Abstract
Background
The nature and contribution of different pregnancy related complications to future cardiovascular disease (CVD) and its risk factors, as well as mechanisms underlying these associations remain unclear.
Methods and Results
We studied associations of pregnancy diabetes, hypertensive disorders of pregnancy (HDP), preterm delivery and size for gestational age with calculated 10 year CVD risk (based on the Framingham score) and a wide range of cardiovascular risk factors measured 18 years after pregnancy (mean age at outcome assessment: 48 years) in a prospective cohort of 3,416 women. Gestational diabetes (GDM) was positively associated with fasting glucose and insulin, even after adjusting for potential confounders whilst HDP were associated with BMI, waist circumference, blood pressure, lipids and insulin. Large for gestational age (LGA) was associated with greater waist circumference and glucose concentrations, whilst small for gestational age (SGA) and preterm delivery were associated with higher blood pressure. The association with the calculated 10 year CVD risk based on the Framingham prediction score was OR=1.31 (95%CI: 1.11, 1.53) for preeclampsia and 1.26 (0.95, 1.68) for GDM compared to women without preeclampsia and GDM respectively.
Conclusions
HDP and pregnancy diabetes are independently associated with an increased calculated 10 year CVD risk. Preeclampsia may be the better predictor of future CVD since it was associated with a wider range of cardiovascular risk factors. Our results suggest that pregnancy may be an important opportunity for early identification of women at increased risk of CVD later in life.
Keywords: cardiovascular disease risk factors, long term follow-up, longitudinal cohort study, prediction, pregnancy
Introduction
Cardiovascular disease (CVD) is the leading cause of death in women, accounting for a quarter of deaths in both high income and low and middle income settings.1 It is increasingly recognised that women experiencing common pregnancy related complications: gestational diabetes (GDM),2, 3 preeclampsia,4 intra-uterine-growth retardation,5 and preterm delivery6, 7 are at increased risk of future CVD. Therefore, it has been suggested that pregnancy offers an opportunity to identify women at-risk of future CVD.8-10 But whether these pregnancy complications have separate, independent effects on future cardiovascular risk, and if so, how their relative and absolute associations differ from each other remains unclear. Such information is important for exploring whether there are common underlying pathways between these conditions and future cardiovascular risk and for considering the most efficient methods for using pregnancy complications to target preventive initiatives in women.
It is well established that women with GDM are at increased risk of developing diabetes later in life2 and diabetes is an established cardiovascular risk factor.11 Several studies have reported on associations of pregnancy related complications with other cardiovascular risk factors post-partum such as increased lipid concentrations, insulin resistance, increased levels of inflammatory markers, higher prevalence of metabolic syndrome, and vascular dysfunction (reviewed in 12). However, most studies have either limited sample sizes,12 have measured single cardiovascular risk biomarkers, or have measured these shortly after pregnancy, with few exceptions e.g.13-16. Therefore, it is uncertain whether such associations persist in the longer term but prior to actual CVD events.
Here we examine – and compare - associations of pregnancy diabetes (pre-gestational diabetes, GDM and glycosuria), hypertensive disorders of pregnancy (HDP: gestational hypertension and preeclampsia), size for gestational age and preterm delivery with a wide range of cardiovascular risk factors and the calculated 10-year risk of CVD based on the Framingham risk score, in women in early middle age. In doing so we aim to identify specific pathways linking pregnancy related complications with future cardiovascular health.
Methods
The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective population-based birth cohort study that recruited some 14,541 pregnancies resident in Avon, UK with expected dates of delivery 1st April 1991 to 31st December 1992 (http://www.alspac.bris.ac.uk.). 13,617 women with a live singleton birth consented to have their obstetric data abstracted from medical records. Ethical approval for this study was obtained from the ALSPAC Law and Ethics Committee and the Local Research Ethics Committee.
Pregnancy complications
Research midwives, using a standard protocol, abstracted information on clinical diagnoses of GDM and glycosuria for the index pregnancy from the antenatal, pregnancy and postnatal medical records of all women, as previously described.17 Information on glycosuria (recorded as none, trace, +, ++, +++ or more) was abstracted from the records of each antenatal clinic visit made by the woman (median number, 14 per woman). The practice in the UK at the time was for all women to be offered urine tests for glycosuria at each antenatal clinic visit. Universal screening of women with a random or fasting blood glucose level or with an oral glucose tolerance test was not undertaken and diagnostic tests for GDM (most commonly an oral glucose tolerance test) will only have been undertaken in women with established risk factors (family history, previous history of GDM or macrosomic birth, south Asian ethnicity) or glycosuria. Glycosuria was defined as a record of at least ++ (equal to 13.9 mmol/l or 250 mg/100 ml) on at least two occasions at any time during the pregnancy.17 Whilst glycosuria has been suggested as being a poor indicator of GDM, we have previously shown its face validity by demonstrating a clear association with birth size and macrosomia.17 In addition, at recruitment to the study, women were asked about existing diabetes, any previous history of GDM, whether they had ever been diagnosed with high blood pressure/hypertension and if so whether or not this was restricted to blood pressure in previous pregnancies. Using these data, we classified women into one of four mutually exclusive categories: no evidence of glycosuria or diabetes (hereafter referred to as ‘healthy women’); pre-gestational diabetes before the pregnancy; GDM (i.e. a diagnosis in the medical records of GDM in any woman with no history of pre-gestational diabetes); and glycosuria (i.e. ++ glycosuria on two occasions in women with no evidence of pre-gestational or GDM).
Obstetric data abstractions also included every measurement of systolic and diastolic blood pressure and proteinuria entered into the medical records and the corresponding gestational age and date at the time of these measurements. These measurements were obtained in routine clinical practice by trained midwives and obstetricians. The median number (interquartile range) of blood pressure measurements in pregnancy was 14 (11, 16) and that of urine measurements was 11 (10, 14). We applied the International Society for the Study of Hypertension in Pregnancy18 criteria to all of the clinic data in order to determine women with preeclampsia and those with gestational hypertension. Using these criteria preeclampsia was defined as a systolic blood pressure ≥ 140 mmHg or a diastolic blood pressure ≥ 90 mmHg, measured on at least two occasions after 20 weeks of gestation, with proteinuria, diagnosed if the protein reading on dipstick testing (Albustix; Ames Company, Elkhart, Indiana) was at least 1+ (30mg/dl), occurring at the same time as the elevated blood pressure.18 Gestational hypertension was defined as the same pattern of elevated blood pressure but without proteinuria.18 Thus, all women were categorised into one of three mutually exclusive categories of no HDP, gestational hypertension or preeclampsia. Fifty-six women without HDP, 42 and nine with gestational hypertension and preeclampsia respectively, reported having hypertension prior to pregnancy. When these women were excluded from the analysis, results were unchanged to those presented here.
Gestational age and birth weight were ascertained from obstetric records
Size for gestational age was categorised as small (SGA, a birth weight lower than the 10th percentile of birth weight for gestational age; appropriate (AGA, between the 10-90th percentiles for gestational age); and large (LGA, higher than the 90th percentile for gestational age), using the study population centiles. Using sex specific centiles yielded virtually the same classification to SGA, AGA and LGA. Pre-term birth was defined as under 37 weeks gestation.
Mothers’ follow-up assessment
4,376 women attended the follow-up clinic. Mean follow up time was 18 years (range: 16-20 years). Weight and height were measured in light clothing and without shoes. Weight was measured to the nearest 0.1kg using Tanita scales. Height was measured to the nearest 0.1cm using a Harpenden stadiometer. Waist circumference was measured twice to the nearest 1mm at the mid-point between the lower ribs and the pelvic bone with a flexible tape. The mean of the two measures is used here. Blood pressure was measured whilst women were lying down, using an Omron M6 monitor (Omron Healthcare UK Ltd. Milton Keynes, UK). Two readings of systolic and diastolic blood pressure were recorded on each arm and the mean of these four readings used here.
