Skip to main content

Mid-upper arm circumference predicts the risk of gestational diabetes in early pregnancy

Abstract

Background

The present work aimed to assess the value of mid-upper arm circumference (MUAC) at 8 to 12 weeks in predicting the occurrence of gestational diabetes mellitus (GDM).

Methods

According to eligibility criteria, 328 women with singleton pregnancies who underwent routine antenatal check-ups at Qinhuangdao Maternal and Child Health Hospital from September 2017 to September 2020 were included. The patients were divided into the gestational diabetes mellitus (GDM) and non-GDM groups according to oral glucose tolerance test (OGTT) data from gestation weeks 24 to 28. Clinical data were compared between the two groups. Logistic regression analysis was performed to determine factors independently predicting GDM. Receiver operating characteristic (ROC) curve analysis was employed to analyze the value of MUAC in predicting the occurrence of GDM. The optimal cut-off points were calculated.

Results

In logistic regression analysis, pre-pregnancy weight, waist circumference, MUAC, UA, TG, and HDL-C independently predicted the occurrence of GDM (P < 0.05). MUAC retained statistical significance upon adjustment for various confounders (OR = 8.851, 95%CI: 3.907–20.048; P < 0.001). ROC curve analysis revealed good diagnostic potential for MUAC in GDM (AUC = 0.742, 95%CI: 0.684–0.800, P < 0.001), with a cut-off of 28.5 cm, sensitivity and specificity were 61% and 77%, respectively.

Conclusion

Pregnant women with MUAC >28.5 cm are prone to develop GDM during pregnancy, indicating that MUAC as an important predictive factor of GDM in early pregnancy.

Peer Review reports

Introduction

Gestational diabetes mellitus (GDM) represents a major perinatal complication, whose prevalence is rising with the increasing number of overweight or obese women of childbearing age, with the current global pooled standardized prevalence of GDM being 14.0% [1]. GDM attracts increasing attention and focus for causing major adverse pregnancy outcomes and enhancing the risk of birth trauma, and hypoglycaemia in the immediate postpartum period, fetal macrosomia (excessive birth weight), shoulder dystocia, diabetes, hypertension, obesity, heart disease and other metabolic diseases in the offspring and the mother [2,3,4].The diagnosis of GDM is established after an oral glucose tolerance test (OGTT) at pregnancy weeks 24 to 28; however, even before this screening, pregnant women at elevated risk of GDM already show a trend of increased blood glucose, which is harmful for mother and child [5]. A previous study suggested that early detection and strict management of patients at high risk of GDM can improve maternal and infant outcomes [6]. Therefore, predicting the risk of GDM in early pregnancy and providing timely and targeted interventions to individuals at high risk of the disease may help improve maternal and infant outcomes.

Mid-upper arm circumference (MUAC) is a simple and easily obtained anthropometric parameter that was previously used to assess muscle mass and nutritional status or quality of life in humans [7,8,9]. A recent study found new applications for this old index [10]: MUAC is associated with overweight and obesity in young people [11], making it a simple tool to detect abdominal obesity and insulin resistance in diabetic individuals [12]. Currently, only a few studies have examined the correlation between MUAC and gestational diabetes, and it remains unclear whether MUAC could predict metabolic abnormalities and represent a new marker for predicting the occurrence of GDM.

Materials and methods

Study population

Pregnancies who underwent routine antenatal examinations in the Obstetrics Clinic of Qinhuangdao Maternal and Child Health Hospital between September 2017 and September 2020 were enrolled in this retrospective study. Figure 1 shows the detailed screening process for eligible participants. Inclusion criteria were singleton pregnancy, primipara and complete information about anthropometric measures and serum indexes. Exclusion criteria were diagnosis of diabetes mellitus prior to pregnancy, family history of diabetes, alcohol and smoking history, cancer, hypertension, cardiac disorders, thyroid disorders, and severe liver and kidney disorders. Ultimately, 328 women were included. This study had approval from the Ethics Committee of Qinhuangdao Maternal and Child Health Hospital and Qinhuangdao First Hospital.

