The effects of maternal pre-pregnancy BMI, pregnancy smoking and alcohol consumption on molar-incisor hypomineralisation: A negative control study

Objectives: (1) To explore the associations between modifiable maternal pregnancy exposures: pre-pregnancy body mass index (BMI), pregnancy smoking and alcohol consumption with offspring molar-incisor hypomineralisation (MIH). (2) To explore for the presence of residual confounding using negative control analyses. Methods: This study used data from Avon Longitudinal Study of Parents and Children (ALSPAC), a UK prospective birth cohort. We defined offspring MIH using prospectively collected questionnaire data. We used logistic regression to explore confounder adjusted associations between maternal pre-pregnancy BMI and smoking and alcohol consumption during pregnancy with MIH. We included negative control exposure (paternal BMI, smoking and alcohol around the time of pregnancy) and outcome (offspring dental trauma) analyses to explore for the presence of residual confounding. Results: 5536 mother/offspring pairs were included (297 MIH cases [5.4%]). We found a positive association between maternal mean BMI and offspring MIH (OR per 1‐kg/m2 difference in maternal BMI: 1.04, 95% CI: 1.00, 1.08). In subsequent analyses, we found evidence that this effect was non-linear and being driven by women in the highest BMI quintiles. In negative control analyses, we found no evidence of association between paternal BMI and offspring MIH (OR: 0.94, 95% CI: 0.89, 1.00) and maternal BMI and offspring dental trauma (OR: 0.99, 95% CI: 0.96, 1.02). There was no clear evidence of an association for maternal pregnancy smoking (OR: 0.76, 95%CI: 0.46, 1.22) and alcohol consumption (OR: 0.79, 95% CI: 0.56, 1.21) with offspring MIH with results imprecisely estimated. Conclusion: In summary we found evidence of a possible intrauterine effect of high maternal pre-pregnancy BMI on offspring MIH. We did not find robust evidence for an intrauterine effect of maternal pregnancy smoking or alcohol consumption on offspring MIH. Our findings provide further support for women of reproductive age to maintain a healthy weight. Future studies are warranted to explore possible mechanisms on how the pregnancy environment may relate to offspring MIH.

Effect of Maternal Prepregnancy/Early‐Pregnancy Body Mass Index and Pregnancy Smoking and Alcohol on Congenital Heart Diseases: A Parental Negative Control Study

Background Congenital heart diseases (CHDs) are the most common congenital anomaly. The causes of CHDs are largely unknown. Higher prenatal body mass index (BMI), smoking, and alcohol consumption are associated with increased risk of CHDs. Whether these are causal is unclear. Methods and Results Seven European birth cohorts, including 232 390 offspring (2469 CHD cases [1.1%]), were included. We applied negative exposure paternal control analyses to explore the intrauterine effects of maternal BMI, smoking, and alcohol consumption during pregnancy, on offspring CHDs and CHD severity. We used logistic regression, adjusting for confounders and the other parent's exposure and combined estimates using a fixed‐effects meta‐analysis. In adjusted analyses, maternal overweight (odds ratio [OR], 1.15 [95% CI, 1.01–1.31]) and obesity (OR, 1.12 [95% CI, 0.93–1.36]), compared with normal weight, were associated with higher odds of CHD, but there was no clear evidence of a linear increase in odds across the whole BMI distribution. Associations of paternal overweight, obesity, and mean BMI were similar to the maternal associations. Maternal pregnancy smoking was associated with higher odds of CHD (OR, 1.11 [95% CI, 0.97–1.25]) but paternal smoking was not (OR, 0.96 [95% CI, 0.85–1.07]). The positive association with maternal smoking appeared to be driven by nonsevere CHD cases (OR, 1.22 [95% CI, 1.04–1.44]). Associations with maternal moderate/heavy pregnancy alcohol consumption were imprecisely estimated (OR, 1.16 [95% CI, 0.52–2.58]) and similar to those for paternal consumption. Conclusions We found evidence of an intrauterine effect for maternal smoking on offspring CHDs, but no evidence for higher maternal BMI or alcohol consumption. Our findings provide further support for the importance of smoking cessation during pregnancy.