Women were asked to fast for at least 8 hours prior to attending clinic. Blood samples were obtained, centrifuged, separated and frozen at −80°C within 30 minutes. Plasma glucose was measured by automated enzymatic (hexokinase) method with CV of < 3%. Plasma insulin was measured by an ELISA (Mercodia, Uppsala, Sweden) that does not cross-react with proinsulin and proinsulin was also measured by an ELISA (Mercodia, Uppsala, Sweden) that is a solid phase two-site enzyme immunoassay for the quantification of human pro-insulin. The coefficients of variation are: within assay 3.2%; between assay 5.2%; total assay: 6.1%. There is no cross reactivity with insulin or c-peptide. Lipids were measured by automated analyser with enzymatic methods. C-reactive protein was measured by automated particle-enhanced immunoturbidimetric assay (Roche UK, Welwyn Garden City, UK).
Other variables
Maternal age at delivery and parity and were obtained from obstetric records. Information on pre-pregnancy weight and height, maternal smoking in pregnancy, maternal education, and household social class were based on questionnaire responses. Maternal education was categorised as below or above university level. The highest parental occupation was used to allocate the children to family social class groups (classes I (professional / managerial) to V (unskilled manual workers), using the 1991 British Office of Population and Census Statistics (OPCS) classification). Maternal smoking in pregnancy was categorised as -never smoked; smoked before pregnancy or in the first trimester and then stopped, and smoked throughout pregnancy. Information on diabetes and CVD diagnosed during follow-up was collected by a questionnaire completed 18 years after the index pregnancy. Women reported having been told they had a heart attack, heart failure, angina and/or stroke.
CVD Framingham risk score
The 10-year CVD Framingham risk score (i.e. the risk of a CVD event, expressed as a percentage) was calculated using information on age, total cholesterol, HDL cholesterol, systolic blood pressure, diabetes (based on fasting glucose) and treatment for hypertension obtained at the follow-up assessment, and smoking status reported on a questionnaire completed at 18 years post-pregnancy, using the equations given on the study web-site19,20.
In a modified version of the score, we removed diabetes from the equation. This was done in order to examine whether the increased risk of developing diabetes, particularly in women with GDM, was the main driver of results. The correlation of this version with the main risk score was 0.985.
Statistical analysis
Values of insulin, proinsulin, triglycerides, and CRP were log transformed to make the data follow normal distributions. Linear regression models were used to assess associations of diabetes/glycosuria, HDP, size for gestational age and preterm delivery with outcomes. In the basic model (model 1) we adjusted for maternal age at follow-up. In the fully adjusted model (model 2) we also included pre-pregnancy BMI, maternal education, parity and smoking during pregnancy. In model 3 we also mutually adjusted each exposure of interest for the remaining three pregnancy complications to examine whether associations remained (except for models with HDP as the exposure, in which we only adjust for diabetes/glycosuria). Regression coefficients are mean differences in the outcome in each category of exposure compared to the reference category that has a null value of 0. When the outcomes were log transformed (insulin, proinsulin, triglycerides, and CRP), coefficients were back transformed (exponentiated) to obtain ratios of geometric means for each exposure category in relation to the reference category that has the null value of 1, as were the values of the 95% confidence intervals.
We derived the odds of CVD by dividing the Framingham risk score by (1-score). We then used the natural log of the odds in multivariable linear regression models and back transformed (exponentiated) coefficients to obtain the OR of having a CVD event over 10 years, with a null value of 1 in the reference category. We examined whether replacing maternal education with household social class, or whether using the modified score without diabetes made a difference to results. Finally, we repeated analyses excluding 31 women who reported having CVD on the follow-up questionnaire and restricting analyses to include only nulliparous women (n=1,614).
Results
Of the 4,376 women who attended the clinic (the eligible cohort), 3,364 (for BMI and blood pressure) and 3,224 (blood measures) were included in analyses. Characteristics of women included in this study compared to women in the eligible cohort who were excluded from analyses due to missing data, are presented in Table 1. Women included in the analyses were slightly older at the birth of the index child, were less likely to belong to a manual social class and to have a preterm birth. They were less likely to smoke and had a lower mean BMI, waist circumference, insulin, proinsulin and triglyceride levels at follow-up. No other differences were found.
Table 1.
Characteristics of eligible women included and excluded from analyses.
| Excluded | N | Included in analyses* | p-value | |
|---|---|---|---|---|
| Age at birth of index child, mean (SD) | 29.3 (4.7) | 812 | 29.8 (4.4) | 0.01 |
| Manual social class, % (N) | 13.8 (92) | 716 | 10.5 (349) | 0.01 |
| University level education, % (N) | 16.7 (118) | 759 | 19.8 (678) | 0.09 |
| Pre-pregnancy BMI (kg/m2), mean (SD) | 22.7 (3.8) | 527 | 22.5 (3.3) | 0.08 |
| 3+ parity, % (N) | 5.2 (36) | 749 | 4.0 (139) | 0.15 |
| No smoking during pregnancy, % (N) | 84.2 (627) | 797 | 86.0 (2,937) | 0.19 |
| Pregnancy diabetes, % (N) | 735 | 0.97 | ||
| None | 96.3 (660) | 96.1 (3,279) | ||
| GDM | 0.4 (3) | 0.5 (18) | ||
| Pre-gestational diabetes | 0.4 (3) | 0.4 (13) | ||
| Glycosuria | 2.9 (17) | 3.0 (106) | ||
| HDP, % (N) | 846 | 0.94 | ||
| None | 83.6 (660) | 83.9 (2,868) | ||
| Gestational hypertension | 14.2 (115) | 14.1 (479) | ||
| Preeclampsia | 2.3 (19) | 2.1 (69) | ||
| Preterm birth % (N) | 6.4 (52) | 865 | 4.3 (145) | 0.03 |
| Size for gestational age, % (N) | 501 | 0.10 | ||
| SGA | 11.1 (50) | 8.1 (278) | ||
| AGA | 79.3 (356) | 81.8 (2,793) | ||
| LGA | 9.6 (43) | 10.1 (345) | ||
| Follow-up measures | ||||
| Age (years), mean (SD) | 47.8 (4.6) | 1,071 | 48.0 (4.4) | 0.13 |
| BMI (kg/m2), mean (SD) | 27.0 (5.7) | 1,067 | 26.4 (5.1) | 0.003 |
| Waist circumference (cm), mean (SD) | 85.4 (13.0) | 1,068 | 84.0 (12.0) | 0.0001 |
| SBP (mmHg), mean (SD) | 118.1 (12.2) | 982 | 118.2 (12.5) | 0.92 |
| DBP (mmHg), mean (SD) | 71.8 (8.0) | 982 | 71.6 (8.1) | 0.62 |
| Glucose (mmol/l), mean (SD) | 5.29 (0.87) | 1,003 | 5.29 (1.01) | 0.95 |
| Insulin (m U/l), mean (SD) | 4.98 (1.94) | 1,002 | 4.76 (1.82) | 0.05 |
| Proinsulin (pmol/l), mean (SD) | 6.04 (1.84) | 1,003 | 5.79 (1.71) | 0.03 |
| Triglycerides (mmol/l), mean (SD) | 0.95 (1.56) | 1,003 | 0.92 (1.53) | 0.07 |
| HDL (mmol/l), mean (SD) | 1.46 (0.40) | 1,003 | 1.48 (0.39) | 0.19 |
| LDL (mmol/l), mean (SD) | 2.97 (0.82) | 1,003 | 2.98 (0.80) | 0.70 |
| C-reactive protein (mg/l), mean (SD) | 1.13 (3.15) | 1,003 | 1.09 (3.10) | 0.41 |
| Non-smokers, % (N) | 85.4 (626) | 733 | 89.0 (2,560) | 0.007 |
Included are women who contributed to any analysis, thus the number varies between 2,877 women included in analyses using smoking at 18 years post pregnancy and 3,416 women who contributed to at least one exposure/outcome pair analysed.
