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Open Access

Peer-reviewed

Research Article

Association of body composition in early pregnancy with gestational diabetes mellitus: A meta-analysis

  • Fatemeh Alsadat Rahnemaei ,

    Contributed equally to this work with: Fatemeh Alsadat Rahnemaei, Fatemeh Abdi, Reza Pakzad, Seyedeh Hajar Sharami, Fatemeh Mokhtari, Elham Kazemian

    Roles Conceptualization, Data curation

    Affiliation Department of Obstetrics & Gynecology, Midwifery, Reproductive Health Research Center, Al-zahra Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran

    ⨯
  • Fatemeh Abdi ,

    Contributed equally to this work with: Fatemeh Alsadat Rahnemaei, Fatemeh Abdi, Reza Pakzad, Seyedeh Hajar Sharami, Fatemeh Mokhtari, Elham Kazemian

    Roles Conceptualization, Visualization, Writing – original draft, Writing – review & editing

    abdi@sbmu.ac.ir, fatemeh.abdi87@yahoo.com

    Affiliation Non-communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran

    ⨯
  • Reza Pakzad ,

    Contributed equally to this work with: Fatemeh Alsadat Rahnemaei, Fatemeh Abdi, Reza Pakzad, Seyedeh Hajar Sharami, Fatemeh Mokhtari, Elham Kazemian

    Roles Formal analysis, Methodology

    Affiliation Epidemiology, Faculty of Health, Ilam University of Medical Sciences, Ilam, Iran

    ⨯
  • Seyedeh Hajar Sharami ,

    Contributed equally to this work with: Fatemeh Alsadat Rahnemaei, Fatemeh Abdi, Reza Pakzad, Seyedeh Hajar Sharami, Fatemeh Mokhtari, Elham Kazemian

    Roles Data curation

    Affiliation Department of Obstetrics & Gynecology, Reproductive Health Research Center, Al-zahra Hospital, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran

    ⨯
  • Fatemeh Mokhtari ,

    Contributed equally to this work with: Fatemeh Alsadat Rahnemaei, Fatemeh Abdi, Reza Pakzad, Seyedeh Hajar Sharami, Fatemeh Mokhtari, Elham Kazemian

    Roles Data curation

    Affiliation Department of Midwifery, Reproductive Health, Faculty of Nursing and Midwifery, Isfahan University of Medical Sciences, Isfahan, Iran

    ⨯
  • Elham Kazemian

    Contributed equally to this work with: Fatemeh Alsadat Rahnemaei, Fatemeh Abdi, Reza Pakzad, Seyedeh Hajar Sharami, Fatemeh Mokhtari, Elham Kazemian

    Roles Writing – review & editing

    Affiliation Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA, Unites States of America

    ⨯

Abstract

Introduction

Body composition as dynamic indices constantly changes in pregnancy. The use of body composition indices in the early stages of pregnancy has recently been considered. Therefore, the current meta-analysis study was conducted to investigate the relationship between body composition in the early stages of pregnancy and gestational diabetes.

Method

Valid databases searched for papers published from 2010 to December 2021 were based on PRISMA guideline. Newcastle Ottawa was used to assess the quality of the studies. For all analyses, STATA 14.0 was used. Mean difference (MD) of anthropometric indices was calculated between the GDM and Non-GDM groups. Pooled MD was estimated by “Metan” command, and heterogeneity was defined using Cochran’s Q test of heterogeneity, and I 2 index was used to quantify heterogeneity.

Results

Finally, 29 studies with a sample size of 56438 met the criteria for entering the meta-analysis. Pooled MD of neck circumference, hip circumference, waist hip ratio, and visceral adipose tissue depth were, respectively, 1.00 cm (95% CI: 0.79 to 1.20) [N = 5; I^2: 0%; p: 0.709], 7.79 cm (95% CI: 2.27 to 13.31) [N = 5; I2: 84.3%; P<0.001], 0.03 (95% CI: 0.02 to 0.04) [N = 9; I2: 89.2%; P<0.001], and 7.74 cm (95% CI: 0.11 to 1.36) [N = 4; I^2: 95.8%; P<0.001].

Conclusion

Increased neck circumference, waist circumference, hip circumference, arm circumference, waist to hip ratio, visceral fat depth, subcutaneous fat depth, and short stature increased the possibility of developing gestational diabetes. These indices can accurately, cost-effectively, and affordably assess the occurrence of gestational diabetes, thus preventing many consequences with early detection of gestational diabetes.

Citation: Rahnemaei FA, Abdi F, Pakzad R, Sharami SH, Mokhtari F, Kazemian E (2022) Association of body composition in early pregnancy with gestational diabetes mellitus: A meta-analysis. PLoS ONE 17(8): e0271068. https://doi.org/10.1371/journal.pone.0271068

Editor: Rajakumar Anbazhagan, National Institute of Child Health and Human Development (NICHD), NIH, UNITED STATES

Received: March 16, 2022; Accepted: June 22, 2022; Published: August 15, 2022

Copyright: © 2022 Rahnemaei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This study was funded by Alborz University of Medical Sciences and the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: GDM, gestational diabetes mellitus; GWG, gestational weight gain; ICD9, International Classification of Diseases, 9th Revision-Clinical Modification; H, height; WGDP, weight gained during pregnancy; HOMA-IR, homeostasis model assessment insulin resistance; WHR, Waist/Hip Ratio; QUICKI, quantitative insulin sensitivity check index; VAD, Visceral Adipose Tissue Depth; BMI, Body Mass Index; VFM, visceral fat mass; PBF, percentage body fat; IR, insulin resistance; WC, waist circumference; SAT, subcutaneous tissues thickness; TAT, total adipose tissues thickness; VAT, visceral tissues thickness; ASFT, abdominal subcutaneous fat thickness; FBG, fasting blood glucose; NC, Neck circumference; ISI, insulin sensitivity index; ASM, appendicular skeletal muscle mass; FM, fat mass; HbA1c, glycosylated hemoglobin A1c; SFT, subcutaneous fat thickness; IADPSG, International Association of Diabetes and Pregnancy Study Groups; FMP, fat mass percentage; SMMP, skeletal muscle mass percentage; FMI, Fat mass index; BFI, Body Fat Index = (pre-peritoneal fat x subcutaneous fat/height); FFM, fat free mass; MM, muscular mass; PP, Pre pregnancy; PPBMI, Pre pregnancy BMI; ADA, American Diabetes Association; WHO, World health Organization; ACOG, American College of Obstetricians and Gynecologists; AC, arm circumference; MUAC, mid upper arm circumference; NS, Not Significant

Introduction

Gestational diabetes mellitus (GDM) is defined as carbohydrate intolerance with varying degrees that is first diagnosed in pregnancy [1]. GDM usually begins in the second half of pregnancy when the mother is unable to secrete enough insulin to compensate for the nutritional increase in pregnancy and the possible increase in fat and anti-insulin hormones that occur during pregnancy (such as human placental hormone, cortisol, and prolactin) [2]. GDM has many maternal and fetal consequences that can be both short-term and long-term [3].