Fig. 1
figure 1

Study flowchart. MUAC: mid-upper arm circumference; WHR: waist-hip ratio; OGTT: oral glucose tolerance test

All patients were examined by the 75 g OGTT at gestation weeks 24 to 28. GDM diagnosis followed the International Association of Diabetes and Pregnancy Study Groups (IADPSG) guidelines [13]: fasting plasma glucose (FPG) level ≥ 5.1 mmol/L, 1-h post-glucose (1hPG) level ≥ 10.0 mmol/L, or 2-h post-glucose (2hPG) level ≥ 8.5 mmol/L. Pregnant women with normal glucose levels were included as controls. The non-GDM (n = 225) and GDM (n = 103) groups were determined based on OGTT data.

Measurements and definitions

General data collection

Maternal age, and pre-pregnancy weight (accuracy of 0.1 kg) and height (accuracy of 0.1 cm) were collected and recorded at the initial antenatal visit. Pre-pregnancy body mass index (BMI) was obtained as BMI = weight/height2 (kg/m2). Based on Chinese National Health Commission criteria, the patients were assigned to two weight groups: underweight and normal weight group, BMI < 24.0 kg/m2, overweight and obesity group, BMI ≥ 24.0 kg/m2 [14].

A pregnancy profile was generally created between 8 and 12 weeks of gestation. MUAC, waist circumference, and systolic and diastolic blood pressure levels were collected at this period. Measurements were completed by two trained examiners, with the participants standing relaxed with feet shoulder width apart and the arms drooped naturally. MUAC was measured on the right upper arm at the midpoint of the acromion and olecranon processes using a flexible tape. Waist circumference measurement was performed at the umbilical level after normal expiration. Hip circumference measurement was carried out at the most prominent part of the buttocks, accurate to 0.1 cm. Waist-to-hip ratio (WHR) was obtained as WC (cm)/HC (cm). In women, a WHR ≥ 0.85 was considered to indicate central obesity. Right brachial artery blood pressure was obtained with a mercury sphygmomanometer in the resting state. Blood pressure was measured twice and averaged for analysis.

Laboratory tests

All participants underwent an 8–12 h fasting at night, and venous blood samples were collected in the next morning. An automatic biochemical analyzer (Hitachi 7060 type, Ibaraki, Japan) was employed for triglyceride (TG), serum total cholesterol (TC), high-density lipoprotein-cholesterol (HDL-C), low-density lipoprotein-cholesterol (LDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transpeptidase (GGT), and uric acid (UA) detection in early pregnancy (8 to 12 weeks), as well as plasma glucose at fasting, 1-h plasma glucose (1hPG), and 2-h plasma glucose (2hPG) in the second trimester (24 to 28 weeks). Analytical techniques included the continuous monitoring and two-point terminal methods. Regarding the accuracy of blood glucose detection, the coefficients of variation of intra-assay and inter-assay were less than 2% and 4.2%, respectively. The relative deviation between the measured and target values was ≤ ± 5%; blood glucose concentrations of 0.02 to 40.00 mmol/L were in the linear range (r ≥ 0.99), with an absolute deviation of ± 0.2 mmol/L.

Statistical analysis

Data analysis used SPSS 25.0. To compare differences between the non-GDM and GDM groups, the two-sample t-test was used for continuous variates, described as mean ± standard deviation, while the chi-square (χ2) test was employed for categorical variates, represented as number and percentage. Binary logistic regression analysis was carried out to identify risk factors for GDM. Furthermore, independent variables that showed significant differences were divided into three tertiles, with the first tertile considered a reference for trend analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined. The association of MUAC with GDM was analyzed. The Crude Model had no adjustments. Age (Model 1), pre-pregnancy BMI, waist circumference, hip circumference, TG, UA, and HDL-C (Model 2) were adjusted. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated to determine the diagnostic potential of MUAC for GDM, and the cut-off was obtained simultaneously. P < 0.05 indicated statistical significance.