In the whole ALSPAC cohort (N=14,541), 45% of women had at least one pregnancy related complication. 38% of the eligible cohort (N=1,663) had at least one pregnancy related complication and of the women included in analyses, 35.8% (N=1,204) had at least one pregnancy related complication. 1,002 women (29.8%) had one complication, 175 (5.2%) had two, 26 (0.8%) had three and one woman had all four. The prevalence of each pregnancy related complication is given in Table 1 and Figure 1 shows the overlap between pregnancy related complications. Four women with GDM also had preeclampsia and an additional four had gestational hypertension. In women with pre-gestational diabetes equivalent numbers were 4 and 1, and for women with glycosuria, 17 and 2, respectively All 13 women with pre-gestational diabetes were diagnosed before the age of 29 and two were treated by diet only. Thirty-one women (1.3%) were diagnosed with CVD during follow-up. Overall in the cohort future risk of CVD based on the 10 year Framingham score was low (as would be expected given that the mean age of participants is 48 years), with a median predicted risk of 3.0% (inter-quartile range: 2.2-4.2%), with some evidence that this varied by pregnancy related complications (Figure 2).
Figure 1.
Venn diagram of pregnancy related complications.
Figure 2.
Median (inter-quartile range) Framingham CVD score by pregnancy related complications.
Associations of pregnancy diabetes with cardiovascular risk factors are presented in Table 2. Women with glycosuria and GDM had a greater mean BMI and waist circumference 18 years after the index pregnancy compared to women without pregnancy diabetes in the basic, age adjusted model (model 1), but associations were attenuated to the null in the confounder adjusted model (model 2). Glycosuria and GDM were also associated with higher levels of insulin, proinsulin and triglycerides in model 1. In model 2 associations for insulin and proinsulin persisted, though confidence intervals for GDM included the null. Women with pre-gestational diabetes, GDM and glycosuria all had higher fasting glucose levels compared to women without pregnancy diabetes. The greatest mean difference was in women with pre-gestational diabetes (mean difference compared to women without preganancy diabetes: 6.82 mmol/l), then in women with GDM (2.35 mmol/l) and then women with glycosuria (0.28 mmol/l), when adjusting for potential confounders (model 2). Women with pre-gestational diabetes also had lower LDL cholesterol compared to women without pregnancy diabetes. No evidence of associations of pregnancy diabetes with blood pressure or HDL cholesterol was found. There was evidence of increased calculated risk of a CVD event over 10 years in women with glycosuria, GDM and pre-gestational diabetes in model 1; upon adjustment for potential confounders the odds ratio was 1.10 for women with glycosuria, 1.26 in women with GDM and 1.56 in women with pre-gestational diabetes with confidence intervals for glycosuria and GDM spanning the null. Overall, adjusting for HDP, preterm birth and size for gestational age (model 3) did not substantially change results.
Table 2.
Multivariable associations of pre-gestational diabetes, GDM and glycosuria with cardiovascular risk factors measured 18 years after the index pregnancy.
| Outcome | No glycosuria or diabetes N=3,061 |
Glycosuria N=99 |
GDM N=17 |
Pre-gestational diabetes N=10 |
|---|---|---|---|---|
| Reference | Mean difference (95%CI) |
Mean difference (95%CI) |
Mean difference (95%CI) |
|
| BMI (kg/m2)* N=3,364 | ||||
| Age adjusted mean (SE) | 29.41 (0.96) | 30.83 (1.07) | 34.66 (1.54) | 29.17 (1.71) |
| M1 | 0 | 1.42 (0.42, 2.43) | 5.25 (2.89, 7.60) | −0.24 (−3.01, 2.53) |
| M2 | 0 | 0.19 (−0.47, 0.86) | −1.07 (−2.64, 0.50) | −1.49 (−3.32, 0.34) |
| M3 | 0 | 0.17 (−0.49, 0.84) | −1.37 (−2.94, 0.21) | −1.70 (−3.53, 0.12) |
|
|
||||
| Waist circumference (cm) N=3,358 | ||||
| Age adjusted mean (SE) | 84.77 (2.26) | 88.11 (2.52) | 97.95 (3.62) | 83.88 (4.01) |
| M1 | 0 | 3.34 (0.99, 5.70) | 13.18 (7.64, 18.71) | −0.89 (−7.40,5.62) |
| M2 | 0 | 0.78 (−0.98, 2.53) | 0.17 (−3.98, 4.32) | −3.43 (−8.28, 1.42) |
| M3 | 0 | 0.65 (−1.10, 2.40) | −0.87(−5.04, 3.31) | −4.26 (−9.10, 0.58) |
|
|
||||
| SBP (mmHg)* N=3,364 | ||||
| Age adjusted mean (SE) | 103.12 (2.34) | 105.57 (2.61) | 108.24 (3.75) | 109.02 (4.16) |
| M1 | 0 | 2.46 (0.01, 4.90) | 5.12 (−0.62, 10.86) | 5.90 (−0.85, 12.65) |
| M2 | 0 | 1.55 (−0.83, 3.92) | 0.18 (−5.44, 5.80); | 4.82 (−1.74, 11.38) |
| M3 | 0 | 1.57 (−0.73, 3.87) | −0.97 (−6.46, 4.52) | 3.60 (−2.76, 9.97) |
|
|
||||
| DBP (mmHg)* N=3,364 | ||||
| Age adjusted mean (SE) | 68.77 (1.54) | 70.37 (1.72) | 69.31 (2.47) | 68.83 (2.74) |
| M1 | 0 | 1.60 (−0.01, 3.20) | 0.54 (−3.23, 4.31) | 0.06 (−4.38, 4.49) |
| M2 | 0 | 1.03 (−0.54, 2.60) | −2.50 (−6.20, 1.21) | −0.60 (−4.92, 3.73) |
| M3 | 0 | 1.06 (−0.47, 2.60) | −3.26 (−6.91, 0.39) | −1.25 (−5.48, 2.99) |
|
|
||||
| Glucose (mmol/l) | ||||
| Age adjusted mean (SE) | 4.93 (0.17) | 5.26 (0.19) | 7.51 (0.27) | 11.81 (0.31) |
| M1 | 0 | 0.34 (0.17, 0.52) | 2.58 (2.18, 2.99) | 7.13 (6.58, 7.67) |
| M2 | 0 | 0.28 (0.11, 0.46) | 2.35 (1.94, 2.76) | 6.82 (6.32, 7.32) |
