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Background <p>This study aimed to compare fetal soft tissue thickness, particularly anterior abdominal wall thickness (AAWT), between GDM and non-GDM pregnancies, and to preliminarily explore its association with LGA as a secondary analysis.</p> Methods <p>A retrospective analysis was conducted on 250 pregnant women who underwent regular prenatal examinations at our hospital from January 2021 to February 2022, consisting of 125 cases in the GDM group and 125 in the non-GDM group. Ultrasound measurements of conventional fetal biometric parameters (biparietal diameter [BPD], head circumference [HC], abdominal circumference [AC], and femur length [FL]) and AAWT, fetal thigh soft tissue thickness [FTSTT], thigh muscle thickness [TM], and thigh fat thickness [TF]) were performed at 24–26, 30–32, and 36–38 weeks of gestation. Differences between the two groups were compared. Correlations between each indicator and birth weight were analyzed, along with their individual predictive efficacy for LGA. Predictor variables were selected using univariate and multivariate logistic regression as well as LASSO regression to construct an LGA risk prediction model. Its performance was compared with a estimated fetal weight (EFW) prediction model. Finally, a nomogram was developed for visual prediction.</p> Results <p>Maternal age, pre-pregnancy BMI, and neonatal birth weight were significantly higher in the GDM group than in the non-GDM group (<i>P</i> &lt; 0.05). Fetal AAWT was also significantly greater in the GDM group across all measured gestational periods (<i>P</i> &lt; 0.05). In LGA fetuses, AAWT and AC, and other parameters were significantly larger than in non-LGA fetuses at all gestational weeks (<i>P</i> &lt; 0.05). Among all indicators, AAWT exhibited the strongest correlation with birth weight, and this correlation strengthened with advancing gestation (<i>r</i> = 0.666 at 36–38 weeks). ROC curve analysis showed that AAWT at 36–38 weeks had the highest predictive efficacy for LGA (AUC = 0.940). The multivariate logistic regression model identified AAWT at 30–32 weeks, AAWT at 36–38 weeks, and AC as independent predictors of LGA. The logistic regression model constructed based on these factors demonstrated good predictive performance (AUC = 0.966) and outperformed the EFW prediction model. Based on this model, a clinically practical nomogram was subsequently established.</p> Conclusions <p>Fetal AAWT is significantly greater in GDM pregnancies than in non-GDM pregnancies and is strongly associated with LGA. A preliminary exploratory model incorporating AAWT and AC demonstrated potential utility in identifying LGA, but these findings require validation in larger, LGA-enriched cohorts. The nomogram should be considered hypothesis-generating rather than ready for clinical implementation.</p>

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Differences in fetal soft tissue thickness between GDM and non-GDM pregnancies and an exploratory nomogram for LGA prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Qiaoli Zeng,
  • Chunyan Hu,
  • Yuqiao Liang,
  • Jiaxin Liu,
  • Yixing Chen,
  • Dan Zhao,
  • Yue Wei,
  • Runmin Guo

摘要

Background

This study aimed to compare fetal soft tissue thickness, particularly anterior abdominal wall thickness (AAWT), between GDM and non-GDM pregnancies, and to preliminarily explore its association with LGA as a secondary analysis.

Methods

A retrospective analysis was conducted on 250 pregnant women who underwent regular prenatal examinations at our hospital from January 2021 to February 2022, consisting of 125 cases in the GDM group and 125 in the non-GDM group. Ultrasound measurements of conventional fetal biometric parameters (biparietal diameter [BPD], head circumference [HC], abdominal circumference [AC], and femur length [FL]) and AAWT, fetal thigh soft tissue thickness [FTSTT], thigh muscle thickness [TM], and thigh fat thickness [TF]) were performed at 24–26, 30–32, and 36–38 weeks of gestation. Differences between the two groups were compared. Correlations between each indicator and birth weight were analyzed, along with their individual predictive efficacy for LGA. Predictor variables were selected using univariate and multivariate logistic regression as well as LASSO regression to construct an LGA risk prediction model. Its performance was compared with a estimated fetal weight (EFW) prediction model. Finally, a nomogram was developed for visual prediction.

Results

Maternal age, pre-pregnancy BMI, and neonatal birth weight were significantly higher in the GDM group than in the non-GDM group (P < 0.05). Fetal AAWT was also significantly greater in the GDM group across all measured gestational periods (P < 0.05). In LGA fetuses, AAWT and AC, and other parameters were significantly larger than in non-LGA fetuses at all gestational weeks (P < 0.05). Among all indicators, AAWT exhibited the strongest correlation with birth weight, and this correlation strengthened with advancing gestation (r = 0.666 at 36–38 weeks). ROC curve analysis showed that AAWT at 36–38 weeks had the highest predictive efficacy for LGA (AUC = 0.940). The multivariate logistic regression model identified AAWT at 30–32 weeks, AAWT at 36–38 weeks, and AC as independent predictors of LGA. The logistic regression model constructed based on these factors demonstrated good predictive performance (AUC = 0.966) and outperformed the EFW prediction model. Based on this model, a clinically practical nomogram was subsequently established.

Conclusions

Fetal AAWT is significantly greater in GDM pregnancies than in non-GDM pregnancies and is strongly associated with LGA. A preliminary exploratory model incorporating AAWT and AC demonstrated potential utility in identifying LGA, but these findings require validation in larger, LGA-enriched cohorts. The nomogram should be considered hypothesis-generating rather than ready for clinical implementation.