Blood pressure change across pregnancy in white British and Pakistani women: analysis of data from the Born in Bradford cohort

Social inequalities in pregnancy metabolic profile: findings from the multi-ethnic Born in Bradford cohort study

BACKGROUND

Lower socioeconomic position (SEP) associates with adverse pregnancy and perinatal outcomes and with less favourable metabolic profile in nonpregnant adults. Socioeconomic differences in pregnancy metabolic profile are unknown. We investigated association between a composite measure of SEP and pregnancy metabolic profile in White European (WE) and South Asian (SA) women.

METHODS

We included 3,905 WE and 4,404 SA pregnant women from a population-based UK cohort. Latent class analysis was applied to nineteen individual, household, and area-based SEP indicators (collected by questionnaires or linkage to residential address) to derive a composite SEP latent variable. Targeted nuclear magnetic resonance spectroscopy was used to determine 148 metabolic traits from mid-pregnancy serum samples. Associations between SEP and metabolic traits were examined using linear regressions adjusted for gestational age and weighted by latent class probabilities.

RESULTS

Five SEP sub-groups were identified and labelled 'Highest SEP' (48% WE and 52% SA), 'High-Medium SEP' (77% and 23%), 'Medium SEP' (56% and 44%) 'Low-Medium SEP' (21% and 79%), and 'Lowest SEP' (52% and 48%). Lower SEP was associated with more adverse levels of 113 metabolic traits, including lower high-density lipoprotein (HDL) and higher triglycerides and very low-density lipoprotein (VLDL) traits. For example, mean standardized difference (95%CI) in concentration of small VLDL particles (vs. Highest SEP) was 0.12 standard deviation (SD) units (0.05 to 0.20) for 'Medium SEP' and 0.25SD (0.18 to 0.32) for 'Lowest SEP'. There was statistical evidence of ethnic differences in associations of SEP with 31 traits, primarily characterised by stronger associations in WE women e.g., mean difference in HDL cholesterol in WE and SA women respectively (vs. Highest-SEP) was -0.30SD (-0.41 to -0.20) and -0.16SD (-0.27 to -0.05) for 'Medium SEP', and -0.62SD (-0.72 to -0.52) and -0.29SD (-0.40 to -0.20) for 'Lowest SEP'.

CONCLUSIONS

We found widespread socioeconomic differences in metabolic traits in pregnant WE and SA women residing in the UK. Further research is needed to understand whether the socioeconomic differences we observe here reflect pre-conception differences or differences in the metabolic pregnancy response. If replicated, it would be important to explore if these differences contribute to socioeconomic differences in pregnancy outcomes.

Maternal race and pre-eclampsia: Cohort study and systematic review with meta-analysis

OBJECTIVES

To examine the association between race and pre-eclampsia and gestational hypertension after adjustment for factors in maternal characteristics and medical history in a screening study from the Fetal Medicine Foundation (FMF) in England, and to perform a systematic review and meta-analysis of studies on pre-eclampsia.

DESIGN

Prospective observational study and systematic review with meta-analysis.

SETTING

Two UK maternity hospitals.

POPULATION

A total of 168 966 women with singleton pregnancies attending for routine ultrasound examination at 11-13 weeks of gestation without major abnormalities delivering at 24 weeks or more of gestation.

METHODS

Regression analysis examined the association between race and pre-eclampsia or gestational hypertension in the FMF data. Literature search to December 2021 was carried out to identify peer-reviewed publications on race and pre-eclampsia.

MAIN OUTCOME MEASURE

Relative risk of pre-eclampsia and gestational hypertension in women of black, South Asian and East Asian race by comparison to white women.

RESULTS

In black women, the respective risks of total-pre-eclampsia and preterm-pre-eclampsia were 2-fold and 2.5-fold higher, respectively, and risk of gestational hypertension was 25% higher; in South Asian women there was a 1.5-fold higher risk of preterm pre-eclampsia but not of total-pre-eclampsia and in East Asian women there was no statistically significant difference in risk of hypertensive disorders. The literature search identified 19 studies that provided data on several million pregnancies, but 17 were at moderate or high-risk of bias and only three provided risks adjusted for some maternal characteristics; consequently, these studies did not provide accurate contributions on different racial groups to the prediction of pre-eclampsia.

CONCLUSION

In women of black and South Asian origin the risk of pre-eclampsia, after adjustment for confounders, is higher than in white women.