| M3 | 0 | 0.28 (0.10, 0.46) | 2.25 (1.83, 2.67) | 6.78 (6.28, 7.28) |
|
|
||||
| HDL (mmol/l) | ||||
| Age adjusted mean (SE) | 0.79 (0.07) | 0.70 (0.08) | 0.61 (0.12) | 0.87 (0.13) |
| M1 | 0 | −0.09 (−0.16, −0.01) | −0.17 (−0.35, 0.01) | 0.10 (−0.13, 0.34) |
| M2 | 0 | −0.05 (−0.13, 0.02) | −0.01 (−0.19, 0.16) | 0.13 (−0.08, 0.33) |
| M3 | 0 | −0.05 (−0.13, 0.02) | 0.0002 (−0.17, 0.17) | 0.14 (−0.07,0.35) |
|
|
||||
| LDL (mmol/l) | ||||
| Age adjusted mean (SE) | 1.29 (0.15) | 1.33 (0.17) | 1.15 (0.24) | 0.34 (0.27) |
| M1 | 0 | 0.01 (−0.14, 0.17) | −0.14 (−0.51, 0.23) | −0.86 (−1.35, −0.37) |
| M2 | 0 | 0.02 (−0.14, 0.18) | −0.23 (−0.60, 0.13) | −0.97 (−1.41, −0.53) |
| M3 | 0 | 0.02 (−0.14,0.17) | −0.27 (−0.64,0.10) | −1.00 (−1.44,−0.55) |
| Reference |
Ratio of geometric
means (95%CI) |
Ratio of geometric
means (95%CI) |
Ratio of geometric
means (95%CI) |
|
| Insulin (m U/l) | ||||
| Age adjusted geometric mean (95%CI) | 6.70 (5.34, 8.40) | 8.47 (6.59, 10.89) | 10.40 (7.27, 14.87) | 5.58 (3.61, 8.62) |
| M1 | 1 | 1.27 (1.13, 1.42) | 1.55 (1.17, 2.04) | 0.83 (0.57, 1.20) |
| M2 | 1 | 1.19 (1.06, 1.33) | 1.12 (0.86, 1.46) | 0.74 (0.52, 1.05) |
| M3 | 1 | 1.19 (1.06, 1.33) | 1.10 (0.84, 1.43) | 0.73 (0.52, 1.04) |
| Proinsulin (pmol/l) | ||||
| Age adjusted geometric mean (95%CI) | 4.58 (3.74, 5.62) | 5.63 (4.49, 7.06) | 7.70 (5.58, 10.64) | 3.92 (2.65, 5.80) |
| M1 | 1 | 1.23 (1.11, 1.37) | 1.67 (1.30, 2.15) | 0.85 (0.61, 1.19) |
| M2 | 1 | 1.15 (1.05, 1.27) | 1.24 (0.98, 1.57) | 0.76 (0.56, 1.05) |
| M3 | 1 | 1.15 (1.04, 1.27) | 1.21 (0.95, 1.53) | 0.76 (0.56, 1.04) |
| Triglycerides (mmol/l) | ||||
| Age adjusted geometric mean (95%CI) | 0.64 (0.54, 0.75) | 0.71 (0.59, 0.85) | 0.71 (0.55, 0.91) | 0.55 (0.41, 0.75) |
| M1 | 1 | 1.11 (1.02, 1.21) | 1.11 (0.91, 1.35) | 0.87 (0.67, 1.13) |
| M2 | 1 | 1.08 (0.99, 1.17) | 0.96 (0.79, 1.16) | 0.82 (0.64, 1.06) |
| M3 | 1 | 1.07 (0.99, 1.16) | 0.93 (0.77, 1.13) | 0.81 (0.62, 1.04) |
| C-reactive protein (mg/l) | ||||
| Age adjusted geometric mean (95%CI) | 1.20 (0.78, 1.84) | 1.38 (0.86, 2.22) | 1.31 (0.66, 2.58) | 2.38 (1.05, 5.44) |
| M1 | 1 | 1.15 (0.92, 1.44) | 1.07 (0.63, 1.81) | 1.96 (0.97, 3.96) |
| M2 | 1 | 1.01 (0.82, 1.25) | 0.57 (0.35, 0.94) | 1.55 (0.80, 2.98) |
| M3 | 1 | 1.02 (0.83, 1.25) | 0.56 (0.34, 0.92) | 1.57 (0.81, 3.02) |
| OR for predicted CVD event in next 10 years based on Framingham score N=2,172 | ||||
| Reference | OR (95%CI) | OR (95%CI) | OR (95%CI) | |
| Mean score, % (SE) | 3.69 (0.06) | 4.11 (0.33) | 5.52 (0.75) | 5.37 (0.99) |
| M1** | 1 | 1.12 (0.97, 1.29) | 1.43 (1.04, 1.96) | 1.56 (1.03, 2.37) |
| M2 | 1 | 1.10 (0.97, 1.25) | 1.26 (0.95, 1.68) | 1.56 (1.07, 2.26) |
| M3 | 1 | 1.11 (0.98, 1.25) | 1.23 (0.93, 1.25) | 1.55 (1.07, 2.24) |
Shaded rows include ratios of geometric means (of insulin, proinsulin, triglycerides and CRP) or odds ratios of CVD obtained by back transforming coefficients since outcomes were log transformed in analyses. These are interpreted as percentage (relative) differences, for example the results from model 3 for pro-insulin show a 16% increase in those with glycosuria compared with those with no pregnancy diabetes. Similarly for CVD events, pre-gestational diabetes is associated with a 55% increase in the predicted odds of an event.
M1: adjusting for age at measurement.
M2: additional adjustment for pre-pregnancy BMI, education (yes/no university level), parity, and smoking during pregnancy.
M3: additional adjustment for HDP, preterm delivery and size for gestational age.
N for exposure categories are 3,487, 105, 18 and 14, for no diabetes/glycosuria, glycosuria, gestational diabetes, and pre-gestational diabetes.
Crude estimate, not adjusted for any covariables.
Associations of HDP with cardiovascular risk factors are presented in Table 3. Both gestational hypertension and preeclampsia were associated with greater BMI, waist, systolic and diastolic blood pressure, insulin, proinsulin and triglycerides and lower HDL cholesterol in both the basic and confounder adjusted models. In the confounder adjusted models point estimates for the associations of HDP and preeclampsia with HDL cholesterol, insulin and triglycerides were of similar magnitude but confidence intervals for preeclampsia (but not for HDP) included the null (because of the small number of women with preeclampsia). Preeclampsia was also associated with higher glucose and C-reactive protein, whilst associations for gestational hypertension were weaker. No associations with LDL cholesterol were noted. The calculated risk of a CVD event over 10 years was elevated in women with HDP compared to those without (models 1 and 2). Adjusting for pregnancy diabetes did not alter results (model 3).
Table 3.
Multivariable associations of HDP with cardiovascular risk factors measured 18 years after the index pregnancy.
| Outcome | No HDP N=2,688 |
Gestational hypertension N=438 |
Preeclampsia N=61 |
|---|---|---|---|
| Reference | Mean difference (95%CI) |
Mean difference (95%CI) |
|
| BMI (kg/m2)* N=3,364 | |||
| Age adjusted mean (SE) | 28.89 (0.95) | 31.22 (0.96) | 33.16 (1.13) |
| M1 | 0 | 2.33 (1.84, 2.82) | 4.26 (3.07, 5.46) |