Health-Related Quality of Life Among Pregnant Women with Pre-pregnancy Smoking in China: A National Cross-sectional Study (Preprint)

BACKGROUND Previous researches didn’t explore the influence of smoking before pregnancy on the health-related quality of life (HRQoL)of Chinese pregnant women, which is a big population in the largest developing country in the world and cannot be neglected. OBJECTIVE To evaluate the HRQoL of pregnant women in China with different smoking status and further to estimate the association between pre-pregnancy smoking and HRQoL. METHODS A nationwide-based cross-sectional study was conducted to determine the association between different smoking status (smoking currently, quit smoking, never smoking) and HRQoL in pregnant women across China. A web-based questionnaire was administered during prenatal examinations. EuroQoL Group’s five-dimension (EQ-5D-5L) scale with EuroQoL Group’s visual analog scale (EQ-5D VAS) scale were used for measuring HRQoL. RESULTS A total of 16,811 participants were included in the study. Significant difference in EQ-5D VAS was detected between non-smokers and ex-smokers (P<.001). Among ex-smokers, the proportion of pregnant women who suffer from depression/anxiety is higher compared with non-smokers (P<.001). We found that the increased cigarette consumption before pregnancy could result in lower EQ-5D VAS (P=.04) and EQ-5D index (P=.005) of pregnant women in China. CONCLUSIONS Chinese pregnant women with smoking history tend to have lower HRQoL. Smoking cessation during pregnancy doesn’t not significantly improve the HRQoL of Chinese pregnant women compared to smokers. Compared to non-smokers, ex-smokers are more likely to suffer from depression/anxiety. Among ex-smokers, the more cigarettes the Chinese pregnant women smoked, the lower their HRQoL. We suggest that the Chinese government should strengthen the education of stopping smoking and avoiding second-hand smoke for women who have pregnancy plan and their family members.

Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders? Findings from a UK birth cohort with independent validation

Background Prediction of pregnancy-related disorders is usually done based on established and easily measured risk factors. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders. Methods We used data collected from women in the Born in Bradford (BiB; n = 8212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n = 859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of (1) risk factors (maternal age, pregnancy smoking, body mass index (BMI), ethnicity and parity) to (2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24–28 weeks gestation) and (3) combined risk factors and metabolites. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a multi-ethnic study of obese pregnant women. Results Maternal age, pregnancy smoking, BMI, ethnicity and parity were retained in the combined risk factor and metabolite models for all outcomes apart from PTB, which did not include maternal age. In addition, 147, 33, 96, 51 and 14 of the 156 metabolite traits were retained in the combined risk factor and metabolite model for GDM, HDP, SGA, LGA and PTB, respectively. These include cholesterol and triglycerides in very low-density lipoproteins (VLDL) in the models predicting GDM, HDP, SGA and LGA, and monounsaturated fatty acids (MUFA), ratios of MUFA to omega 3 fatty acids and total fatty acids, and a ratio of apolipoprotein B to apolipoprotein A-1 (APOA:APOB1) were retained predictors for GDM and LGA. In BiB, discrimination for GDM, HDP, LGA and SGA was improved in the combined risk factors and metabolites models. Risk factor area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56, 0.63)). Combined risk factor and metabolite models AUC 95% (CI): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63, 0.70)). For GDM, HDP and LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24–28 weeks and 15–18 weeks gestation confirmed similar patterns of results, but AUCs were attenuated. Conclusions Our results suggest a combined risk factor and metabolite model improves prediction of GDM, HDP and LGA, and SGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.