Several risk factors increase GDM, including aging, GDM history, body mass index (BMI) greater than 30 kg/m2, family history of diabetes, history of a macrosomic infant weighing 4.5 kg, and race [4]. Other maternal complications include shoulder dystocia, preeclampsia, cesarean section, type-2 diabetes, metabolic syndrome, and cardiovascular disease [5–7]. Neonatal complications also include macrosomia, neonatal trauma, hypoglycemia, and other metabolic disorders of the neonatal period [8, 9].

Many maternal and neonatal complications can be improved by careful monitoring of blood glucose during pregnancy, medical treatments (insulin and metformin), diet, physical activity, and lifestyle changes [10, 11].

In 2010, the International Association of Diabetes and Pregnancy Study Groups (IADPSG) developed new diagnostic criteria for GDM, based for the first time on adverse pregnancy outcomes [12]. In 2013, the World Health Organization (WHO) defined the IADPSG criteria adjusted during the 75 g OGTT threshold to 1.75 times the odds ratio for adverse pregnancy outcomes by reducing fasting glucose concentrations by 5.1 ≥, 1-h ≥ 10, and/or 2-h ≥ 8.5 mmol per liter [13].

The global prevalence of gestational diabetes is estimated 1 to 28%; this difference is due to differences in the criteria for measuring GDM, age, race, ethnicity, lifestyle, and history of the populations in which the prevalence was measured [14–16].

Normal pregnancy is characterized by a physiological reduction of 50–60% in insulin sensitivity [17]. Studies have reported that the likelihood of GDM increases with maternal weight gain, especially in early pregnancy. Numerous studies have been conducted worldwide to identify effective risk predictors to support early prevention or treatment [18, 19].

Measurement of body composition seems to be a practical method for potential screening of GDM [20]. Body composition is a risk factor for conditions such as diabetes, preeclampsia, and gestational hypertension [21, 22]. Obesity is a powerful predictor of GDM, and abdominal obesity is a powerful factor in the development of GDM and future diabetes [23, 24]. However, obesity is a complex process in which the distribution of body fat is involved, and body fat leads to adverse metabolic and cardiovascular consequences [25]. Studies show that increasing body composition, especially body fat, is closely related to glucose metabolism in humans [26]. But data on body composition and anthropometric indices are low. Studies show that weight gain in the first 2–3 months is composed of more fat mass, and patients with higher BMI gain more fat mass [15, 16] which can affect subsequent maternal insulin resistance [27].

However, there are other anthropometric indices that have been considered recently. In addition to showing more accurate information about body composition, they can also predict pregnancy outcomes, including GDM in pregnant women. For example, measurement of visceral abdominal adipose tissue (VAT) [28], neck circumference (NC), hip circumference (HC) and waist circumference (WC) [29], percentage of skeletal muscle mass and percentage of fat mass [30], and central obesity [31] can be used as an approach to predict occurrence GDM. Previous meta-analysis studies have shown a direct relationship with indices of general body obesity including WC, waist to hip ratio (WHR), and VAT with GDM [32].

In this study, according to the time period searched (1985–2020), a small number of studies were analyzed; in addition, a small number of anthropometric indices indicating the body composition were examined. Therefore, the present study was performed by reviewing the updated studies and all anthropometric indices expressed in the studies and using an accurate model in the early stages of pregnancy in a systematic review and meta-analysis to investigate the relationship between anthropometric indices expressing body composition and GDM.

Materials and methods

This study was approved by Alborz University of Medical Sciences (ethnical code: IR.ABZUMS.REC.1400.241). Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were observed in the report of the study. PRISMA contains 27 items related to the content of a systematic and meta-analysis, and includes abstracts, methods, results, discussions, and financial resource [33, 34]. Participant consent for this study is not applicable. This study was registered on PROSPERO website by "CRD42022302813" ID.

Search strategy

PubMed, Web of Science, Scopus, Google Scholar, and ProQuest were searched from 2010 to December 2021. MESH keywords and search strategy were as below:

  1. ’Gestational diabetes’ [tiab], OR ’GD’ [tiab], OR ’Gestational Diabetes Mellitus’ [tiab], OR ’GDM’ [tiab], OR ’pregnancy induced diabetes’[tiab]
  2. ’Anthropometric indicators’ [tiab], ’Anthropometric indices’ [tiab], OR ’body size’[tiab], OR ’body composition’ [tiab] OR, ’Waist/Hip Ratio’ [tiab], OR ’WHR’ [tiab], OR ’ visceral fat mass’ [tiab], OR ’VFM’ [tiab], OR ’ Neck circumference’ [tiab], OR ’hip circumference’ [tiab], OR ’ waist circumference’ [tiab], OR ’ subcutaneous adipose tissue’ [tiab], OR ’ skeletal muscle mass percentage’ [tiab], ’total adipose tissue thickness’ [tiab], OR ’subcutaneous adipose tissue’[tiab], OR ’Subcutaneous fat thickness’ [tiab], OR ’visceral adipose tissue depth’ [tiab], OR ’skinfold thickness’ [tiab], OR ’mid upper arm circumference’ [tiab], OR ’subcutaneous fat thickness’ [tiab], OR ’fat mass percentage’ [tiab], OR ’fat mass index’ [tiab], OR ’muscle mass percentage’ [tiab], OR ’Skinfold Thickness’ [tiab]
  3. ’Pregnancy’ [tiab], OR ’Pregnancies’ [tiab], OR ’Gestation’[tiab], OR ’early pregnancy’ [tiab]
  4. #1 AND #2
  5. #1 AND #2 AND #3

Eligibility criteria

Inclusion and exclusion criteria.

We set our inclusion and exclusion criteria based on PICO criteria (population, intervention, comparison, outcome, and study design) (Table 1).

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Table 1. PICO criteria.

https://doi.org/10.1371/journal.pone.0271068.t001

Study selection

The initial search yielded 3523 results. The eligibility of these articles was independently evaluated by two authors, and disagreements were resolved by consensus. In the first stage, 2108 irrelevant or duplicate articles were excluded. After reviewing the titles and abstracts of the remaining articles, 918 more papers were excluded. In the evaluation of the full texts, 139 ineligible articles were excluded out of the remaining 180 articles. Finally, a total of 41 eligible articles were reviewed and 29 articles meets criteria to meta-analysis (Fig 1).

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Fig 1. PRISMA flowchart of selected studies.

https://doi.org/10.1371/journal.pone.0271068.g001

Quality assessment

Newcastle Ottawa scale (NOS) was used to measure the quality of studies. This scale is used to measure the quality of cohort and case control studies. The validity and reliability of this tool have been proven in various studies [35, 36].

