Endocrine connections · 2026
A nomogram integrating first-trimester metabolic and angiogenic markers for predicting adverse pregnancy outcomes in gestational diabetes mellitus: development and internal validation.
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Fu Jingli, Zhong Xiaoyan, Li Dan, Zhuang Siying, Yu Weiwei
Аннотация
BACKGROUND: Gestational diabetes mellitus (GDM) is associated with a significantly increased risk of adverse pregnancy outcomes. This study aimed to develop and internally validate a nomogram integrating integrating first-trimester metabolic and angiogenic markers to predict composite adverse pregnancy outcomes in GDM.
METHODS: We conducted a retrospective cohort study of 1,000 GDM patients (training set n=700, validation set n=300). Multivariable logistic regression was used to identify independent predictors and construct a nomogram. Model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration (Hosmer-Lemeshow test, calibration plots), and decision curve analysis (DCA).
RESULTS: Six variables were retained as independent predictors: pre-pregnancy BMI, FPG, TG, HOMA-IR, PIGF, and sFlt-1. The full model showed excellent discrimination in internal validation, with an AUC of 0.87 (95% CI 0.84-0.90) in the training set and 0.84 (95% CI 0.80-0.88) in the validation set, significantly outperforming a metabolic-only model without PIGF/sFlt-1 (AUC 0.82, p=0.01). Calibration was good (Hosmer-Lemeshow p=0.36 and 0.42, respectively). DCA demonstrated clinical net benefit across threshold probabilities of 0.15-0.35. Subgroup analyses confirmed stable performance across age, BMI, treatment, and parity.
CONCLUSION: The nomogram incorporating first-trimester metabolic and angiogenic markers accurately predicts adverse pregnancy outcomes in GDM patients in this internal validation cohort.
METHODS: We conducted a retrospective cohort study of 1,000 GDM patients (training set n=700, validation set n=300). Multivariable logistic regression was used to identify independent predictors and construct a nomogram. Model performance was assessed by discrimination (area under the receiver operating characteristic curve [AUC]), calibration (Hosmer-Lemeshow test, calibration plots), and decision curve analysis (DCA).
RESULTS: Six variables were retained as independent predictors: pre-pregnancy BMI, FPG, TG, HOMA-IR, PIGF, and sFlt-1. The full model showed excellent discrimination in internal validation, with an AUC of 0.87 (95% CI 0.84-0.90) in the training set and 0.84 (95% CI 0.80-0.88) in the validation set, significantly outperforming a metabolic-only model without PIGF/sFlt-1 (AUC 0.82, p=0.01). Calibration was good (Hosmer-Lemeshow p=0.36 and 0.42, respectively). DCA demonstrated clinical net benefit across threshold probabilities of 0.15-0.35. Subgroup analyses confirmed stable performance across age, BMI, treatment, and parity.
CONCLUSION: The nomogram incorporating first-trimester metabolic and angiogenic markers accurately predicts adverse pregnancy outcomes in GDM patients in this internal validation cohort.