| M2 | 0 | 0.58 (0.24, 0.91) | 1.01 (0.20, 1.82) |
| M3 | 0 | 0.59 (0.25, 0.92) | 1.09 (0.28,1.90) |
| Waist circumference (cm) N=3,358 | |||
| Age adjusted mean (SE) | 83.63 (2.24) | 88.76 (2.27) | 92.38 (2.66) |
| M1 | 0 | 5.12 (3.97, 6.27) | 8.74 (5.90, 11.58) |
| M2 | 0 | 1.50 (0.61, 2.38) | 1.86 (−0.30, 4.02) |
| M3 | 0 | 1.51 (0.63, 2.40) | 1.91 (−0.26, 4.08) |
| SBP (mmHg)* N=3,364 | |||
| Age adjusted mean (SE) | 100.81 (2.25) | 110.20 (2.29) | 111.05 (2.68) |
| M1 | 0 | 9.39 (8.23, 10.55) | 10.24 (7.40, 13.09) |
| M2 | 0 | 8.33 (7.17, 9.49) | 8.27 (5.45, 11.09) |
| M3 | 0 | 8.31 (7.15, 9.47) | 8.31 (5.48,11.14) |
| DBP (mmHg)* N=3,364 | |||
| Age adjusted mean (SE) | 67.53 (1.50) | 72.66 (1.52) | 74.03 (1.78) |
| M1 | 0 | 5.12 (4.35, 5.89) | 6.50 (4.61, 8.39) |
| M2 | 0 | 4.48 (3.71, 5.25) | 5.29 (3.41, 7.16) |
| M3 | 0 | 4.49 (3.71, 5.26) | 5.47 (3.59, 7.36) |
| Glucose (mmol/l) | |||
| Age adjusted mean (SE) | 4.92 (0.19) | 5.08 (0.19) | 5.43 (0.23) |
| M1 | 0 | 0.16 (0.06, 0.26) | 0.51 (0.26, 0.75) |
| M2 | 0 | 0.08 (−0.02, 0.18) | 0.37 (0.13, 0.62) |
| M3 | 0 | 0.06 (−0.03, 0.15) | 0.31 (0.09, 0.53) |
| HDL (mmol/l) | |||
| Age adjusted mean (SE) | 0.83 (0.07) | 0.74 (0.07) | 0.70 (0.09) |
| M1 | 0 | −0.08 (−0.12, −0.05) | −0.12 (−0.22, −0.03) |
| M2 | 0 | −0.04 (−0.08, −0.01) | −0.05 (−0.14, 0.04) |
| M3 | 0 | −0.05 (−0.09, −0.01) | −0.05 (−0.14, 0.04) |
| LDL (mmol/l) | |||
| Age adjusted mean (SE) | |||
| M1 | 0 | 0.03 (−0.05, 0.11) | 0.10 (−0.10, 0.29) |
| M2 | 0 | 0.02 (−0.06,0.10) | 0.07 (−0.13,0.27) |
| M3 | 0 | 0.02 (−0.06,0.10) | 0.08 (−0.12,0.27) |
| Reference |
Ratio of geometric means
(95%CI) |
Ratio of geometric
means (95%CI) |
|
| Insulin (m U/l) | |||
| Age adjusted geometric mean (95%CI) | 6.51 (5.19, 8.16) | 7.68 (6.10, 9.66) | 8.57 (6.55, 11.23) |
| M1 | 1 | 1.18 (1.11, 1.25) | 1.32 (1.13, 1.53) |
| M2 | 1 | 1.08 (1.02, 1.15) | 1.12 (0.97, 1.29) |
| M3 | 1 | 1.08 (1.02, 1.15) | 1.12 (0.97, 1.29) |
| Proinsulin (pmol/l) | |||
| Age adjusted geometric mean (95%CI) | 4.47 (3.64, 5.48) | 5.16 (4.20, 6.35) | 5.93 (4.65, 7.56) |
| M1 | 1 | 1.16 (1.09, 1.22) | 1.33 (1.16, 1.52) |
| M2 | 1 | 1.06 (1.01, 1.12) | 1.15 (1.01, 1.30) |
| M3 | 1 | 1.07 (1.01, 1.12) | 1.13 (1.00, 1.29) |
| Triglycerides (mmol/l) | |||
| Age adjusted geometric mean (95%CI) | 0.63 (0.54, 0.74) | 0.68 (0.58, 0.81) | 0.70 (0.58, 0.84) |
| M1 | 1 | 1.09 (1.04, 1.14) | 1.11 (1.00, 1.23) |
| M2 | 1 | 1.05 (1.01, 1.10) | 1.04 (0.94, 1.16) |
| M3 | 1 | 1.05 (1.01, 1.10) | 1.05 (0.94, 1.16) |
| C-reactive protein (mg/l) | |||
| Age adjusted geometric mean (95%CI) | 1.14 (0.74, 1.74) | 1.43 (0.93, 2.22) | 1.90 (1.14, 3.16) |
| M1 | 1 | 1.26 (1.13, 1.41) | 1.67 (1.26, 2.21) |
| M2 | 1 | 1.07 (0.96, 1.19) | 1.21 (0.93, 1.58) |
| M3 | 1 | 1.07 (0.96, 1.19) | 1.25 (0.96, 1.64) |
| OR for predicted CVD event in next 10 years based on Framingham score N=2,172 | |||
| Reference | OR (95%CI) | OR (95%CI) | |
| Mean score, % (SE) | 3.55 (0.06) | 4.58 (0.15) | 5.09 (0.41) |
| M1** | 1 | 1.28 (1.21, 1.39) | 1.42 (1.19, 1.69) |
| M2 | 1 | 1.27 (1.19, 1.35) | 1.31 (1.11, 1.53) |
| M3 | 1 | 1.27 (1.19, 1.35) | 1.30 (1.11, 1.52) |
Shaded rows include ratios of geometric means (of insulin, proinsulin, triglycerides and CRP) or odds ratios of CVD obtained by back transforming coefficients since outcomes were log transformed in analyses. These are interpreted as percentage (relative) differences, for example the results from model 3 for pro-insulin show a 7% and 12% increase in those with gestational hypertension and pre-eclampsia compared with those with no HDP, respectively, and equivalent results for CVD events would be interpreted as 27% and 30% increases in the predicted odds of an event.
M1: adjusting for age at measurement.
M2: additional adjustment for pre-pregnancy BMI, education (yes/no university level), parity, and smoking during pregnancy.
M3: additional adjustment for pregnancy diabetes.
N for HDP, gestational hypertension and pre-eclampsia are 3,044, 504, and 76.
Crude estimate, not adjusted for any covariables.
Mothers to LGA babies had higher mean BMI, waist circumference, glucose, insulin, proinsulin, triglycerides and C-reactive protein and lower HDL cholesterol levels than women with an AGA baby in the age adjusted model (model 1). When adjusting for confounders, only the associations with waist circumference and glucose remained. The association with glucose was attenuated in model 3 after adjustment for other pregnancy related complications and was due to the adjustment for pregnancy diabetes (result not shown). Women with a SGA baby had higher systolic and diastolic blood pressure compared to women with an AGA baby in both models 1 and 2. Adjusting for pregnancy diabetes, HDP and preterm birth did not alter results (model 3). An initial association of LGA with the calculated 10 year CVD risk was attenuated to the null when adjusting for confounders, whilst SGA was associated with a 10% increased risk in model 2. Associations of size for gestational age with outcomes are presented in Table 4.
Table 4.
Multivariable associations of size-for-gestational-age with cardiovascular risk factors measured 18 years after the index pregnancy.
