 2014), and Australia (6.6% obesity and 11.5% overweight) (Doherty et al. 2006). The comparisons above indicate that Kuwait has one of the highest estimates of prepregnancy obesity and overweight, with nearly two‐thirds of women classified as either obese or overweight before pregnancy. Public health efforts should focus on supporting weight management among women of childbearing age, starting from the preconception period, and continuing throughout the life‐course. This is particularly important given Kuwait's elevated proportion of delivery by cesarean section, prematurity (Al‐Taiar et al. 2020), and childhood obesity (Al‐Taiar, Alqaoud, Sharaf Alddin, et al. 2021; Al‐Taiar, Alqaoud, Ziyab, et al. 2021), all of which have been linked to maternal obesity in numerous studies.

Perceptions of body weight status play a crucial role in weight management (Hazzard et al. 2017; Lynch et al. 2009; Riley et al. 1998; Yu et al. 2023). Underestimation of body weight may predispose individuals to obesity (Flynn and Fitzgibbon 1998), while overestimation among those who are normal or underweight may lead to unhealthy weight control practices (Park 2011). Our findings indicate that the majority of both obese and normal weight women correctly assessed their adiposity status before pregnancy (Figure 1). This is different from other settings, where a large number of women with normal weight perceived themselves as overweight or obese (Dorosty et al. 2014; Fatimah et al. 1995). Interestingly, most of those who underestimated their body weight status were actually classified as overweight (out of 373 overweight women, only 43.43% recognized they were overweight before pregnancy), which is consistent with findings from developed countries, where approximately 25%–50% of actually overweight individuals fail to perceive themselves as such, believing their body weight to be healthy (Jackson et al. 2015; Robinson et al. 2020; Yaemsiri et al. 2011). There are several reasons for this misclassification, particularly among overweight group, including the fact that heavier body weight has become more of a “normal” thing, which resulted in those who are in the overweight category not recognizing that they have overweight (Robinson 2017; Robinson et al. 2020). Another contributing factor could be that media portrayals of obesity often feature individuals with severe obesity, distorting public perceptions of what constitutes a normal weight (Al‐Taiar, Alqaoud, et al. 2022; Campbell et al. 2006; Lundahl et al. 2014).

Prepregnancy underweight is associated with various adverse pregnancy and birth outcomes (Han et al. 2011; Liu et al. 2016; Nakanishi et al. 2022; Qu et al. 2019). In our setting, only 2.4% of women were classified as underweight before pregnancy, a figure lower than that reported in China (13.2) (Sun et al. 2020), the Netherlands (16.2%) (Gaillard et al. 2013), and France (7.6%) (Boudet‐Berquier et al. 2017), but similar to findings from studies in the region, including United Arab Emirates (2.7%) (Taha et al. 2022), and Saudi Arabia (2.2%) (Wahabi et al. 2021). Despite the relatively small number of women classified as underweight before pregnancy, targeting these women should be part of efforts that aim to promote a healthy weight before conception.

Identifying women at risk of prepregnancy obesity is crucial for guiding efforts to promote a healthy weight prior to pregnancy. Our objective was to identify a set of sociodemographic and reproductive factors that could inform interventions aimed at optimizing prepregnancy weight and enhancing their effectiveness. In multivariable analysis, we found that age at recruitment, age of menarche, and seeking treatment to aid conception are three indicators that can be used to identify women who may benefit from support to achieve a healthy weight before pregnancy. Women aged 30 years or older were more likely to have obesity or overweight, consistent with findings from previous studies (Boudet‐Berquier et al. 2017; Sun et al. 2020; Taha et al. 2022). Additionally, women with an earlier age of menarche were also more likely to be obese or overweight before pregnancy (Table 3). Although age at menarche has not been widely explored in studies on prepregnancy obesity, it has been linked to obesity in females more broadly (Al‐Awadhi et al. 2013; Matsuo et al. 2022; Nieczuja‐Dwojacka et al. 2018; Wronka 2010). The significant association between receiving treatment to aid conception and prepregnancy obesity may be due to underlying conditions such as polycystic ovary syndrome or other endocrine disorders that predispose to obesity and hinder conception. It is important to note that these factors do not directly cause prepregnancy obesity but can serve as useful markers for identifying women at risk, thereby guiding efforts to maintain a healthy weight prior to conception.

