Preconception metabolic indicators predict gestational diabetes and offspring birthweight

Abstract

Pregnancy conditions such as gestational diabetes (GDM) and macrosomia lead to an increased risk of diabetes and cardiovascular disease in the offspring, perpetuating a cycle of poor health. We hypothesized that (1) pre-pregnancy indicators of metabolism would be associated with GDM and birthweight; and (2) the lipid accumulation product (LAP; incorporating waist circumference and triglycerides) and visceral adiposity index (VAI; incorporating waist circumference, triglycerides, and HDL-c) would be better predictors of GDM and birthweight than other indicators. Data from the Cardiovascular Risk in Young Finns Study were linked to the Finnish birth registry for 349 women. BMI, triglycerides, waist circumference, insulin, HOMA-IR, LAP, and VAI at the visit prior to the pregnancy were examined as predictors of GDM and large-for-gestational-age (LGA) using logistic regression with adjustment for age, parity, and smoking. Waist circumference was the strongest predictor of GDM (adjusted odds ratio [aOR] 1.66, 95% confidence interval 1.16–2.38) and LGA (aOR 1.41, 1.00–1.99). For GDM, all markers had similar discrimination; for LGA, the area under the receiver operating curve for waist circumference was significantly higher than for BMI (p < 0.01). This analysis suggests that pregnancy and even offspring health is affected by risk factors outside the immediate time period of pregnancy.

Chinese abstract

妊娠期糖尿病(GDM)和巨大儿等妊娠期情况导致其后代糖尿病和心血管疾病的风险增加，延续不良的健康循环。我们假设(1)孕前代谢指标可能与GDM和出生体重有关；(2) 脂质蓄积指数(LAP；包括腰围和甘油三酯)和内脏代谢指数（VAI；包括腰围、甘油三酯和HDL-c），比其他指标更好地预测GDM和出生体重。将年轻芬兰人的心血管风险研究数据联系到芬兰出生登记的349名女性，孕前访问时测定的BMI、甘油三酯、腰围、胰岛素、HOMA-R、LAP、 VAI作为GDM 和大于胎龄儿（LGA）的预测因子， 调整年龄、产次、吸烟因素后进行logistic回归分析。腰围是最强的GDM和LGA的预测因子，GDM(调整优势比[aOR]1.66,95%置信区间1.16-2.38)和LGA([aOR1.41,1.00-1.99)。所有指标对GDM有相似的预测力；对于LGA，腰围的ROC曲线下面积显著高于BMI(p<0.01)。这一分析表明, 接近妊娠时期的外部风险因素影响妊娠期甚至后代的健康。

• Birth weight
• diabetes
• fetal growth retardation
• gestational
• waist circumference

Acknowledgements

Many thanks to Dr Henry Kahn, who provided the initial suggestion for this paper as well as many useful comments during analysis and revision.

Supplementary material available online

Supplementary Tables S1 and S2.