| Outcome | SGA=262 | AGA N=2,630 |
LGA N=332 |
|---|---|---|---|
| Mean difference (95%CI) |
Reference | Mean difference (95%CI) |
|
| BMI (kg/m2)* N=3,364 | |||
| Age adjusted mean (SE) | 29.35 (0.99) | 29.69 (0.96) | 31.72 (1.01) |
| M1 | −0.34 (−0.97, 0.29) | 0 | 2.03 (1.45, 2.60) |
| M2 | −0.08 (−0.50, 0.34) | 0 | 0.23 (−0.15, 0.62) |
| M3 | −0.17 (−0.59, 0.25) | 0 | 0.26 (−0.12, 0.65) |
| Waist circumference (cm) N=3,358 | |||
| Age adjusted mean (SE) | 84.37 (2.32) | 85.56 (2.25) | 90.96 (2.36) |
| M1 | −1.18 (−2.66, 0.29) | 0 | 5.40 (4.06, 6.74) |
| M2 | −0.78 (−1.90, 0.33) | 0 | 1.72 (0.70, 2.74) |
| M3 | −1.00 (−2.12, 0.12) | 0 | 1.74 (0.72, 2.76) |
| SBP (mmHg)* N=3,364 | |||
| Age adjusted mean (SE) | 105.19 (2.42) | 102.85 (2.34) | 103.95 (2.46) |
| M1 | 2.35 (0.81,3.88) | 0 | 1.10 (−0.30, 2.50) |
| M2 | 2.67 (1.16,4.18) | 0 | −0.33 (−1.71, 1.05) |
| M3 | 1.93 (0.46,3.41) | 0 | −0.40 (−1.74,0.95) |
| DBP (mmHg)* N=3,364 | |||
| Age adjusted mean (SE) | 70.50 (1.59) | 68.50 (1.54) | 69.20 (1.61) |
| M1 | 2.00 (0.99, 3.01) | 0 | 0.70 (−0.21, 1.62) |
| M2 | 2.17 (1.17, 3.16) | 0 | −0.20 (−1.10, 0.71) |
| M3 | 1.72 (0.75, 2.70) | 0 | −0.15 (−1.05, 0.74) |
| Glucose (mmol/l) | |||
| Age adjusted mean (SE) | 4.93 (0.20) | 5.00 (0.19) | 5.23 (0.20) |
| M1 | −0.07 (−0.19, 0.06) | 0 | 0.24 (0.12, 0.35) |
| M2 | −0.08 (−0.20, 0.05) | 0 | 0.15 (0.04, 0.27) |
| M3 | −0.06 (−0.17, 0.05) | 0 | 0.05 (−0.05, 0.15) |
| HDL (mmol/l) | |||
| Age adjusted mean (SE) | 0.82 (0.08) | 0.79 (0.07) | 0.73 (0.08) |
| M1 | 0.03 (−0.02, 0.08) | 0 | −0.07 (−0.11, −0.02) |
| M2 | 0.03 (−0.01, 0.08) | 0 | −0.01 (−0.06, 0.03) |
| M3 | 0.04 (−0.01, 0.09) | 0 | −0.01 (−0.06, 0.03) |
| LDL (mmol/l) | |||
| Age adjusted mean (SE) | 1.32 (0.16) | 1.35 (0.15) | 1.42 (0.16) |
| M1 | −0.03 (−0.13, 0.07) | 0 | 0.08 (−0.02, 0.17) |
| M2 | −0.04 (−0.14, 0.06) | 0 | −0.01 (−0.06, 0.03) |
| M3 | −0.05 (−0.15, 0.05) | 0 | 0.05 (−0.04, 0.14) |
|
Ratio of geometric
means (95%CI) |
Reference | Ratio of geometric means (95%CI) | |
| Insulin (m U/l) | |||
| Age adjusted geometric mean (SE) | 6.73 (5.35, 8.1) | 6.88 (5.48, 8.64) | 7.58 (5.97, 9.62) |
| M1 | 0.98 (0.91, 1.06) | 1 | 1.10 (1.03, 1.18) |
| M2 | 0.99 (0.92, 1.06) | 1 | 0.99 (0.93, 1.06) |
| M3 | 0.98 (0.91, 1.06) | 1 | 0.99 (0.93, 1.06) |
| Proinsulin (pmol/l) | |||
| Age adjusted geometric mean (SE) | 4.68 (3.79, 5.78) | 4.68 (3.82, 5.75) | 5.27 (4.25, 6.53) |
| M1 | 1.00 (0.93, 1.07) | 1 | 1.13 (1.06, 1.20) |
| M2 | 1.00 (0.94, 1.07) | 1 | 1.02 (0.96, 1.08) |
| M3 | 0.99 (0.93, 1.06) | 1 | 1.02 (0.96, 1.08) |
| Triglycerides (mmol/l) | |||
| Age adjusted geometric mean (SE) | 0.63 (0.53, 0.74) | 0.65 (0.55, 0.76) | 0.71 ((0.60, 0.84) |
| M1 | 0.97 (0.92, 1.02) | 1 | 1.10 (1.04, 1.15) |
| M2 | 0.97 (0.92, 1.02) | 1 | 1.04 (0.99, 1.09) |
| M3 | 0.96 (0.91, 1.01) | 1 | 1.04 (0.99, 1.09) |
| C-reactive protein (mg/l) | |||
| Age adjusted geometric mean (SE) | 1.31 (0.84, 2.04) | 1.31 (0.84, 2.04) | 1.41 (0.90, 2.22) |
| M1 | 1.09 (0.94, 1.26) | 1 | 1.18 (1.03, 1.34) |
| M2 | 1.10 (0.96, 1.26) | 1 | 0.96 (0.85, 1.08) |
| M3 | 1.09 (0.95, 1.25) | 1 | 0.97 (0.85, 1.09) |
| OR for predicted CVD event in next 10 years based on Framingham score N=2,172 | |||
| OR (95%CI) | Reference | OR (95%CI) | |
| Mean score, % (SE) | 3.94 (0.20) | 3.65 (0.06) | 4.12 (0.18) |
| M1** | 1.05 (0.96, 1.15) | 1 | 1.12 (1.04, 1.21) |
| M2 | 1.10 (1.01, 1.19) | 1 | 1.00 (0.93, 1.08) |
| M3 | 1.08 (0.99, 1.16) | 1 | 0.99 (0.92, 1.06) |
Shaded rows include ratios of geometric means (of insulin, proinsulin, triglycerides and CRP) or odds ratios of CVD obtained by back transforming coefficients since outcomes were log transformed in analyses. These are interpreted as percentage (relative) differences, for example the results from model 3 for pro-insulin show a 2% increase in women who delivered a LGA baby compared with those who delivered an AGA baby (though confidence intervals include the null value).
M1: adjusting for age at measurement.
M2: additional adjustment for pre-pregnancy BMI, education (yes/no university level), parity, and smoking during pregnancy.
M3: additional adjustment for pregnancy diabetes, HDP and preterm delivery.
SGA: N=274 AGA: N=2,751, LGA: N=339.
Crude association.
Preterm birth was associated with systolic blood pressure and more weakly with diastolic blood pressure, but these associations were attenuated upon adjustment for other pregnancy related complications. Specifically, the attenuation was due to the adjustment for HDP (results not shown). When the analysis was restricted to women without HDP, there was no longer any evidence of an association between preterm birth and systolic or diastolic blood pressure (results not shown). There was no strong evidence of associations of preterm birth with any of the other cardiovascular risk outcomes or with the calculated 10 year CVD risk (Table 5).
Table 5.
Multivariable associations of preterm birth with cardiovascular risk factors measured 18 years after the index pregnancy.
| Outcome | Not Preterm N=3,085 |
Preterm N=139 |
|---|---|---|
| Reference | Mean difference (95%CI) |
|
| BMI (kg/m2)* N=3,364 | ||
| Age adjusted mean (SE) | 29.54 (0.97) | 29.17 (1.04) |
| M1 | 0 | −0.36 (−1.21, 0.49) |
| M2 | 0 | −0.04 (−0.60, 0.53) |
| M3 | 0 | −0.14 (−0.71, 0.42) |
| Waist circumference (cm) N=3,358 | ||
| Age adjusted mean (SE) | 85.12 (2.27) | 83.70 (2.44) |
| M1 | 0 | −1.43 (−3.43, 0.57) |
| M2 | 0 | −0.77 (−2.25 ,0.72) |
| M3 | 0 | −1.01 (−2.51, 0.48) |
| SBP (mmHg)* N=3,364 | ||
| Age adjusted mean (SE) | 103.06 (2.34) | 105.02 (2.52) |
| M1 | 0 | 1.97 (−0.10, 4.03) |
| M2 | 0 | 2.21 (0.20, 4.22) |
| M3 | 0 | 1.55 (−0.42, 3.52) |
| DBP (mmHg)* N=3,364 | ||
| Age adjusted mean (SE) | 68.75 (1.54) | 69.88 (1.66) |
| M1 | 0 | 1.13 (−0.23, 2.48) |
| M2 | 0 | 1.26 (−0.07, 2.58) |
| M3 | 0 | 0.85 (−0.46, 2.16) |
| Glucose (mmol/l) | ||
| Age adjusted mean (SE) | 4.95 (0.19) | 5.10 (0.20) |
| M1 | 0 | 0.15 (−0.02, 0.31) |
| M2 | 0 | 0.15 (−0.01, 0.32) |
| M3 | 0 | 0.12 (−0.03, 0.27) |
| HDL (mmol/l) | ||
| Age adjusted mean (SE) | 0.80 (0.07) | 0.86 (0.08) |
| M1 | 0 | 0.07 (0.004, 0.13) |
| M2 | 0 | 0.07 (0.002, 0.13) |
| M3 | 0 | 0.07 (0.01, 0.14) |
| LDL (mmol/l) | ||
| Age adjusted mean (SE) | 1.34 (0.15) | 1.27 (0.16) |
| M1 | 0 | −0.08 (−0.21, 0.06) |
| M2 | 0 | −0.08 (−0.21, 0.06) |
| M3 | 0 | −0.09 (−0.23, 0.05) |
| Reference | Ratio of geometric means (95%CI) | |
| Insulin (m U/l) | ||
| Age adjusted mean (SE) | 6.79 (5.41, 8.52) | 6.93 (5.43, 8.85) |
| M1 | 1 | 1.02 (0.92, 1.13) |
| M2 | 1 | 1.03 (0.94, 1.14) |
| M3 | 1 | 1.02 (0.93, 1.13) |
| Proinsulin (pmol/l) | ||
| Age adjusted mean (SE) | 4.64 (3.78, 5.70) | 4.72 (3.78, 5.88) |
| M1 | 1 | 1.02 (0.93, 1.11) |
| M2 | 1 | 1.03 (0.94, 1.12) |
| M3 | 1 | 1.01 (0.93, 1.11) |
| Triglycerides (mmol/l) | ||
| Age adjusted mean (SE) | 0.64 (0.55, 0.76) | 0.64 (0.54, 0.76) |
| M1 | 1 | 0.99 (0.92, 1.07) |
| M2 | 1 | 1.00 (0.93, 1.07) |
| M3 | 1 | 0.97 (0.91, 1.05) |
| C-reactive protein (mg/l) | ||
| Age adjusted mean (SE) | 1.21 (0.79, 1.87) | 1.19 (0.75, 1.89) |
| M1 | 1 | 0.98 (0.81, 1.19) |
| M2 | 1 | 0.99 (0.83, 1.18) |
| M3 | 1 | 0.94 (0.78, 1.13) |
| OR for predicted CVD event in next 10 years based on Framingham score N=2,172 | ||
| Reference | OR (95%CI) | |
| Mean score, % (SE) | 3.72 (0.06) | 3.72 (0.26) |
| M1** | 1 | 0.96 (0.86, 1.07) |
| M2 | 1 | 0.99 (0.90, 1.10) |
| M3 | 1 | 0.98 (0.89, 1.09) |
Shaded rows include ratios of geometric means (of insulin, proinsulin, triglycerides and CRP) or odds ratios of CVD obtained by back transforming coefficients since outcomes were log transformed in analyses. These are interpreted as percentage (relative) differences, for example the results from model 3 for pro-insulin show a 1% increase in women with a preterm delivery (though confidence intervals include the null value).