Similar to the findings in other studies (Boudet‐Berquier et al. 2017; Gaillard et al. 2013; Sun et al. 2020), we found mother's education and husband's education are inversely related to prepregnancy obesity and overweight in univariate analysis, though this was not statistically significant in the final model. Studies examining the link between education and prepregnancy obesity yield inconsistent results (Gaillard et al. 2013; Hashan et al. 2020; Liang et al. 2020; Taha et al. 2022). It has been hypothesized that women with better education may possess greater knowledge and engage in healthier behavior, or reside in environments less conducive to obesity (Boudet‐Berquier et al. 2017). A systematic review of studies investigating the association between education and overweight/obesity in general noted that the significance of the association diminished when accounting for publication bias and concluded that the link between education and overweight/obesity is affected by age, region, gender, and observation period (Kim et al. 2017). Generally, reverse causality is the predominant interpretation for the association between education and overweight/obesity (Benson et al. 2018; von Hippel and Lynch 2014). Neither maternal nor paternal monthly income showed a significant association with prepregnancy obesity or overweight, which is different from that reported in other studies (Taha et al. 2022). Research exploring the relationship between family income and prepregnancy obesity shows conflicting findings, with some studies reporting higher income as a predisposing factor (Abdulmalik et al. 2019) while others suggest it as a protective factor (Liang et al. 2020; Taha et al. 2022). A review of the literature on the association between income and obesity in general revealed that the association lost statistical significance after accounting for publication bias, with studies on reverse causality providing more consistent evidence (Kim and von dem Knesebeck 2018).

This study has several strengths, including the collection of high‐quality data through personal interviews and a large sample size, which was sufficient to precisely estimate the prevalence of prepregnancy obesity. Nonetheless, the study has some limitations to consider. First, prepregnancy weight and height were self‐reported, although current weight and height were measured in a standardized manner. Research suggests that women of reproductive age may underestimate their BMI by 0.8 kg/m2, though this was not statistically significant among women with a high school education (Brunner Huber 2007). In another study, investigators compared self‐reported prepregnancy weight with the weight measured at the first prenatal visit and found that BMI classification remained consistent (Holland et al. 2013). In our data, it was reassuring to observe that measured height during pregnancy was nearly identical to self‐reported height. Finally, while BMI does not directly measure body fat, it is widely used in epidemiological studies due to its simplicity, low cost, and practicality, especially when compared to direct body fat measurement, which requires specialized and costly equipment.

5. Conclusion

Obesity and overweight prior to pregnancy are extremely high in Kuwait, with more than two‐thirds of women classified as either obese or overweight. This warrants systematic control measures to mitigate the short‐term and long‐term consequences of obesity on mothers and their offspring. Efforts should be made to correct the misperceptions of healthy weight, as one in five women with obesity and more than half of those with overweight, underestimate their body weight. Public health interventions to reduce obesity among all women, particularly those of reproductive age, should be implemented, along with lifestyle counseling to optimize prepregnancy weight. These efforts should specifically target women aged 30 years or older, those with an earlier age of menarche, or those seeking help to conceive.

Funding

Research reported in this article was conducted within a funded project supported by Kuwait University: Research Project No. MC01/15.

Ethics Statement

This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the ethics committee at the Ministry of Health, Kuwait (Ref: project 173/2014; date: February 14, 2017) and the Institutional Review Board at Old Dominion University Ref: 1517949). Written informed consent was taken from each study participant before recruitment.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

Data analyzed in this study will be available from the corresponding author upon reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

Data analyzed in this study will be available from the corresponding author upon reasonable request.

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