The effect of maternal pre-/early-pregnancy BMI and pregnancy smoking and alcohol on congenital heart diseases: a parental negative control study

BackgroundCongenital heart diseases (CHDs) are the most common congenital anomaly. The causes of CHDs are largely unknown. Higher prenatal body mass index (BMI), smoking and alcohol consumption are associated with increased risk of CHDs. Whether these are causal is unclear.Methods and ResultsSeven European birth cohorts including 232,390 offspring (2,469 CHD cases [1.1%]) were included. We applied negative exposure paternal control analyses to explore the intrauterine effects of maternal BMI, smoking and alcohol consumption during pregnancy, on offspring CHDs and CHD severity. We used logistic regression and combined estimates using a fixed-effects meta-analysis. Analyses of BMI categories resulted in similar increased odds of CHD in overweight (mothers OR: 1.15 (1.01, 1.31) and fathers 1.10 (0.96, 1.27)) and obesity (mothers OR: 1.12 (0.93, 1.36) and fathers 1.16 (0.90, 1.50)). The association of mean BMI with CHD was null. Maternal smoking was associated with increased odds of CHD (OR: 1.11 (0.97, 1.25)) but paternal smoking was not (OR: 0.96 (0.85, 1.07)). The difference increased when removing offspring with genetic/chromosomal defects (mothers OR: 1.15 (1.01, 1.32) and fathers 0.93 (0.83, 1.05)). The positive association with maternal pregnancy smoking appeared to be driven by non-severe CHD cases (OR: 1.22 (1.04, 1.44)). Associations with maternal (OR: 1.16 (0.52, 2.58)) and paternal (OR: 1.23 (0.74, 2.06)) moderate/heavy pregnancy alcohol consumption were similar.ConclusionsWe found evidence of an intrauterine effect for maternal smoking on offspring CHDs, but no evidence for higher maternal BMI or alcohol consumption. Our findings provide further support for why smoking cessation is important during pregnancy.

Evidence for a Causal Relationship Between Academic Achievement and Cigarette Smoking

Introduction Academic achievement (AA) is associated with smoking rates. Can we determine the degree to which this relationship is likely a causal one? Methods We predict smoking in male conscripts (mean age 18.2) assessed from 1984 to 1991 (N = 233 248) and pregnant females (mean age 27.7) receiving prenatal care 1972–1990 (N = 494 995) from AA assessed in all students at 16. Instrumental variable (IV) analyses used the instrument month-of-birth as in each school year, older children have high AA. Co-relative analyses used AA-smoking associations in the population, cousins and siblings to predict the AA-smoking relationship in MZ twins, thereby controlling for familial confounding. Results In males, higher AA was associated with a substantial decrease in risk for smoking (odds ratio [OR] [95% confidence intervals [CIs]] per standard deviation [SD] = 0.41 [0.40–0.41]) while the parallel figures obtain from our IV and co-relative analyses were 0.47 (0.39–0.57) and 0.51 (0.43–0.60), respectively. In females, these figures for pre-pregnancy smoking were, respectively, 0.39 (0.39–0.39), 0.50 (0.46–0.54) and 0.54 (0.51–0.58). Results for heavy versus light smoking suggested a causal effect but were inconsistent across methods. However, among females smoking prior to pregnancy, AA predicted a reduced risk for continued smoking with ORs for uncontrolled, IV, and co-relative analyses equaling, respectively, were 0.54 (0.53–0.55) 0.68 (0.56–0.82) and 0.78 (0.66–0.91), respectively. Conclusions Two different methods produced consistent evidence that higher AA has a causal effect on reducing smoking rates and increasing cessation rates in smoking pregnant females. Improving AA may result in meaningful gains in population health through reduced smoking. Implications This study provides consistent evidence across two different methods that high AA is causally related to reduced rates of smoking and increasing rates of smoking cessation among pregnant women. Our results suggest that interventions that improve educational achievement in adolescence would reduce tobacco consumption, thereby improving public health.