Data extraction

Two authors independently performed the study selection and validity assessment and resolved any disagreements by consulting a third researcher. Author, year, study design, geographic region, maternal age, diagnostic criteria of GDM, anthropometric indices, accompanying factors, results, and quality assessment scores were extracted from articles.

Statistical analysis

All analyses were conducted with STATA 14.0 (College Station, Texas). For each study, mean value and standard deviation (SD) of anthropometric indices were extracted; if IQR was reported, we changed it to SD with IQR/1.35. Then, the mean difference (MD) of anthropometric indices was calculated between GDM and non-GDM group for each study. Then, standard error (SE) of MD was calculated for each study using the following formula:

Where, , n1, , and n2 are variance values, and samples size in GDM and control groups, respectively. Then, pooled MD was calculated by “Metan” command [37]. Heterogeneity was determined using Cochran’s Q test of heterogeneity, and the I 2 index was used to quantify heterogeneity. In accordance with Higgins classification approach, I 2 values above 0.7 were considered as having high heterogeneity. To estimate the pooled MD for anthropometric indices, the fixed-effect model was used; when heterogeneity was greater than 0.7, the random effects model was used. The meta-regression analysis was used to examine the effect of publication year, age, sample size, and study design as factors affecting heterogeneity among studies. The “meta bias” command [38] was used to check for publication bias, and if there was any publication bias, the pooled MD was adjusted with the “meta trim” command using the trim-and-fill method [39]. In all analyses, significance level was considered 0.05.

Results

Twenty-nine studies with a sample size of 56,438 met the meta-analysis inclusion criteria (Table 1). Fig 1 shows the flowchart of the study selection process. Anthropometric indices values for the groups with and without GDM of included studies are given in Table 5.

Pooled MD of anthropometric indices

Table 2 shows the pooled MD of all anthropometric indices. As shown in Table 2, twelve studies were carried out for waist circumference, five studies for neck and hip circumference, nine studies for waist hip ratio and height, six studies for subcutaneous adipose tissues, four studies for visceral adipose tissue depth, three studies for mid upper arm circumference, two studies for fat mass index and skeletal muscle mass percentage, and one study for other indices. Fig 2 shows the pooled MD of waist circumference for included studies. The lowest and highest MDs were reported by Kansu-Celik et al. [40] in Turkey (MD: -1.67; 95% CI: -11.30 to 7.96) and Aydin et al. [41] in Turkey (MD: 13.10; 95% CI: 6.13 to 20.07). Based on random effects model, the pooled MD for waist circumference was 6.83 cm (95% CI: 5.37 to 8.30). In other words, the mean values of waist circumference in people with GDM were higher than that in non-GDM people. Forest plot of other anthropometric indices was provided in supplements 1 to 21, and pooled MD is shown in Table 2 and Fig 3. Pooled MD of neck circumference, hip circumference, waist hip ratio, and visceral adipose tissue depth was 1.00 cm (95% CI: 0.79 to 1.20) [N = 5; I^2: 0%; p: 0.709]; 7.79 cm (95% CI: 2.27 to 13.31) [N = 5; I^2: 84.3%; P<0.001]; 0.03 (95% CI: 0.02 to 0.04) [N = 9; I^2: 89.2%; p<0.001] and 7.74 cm (95% CI: 0.11 to 1.36) [N = 4; I^2: 95.8%; P<0.001], respectively, which indicates that the average of these indices was higher in the GDM group. An adverse pattern was observed for the height and skeletal muscle mass percentage, which pooled MD for the height, and skeletal muscle mass percentage was -0.24 cm (95% CI: -0.37 to -0.10) [N = 9; I^2: 0%; p:0.975]; and -2.11 (95% CI: -3.61 to -0.61) [N = 2; I^2: 83.2%; p:0.015], respectively, which indicates that the average of these indices was higher in the non-GDM group. In other words, in general, people with non-GDM had a mean height and skeletal muscle mass percentage higher than GDM people. Although pooled MD was higher for subcutaneous adipose tissues in the GDM group, this difference was not significant (2.15 [95% CI: -1.66 to 5.96]). The pooled MD of other indices are given in Table 2 and Fig 3.

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Fig 2. Forest plot for MD of waist circumference (cm) between GMD and non-GDM group based on a random effects model.

Each study is distinguished by its author (year) and countries. Each line segment’s midpoint shows the MD estimate; the length of line segment indicates 95% confidence interval (CI) in each study, and the diamond mark illustrates the pooled estimate of MD.

https://doi.org/10.1371/journal.pone.0271068.g002

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Fig 3. Pooled MD and 95% confidence interval of anthropometric index.

The diamond mark illustrates the pooled MD, and the length of the diamond indicates 95% CI.

https://doi.org/10.1371/journal.pone.0271068.g003

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Table 2. Pooled MD (95% confidence interval) and heterogeneity of anthropometric indices.

https://doi.org/10.1371/journal.pone.0271068.t002

Heterogeneity and meta-regression results

Table 2 shows significant heterogeneity between different studies for waist circumference, hip circumference, waist/hip ratio, visceral adipose tissue depth, subcutaneous adipose tissue (Cochran’s Q test P-value < 0.001 for all lipid profiles) so that the I2 index was above 70% for all mentioned indices. Table 3 shows the meta-regression results to investigate the effect of publication year, age, sample size, and study design on heterogeneity between studies. Accordingly, none of the variables had a significant role on heterogeneity between studies (P>0.05 for all). Fig 4 shows the result of meta-regression for association between pooled MD of waist circumference with age (A) and publication year (B).

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Fig 4.

Association between pooled mean difference (MD) of waist circumference with age (A) and publication year (B) by means of meta regression. The size of circles indicates the precision of each study. There is no significant association with respect to the pooled MD of waist circumference with age publication year.

https://doi.org/10.1371/journal.pone.0271068.g004

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Table 3. Results of the univariate meta-regression analysis on the heterogeneity of the determinant.

https://doi.org/10.1371/journal.pone.0271068.t003

Table 4 shows the publication bias results based on the Egger’s test and the fill and trim method. There was a significant publication bias for waist circumference (coefficient: 1.95; P: 0.019) and hip circumference (coefficient: 3.06; P: 0.028). According to the fill and trim method, the value of adjusted pooled MD for waist circumference and hip circumference was 5.35 (95% CI: 3.81–6.88) and 7.80 (95% CI: 2.76–13.31), which was not significantly different from the pooled MD calculated for waist circumference (6.83 [95% CI: 5.37–8.30]) and hip circumference (7.79 [95% CI: 2.27–13.31]). In other words, the publication bias had no cosiderable effect on the result of meta analysis. No publication bias was observed for other anthropometric indices including neck circumference, waist/hip ratio, height, visceral adipose tissue depth, and subcutaneous adipose tissue. Details of the studies are listed in Table 5.