Results

Clinical data

The 328 pregnant women were 21–40 years old, with an average age of 29.12 ± 4.55 years. Pre-pregnancy weight, BMI, MUAC, TG, UA, FPG, 1hPG, and 2hPG in 24 to 28 weeks were significantly higher in the GDM group compared with the non-GDM group, while HDL-C was significantly lower (all P<0.05). However, age, systolic and diastolic blood pressure levels, ALT, AST, GGT, TC, and LDL-C were similar in both groups (all P>0.05). Although waist circumference and hip circumference had significant differences (P < 0.001), central obesity classified by WHR had no significant difference (P>0.05) (Table 1).

Table 1 Clinical features [\(\stackrel{-}{\varvec{x}}\pm \mathbf{s}\), n (%)]

GDM risk factors

To analyze factors associated with GDM, univariate logistic regression analysis was performed. The analysis included maternal pre-pregnancy weight, pre-pregnancy BMI, MUAC, waist circumference, hip circumference, TG, UA, and HDL-C in early pregnancy, and FPG, 1hPG, and 2hPG in mid-pregnancy OGTT as independent variables. The results showed that waist circumference, hip circumference, UA, TG, and HDL-C independently predicted the development of GDM (all P<0.05, Table 2). Additionally, these independent variables were also presented for each tertile (Tertiles 1–3).

Table 2 Univariate logistic regression analysis of factors associated with GDM

Association of MUAC with GDM

We further evaluated the association of MUAC with GDM. As a continuous variate, MUAC markedly contributed to the risk of GDM in all three models, with ORs of 1.480 (1.307–1.675), 1.476 (1.303–1.672), and 1.610(1.359–1.909), respectively. Consistently, MUAC as a categorical variable increased the risk of GDM with elevated categories from Tertiles 1 to 3. In the fully adjusted model (Model 2), the top tertile exhibited a 7.827-fold risk of GDM with the first tertile as a reference (Table 3).

Table 3 Logistic regression analysis of the relationship between MUAC and GDM in different models

Diagnostic potential of MUAC for predicting GDM

ROC curve analysis was performed to determine the potential of MUAC to predict the occurrence of GDM. The cut-off value of MUAC for diagnosing GDM was 28.5 cm, and the associated AUC, sensitivity and specificity were 0.742 (95%CI: 0.684–0.800, P < 0.001), 61% and 77%, respectively (Fig. 2). AUC above 0.5 is considered to indicate good diagnostic potential.

Fig. 2
figure 2

ROC curve analysis of MUAC for predicting gestational diabetes. AUC = 0.742 (95%CI: 0.684–0.800, P < 0.001), cut-off = 28.5 cm, sensitivity = 61% and specificity = 77%

Discussion

The potential of MUAC as an early predictor of GDM in pregnant women was evaluated in the current work. The results showed a significant positive association between MUAC and GDM: for each 1-cm increment in MUAC, the risk of GDM was increased by 1.610-fold. Logistic regression analysis after adjustment for potential confounders and considering MUAC categories demonstrated that the relative risk of GDM in pregnant women in the top tertile of MUAC was 7.827-fold that of the first tertile. The AUC of MUAC for GDM prediction was 0.742, indicating that this risk factor correctly classified 74.2% of high-risk individuals. Therefore, MUAC as a potential factor for predicting GDM has a certain value.