Smoking During Pregnancy Among Immigrant Women With Same-Origin and Swedish-Born Partners

Introduction Although ethnically mixed couples are on the rise in industrialized countries, their health behaviors are poorly understood. We examined the associations between partner’s birthplace, age at immigration, and smoking during pregnancy among foreign-born women. Methods Population-based register study including all pregnancies resulting in a livebirth or stillbirth in Sweden (1991–2012) with complete information on smoking and parental country of birth. We compared the prevalence of smoking during pregnancy between women in dual same-origin foreign-born unions (n = 213 111) and in mixed couples (immigrant women with a Swedish-born partner) (n = 111 866) using logistic regression. Swedish-born couples were used as a benchmark. Results The crude smoking rate among Swedish women whose partners were Swedish was 11%. Smoking rates of women in dual same-origin foreign-born unions varied substantially by birthplace, from 1.3% among women from Asian countries to 23.2% among those from other Nordic countries. Among immigrant groups with prevalences of pregnancy smoking higher than that of women in dual Swedish-born unions, having a Swedish-born partner was associated with lower odds of smoking (adjusted odds ratios: 0.72–0.87) but with higher odds among immigrant groups with lower prevalence (adjusted odds ratios: 1.17–5.88). These associations were stronger among women immigrating in adulthood, whose smoking rates were the lowest. Conclusions Swedish-born partners “pull” smoking rates of immigrant women toward the level of smoking of Swedish-born women, particularly among women arrived during adulthood. Consideration of a woman’s and her partner’s ethnic background and life stage at migration may help understand smoking patterns of immigrant women. Implications We found that having a Swedish-born partner is associated with higher rates of smoking during pregnancy among immigrants from regions where women smoke less than Swedish women, but with lower smoking rates among immigrants from regions where women smoke more. This implies that prevention efforts should concentrate on newly arrived single women from low prevalence regions, such as Africa and Asia, whereas cessation efforts may target women from high prevalence regions, such as other European countries. These findings suggest that pregnancy smoking prevention or cessation interventions may benefit from including partners and approaches culturally tailored to mixed unions.

Do nuclear magnetic resonance (NMR)-based metabolomics improve the prediction of pregnancy-related disorders?

BackgroundPrediction of pregnancy-related disorders is mostly done based on established and easily measured risk factors. However, these measures are at best moderate at discriminating between high and low risk women. Recent advances in metabolomics may provide earlier and more accurate prediction of women at risk of pregnancy-related disorders.Methods and FindingsWe used data collected from women in the Born in Bradford (BiB; n=8,212) and UK Pregnancies Better Eating and Activity Trial (UPBEAT; n=859) studies to create and validate prediction models for pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We used ten-fold cross-validation and penalised regression to create prediction models. We compared the predictive performance of 1) risk factors (maternal age, pregnancy smoking status, body mass index, ethnicity and parity) to 2) nuclear magnetic resonance-derived metabolites (N = 156 quantified metabolites, collected at 24-28 weeks gestation) and 3) risk factors and metabolites combined. The multi-ethnic BiB cohort was used for training and testing the models, with independent validation conducted in UPBEAT, a study of obese pregnant women of multiple ethnicities.In BiB, discrimination for GDM, HDP, LGA and SGA was improved with the addition of metabolites to the risk factors only model. Risk factors area under the curve (AUC 95% confidence interval (CI)): GDM (0.69 (0.64, 0.73)), HDP (0.74 (0.70, 0.78)) and LGA (0.71 (0.66, 0.75)), and SGA (0.59 (0.56,0.63)). Combined AUC 95% (CI)): GDM (0.78 (0.74, 0.81)), HDP (0.76 (0.73, 0.79)) and LGA (0.75 (0.70, 0.79)), and SGA (0.66 (0.63,0.70)). For GDM, HDP, LGA, but not SGA, calibration was good for a combined risk factor and metabolite model. Prediction of PTB was poor for all models. Independent validation in UPBEAT at 24-28 weeks and 15-18 weeks gestation confirmed similar patterns of results, but AUC were attenuated. A key limitation was our inability to identify a large general pregnancy population for independent validation.ConclusionsOur results suggest metabolomics combined with established risk factors improves prediction GDM, HDP and LGA, when compared to risk factors alone. They also highlight the difficulty of predicting PTB, with all models performing poorly.Author SummaryBackgroundCurrent methods used to predict pregnancy-related disorders exhibit modest discrimination and calibration.Metabolomics may enable improved prediction of pregnancy-related disorders.Why Was This Study Done?We require tools to identify women with high-risk pregnancies earlier on, so that antenatal care can be more appropriately targeted at women who need it most and tailored to women’s needs and to facilitate early intervention.It has been suggested that metabolomic markers might improve prediction of future pregnancy-related disorders. Previous studies tend to be small and rarely undertake external validation.What Did the Researchers Do and Find?Using BiB (8,212 pregnant women of multiple ethnicities), we created prediction models, using established risk factors and 156 NMR-derived metabolites, for five pregnancy-related disorders. These were gestational diabetes mellitus (GDM), hypertensive disorders of pregnancy (HDP), small for gestational age (SGA), large for gestational age (LGA) and preterm birth (PTB). We sought external validation in UPBEAT (859 obese pregnant women).We compared the predictive discrimination (area under the curve - AUC) and calibration (calibration slopes) of the models. The prediction models we compared were 1) established risk factors (pregnancy smoking, maternal age, body mass index (BMI), maternal ethnicity and parity) 2) NMR-derived metabolites measured in the second trimester and 3) a combined model of risk factors and metabolites.Inclusion of metabolites with risk factors improved prediction of GDM, HDP, LGA and SGA in BiB. Prediction of PTB was poor with all models. Result patterns were similar in validation using UPBEAT, particularly for GDM and HDP, but AUC were attenuated.What Do These Findings Mean?These findings indicate that combining current risk factor and metabolomic data could improve the prediction of GDM, HDP, LGA and SGA. These findings need to be validated in larger, general populations of pregnant women.