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Table 4. Result of publication bias for anthropometric indices and fill and trim method result of adjusting publication bias.

https://doi.org/10.1371/journal.pone.0271068.t004

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Table 5. Details of studies included in the systematic review.

https://doi.org/10.1371/journal.pone.0271068.t005

Discussion

The current study set to investigate the relationship between body composition and GDM as a systematic review and meta-analysis. The results indicate that anthropometric indices such as WC, NC, HC, WHR, VAT, SAT, Height, and MUAC are associated with GDM; an increase in the indices of WC, NC, HC, WHR, VAT, SAT, and MUAC increase developing GDM, also short stature increases the susceptibility to GDM.

We investigated that VAT and SAT are associated with GDM. Alwash et al.(2021) found that all three obesity phenotypes were significantly associated with the risk of developing GDM. In addition, visceral obesity was a stronger risk factor for GDM than other obesity phenotypes [32]. Yao et al.(2020) also stated that the risk of GDM is associated with maternal central obesity in early pregnancy [77]. In the case of central and visceral body fats, Benevides et al.(2020) reported that the cut-off point for subcutaneous, visceral, and total abdominal fat to predict GDM varied between studies in the first and second trimesters of pregnancy. No study confirmed a model for predicting GDM using subcutaneous and visceral fat measurements [78].

De Souza et al.(2015) determined the relationship between SAT depth, TAT depth, and VAT depth in the first trimester of pregnancy and the occurrence of GDM in mid-pregnancy. It was observed that increasing the depth of VAT and TAT independently of BMI could predict the risk of dysglycemia in later stages of pregnancy [69]. Similarly, Balani et al. (2018) showed that visceral adipose mass in obese women can be a predictor of GDM [57]. Increased VAT depth, but not SAT depth, was associated with an increased risk of GDM after adjusting for confounding factors. VAT depth ≥ 4.27 cm is more sensitive compared to the National Institute of Health and Care Excellence criteria and similar feature for the diagnosis of GDM [79]. In addition, Alves et al.(2020) observed an increase in VAT depth in sonographic measurements in early pregnancy; GDM was associated with a higher risk [28]. One of the strengths of the present study is the assessment of most indices of body composition and their relationship with GDM and the large number of up-to-date studies that lead to the investigation of more samples.

The results of the present study also showed WC, HC and WHR are associated with GDM. Various studies have shown an association between WC and WHR-based central obesity around the hip with the occurrence of GDM [31]. However, the data are also contradictory; for example, Basraon et al.(2016) showed that WHR could not replace BMI as a risk factor in pregnancy for GDM [67]. But, Yao et al.(2020) in his subgroup analysis showed that higher levels of central maternal obesity in the first stage have a similar risk of GDM in the first and second trimesters of pregnancy [77]. However, Tornaghi et al.(1994) provided evidence of the superiority of maternal central obesity regarding mid-pregnancy (18–22 weeks) in identifying obesity-related complications in pregnancy. In other words, the factors expressing central obesity in the mother’s body can better predict the risk of GDM than BMI [80]. Central obesity is expressed as a risk factor for insulin resistance associated with deposition and abnormal fat function. WC as one of the indices of central obesity leads to an increased risk of GDM. Multivariate regression analysis with consideration of other risk factors showed that WC ≥ 80 cm could not predict the risk of GDM. However, Ebrahimi-Mameghani et al.(2013) concluded that WC≥88 cm is a significant predictor of GDM (OR: 3.77) [41, 75]. Han et al. (2018) also observed that the risk of GDM increases with WC≥78.5 cm increase [75]. WC at gestation weeks 20–24, pre-pregnancy BMI, and gestational BMI can predict the occurrence of GDM. WC 100 cm with 84% sensitivity and 70% specificity predicts GDM risk [50]. Although other studies have shown that at gestation weeks 20–24, WC: 85.5–88.5 cm was the optimal cut-off point for GDM prediction (Sens/Spec balance between 87.1/41.1% and 77.4/56.9%) [73].

Kansu-Celik et al.(2018) observed a significant relationship between 50g GCT and WC, and SAT thickness. He showed that SAT predicts thickness greater than 16.75 mm GDM with a sensitivity of 71.7% and a specificity of 87.6% [40]. In adults, WHR is independently associated with complications after relative weight adjustment, i.e. the use of relative weight and body shape at the same time provides a better estimate of the risk of disease than either alone [81]. In women with WHR<0.85, one or more risk factors increased the risk of GDM by 1.99 times, and in women with WHR≥0.85 but without fixed risk factors, the risk of GDM increased by 2.41 times, and in women with fixed risk factors, it increased by 6.22 times. Similar but weak results were observed for WC≥88 cm [31].

We have shown that increased NC also leads to GDM. Hancerliogullari et al.(2020) also stated that NC in women with GDM are significantly higher [29] and NC is assumed to be a better marker than WC for determining metabolic syndrome and its key features. It is also easy to measure and it is replicable [82, 83]. Barforoush et al.(2021) also stated that NC more than 34.3 cm in Iranian women could predict GDM [46].

In this study we reported that short stature increases the susceptibility to GDM. Height in adulthood is an indices of genetic, early and childhood factors and their interactions. Although the biological mechanism associated with adult height and GDM is unknown, several pathways have been suggested. For example, malnutrition of the fetus may lead to low birth weight, which is associated with shorter height in adulthood, and may also be associated with metabolic disorders in adulthood. Height has different variations in different populations [84, 85]. In an analysis of 135861 pregnant women, height was found to be inversely related to the occurrence of GDM. Of course, this relationship can also vary between different races [86].

Body composition in pregnancy has a dynamic process; for example, changes in weight gain and free body adipose mass during pregnancy are clearly observed [87].

Measuring maternal body composition during pregnancy is challenged by existing in-vivo measurement methods that cannot distinguish between maternal and fetal reserves [88] and look at the mother and fetus as a whole. In addition, some pregnancy-induced changes in body composition violate the assumptions that underlie many commonly available measurement methods and require special pregnancy modifications (which often vary at different gestational ages) [89].

The composition of the mother’s body changes during pregnancy to support optimal fetal growth. In the first few months of pregnancy, changes in the composition of the mother’s body indicate the readiness of the female body for fetal growth. Especially, the uterine and breast tissue that makes up the mother unit grows and the blood volume increases. In late pregnancy, more pronounced growth of the embryonic unit (including the fetus, amniotic fluid, and placenta) occurs along with the continued growth of maternal tissue and further increase in blood volume. At the time of delivery, the fetal unit accounts for approximately one-third of the total GWG [90].

