Objective
Knowledge about the causes of placental dysfunction disorders is limited. We performed an intergenerational study, focusing on the risks of placental dysfunction disorders in mothers and fathers who had been born small for gestational age (SGA).
Study Design
Using linked generational data from the Swedish Medical Birth Register from 1973-2006, we identified 321,383 mother-offspring units and 135,637 mother-father-offspring units.
Results
Compared with mothers who had not been born SGA, mothers who had been born SGA had the following adjusted odds ratios: late preeclampsia, 1.41 (95% confidence interval [CI], 1.26–1.57); early preeclampsia, 1.87 (95% CI, 1.38–2.35); placental abruption, 1.60 (95% CI, 1.23–2.09); spontaneous preterm birth, 1.11 (95% CI, 1.00–1.23); and stillbirth, 1.24 (95% CI, 0.84–1.82). Compared with parents who had not been born SGA, the risk of preeclampsia was more than 3-fold increased if both parents had been born SGA, whereas if only the mother had been born SGA, the corresponding risk was increased by only 50%.
Conclusion
There is an intergenerational recurrence of placental dysfunction disorders on the maternal side and most likely also on the paternal side.
Abnormal placentation is associated with the birth of a small-for-gestational-age (SGA) infant, preeclampsia, placental abruption, spontaneous preterm birth, and stillbirth. Each outcome has a recurrence risk in successive births to the same woman, and there are recent reports that these adverse outcomes predispose to each other. The underlying reason for this may be that abnormal placentation also tends to recur in subsequent pregnancies.
For Editors’ Commentary, see Table of Contents
See Journal Club, page 506
Recurrence of adverse pregnancy outcomes across generations has not been extensively investigated. There are studies that support the fact that women or men who had been born SGA, born preterm, or born in pregnancies that were affected by preeclampsia are at increased risks of the corresponding complication when she or his partner get pregnant. There are also a few studies that suggest that disorders that are associated with abnormal placentation may predispose to each other through generations, at least from mother to daughter, with increased risks of preterm birth and preeclampsia in mothers who had been born SGA.
Intergenerational studies may increase our knowledge about the causes of placental dysfunction disorders and their potential correlations. Associations that occur across generations may reflect a family lifestyle or shared genetic causes. Fetal genes, which are inherited from both the mother and father, may be of importance for successful placentation. A genetic predisposition for placental dysfunction disorders may be of further interest, because placental dysfunction disorders have been associated with mother and offspring risks of cardiovascular diseases; genetic factors could be underlying these associations.
Knowledge concerning recurrence of placental dysfunction disorders across generations is limited. Previous studies have investigated only specific single outcomes and assessed recurrence in either mother and offspring or father and offspring. Information on both parents’ birth characteristics in the same analysis might provide new knowledge concerning the maternal and paternal genetic contribution to the cause of placental dysfunction disorders.
To test our hypothesis that placental dysfunction disorders are inherited across generations, we performed a population-based study that included 324,383 mother-offspring units and investigated associations between mothers who had been born SGA and risks of early and late preeclampsia, placental abruption, spontaneous preterm birth, and stillbirth. To further explore the maternal and paternal influence on placental dysfunction disorders, we created 135,637 mother-father-offspring units and estimated the effect of mother, father, or both parents being born SGA on the risks of preeclampsia, placental abruption, and spontaneous preterm birth.
Materials and Methods
The Swedish National Board of Health and Welfare gave access to information from the Swedish Medical Birth Register; Statistics Sweden provided data from the Multi-Generation Register and the Education Register. Individual record linkage between the registries was possible through each individual’s unique personal registration number, which is assigned to Swedish residents at birth or immigration.
The Swedish Medical Birth Register contains data on >98% of all births in Sweden since 1973 and includes demographic data, information on reproductive history and complications during pregnancy, delivery, and the neonatal period. In Sweden, antenatal care is standardized and free of charge. During the first antenatal visit, usually taking place at the end of the first trimester, the mother is interviewed about her medical and obstetric history, including height, weight, and smoking habits. Complications during pregnancy and delivery are classified according to the International Classification of Diseases (ICD), as noted by the responsible doctor at discharge from hospital. Information on each pregnancy and delivery is forwarded to the Birth Register through copies of standardized antenatal, obstetric, and pediatric records.
Study population and parental exposures at birth
By means of the personal registration number, we identified women who had been born in 1973 and later who were included in the Birth Register both as infants and mothers. From data recorded from 1973-2006, we identified a cohort of 326,547 births, with data also on births of the mothers (mother-offspring units). Only single births in both generations were included.
Mothers were classified as being born SGA or not. SGA was defined as a birthweight of 2 SDs or more below the mean birthweight for gestational age according to the sex-specific Swedish fetal growth curve. Gestational age at birth of the mothers was calculated from the last menstrual period. We defined births with a birthweight for gestational age ≥5 SDs above or below the mean for a given gestational age as being misclassified. We excluded 2164 births in which the mother’s birthweight and/or gestational age were missing or misclassified.
The fathers were identified through linkage of the Birth Register to the Multi-Generation Register. If the fathers had been born in Sweden in 1973 or later, we obtained information on their births from the Birth Register. The fathers were classified as being born SGA or not in the same way as the mothers. Fathers to stillborn infants could not be identified in the Multi-Generation Register; therefore, analyses within the mother-father-offspring cohort were restricted to live births.
