Expert review of clinical immunology · 2024
Serum adipocytokines and inflammatory cytokines in pregnant women with gestational diabetes mellitus: clinical utility and development of a risk prediction model.
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Liu Kezhuo, Wang Huihui
Аннотация
BACKGROUND: This study analyzed the clinical utility of serum adipocytokines and inflammatory cytokines in gestational diabetes mellitus (GDM) and developed a quantitative nomogram prediction model.
RESEARCH DESIGN & METHODS: General data were collected. Fasting venous blood was taken and levels of fasting plasma glucose (FPG), serum adipocytokines, and inflammatory cytokines were assessed. The main risk factors for GDM were analyzed by implementing univariate and multivariate logistic regression analysis. The weights of the main risk factors were assigned, and the nomogram prediction model for GDM was developed by R software. The efficacy of the nomogram model for GDM prediction was measured and analyzed by the receiver operating characteristic (ROC) curve and calibration curve.
RESULTS: The observation group possessed a higher proportion of family history of diabetes, raised FPG, LEP, Visfatin, hs-CRP, IL-6, and TNF-α contents, and lower ADP contents (all p < 0.05). Multivariate logistic regression analysis displayed that LEP, ADP, and IL-6 were the main risk factors for GDM (p < 0.05). Calibration curve was basically consistent with the original curve, suggesting good accuracy.
CONCLUSION: Serum adipocytokines and inflammatory cytokines were the main risk factors for GDM. Developing a nomogram model can facilitate early diagnosis of GDM by physicians, allowing for timely interventions.
RESEARCH DESIGN & METHODS: General data were collected. Fasting venous blood was taken and levels of fasting plasma glucose (FPG), serum adipocytokines, and inflammatory cytokines were assessed. The main risk factors for GDM were analyzed by implementing univariate and multivariate logistic regression analysis. The weights of the main risk factors were assigned, and the nomogram prediction model for GDM was developed by R software. The efficacy of the nomogram model for GDM prediction was measured and analyzed by the receiver operating characteristic (ROC) curve and calibration curve.
RESULTS: The observation group possessed a higher proportion of family history of diabetes, raised FPG, LEP, Visfatin, hs-CRP, IL-6, and TNF-α contents, and lower ADP contents (all p < 0.05). Multivariate logistic regression analysis displayed that LEP, ADP, and IL-6 were the main risk factors for GDM (p < 0.05). Calibration curve was basically consistent with the original curve, suggesting good accuracy.
CONCLUSION: Serum adipocytokines and inflammatory cytokines were the main risk factors for GDM. Developing a nomogram model can facilitate early diagnosis of GDM by physicians, allowing for timely interventions.