Accordingly, central obesity is associated with more obesity-related complications [91]. In contrast, peripheral obesity has been suggested to eliminate or even protect against some of the risks associated with obesity [92]. CT, MRI, body densitometry, or WHR are better indices of central obesity than BMI but are impractical as screening tools in pregnancy. SAT measurement can be used as an alternative measure of central obesity [93] as it is associated with a wide range of cardiovascular and metabolic risk factors. SAT can be easily and accurately measured by ultrasound [94]. BMI can also be potentially useful as a direct and inexpensive method for assessing central fat distribution [95]. In adults, BMI can predict outcomes such as type-2 diabetes and hypertension[81]. Although a sufficient number of studies examining the relationship between BMI and GDM have been performed in the past [96, 97].

Conclusion

Body composition indices such as WC, HC, WHR, AC, VAT, SAT, and height can relate more effectively and accurately to GDM. These available anthropometric indices can be used as a tool to assess the occurrence of GDM in an accessible, cost-effective, and high-precision manner.

Limitation

One of the limitations of the study is the difference in the critical values of the criteria used to diagnose GDM, which may affect the decision on the absence or occurrence of GDM based on different indices. In addition, studies conducted in different populations and races, which is a determining factor in body composition and can affect both body composition and the occurrence of GDM, have not been considered in the present study. Also, the small number of studies performed on some anthropometric indices is another limitation of the study, which makes it difficult to draw conclusions about such indices.

Supporting information

S1 File. Forest plot of different anthropometric indices between GMD and non-GDM group.

https://doi.org/10.1371/journal.pone.0271068.s001

(DOC)

S1 Checklist. PRISMA-2009-checklist.

https://doi.org/10.1371/journal.pone.0271068.s002

(DOC)