Associations of Maternal Non-Nutritive Sweetener Intake During Pregnancy with Childhood BMI Trajectory

Objectives To examine the extent to which non-nutritive sweetener (NNS; e.g., aspartame) intake during pregnancy is associated with offspring BMI z-score trajectory from birth to 15 years. Methods We included 1683 mother-child pairs from Project Viva, a prospective pre-birth-cohort recruited in 1999–2002 in Massachusetts. The main exposure was maternal NNS intake assessed in the 1st and 2nd trimesters of pregnancy using a food frequency questionnaire. We defined NNS as servings/day of diet soda + NutraSweet (aspartame packets) averaged across 1st and 2nd trimesters. We used linear regression models to examine associations of maternal NSS intake with offspring BMI z-score at each in-person research visit (birth, 6 months, 3 years, 7 years, and 12 years). We also used mixed-effects models to examine associations with BMI z-score trajectory from birth to 15 years, including both research and clinical measures of BMI for 1570 participants with ≥3 BMI z-score values. We adjusted all models for maternal pre-pregnancy BMI, age, race/ethnicity, education, parity, and pregnancy smoking status. Results 70% of mothers were white and mean (SD) pre-pregnancy BMI was 24.6 (5.2) kg/m2. Mean (SD; IQR) intake of NNS was 0.23 (0.55; 0.22) servings/day. While maternal NNS intake (per servings/day) was not associated with BMI z-score at birth (β −0.03 units; 95% CI −0.14, 0.08), NNS was associated with higher BMI z-score at 6 months (β 0.17; 0.06, 0.28), 3 years (β 0.13; 0.03, 0.24), 7 years (β 0.16; 0.04, 0.29) and 12 years (β 0.16; 0.01, 0.31). Based on the BMI z-score trajectory, the associations of NNS intake (Q4 vs. Q1) with BMI z-score became stronger with increasing age from 3–14 years (pinteraction with age: &lt;0.01); e.g., 3 years (β 0.13; 0.02, 0.23), 7 years (β 0.24; 0.11, 0.37) and 12 years (β 0.38; 0.20, 0.57). Conclusions Our findings showed higher maternal NNS intake during pregnancy was associated with higher BMI z-score from childhood to early adolescence, and the associations strengthened with increasing age. Funding Sources US National Institutes of Health (R01 HD034568, UH3 OD023286).