Obesity and overweight were independently risk factors for GDM, the risk of GDM is increased almost 4-fold in women with obese and 9-fold in women with severely obese, compared to normal-weight pregnant women [15]. Excessive gestational weight gain prior to GDM screening test was a major risk factor for the development of GDM [16, 17]. Moreover, the association between GDM and pregnancy weight gain was mainly attributed to weight gain in early pregnancy [18]. However, GDM is diagnosed is at 24 to 28 weeks of gestation, that has lost the opportunity for early intervention. Thus, there are more studies required to explore predictive factors for early identification of GDM to facilitate targeted interventions in those most likely to benefit, this is of great significance to reduce the risk of GDM. BMI, waist circumference, and waist-to-hip ratio are commonly used to evaluate levels of obesity. These parameters changed significantly with the increase of uterine volume during pregnancy, and this affected the judgment of obesity or overweight in pregnancy. Previous studies demonstrated a significant association between maternal BMI and MUAC, MUAC can be used as a surrogate for BMI as it is measured easier and has less variability during the period of gestation unlike BMI [19]. Maternal MUAC is associated with the degree of obesity, body fat content, and the development of gestational diabetes [20]. A study on assessing 2912 pregnant women established a correlation between BMI and MUAC, the detection rate in overweight patients was 75% when MUAC ≥ 27 cm [21]. An UK research showed that the GDM prediction model combined maternal age, MUAC, systolic blood pressure, glucose, triglyceride, and HbA1c can well predict the risk of GDM in pregnant women [22]. Another study on the development of an early prediction tool for gestational diabetes employed MUAC, age, systolic blood pressure, HbA1c, and adiponectin reported a positive predictive value of 50% [23]. A random effect meta-analysis of 11 cohort studies showed that the risk of GDM was positively associated with maternal central obesity. There are many evaluation measures for maternal central obesity, such as waist circumference/waist-hip ratio, abdominal subcutaneous fat thickness and body fat index, but the predictive value of these measures is unclear [24]. In this study, the proportions of overweight and obesity in pregnant women were elevated in the GDM group compared with the non-GDM group, limited differences were detected (P = 0.026), but central obesity rate was no significant difference in both groups. Therefore, we speculated that high levels of MUAC in early pregnancy may independently predict the risk of GDM. However, MUAC in different races had different cutoff values in GDM prediction [25]. We determined an optimal cutoff of 28.5 cm, resulting in sensitivity and specificity of 61% and 77%, respectively, which indicated a medium predictive ability. Therefore, this study provided evidence for forecasting risk of GDM by MUAC in early pregnancy.

The Developmental Origin of Health and Disease (DOHaD) approach emphasizes that the intrauterine environment early in life has a significant impact on health and disease in adulthood [26]. GDM is considered to have an impact on placental development and function, the expression of parathyroid hormone-related protein (PTH-rP) and its receptor PTH-R1 in placenta are higher in GDM pregnant women with abnormal OGTT at fasting glucose compared to women with abnormal 60’ or 120’ glycemia, and the incidence of neonatal 1-minute Apgar score < 7 is higher in placental PTH-rP positive GDM women [27]. In pregnancies with maternal GDM, the placenta is exposed to environmental changes, such as increased inflammation and oxidative stress, dyslipidemia, and altered hormone levels, leading to abnormal fetal growth and development as well as metabolic and cardiovascular abnormalities in the offspring [28]. Many researches suggested that active perinatal management of pregnant women at elevated risk of GDM may reduce the incidence of GDM, which could further prevent or delay the development of long-term chronic diseases [29]. In a Finnish study, 293 pre-pregnant obese women and women in early pregnancy were randomized into the intervention and control groups. The intervention group underwent lifestyle interventions, and the results showed that moderate individualized lifestyle interventions decrease GDM occurrence in high-risk pregnant women by 39% [30]. In a Chinese randomized controlled trial of overweight and obese pregnant women, exercise interventions starting in early pregnancy significantly reduced GDM occurrence, with a 50% decline in the relative risk of GDM [31]. A meta-analysis [32] showed that pregnant women with GDM gained weight before 24 weeks of gestation, suggesting that interventions in overweight and obese pregnant women at elevated risk of GDM should be started at the earliest time, as starting interventions in mid- and late pregnancy does not exhibit a significant improvement in adverse pregnancy outcomes. This evidence suggested that early intervention is key to reducing the risk of GDM.