References

  1. 1. McIntyre HD, Catalano P, Zhang C, Desoye G, Mathiesen ER, Damm P. Gestational diabetes mellitus. Nature reviews Disease primers. 2019;5(1):1–19.
  2. 2. Afkhami M, Rashidi M. Gestational diabetes mellitus. Hormozgan Medical Journal. 2007;11(1):1–12.
  3. 3. Wang H, Li N, Chivese T, Werfalli M, Sun H, Yuen L, et al. IDF diabetes atlas: estimation of global and regional gestational diabetes mellitus prevalence for 2021 by International Association of Diabetes in Pregnancy Study Group’s Criteria. Diabetes research and clinical practice. 2022;183:109050. pmid:34883186
  4. 4. Ali AD, Mehrass AA-KO, Al-Adhroey AH, Al-Shammakh AA, Amran AA. Prevalence and risk factors of gestational diabetes mellitus in Yemen. International journal of women’s health. 2016;8:35. pmid:26869814
  5. 5. Bellamy L, Casas J-P, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. The Lancet. 2009;373(9677):1773–9. pmid:19465232
  6. 6. Fadl H, Magnuson A, Östlund I, Montgomery S, Hanson U, Schwarcz E. Gestational diabetes mellitus and later cardiovascular disease: a S wedish population based case–control study. BJOG: An International Journal of Obstetrics & Gynaecology. 2014;121(12):1530–6.
  7. 7. Bener A, Saleh NM, Al-Hamaq A. Prevalence of gestational diabetes and associated maternal and neonatal complications in a fast-developing community: global comparisons. International journal of women’s health. 2011;3:367. pmid:22140323
  8. 8. Domanski G, Lange AE, Ittermann T, Allenberg H, Spoo RA, Zygmunt M, et al. Evaluation of neonatal and maternal morbidity in mothers with gestational diabetes: a population-based study. BMC pregnancy and childbirth. 2018;18(1):1–11.
  9. 9. Prakash GT, Das AK, Habeebullah S, Bhat V, Shamanna SB. Maternal and neonatal outcome in mothers with gestational diabetes mellitus. Indian journal of endocrinology and metabolism. 2017;21(6):854. pmid:29285448
  10. 10. Crowther CA, Hiller JE, Moss JR, McPhee AJ, Jeffries WS, Robinson JS. Effect of treatment of gestational diabetes mellitus on pregnancy outcomes. New England journal of medicine. 2005;352(24):2477–86. pmid:15951574
  11. 11. Au CP, Raynes-Greenow CH, Turner RM, Carberry AE, Jeffery HE. Body composition is normal in term infants born to mothers with well-controlled gestational diabetes mellitus. Diabetes care. 2013;36(3):562–4. pmid:23223404
  12. 12. Diabetes IAo Panel PSGC. International association of diabetes and pregnancy study groups recommendations on the diagnosis and classification of hyperglycemia in pregnancy. Diabetes care. 2010;33(3):676–82. pmid:20190296
  13. 13. Nallaperumal S, Bhavadharini B, Mahalakshmi MM, Maheswari K, Jalaja R, Moses A, et al. Comparison of the world health organization and the International association of diabetes and pregnancy study groups criteria in diagnosing gestational diabetes mellitus in South Indians. Indian Journal of Endocrinology and Metabolism. 2013;17(5):906. pmid:24083175
  14. 14. Behboudi-Gandevani S, Amiri M, Bidhendi Yarandi R, Ramezani Tehrani F. The impact of diagnostic criteria for gestational diabetes on its prevalence: a systematic review and meta-analysis. Diabetology & metabolic syndrome. 2019;11(1):1–18. pmid:30733833
  15. 15. Hunt KJ, Schuller KL. The increasing prevalence of diabetes in pregnancy. Obstetrics and gynecology clinics of North America. 2007;34(2):173–99. pmid:17572266
  16. 16. Zhang C, Ning Y. Effect of dietary and lifestyle factors on the risk of gestational diabetes: review of epidemiologic evidence. The American journal of clinical nutrition. 2011;94(suppl_6):1975S-9S. pmid:21613563
  17. 17. Sorbye L, Skjaerven R, Klungsoyr K, Morken N-H. Gestational diabetes mellitus and interpregnancy weight change: A population-based cohort study. PLoS medicine. 2017;14(8):e1002367. pmid:28763446
  18. 18. Hao M, Lin L. Fasting plasma glucose and body mass index during the first trimester of pregnancy as predictors of gestational diabetes mellitus in a Chinese population. Endocrine journal. 2017:EJ16-0359. pmid:28420856
  19. 19. Farina A, Eklund E, Bernabini D, Paladino M, Righetti F, Monti G, et al. A first-trimester biomarker panel for predicting the development of gestational diabetes. Reproductive sciences. 2017;24(6):954–9. pmid:27837083
  20. 20. Tatsukawa Y, Misumi M, Kim YM, Yamada M, Ohishi W, Fujiwara S, et al. Body composition and development of diabetes: a 15-year follow-up study in a Japanese population. European journal of clinical nutrition. 2018;72(3):374–80. pmid:29362458
  21. 21. Shao J-t Qi J-q. The relationship between body adiposity index and pregnancy-induced hypertension in third-trimester pregnant women. Blood pressure monitoring. 2017;22(5):279–81. pmid:28591007
  22. 22. Staelens AS, Vonck S, Molenberghs G, Malbrain ML, Gyselaers W. Maternal body fluid composition in uncomplicated pregnancies and preeclampsia: a bioelectrical impedance analysis. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2016;204:69–73. pmid:27525683
  23. 23. Neeland IJ, Turer AT, Ayers CR, Powell-Wiley TM, Vega GL, Farzaneh-Far R, et al. Dysfunctional adiposity and the risk of prediabetes and type 2 diabetes in obese adults. Jama. 2012;308(11):1150–9. pmid:22990274
  24. 24. Freemantle N, Holmes Ja, Hockey A, Kumar S. How strong is the association between abdominal obesity and the incidence of type 2 diabetes? International journal of clinical practice. 2008;62(9):1391–6. pmid:18557792
  25. 25. Després J-P. Body fat distribution and risk of cardiovascular disease: an update. Circulation. 2012;126(10):1301–13. pmid:22949540
  26. 26. PEIRIS AN, STRUVE MF, MUELLER RA, LEE MB, KISSEBAH AH. Glucose metabolism in obesity: influence of body fat distribution. The Journal of Clinical Endocrinology & Metabolism. 1988;67(4):760–7. pmid:3047162
  27. 27. Vidanalage CK, Senarth U, Silva K, Lekamge U, Liyanage I. Effects of initial body mass index on development of gestational diabetes in a rural Sri Lankan population: A case-control study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2016;10(2):S110–S3.
  28. 28. Alves JG, Souza ASR, Figueiroa JN, de Araújo CAL, Guimarães A, Ray JG. Visceral adipose tissue depth in early pregnancy and gestational diabetes mellitus-a cohort study. Scientific reports. 2020;10(1):1–4.
  29. 29. Hancerliogullari N, Kansu-Celik H, Asli Oskovi-Kaplan Z, Kisa B, Engin-Ustun Y, Ozgu-Erdinc AS. Optimal maternal neck and waist circumference cutoff values for prediction of gestational diabetes mellitus at the first trimester in Turkish population; a prospective cohort study. Gynecological Endocrinology. 2020:1–4. pmid:32274939
  30. 30. Liu Y, Liu J, Gao Y, Zheng D, Pan W, Nie M, et al. The body composition in early pregnancy is associated with the risk of development of gestational diabetes mellitus late during the second trimester. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy. 2020;13:2367. pmid:32753921
  31. 31. Zhu Y, Hedderson MM, Quesenberry CP, Feng J, Ferrara A. Central Obesity Increases the Risk of Gestational Diabetes Partially Through Increasing Insulin Resistance. Obesity (Silver Spring, Md). 2019;27(1):152–60. pmid:30461219
  32. 32. Alwash SM, McIntyre HD, Mamun A. The association of general obesity, central obesity and visceral body fat with the risk of gestational diabetes mellitus: Evidence from a systematic review and meta-analysis. Obesity Research & Clinical Practice. 2021;15(5):425–30.
  33. 33. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. International Journal of Surgery. 2021;88:105906. pmid:33789826