To assess the parental influence on risks of adverse pregnancy outcomes that are associated with abnormal placentation, we established a mother-offspring cohort and a mother-father-offspring cohort. The mother-offspring units (n = 324,383) were categorized into births with a mother who was born SGA (n = 12,237) or not born SGA (n = 312,146). The mother-father-offspring units (n = 135,637) were categorized into 4 groups: births with no parent who was born SGA (n = 126,326); births with a mother, but not a father, who was born SGA (n = 4885); births with a father, but not a mother, who was born SGA (n = 4240); and births with both parents who had been born SGA (n = 186).
We used the ICD-8 and -9 revisions to identify complications during pregnancy and delivery when the mothers and fathers had been born. Preeclampsia was defined as ICD-8 code 637 and ICD-9 codes 642E-G; placental abruption was defined by ICD-8 code 641.4 and ICD-9 code 641C. In the mother-offspring cohort, we identified 3128 mothers who had been born in pregnancies complicated by preeclampsia and 1085 mothers who had been born after placental abruption. In the mother-father-offspring cohort, we identified 1345 mothers and 1244 fathers who had been born in pregnancies that were complicated by preeclampsia and 482 mothers and 427 fathers who had been born in pregnancies that were complicated by placental abruption. We did not have information on onset of delivery in births before 1990 and were not able to identify spontaneously preterm-born mothers and fathers. Therefore, among the mothers and fathers, preterm births (<37 gestational weeks) included births with both spontaneous and induced onsets of delivery. In the mother-offspring cohort, we identified 13,166 preterm-born mothers; in the mother-father-offspring cohort, there were 6382 and 5481 preterm-born mothers and fathers, respectively.
Exposures at offspring birth
From the data that were collected at the first antenatal visit, we used information about the mother’s height, prepregnancy weight, and smoking habits. We calculated the mother’s body mass index (BMI) as the weight in kilograms divided by the square of height in meters. Information about maternal age and parity was collected at delivery. Information on maternal diseases included chronic hypertension (ICD-9 codes 642A-C, 642H; ICD-10 codes O10-11) and pregestational diabetes mellitus (ICD-9 codes 250, 648A; ICD-10 codes O24-O43). Information on the mother’s education was obtained by linkage with the Education Register as of December 31, 2005. Variables were categorized according to Table 1 .
| Maternal characteristics | n | Rate per 1000 | |||
|---|---|---|---|---|---|
| Preeclampsia (n = 11,631) | Placental abruption (n = 1281) | Spontaneous preterm birth a (n = 14,779) | Stillbirth b (n = 795) | ||
| Age, y | |||||
| ≤24 | 122,974 | 41.1 | 4.0 | 50.0 | 2.6 |
| 25-29 | 148,841 | 33.6 | 4.0 | 44.9 | 2.3 |
| ≥30 | 52,568 | 29.8 | 3.6 | 41.8 | 2.4 |
| Body mass index, kg/m 2 | |||||
| ≤19 | 32,401 | 21.2 | 4.2 | 51.6 | 1.9 |
| 20-24 | 151,812 | 28.2 | 3.9 | 43.4 | 2.1 |
| ≥25 | 98,654 | 51.2 | 3.6 | 43.6 | 3.5 |
| Missing | 41,516 | 38.9 | 4.8 | 59.1 | 1.8 |
| Height, cm | |||||
| ≤163 | 89,537 | 38.3 | 4.4 | 52.9 | 2.9 |
| 164-172 | 160,472 | 34.7 | 3.6 | 42.7 | 2.5 |
| ≥173 | 52,962 | 34.1 | 3.7 | 36.7 | 2.3 |
| Missing | 21,412 | 38.8 | 5.1 | 70.0 | 1.0 |
| Parity | |||||
| 1 | 198,013 | 46.5 | 3.8 | 49.8 | 3.3 |
| ≥2 | 126,370 | 19.3 | 4.2 | 40.9 | 1.2 |
| Smoking habits | |||||
| Nonsmoker | 260,587 | 37.4 | 3.4 | 43.6 | 2.3 |
| Daily smoker | 43,792 | 25.9 | 6.6 | 52.2 | 3.9 |
| Missing | 20,004 | 37.5 | 5.3 | 69.1 | 1.6 |
| Education, y | |||||
| ≤12 | 215,242 | 37.4 | 4.1 | 46.7 | 2.3 |
| ≥13 | 107,600 | 32.9 | 3.5 | 45.2 | 1.6 |
| Missing | 1541 | 24.5 | 9.7 | 61.1 | 92.5 |
| Chronic hypertension | |||||
| Yes | 1301 | 154.5 | 8.5 | 42.2 | 5.4 |
| No | 323,082 | 35.4 | 3.9 | 46.3 | 2.4 |
| Pregestational diabetes mellitus | |||||
| Yes | 1929 | 146.2 | 9.8 | 105.2 | 9.4 |
| No | 322,454 | 35.2 | 3.9 | 46.0 | 2.4 |
| Year of birth | |||||
| 1987-1996 | 24,407 | 43.6 | 4.8 | 57.1 | 2.3 |
| 1997-2001 | 84,630 | 39.2 | 4.2 | 46.4 | 2.7 |
| 2002-2006 | 215,346 | 33.7 | 3.7 | 45.1 | 2.4 |
| Total no/rates | 324,383 | 35.9 | 3.9 | 46.3 | 2.5 |
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