The establishment of early prediction models for GDM has attracted the common attention of many scholars. Different early and pre-pregnancy indicators are used to establish models to predict the risk of GDM. Sirico.et al observed that fetal heart rate (FHR) during the first trimester was associated with the development of pregestational diabetes mellitus and GDM [33, 34], and showed that FHR in the first trimester had a high predictive power for GDM. FHR is an indicator that must be monitored and easily obtained during prenatal examination, which is of great significance for prediction of GDM. This also suggests that we can capitalize on this indicator and combine with other parameters to improve the accuracy of GDM prediction in the future. Benevides.et al [35] identified the risk of GDM by using ultrasound abdominal fat measurement in early pregnancy and found that preperitoneal fat, rather than abdominal subcutaneous and visceral fat, could predict GDM, with an optimal cut-off point of 45.25 mm, a sensitivity of 73% and a specificity of 77% for predicting GDM. Obesity is closely related to the occurrence of GDM. Although abdominal fat ultrasound has a good predictive effect on GDM, abdominal ultrasound is not a routine examination item, and considering its time-consuming and patients’ wishes, it is not suitable for large-scale screening at present. Tenenbaum. et al. [36] developed an early prediction model for GDM by combining obesity, placental and inflammatory biomarkers, such as high BMI, insulin, sCD163, PP13, PAPP-A and TNFα. Combined indicators can improve the detection rate of GDM screening. However, the cost of special blood biomarkers is high and the large-scale application is limited, which is suitable for the in-depth study of the pathogenesis of GDM. Compared with other models, MUAC measurement can be widely used in the early diagnosis of GDM in terms of being simple, practical and cost effective.

In recent years, with overweight and obesity rates, the number of overweight and obese pregnant women has increased, resulting in a significantly increased risk of GDM. Insulin resistance is pronounced in overweight or obese women during pregnancy, thereby increasing the risk of developing GDM. MUAC is an old indicator with new applications but is simple and easy to use, with stable and reliable results and low variability. This and previous studies have further confirmed MUAC as a good indicator for clinical screening and prediction of GDM. It can efficiently assess the risk of developing GDM in a cost-effective manner and assist in the early detection of patients at risk of GDM. Thus, we should recognize the importance of early-life health and strengthen its management, which would reduce chronic diseases in adulthood and improve health across the lifespan.

There were limitations in this research. Firstly, the study was based on a small sample size, because of missing values in MUAC measurements for the pregnant women during the first trimester. And the impact of social and education level on the risk for GDM had not be considered. The pathophysiology of GDM involves β-cell dysfunction and insulin resistance during pregnancy but this study did not measure insulin; consequently, indicators relevant to MUAC and insulin resistance were not evaluated, and the mechanism was not discussed in depth. Despite these limitations, this study showed an association between MUAC and GDM in early pregnancy.

Data availability

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

References

  1. 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 Res Clin Pract. 2022;183:109050.

    Article  PubMed  Google Scholar 

  2. Vounzoulaki E, Khunti K, Abner SC, Tan BK, Davies MJ, Gillies CL. Progression to type 2 diabetes in women with a known history of gestational diabetes: systematic review and meta-analysis. BMJ. 2020;369:m1361.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ye W, Luo C, Huang J, Li C, Liu Z, Liu F. Gestational diabetes mellitus and adverse pregnancy outcomes: systematic review and meta-analysis. BMJ. 2022;377:e067946.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Murray SR, Reynolds RM. Short- and long-term outcomes of gestational diabetes and its treatment on fetal development. Prenat Diagn. 2020;40:1085–91.

    Article  CAS  PubMed  Google Scholar 

  5. Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, Coustan DR, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358:1991–2002.

    Article  PubMed  Google Scholar 

  6. Gabbay-Benziv R, Doyle LE, Blitzer M, Baschat AA. First trimester prediction of maternal glycemic status. J Perinat Med. 2015;43:283–9.

    Article  CAS  PubMed  Google Scholar 

  7. Addo OY, Himes JH, Zemel BS. Reference ranges for midupper arm circumference, upper arm muscle area, and upper arm fat area in US children and adolescents aged 1–20 y. Am J Clin Nutr. 2017;105:111–20.

    Article  CAS  PubMed  Google Scholar 

  8. Rieckmann A, Fisker AB, Øland CB, Nielsen S, Wibaek R, Sørensen TB, et al. Understanding the child mortality decline in Guinea-Bissau: the role of population-level nutritional status measured by mid-upper arm circumference. Int J Epidemiol. 2022;51:1522–32.

    Article  PubMed  Google Scholar 

  9. Won SH, Hiratsuka Y, Suh SY, Bae H, Choi SE, Kim YJ, et al. Mid-upper arm circumference as an Indicator of Quality of Life of patients with Advanced Cancer. J Palliat Care. 2023;38:24–9.