  34. 34. Abdi F, Roozbeh N. The effects of Humulus lupulus L.) hops) on menopausal vasomotor symptoms: a systematic review and meta-analysis. The Iranian Journal of Obstetrics, Gynecology and Infertility. 2016;19(26):9–17.
  35. 35. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. European journal of epidemiology. 2010;25(9):603–5. pmid:20652370
  36. 36. Sedgh G, Bearak J, Singh S, Bankole A, Popinchalk A, Ganatra B, et al. Abortion incidence between 1990 and 2014: global, regional, and subregional levels and trends. The Lancet. 2016;388(10041):258–67.
  37. 37. Rahnemaei FA, Pakzad R, Amirian A, Pakzad I, Abdi F. Effect of gestational diabetes mellitus on lipid profile: A systematic review and meta-analysis. Open Medicine. 2022;17(1):70–86. pmid:34993347
  38. 38. Soltani S, Tabibzadeh A, Zakeri A, Zakeri AM, Latifi T, Shabani M, et al. COVID-19 associated central nervous system manifestations, mental and neurological symptoms: a systematic review and meta-analysis. Reviews in the Neurosciences. 2021. pmid:33618441
  39. 39. Hallajzadeh J, Khoramdad M, Izadi N, Karamzad N, Almasi-Hashiani A, Ayubi E, et al. The association between metabolic syndrome and its components with systemic lupus erythematosus: a comprehensive systematic review and meta-analysis of observational studies. Lupus. 2018;27(6):899–912. pmid:29301471
  40. 40. Kansu-Celik H, Karakaya BK, Tasci Y, Hancerliogullari N, Yaman S, Ozel S, et al. Relationship maternal subcutaneous adipose tissue thickness and development of gestational diabetes mellitus. Interventional medicine & applied science. 2018;10(1):13–8. pmid:30363336
  41. 41. Aydın GA, Özsoy HG, Akdur PÖ, Özgen G. The predictive value of first‐trimester anthropometric and ultrasonographic adipose tissue measurements in gestational diabetes mellitus. Journal of Obstetrics and Gynaecology Research. 2021. pmid:34137118
  42. 42. Budak MS, Kahramanoglu I, Vitale SG, Akgol S, Dilek ME, Kartal S, et al. Maternal abdominal subcutaneous fat thickness as a simple predictor for gestational diabetes mellitus. Journal of perinatal medicine. 2019;47(6):605–10. pmid:31141488
  43. 43. Jitngamsujarit S, Pittyanont S, Thamrongwuttikul C. Waist Circumference at 18 weeks of gestation as a Predictor for Gestational Diabetes Mellitus in Women with Normal Pre-pregnancy Body Mass Index. Thai Journal of Obstetrics and Gynaecology. 2021:177–87.
  44. 44. Saif Elnasr I, Ammar H. Ultrasound markers for prediction of gestational diabetes mellitus in early pregnancy in Egyptian women: observational study. The Journal of Maternal-Fetal & Neonatal Medicine. 2021;34(19):3120–6. pmid:32138572
  45. 45. Cremona A , O’Gorman CS, Ismail KI, Hayes K, Donnelly AE, Hamilton J, et al. A risk-prediction model using parameters of maternal body composition to identify gestational diabetes mellitus in early pregnancy. Clinical Nutrition ESPEN. 2021;45:312–21. pmid:34620334
  46. 46. Barforoush TS, Ghadimi R, Pahlevan Z, Ahmadi N, Delavar MA. The relationship between neck circumference and gestational diabetes mellitus in Iranian women. Clinical Diabetes and Endocrinology. 2021;7(1):1–7.
  47. 47. Zhang R-Y, Wang L, Zhou W, Zhong Q-M, Tong C, Zhang T, et al. Measuring maternal body composition by biomedical impedance can predict risk for gestational diabetes mellitus: a retrospective study among 22,223 women. The Journal of Maternal-Fetal & Neonatal Medicine. 2020:1–8. pmid:32722949
  48. 48. Rocha AdS Bernardi JR, Matos S Kretzer DC, Schöffel AC Goldani MZ, et al. Maternal visceral adipose tissue during the first half of pregnancy predicts gestational diabetes at the time of delivery–a cohort study. PLoS One. 2020;15(4):e0232155. pmid:32353068
  49. 49. Thaware PK, Patterson CC, Young IS, Casey C, McCance DR. Clinical utility of ultrasonography-measured visceral adipose tissue depth as a tool in early pregnancy screening for gestational diabetes: a proof-of-concept study. Diabetic medicine: a journal of the British Diabetic Association. 2019;36(7):898–901. pmid:30672019
  50. 50. Takmaz T, Yalvaç ES, Özcan P, Çoban U, Karasu AFG, Ünsal M. The predictive value of weight gain and waist circumference for gestational diabetes mellitus. Turkish journal of obstetrics and gynecology. 2019;16(3):199. pmid:31673474
  51. 51. Kawanabe S, Nagai Y, Nakamura Y, Nishine A, Nakagawa T, Tanaka Y. Association of the muscle/fat mass ratio with insulin resistance in gestational diabetes mellitus. Endocrine journal. 2019;66(1):75–80. pmid:30393250
  52. 52. Marshall NE, Biel FM, Boone-Heinonen J, Dukhovny D, Caughey AB, Snowden JM. The Association between Maternal Height, Body Mass Index, and Perinatal Outcomes. American journal of perinatology. 2019;36(6):632–40. pmid:30292175
  53. 53. Ulubaşoğlu H, Bakay K, Özdemir AZ, Güven D, Batıoglu S. Can we use waist circumference in the first trimester to screen for gestational diabetes? Journal of Experimental and Clinical Medicine. 2019;36(1):9–12.
  54. 54. Wang Y, Luo BR. The association of body composition with the risk of gestational diabetes mellitus in Chinese pregnant women: A case-control study. Medicine. 2019;98(42):e17576. pmid:31626126
  55. 55. Nombo AP, Mwanri AW, Brouwer-Brolsma EM, Ramaiya KL, Feskens EJ. Gestational diabetes mellitus risk score: a practical tool to predict gestational diabetes mellitus risk in Tanzania. Diabetes research and clinical practice. 2018;145:130–7. pmid:29852237
  56. 56. Anafcheh M, Ahmadzadeh B, Albookordi M, Najafian M. Effect of blood group, height, and weight gain during pregnancy on gestational diabetes mellitus. The Iranian Journal of Obstetrics, Gynecology and Infertility. 2018;21(4):34–42.
  57. 57. Balani J, Hyer SL, Shehata H, Mohareb F. Visceral fat mass as a novel risk factor for predicting gestational diabetes in obese pregnant women. Obstetric medicine. 2018;11(3):121–5. pmid:30214477
  58. 58. Bourdages M, Demers M-É, Dubé S, Gasse C, Girard M, Boutin A, et al. First-Trimester Abdominal Adipose Tissue Thickness to Predict Gestational Diabetes. Journal of Obstetrics and Gynaecology Canada. 2018;40(7):883–7. pmid:29724492
  59. 59. KhushBakht D, Mazhar S, Bhalli A, Rashid A, Khan K, Jahanzaib U. Correlation Between Neck Circumference and Gestational Diabetes Mellitus and Associated Risk Factors During Pregnancy. Cureus. 2018;10(5):e2699. pmid:30062073
  60. 60. Nassr AA, Shazly SA, Trinidad MC, El-Nashar SA, Marroquin AM, Brost BC. Body fat index: A novel alternative to body mass index for prediction of gestational diabetes and hypertensive disorders in pregnancy. European Journal of Obstetrics & Gynecology and Reproductive Biology. 2018;228:243–8. pmid:30014931
  61. 61. D’Ambrosi F, Crovetto F, Colosi E, Fabietti I, Carbone F, Tassis B, et al. Maternal subcutaneous and visceral adipose ultrasound thickness in women with gestational diabetes mellitus at 24–28 weeks’ gestation. Fetal diagnosis and therapy. 2018;43(2):143–7. pmid:28624818
  62. 62. Han Q, Shao P, Leng J, Zhang C, Li W, Liu G, et al. Interactions between general and central obesity in predicting gestational diabetes mellitus in Chinese pregnant women: A prospective population‐based study in Tianjin, China: 单纯性肥胖和中心性肥胖在预测中国孕妇妊娠糖尿病中的交互作用: 一个中国天津前瞻性人群队列研究. Journal of diabetes. 2018;10(1):59–67. pmid:28383185
  63. 63. He F, He H, Liu WQ, Lin JY, Chen BJ, Lin YC, et al. Neck circumference might predict gestational diabetes mellitus in Han Chinese women: A nested case-control study. Journal of Diabetes Investigation. 2017;8(2):168–73. pmid:27589681
  64. 64. Li P, Lin S, Cui JH, Li L, Zhou SS, Fan JH. First-trimester neck circumference as a predictor of the development of gestational diabetes mellitus. Diabetes-Metabolism Research and Reviews. 2017;33. pmid:29406042