Maternal blood pressure trajectories and associations with gestational age at birth: a functional data analytic approach

BACKGROUND

Research has revealed group-level differences in maternal blood pressure trajectories across pregnancy. These trajectories are typically constructed using clinical blood pressure data and multivariate statistical methods that are prone to bias and ignore the functional, dynamic process underlying a single blood pressure observation. The aim of this study was to use functional data analysis to explore blood pressure variation across pregnancy, and multivariate methods to examine whether trajectories are related to gestational age at birth.

METHODS

Clinical blood pressure observations were available from 370 women who participated in a longitudinal pregnancy cohort study conducted in Montreal, Quebec, Canada. Functional data analysis was used to smooth blood pressure data and then to conduct a functional principal component analysis to examine predominant modes of variation.

RESULTS

Three eigenfunctions explained greater than 95% of the total variance in blood pressure. The first accounted for approximately 80% of the variance and was characterized by a prolonged-decrease trajectory in blood pressure; the second explained 10% of the variance and captured a late-increase trajectory; and the third accounted for approximately 7% of the variance and captured a mid-decrease trajectory. The prolonged-decrease trajectory of blood pressure was associated with older, and late-increase with younger gestational age at birth.

CONCLUSION

Functional data analysis is a useful method to model repeated maternal blood pressure observations and many other time-related cardiovascular processes. Results add to previous research investigating blood pressure trajectories across pregnancy through identification of additional, potentially clinically important modes of variation that are associated with gestational age at birth.

Inter-ankle Systolic Blood Pressure Difference Is a Marker of Increased Fasting Blood-Glucose in Asian Pregnant Women

OBJECTIVE

This cross-sectional study aimed to determine the relationship between clinical blood pressures and blood pressures measured using Doppler with blood glucose in pregnancy by ethnicity.

METHODS

We recruited 179 (52% White European, 48% Asian) pregnant women at 24-28 weeks of gestation who underwent a glucose tolerance test in an antenatal clinic in Bradford Royal Infirmary, the UK, from 2012 to 2013. Systolic blood pressures in the arm (left and right brachial) and ankle [left and right posterior tibial (PT) and dorsalis pedalis (DP)] blood pressures were measured using a Doppler probe. The inter-arm (brachial) and inter-ankle (PT and DP) systolic blood pressure differences were obtained. A multivariate linear regression model adjusted for age, body mass index, and diabetes risk was used to assess the relationship between blood pressures and blood glucose.

RESULTS

Asian pregnant women had higher blood glucose but lower ankle blood pressures than White Europeans. In White Europeans, brachial blood pressures and clinical blood pressures were positively associated with fasting blood glucose (FBG), but brachial blood pressures did not perform better as an indicator of FBG than clinical blood pressures. In Asians, increased inter-ankle blood pressure difference was associated with increased FBG. For each 10 mmHg increase in the inter-ankle blood pressure difference, FBG increased by 0.12 mmol/L (Beta=0.12, 95%CI: 0.01-0.23).

CONCLUSION

The relationship between blood pressures with blood glucose differed by ethnicity. In Asians, inter-ankle systolic blood pressure difference was positively associated with blood glucose. This is first ever report on ankle blood pressures with blood glucose in pregnancy which suggests future potential as a non-invasive gestational diabetes risk screening tool.

Metabolomics datasets in the Born in Bradford cohort

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks’ gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts’ extensive phenotype data from questionnaires, medical, educational and social record linkage, and other ‘omics data.

Do Mass Spectrometry-Derived Metabolomics Improve the Prediction of Pregnancy-Related Disorders? Findings from a UK Birth Cohort with Independent Validation