    Article  PubMed  Google Scholar 

  10. Reilly JJ. Mid-upper arm circumference (MUAC): new applications for an old measure. Arch Dis Child. 2017;102:1–2.

    Article  PubMed  Google Scholar 

  11. Harshitha RK, Bhargava M. Mid-upper arm circumference and neck circumference to screen for overweight-obesity in young adults in South India. Heliyon. 2022;8:e12173.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Zhu Y, Lin Q, Zhang Y, Deng H, Hu X, Yang X, et al. Mid-upper arm circumference as a simple tool for identifying central obesity and insulin resistance in type 2 diabetes. PLoS ONE. 2020;15:e0231308.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Metzger BE, Buchanan TA. Gestational diabetes. In: Cowie CC, Casagrande SS, Menke A, Cissell MA, Eberhardt MS, Meigs JB, et al. editors. Diabetes in America. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases (US); 2018.

    Google Scholar 

  14. Wang X, Zhang S, Yu W, Li G, Li J, Ji J, et al. Pre-pregnancy body mass index and glycated-hemoglobin with the risk of metabolic diseases in gestational diabetes: a prospective cohort study. Front Endocrinol (Lausanne). 2023;14:1238873.

    Article  PubMed  Google Scholar 

  15. MarchiJ BM. DenckerA ea. Isks associated with obesity in pregnancy,R for the mother and baby: a systematic review of reviews[J]. Obes Rev. 2015;16:621–38.

    Article  Google Scholar 

  16. Peng Y, Han N, Su T, Zhou S, Bao H, Ji Y, et al. Gestational weight gain and the risk of gestational diabetes mellitus: a latent class trajectory analysis using birth cohort data. Diabetes Res Clin Pract. 2021;182:109130.

    Article  CAS  PubMed  Google Scholar 

  17. Yin A, Tian F, Wu X, Chen Y, Liu K, Tong J, et al. Excessive gestational weight gain in early pregnancy and insufficient gestational weight gain in middle pregnancy increased risk of gestational diabetes mellitus. Chin Med J (Engl). 2022;135:1057–63.

    Article  PubMed  Google Scholar 

  18. BerggrenEK G-WS. PresleyL ea. Maternal fat, but not lean, mass is increased among overweight/obese women with excess gestational weight gain[J]. Am J Obstet Gynecol. 2016;214:745.e1-5.

  19. Erratum. Association between mid-upper arm circumference and body mass index in pregnant women to assess their nutritional status. J Family Med Prim Care. 2020;9:5085.

    Article  Google Scholar 

  20. Martin SSMHD. Akwilina Wendelin Mwanri ea. Prevalence of hyperglycemia in pregnancy and influence of body fat on development of hyperglycemia in pregnancy among pregnant womenin urban areas of Arusha region, Tanzania[J]. JBMC Pregnancy Childbirth. 2019;28:315.

    Google Scholar 

  21. Dea SMCJC. The relationship between body mass index and mid-arm circumference in a pregnant population.[J]. J Obstet Gynaecology: J Inst Obstet Gynecol. 2011;31:594–6.

    Article  Google Scholar 

  22. Ola F, Lucilla Q, Poston, Angela C. Fe. Metabolic profiling of pregnant women with obesity: an exploratory study in women at Greater Risk of Gestational Diabetes.[J]. Metabolites. 2022;29:922.

    Google Scholar 

  23. 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:e0167846.

    Article  PubMed  PubMed Central  Google Scholar 

  24. Yao D, Chang Q, Wu QJ, Gao SY, Zhao H, Liu YS, et al. Relationship between Maternal Central Obesity and the risk of gestational diabetes Mellitus: a systematic review and Meta-analysis of Cohort studies. J Diabetes Res. 2020;2020:6303820.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Nombo AP, Mwanri AW, Brouwer-Brolsma EM, Ramaiya KL, Feskens EJM. Gestational diabetes mellitus risk score: a practical tool to predict gestational diabetes mellitus risk in Tanzania. Diabetes Res Clin Pract. 2018;145:130–7.

    Article  PubMed  Google Scholar 

  26. Yajnik CS, Deshmukh US. Maternal nutrition, intrauterine programming and consequential risks in the offspring. Rev Endocr Metab Disord. 2008;9:203–11.