M1: adjusting for age at measurement.
M2: additional adjustment for pre-pregnancy BMI, education (yes/no university level), parity, and smoking during pregnancy.
M3: additional adjustment for pregnancy diabetes, HDP and size for gestational age.
Women with term and preterm deliveries: N=3,219 and N=145, respectively.
Crude association.
Additional analyses
When the modified Framingham score without diabetes was used, the odds ratio of CVD in women with GDM compared to women without pregnancy diabetes was 0.88 (95%CI: 0.67, 1.16, model 3), suggesting that the increased risk of diabetes following GDM is the main driver of their increased risk of CVD. In women with glycosuria the OR was 1.10 (0.97, 1.24). In comparison, results for other pregnancy related complications were unchanged. Results were unchanged to those presented in the tables when 31 women who reported having been diagnosed with CVD during follow-up were excluded from analyses. Results were also comparable to those presented when analyses were restricted to the 1,614 nulliparous women in our sample (results available from authors upon request). Replacing maternal education with household social class also did not alter results.
Discussion
In this general population of women, over a third of women experienced at least one pregnancy related complication. Pre-gestational diabetes, GDM and glycosuria were all associated with higher glucose concentrations 18 years after pregnancy even when controlling for potential confounders including pre-pregnancy BMI. Pregnancy diabetes was associated with higher glucose, GDM and glycosuria were also associated with higher insulin and proinsulin, and glycosuria was associated with higher triglyceride levels. Similarly, mothers to LGA babies had higher glucose levels than mothers to AGA babies - in line with the established linear association of maternal glycemic status and the risk of delivering an LGA infant.21
In comparison, both gestational hypertension and preeclampsia were associated with a greater number of cardiovascular risk factors: BMI, waist circumference, systolic and diastolic blood pressure, insulin, proinsulin, triglycerides, and HDL cholesterol. Mothers to SGA babies had higher systolic and diastolic blood pressure compared to mothers with AGA babies, as did mothers who delivered before term, which is in line with intra-uterine growth restriction and preterm delivery resulting in part from placental insufficiency due to hypertension. No other associations between preterm delivery and cardiovascular risk factors were found in our study. Although others have reported associations between preterm birth and CVD,22, 23 in keeping with the current study, this primarily reflected elective preterm birth due to placental dysfunction rather than spontaneous preterm birth.
It was notable that the positive association of preeclampsia with future blood pressure was similar to that of gestational hypertension with future blood pressure, but in general for other risk factors the magnitudes of association appeared stronger for preeclampsia than for gestational hypertension. Further, our results suggest that preeclampsia could be a stronger marker of future CVD than GDM since it is associated with a greater number of cardiovascular risk factors, whereas GDM is linked to greater glycaemia later in life as would be expected. They also suggest that the mechanisms underlying these associations are different since pregnancy diabetes and HDP were associated with different cardiovascular risk factors. GDM and LGA were associated primarily with fasting glucose and insulin, while HDP were associated with blood pressure, lipids and insulin but not glucose. Moreover, the increased calculated risk of CVD in women with GDM is explained by their increased risk of developing diabetes whilst this was not the case for women with HDP. The fact that associations of both pregnancy diabetes and HDP with cardiovascular risk factors and the calculated risk of CVD were not attenuated in models in which they were mutually adjusted for, also suggests largely independent pathways of risk. However, the prevalence of these two exposures (pregnancy diabetes and HDP) was low.
Limitations and strengths
In our study women are still not at an age in which CVD events are common. Therefore we were not able to directly assess the value of adding information on pregnancy related complications to existing risk scores. Moreover, the number of women experiencing more than one pregnancy complication was limited. Larger studies with longer follow-up could be useful in determining whether HDP, pregnancy diabetes or both could be valuable additions to CVD risk prediction scores for women and whether there are interactions between the different complications in their associations with CVD risk. Additional limitations include the lack of information on maternal metabolic control during pregnancy; our lack of knowledge about exactly how GDM was diagnosed; and the probability that our category of women with glycosuria included a heterogeneous group of women, including those with undiagnosed GDM and hyperglycaemia, but also some with blood glucose levels in the normal range.
Information on complications in other pregnancies was also unavailable so that we could not assess associations of ever or recurrent pregnancy related complicated with outcomes, but results were unaltered when analyses were restricted to nulliparous women. Like other cohort studies, ALSPAC suffers from attrition and there were some differences between women included and excluded from analyses. However, there is no reason to believe that these differences would bias results since to do so associations of interest would have to be different amongst those excluded from analyses. Whilst there is no way to check this, there is no reason to suspect that this would be the case.
Main strengths of the study include its prospective design, the duration of follow-up, the use of medical records to obtain detailed obstetric information, the availability of detailed lifestyle data, as well as the range of cardiovascular risk factors that were directly measured. To summarize, HDP, pregnancy diabetes and SGA are all associated with an increased calculated 10 year CVD risk, and preeclampsia may be a stronger predictor of future CVD compared to GDM, whereas the latter is of course more strongly linked to future dysglycaemia. Our results suggest that pregnancy may provide an opportunity to identify women at increased risk of CVD relatively early in life, thus allowing lifestyle changes and, if required, other interventions aimed at reducing that risk to be implemented earlier on in the lifecourse.
Acknowledgements
We are grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. We thank Dr. Luisa Zuccolo for helpful comments on an early version of this work. We thank Anne Currie and Anne Alexander based at Glasgow Royal Infirmary Department of Biochemistry for the excellent technical input to key laboratory measurements.
