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Clinical Trial
. 2022 Mar 14:13:826665.
doi: 10.3389/fendo.2022.826665. eCollection 2022.

Effects of Paternal Obesity on Fetal Development and Pregnancy Complications: A Prospective Clinical Cohort Study

Jing Lin  1   2 , Wei Gu  1   2 , Hefeng Huang  1   2   3
Affiliations
Clinical Trial

Effects of Paternal Obesity on Fetal Development and Pregnancy Complications: A Prospective Clinical Cohort Study

Jing Lin et al. Front Endocrinol (Lausanne). .

Abstract

Objectives: To evaluate the association between paternal obesity and fetal development and pregnancy complications.

Study design: This prospective cohort clinical trial analyzed data from 7683 women with singleton pregnancies. All study subjects were sequentially divided into four groups based on paternal BMI. We compared the differences in fetal growth and pregnancy complications between different paternal BMI groups by univariate logistic regression and independent t-test. Finally, the independent predictors of SGA and macrosomia were determined.

Results: The incidences of preeclampsia, cesarean section, SGA, macrosomia, and postpartum hemorrhage in the paternal obesity group were significantly higher than the normal BMI group. With the increase of paternal BMI, fetal ultrasound measurement parameter, neonatal and placental weight showed an increasing trend (trend P < 0.05). However, these differences disappeared in the obese group. The test for interaction showed the effect of paternal obesity on SGA and macrosomia was significantly affected by maternal obesity. We also found paternal obesity was an independent predictor of both SGA and macrosomia. Based on the above results, we plotted the Nomograms for clinical prediction.

Conclusion: Paternal obesity can affect fetal growth parameters and placental development, which has an adverse impact on pregnancy outcomes. Optimizing the paternal BMI will help improve the health of the next generation.

Keywords: SGA; cohort study; macrosomia; paternal obesity; preeclampsia.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of participants in the study.
Figure 2
Figure 2
Forest plot summary of logistic regression analysis for risk of SGA and macrosomia. Predictors of SGA (A) and macrosomia (B) pregravid and before pregnancy. Data were presented as odds ratio per standard deviation change in the indicated variable.
Figure 3
Figure 3
The nomogram was developed to predict the incidence rate of SGA (A) and macrosomia (B) based on the significant predictors in the multivariable analysis. Draw an upward vertical line from each variable axis to obtain the point of each variable. Calculate the sum of each variable score and draw an upward vertical line from the total score axis to obtain the predicted incidence of SGA or macrosomia.
See this image and copyright information in PMC

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