    Article  CAS  PubMed  Google Scholar 

  27. Sirico A, Dell’Aquila M, Tartaglione L, Moresi S, Farì G, Pitocco D et al. PTH-rP and PTH-R1 expression in Placentas from pregnancies complicated by gestational diabetes: New insights into the pathophysiology of hyperglycemia in pregnancy. Diagnostics (Basel). 2021;11.

  28. Bedell S, Hutson J, de Vrijer B, Eastabrook G. Effects of Maternal Obesity and gestational diabetes Mellitus on the Placenta: current knowledge and targets for therapeutic interventions. Curr Vasc Pharmacol. 2021;19:176–92.

    Article  CAS  PubMed  Google Scholar 

  29. SaravananP. Gestational diabetes: opportunities for improving maternal and child health[J]. Lancet Diabetes Endocrinol. 2020;8:793–800.

    Article  Google Scholar 

  30. KoivusaloSB RönöK. KlemettiMM ea. Gestational diabetes mellitus can be prevented by lifestyle intervention: the Finnish gestational diabetes Prevention Study(RADIEL): a randomized controlled trial[J]. Diabetes Care. 2016;39:24–30.

    Article  Google Scholar 

  31. Wang C, Wei Y, Zhang X, Zhang Y, Xu Q, Sun Y, et al. A randomized clinical trial of exercise during pregnancy to prevent gestational diabetes mellitus and improve pregnancy outcome in overweight and obese pregnant women. Am J Obstet Gynecol. 2017;216:340–51.

    Article  PubMed  Google Scholar 

  32. Brunner S, Stecher L, Ziebarth S, Nehring I, Rifas-Shiman SL, Sommer C, et al. Excessive gestational weight gain prior to glucose screening and the risk of gestational diabetes: a meta-analysis. Diabetologia. 2015;58:2229–37.

    Article  PubMed  Google Scholar 

  33. Sirico A, Sarno L, Zullo F, Martinelli P, Maruotti GM. Pregestational diabetes and fetal heart rate in the first trimester of pregnancy. Eur J Obstet Gynecol Reprod Biol. 2019;232:30–2.

    Article  PubMed  Google Scholar 

  34. Sirico A, Lanzone A, Mappa I, Sarno L, Słodki M, Pitocco D, et al. The role of first trimester fetal heart rate in the prediction of gestational diabetes: a multicenter study. Eur J Obstet Gynecol Reprod Biol. 2019;243:158–61.

    Article  PubMed  Google Scholar 

  35. Benevides FT, Araujo Júnior E, Maia CSC, Maia EHMSB, Montenegro Junior RM, Carvalho FHC. Evaluation of ultrasound measurements of abdominal fat for the prediction of gestational diabetes in the first and second trimesters of pregnancy. J Matern Fetal Neonatal Med. 2022;35:9821–9.

    Article  CAS  PubMed  Google Scholar 

  36. Tenenbaum-Gavish K, Sharabi-Nov A, Binyamin D, Møller HJ, Danon D, Rothman L, et al. First trimester biomarkers for prediction of gestational diabetes mellitus. Placenta. 2020;101:80–9.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors sincerely appreciate participants and obstetricians of Qinhuangdao Maternal and Child Health Hospital for their selfless assistance.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Contributions

NM and ZN performed the experiments, and were involved in data collection, data interpretation and manuscript drafting. QL and LB conducted study design, data analysis and manuscript drafting. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Qiang Lu.

Ethics declarations

Ethical approval

The present trial followed the Declaration of Helsinki (2000) and had approval from the Ethics committee of Qinhuangdao Hospital for Maternal and Child Health [2021Q088]. The requirement for informed consent was waived by the Institutional Review Board of Ethics committee of Qinhuangdao Hospital for Maternal and Child Health because of the retrospective nature of the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, N., Bai, L., Niu, Z. et al. Mid-upper arm circumference predicts the risk of gestational diabetes in early pregnancy. BMC Pregnancy Childbirth 24, 462 (2024). https://doi.org/10.1186/s12884-024-06664-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12884-024-06664-z

Keywords