Funding Sources: The work presented in this paper was funded by grants from the British Heart Foundation (SP/07/008/24066), Wellcome Trust (Grant ref: WT087997) and United States National Institutes of Health (NIH): National Institute of Diabetes and Digestive and Kidney Diseases (Grant ref: R01 DK077659). AF is funded by a UK Medical Research Council research fellowship (Grant ref: 0701594). The UK Medical Research Council (Grant ref: G074882), the Wellcome Trust (Grant ref: WT076467) and the University of Bristol provide core funding support for ALSPAC. The UK Medical Research Council (G0600705) and the University of Bristol provide core funding for the MRC Centre of Causal Analyses in Translational Epidemiology. The views expressed in this paper are those of the authors and not necessarily those of any funding body or others whose support is acknowledged. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. AF and DAL conceived and designed the study. AF did the analysis and all authors contributed to the interpretation of data, the drafting and revising of the manuscript and have given final approval of the version to be published. A. Fraser had full access to all of the data in the study and takes responsibility for its integrity.
Footnotes
Conflict of Interest Disclosures: None
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
References
- 1.Global burden of disease and risk factors. World Bank; 2006. [Google Scholar]
- 2.Bellamy L, Casas JP, Hingorani AD, Williams D. Type 2 diabetes mellitus after gestational diabetes: a systematic review and meta-analysis. The Lancet. 2009;373:1773–9. doi: 10.1016/S0140-6736(09)60731-5. [DOI] [PubMed] [Google Scholar]
- 3.Shah BR, Retnakaran R, Booth GL. Increased Risk of Cardiovascular Disease in Young Women Following Gestational Diabetes Mellitus. Diabetes Care. 2008;31:1668–9. doi: 10.2337/dc08-0706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Bellamy L, Casas JP, Hingorani AD, Williams DJ. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. BMJ. 2007;335:974. doi: 10.1136/bmj.39335.385301.BE. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Smith G Davey, Sterne J, Tynelius P, Lawlor DA, Rasmussen F. Birth weight of offspring and subsequent cardiovascular mortality of the parents. Epidemiology. 2005;16:563–9. doi: 10.1097/01.ede.0000164790.96316.c0. [DOI] [PubMed] [Google Scholar]
- 6.Hastie CE, Smith GC, MacKay DF, Pell JP. Maternal risk of ischaemic heart disease following elective and spontaneous pre-term delivery: retrospective cohort study of 750 350 singleton pregnancies. Int J Epidemiol. 2011 doi: 10.1093/ije/dyq270. epub ahead of print: 28/01/2011. [DOI] [PubMed] [Google Scholar]
- 7.Smith G Davey, Whitley E, Gissler M, Hemminki E. Birth dimensions of offspring, premature birth, and the mortality of mothers. The Lancet. 2000;356:2066–7. doi: 10.1016/S0140-6736(00)03406-1. [DOI] [PubMed] [Google Scholar]
- 8.Sattar N, Greer IA. Pregnancy complications and maternal cardiovascular risk: opportunities for intervention and screening? BMJ. 2002;325:157–60. doi: 10.1136/bmj.325.7356.157. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Rich-Edwards JW, McElrath TF, Karumanchi SA, Seely EW. Breathing Life Into the Lifecourse Approach: Pregnancy History and Cardiovascular Disease in Women. Hypertension. 2010;56:331–4. doi: 10.1161/HYPERTENSIONAHA.110.156810. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Kaaja RJ, Greer IA. Manifestations of Chronic Disease During Pregnancy. JAMA. 2005;294:2751–7. doi: 10.1001/jama.294.21.2751. [DOI] [PubMed] [Google Scholar]
- 11.Sarwar N, Gao P, Seshasai SR, Gobin R, Kaptoge S, Di AE, Ingelsson E, Lawlor DA, Selvin E, Stampfer M, Stehouwer CD, Lewington S, Pennells L, Thompson A, Sattar N, White IR, Ray KK, Danesh J. Diabetes mellitus, fasting blood glucose concentration, and risk of vascular disease: a collaborative meta-analysis of 102 prospective studies. Lancet. 2010;375:2215–22. doi: 10.1016/S0140-6736(10)60484-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Retnakaran R. Glucose tolerance status in pregnancy: a window to the future risk of diabetes and cardiovascular disease in young women. Curr Diabetes Rev. 2009;5:239–44. doi: 10.2174/157339909789804378. [DOI] [PubMed] [Google Scholar]
- 13.Lauenborg J, Mathiesen E, Hansen T, Glumer C, Jorgensen T, Borch-Johnsen K, Hornnes P, Pedersen O, Damm P. The Prevalence of the Metabolic Syndrome in a Danish Population of Women with Previous Gestational Diabetes Mellitus Is Three-Fold Higher than in the General Population. J Clin Endocrinol Metab. 2005;90:4004–10. doi: 10.1210/jc.2004-1713. [DOI] [PubMed] [Google Scholar]
- 14.Magnussen EB, Vatten LJ, Smith G Davey, Romundstad PR. Hypertensive disorders in pregnancy and subsequently measured cardiovascular risk factors. Obstet Gynecol. 2009;114:961–70. doi: 10.1097/AOG.0b013e3181bb0dfc. [DOI] [PubMed] [Google Scholar]
- 15.Romundstad PR, Magnussen EB, Smith G Davey, Vatten LJ. Hypertension in Pregnancy and Later Cardiovascular Risk. Common Antecedents? Circulation. 2010;122:579–84. doi: 10.1161/CIRCULATIONAHA.110.943407. [DOI] [PubMed] [Google Scholar]
- 16.Gunderson EP, Jacobs DR, Jr, Chiang V, Lewis CE, Tsai A, Quesenberry CP, Jr, Sidney S. Childbearing is associated with higher incidence of the metabolic syndrome among women of reproductive age controlling for measurements before pregnancy: the CARDIA study. Am J Obstet Gynecol. 2009;201:177. doi: 10.1016/j.ajog.2009.03.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Lawlor DA, Fraser A, Lindsay RS, Ness A, Dabelea D, Catalano P, Davey SG, Sattar N, Nelson SM. Association of existing diabetes, gestational diabetes and glycosuria in pregnancy with macrosomia and offspring body mass index, waist and fat mass in later childhood: findings from a prospective pregnancy cohort. Diabetologia. 2010;53:89–97. doi: 10.1007/s00125-009-1560-z. [DOI] [PubMed] [Google Scholar]
- 18.Davey DA, MacGillivray I. The classification and definition of the hypertensive disorders of pregnancy. Am J Obstet Gynecol. 1988;158:892–8. doi: 10.1016/0002-9378(88)90090-7. [DOI] [PubMed] [Google Scholar]
- 19.The Framingham Heart Study [AccessedApril 15, 2011];General cardiovascular disease (10 year risk) 2011 http://www framinghamheartstudy org/risk/gencardio html . Available at: URL: http://www.framinghamheartstudy.org/risk/gencardio.html.
- 20.D’Agostino RB, Sr., Vasan RS, Pencina MJ, Wolf PA, Cobain M, Massaro JM, Kannel WB. General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study. Circulation. 2008;117:743–53. doi: 10.1161/CIRCULATIONAHA.107.699579. [DOI] [PubMed] [Google Scholar]
- 21.The HAPO Study Cooperative Research Group Hyperglycemia and Adverse Pregnancy Outcomes. N Engl J Med. 2008;358:1991–2002. doi: 10.1056/NEJMoa0707943. [DOI] [PubMed] [Google Scholar]
- 22.Irgens HU, Reisaeter L, Irgens LM, Lie RT, Roberts JM. Long term mortality of mothers and fathers after pre-eclampsia: population based cohort study / Pre-eclampsia and cardiovascular disease later in life: who is at risk? BMJ. 2001;323:1213–7. doi: 10.1136/bmj.323.7323.1213. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Smith GC, Pell JP, Walsh D. Pregnancy complications and maternal risk of ischaemic heart disease: a retrospective cohort study of 129 290 births. The Lancet. 2001;357:2002–6. doi: 10.1016/S0140-6736(00)05112-6. [DOI] [PubMed] [Google Scholar]