  65. 65. Yang SH, Kim C, An HS, An H, Lee JS. Prediction of Gestational Diabetes Mellitus in Pregnant Korean Women Based on Abdominal Subcutaneous Fat Thickness as Measured by Ultrasonography. Diabetes & metabolism journal. 2017;41(6):486–91. pmid:29199403
  66. 66. Alptekin H, Cizmecioglu A, Isik H, Cengiz T, Yildiz M, Iyisoy MS. Predicting gestational diabetes mellitus during the first trimester using anthropometric measurements and HOMA-IR. Journal of endocrinological investigation. 2016;39(5):577–83. pmid:26754418
  67. 67. Basraon SK, Mele L, Myatt L, Roberts JM, Hauth JC, Leveno KJ, et al. Relationship of early pregnancy waist-to-hip ratio versus body mass index with gestational diabetes mellitus and insulin resistance. American journal of perinatology. 2016;2(01):114–22. pmid:26352680
  68. 68. White SL, Lawlor DA, Briley AL, Godfrey KM, Nelson SM, Oteng-Ntim E, et al. Early antenatal prediction of gestational diabetes in obese women: Development of prediction tools for targeted intervention. PLoS ONE. 2016;11(12). pmid:27930697
  69. 69. De Souza LR, Berger H, Retnakaran R, Maguire JL, Nathens AB, Connelly PW, et al. First-trimester maternal abdominal adiposity predicts dysglycemia and gestational diabetes mellitus in midpregnancy. Diabetes Care. 2016;39(1):61–4. pmid:26525976
  70. 70. Kennedy NJ, Peek MJ, Quinton AE, Lanzarone V, Martin A, Benzie R, et al. Maternal abdominal subcutaneous fat thickness as a predictor for adverse pregnancy outcome: A longitudinal cohort study. BJOG: An International Journal of Obstetrics and Gynaecology. 2016;123(2):225–32. pmid:26840907
  71. 71. Sina M, Hoy WE, Callaway L, Wang Z. The associations of anthropometric measurements with subsequent gestational diabetes in Aboriginal women. Obesity research & clinical practice. 2015;9(5):499–506. pmid:25797102
  72. 72. Balani J, Hyer S, Johnson A, Shehata H. The importance of visceral fat mass in obese pregnant women and relation with pregnancy outcomes. Obstetric medicine. 2014;7(1):22–5. pmid:27512414
  73. 73. Bolognani CV, Reis L, de Souza SS, Dias A, Rudge MVC, Calderon IDP. Waist circumference in predicting gestational diabetes mellitus. Journal of Maternal-Fetal & Neonatal Medicine. 2014;27(9):943–8.
  74. 74. Gur EB, Ince O, Turan GA, Karadeniz M, Tatar S, Celik E, et al. Ultrasonographic visceral fat thickness in the first trimester can predict metabolic syndrome and gestational diabetes mellitus. Endocrine. 2014;47(2):478–84. pmid:24452873
  75. 75. Ebrahimi-Mameghani M, Mehrabi E, Kamalifard M, Yavarikia P. Correlation between body mass index and central adiposity with pregnancy complications in pregnant women. Health Promotion Perspectives. 2013;3(1):73. pmid:24688955
  76. 76. Suresh A, Liu A, Poulton A, Quinton A, Amer Z, Mongelli M, et al. Comparison of maternal abdominal subcutaneous fat thickness and body mass index as markers for pregnancy outcomes: A stratified cohort study. Australian and New Zealand Journal of Obstetrics and Gynaecology. 2012;52(5):420–6. pmid:23045985
  77. 77. Yao D, Chang Q, Wu Q-J, Gao S-Y, Zhao H, Liu Y-S, et al. Relationship between maternal central obesity and the risk of gestational diabetes mellitus: a systematic review and meta-analysis of cohort studies. Journal of diabetes research. 2020;2020. pmid:32337296
  78. 78. Benevides FT, Araujo Junior E, Maia CSC, Montenegro Junior RM, Carvalho FHC. Ultrasound evaluation of subcutaneous and visceral abdominal fat as a predictor of gestational diabetes mellitus: a systematic review. The Journal of Maternal-Fetal & Neonatal Medicine. 2020:1–11. pmid:32567410
  79. 79. Thaware P, Patterson C, Young I, Casey C, McCance D. Clinical utility of ultrasonography‐measured visceral adipose tissue depth as a tool in early pregnancy screening for gestational diabetes: a proof‐of‐concept study. Diabetic Medicine. 2019;36(7):898–901. pmid:30672019
  80. 80. Tornaghi G, Raiteri R, Pozzato C, Rispoli A, Bramani M, Cipolat M, et al. Anthropometric or ultrasonic measurements in assessment of visceral fat? A comparative study. International journal of obesity and related metabolic disorders: journal of the International Association for the Study of Obesity. 1994;18(11):771–5. pmid:7866479
  81. 81. Wells JCK, Fewtrell MS. Measuring body composition. Arch Dis Child. 2006;91(7):612–7. pmid:16790722
  82. 82. Ben-Noun LL, Laor A. Relationship between changes in neck circumference and cardiovascular risk factors. Experimental & Clinical Cardiology. 2006;11(1):14. pmid:18651013
  83. 83. Zhou J-y, Ge H, Zhu M-f, Wang L-j, Chen L, Tan Y-z, et al. Neck circumference as an independent predictive contributor to cardio-metabolic syndrome. Cardiovascular diabetology. 2013;12(1):1–7. pmid:23680280
  84. 84. Yeung EH, Hu FB, Solomon C, Chen L, Louis G, Schisterman E, et al. Life-course weight characteristics and the risk of gestational diabetes. Diabetologia. 2010;53(4):668–78. pmid:20043144
  85. 85. Rudra CB, Sorensen TK, Leisenring WM, Dashow E, Williams MA. Weight characteristics and height in relation to risk of gestational diabetes mellitus. American journal of epidemiology. 2007;165(3):302–8. pmid:17074967
  86. 86. Brite J, Shiroma E, Bowers K, Yeung E, Laughon S, Grewal J, et al. Height and the risk of gestational diabetes: variations by race/ethnicity. Diabetic medicine. 2014;31(3):332–40. pmid:24308574
  87. 87. Hutcheon JA, Platt RW, Abrams B, Himes KP, Simhan HN, Bodnar LM. A weight-gain-for-gestational-age z score chart for the assessment of maternal weight gain in pregnancy. The American of Clinical Nutrition. 2013;97(5):1062–7. pmid:23466397
  88. 88. Forbes GB. Human body composition: growth, aging, nutrition, and activity: Springer Science & Business Media; 2012.
  89. 89. Widen E, Gallagher D. Body composition changes in pregnancy: measurement, predictors and outcomes. European journal of clinical nutrition. 2014;68(6):643–52. pmid:24667754
  90. 90. Most J, Marlatt KL, Altazan AD, Redman LM. Advances in assessing body composition during pregnancy. European journal of clinical nutrition. 2018;72(5):645–56. pmid:29748651
  91. 91. Björntorp P. " Portal" adipose tissue as a generator of risk factors for cardiovascular disease and diabetes. Arteriosclerosis: An Official Journal of the American Heart Association, Inc. 1990;10(4):493–6. pmid:2196039
  92. 92. Hamdy O, Porramatikul S, Al-Ozairi E. Metabolic obesity: the paradox between visceral and subcutaneous fat. Current diabetes reviews. 2006;2(4):367–73. pmid:18220642
  93. 93. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu C-Y, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116(1):39–48. pmid:17576866
  94. 94. Ruhl CE, Everhart JE, Ding J, Goodpaster BH, Kanaya AM, Simonsick EM, et al. Serum leptin concentrations and body adipose measures in older black and white adults. The American journal of clinical nutrition. 2004;80(3):576–83. pmid:15321795
  95. 95. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child. 1995;73(1):25–9. pmid:7639544
  96. 96. Najafi F, Hasani J, Izadi N, Hashemi‐Nazari SS, Namvar Z, Mohammadi S, et al. The effect of prepregnancy body mass index on the risk of gestational diabetes mellitus: A systematic review and dose‐response meta‐analysis. Obesity Reviews. 2019;20(3):472–86. pmid:30536891
  97. 97. D’Souza R, Horyn I, Pavalagantharajah S, Zaffar N, Jacob C-E. Maternal body mass index and pregnancy outcomes: a systematic review and metaanalysis. American Journal of Obstetrics & Gynecology MFM. 2019;1(4):100041. pmid:33345836