Many women who experience gestational diabetes (GDM), gestational hypertension (GHT), pre-eclampsia (PE), have a spontaneous preterm birth (sPTB) or have an offspring born small/large for gestational age (SGA/LGA) do not meet the criteria for high-risk pregnancies based upon certain maternal risk factors. Tools that better predict these outcomes are needed to tailor antenatal care to risk. Recent studies have suggested that metabolomics may improve the prediction of these pregnancy-related disorders. These have largely been based on targeted platforms or focused on a single pregnancy outcome. The aim of this study was to assess the predictive ability of an untargeted platform of over 700 metabolites to predict the above pregnancy-related disorders in two cohorts. We used data collected from women in the Born in Bradford study (BiB; two sub-samples, n = 2000 and n = 1000) and the Pregnancy Outcome Prediction study (POPs; n = 827) to train, test and validate prediction models for GDM, PE, GHT, SGA, LGA and sPTB. We compared the predictive performance of three models: (1) risk factors (maternal age, pregnancy smoking, BMI, ethnicity and parity) (2) mass spectrometry (MS)-derived metabolites (n = 718 quantified metabolites, collected at 26-28 weeks' gestation) and (3) combined risk factors and metabolites. We used BiB for the training and testing of the models and POPs for independent validation. In both cohorts, discrimination for GDM, PE, LGA and SGA improved with the addition of metabolites to the risk factor model. The models' area under the curve (AUC) were similar for both cohorts, with good discrimination for GDM (AUC (95% CI) BiB 0.76 (0.71, 0.81) and POPs 0.76 (0.72, 0.81)) and LGA (BiB 0.86 (0.80, 0.91) and POPs 0.76 (0.60, 0.92)). Discrimination was improved for the combined models (compared to the risk factors models) for PE and SGA, with modest discrimination in both studies (PE-BiB 0.68 (0.58, 0.78) and POPs 0.66 (0.60, 0.71); SGA-BiB 0.68 (0.63, 0.74) and POPs 0.64 (0.59, 0.69)). Prediction for sPTB was poor in BiB and POPs for all models. In BiB, calibration for the combined models was good for GDM, LGA and SGA. Retained predictors include 4-hydroxyglutamate for GDM, LGA and PE and glycerol for GDM and PE. MS-derived metabolomics combined with maternal risk factors improves the prediction of GDM, PE, LGA and SGA, with good discrimination for GDM and LGA. Validation across two very different cohorts supports further investigation on whether the metabolites reflect novel causal paths to GDM and LGA.

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.

Metabolomics datasets in the Born in Bradford cohort

Metabolomics is the quantification of small molecules, commonly known as metabolites. Collectively, these metabolites and their interactions within a biological system are known as the metabolome. The metabolome is a unique area of study, capturing influences from both genotype and environment. The availability of high-throughput technologies for quantifying large numbers of metabolites, as well as lipids and lipoprotein particles, has enabled detailed investigation of human metabolism in large-scale epidemiological studies. The Born in Bradford (BiB) cohort includes 12,453 women who experienced 13,776 pregnancies recruited between 2007-2011, their partners and their offspring. In this data note, we describe the metabolomic data available in BiB, profiled during pregnancy, in cord blood and during early life in the offspring. These include two platforms of metabolomic profiling: nuclear magnetic resonance and mass spectrometry. The maternal measures, taken at 26-28 weeks' gestation, can provide insight into the metabolome during pregnancy and how it relates to maternal and offspring health. The offspring cord blood measurements provide information on the fetal metabolome. These measures, alongside maternal pregnancy measures, can be used to explore how they may influence outcomes. The infant measures (taken around ages 12 and 24 months) provide a snapshot of the early life metabolome during a key phase of nutrition, environmental exposures, growth, and development. These metabolomic data can be examined alongside the BiB cohorts' extensive phenotype data from questionnaires, medical, educational and social record linkage, and other 'omics data.

Population reference and healthy standard blood pressure range charts in pregnancy: findings from the Born in Bradford cohort study

Women who develop gestational hypertension are at increased risk of adverse perinatal and longer-term outcomes. Reference charts may aid early detection of raised blood pressure (BP) and in doing so reduce adverse outcome risk. We used repeated BP measurements to produce ‘reference’ (whole population) and ‘standard’ (healthy pregnancies only) gestational-age-specific BP charts for all pregnant women (irrespective of ethnicity) and for White British (WB) and Pakistani (P) women. We included 9218 women recruited to the Born in Bradford study with 74,770 BPs. 19% of the whole population, 11% and 25% of WB and P women respectively were defined as healthy pregnancies. For reference and standard charts, for all women and each ethnic group, SBP/DBP at 12 and 20 weeks gestation was similar before rising at 37 weeks. DBP/SBP of reference charts for all women and for each ethnic group were higher than those of the corresponding standard charts. Compared to WB, P women had lower SBP/DBP at 12, 20 and 37 weeks gestation. To conclude; maternal population BP reference charts are higher compared to standard charts (healthy pregnancies) and are influenced by ethnicity.