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The Interplay of Variants Near LEKR and CCNL1 and Social Stress in Relation to Birth Size

  • Anokhi Ali Khan,

    Affiliation Department of Epidemiology and Biostatistics, Medical Research Council Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom

  • Alina Rodriguez ,

    Contributed equally to this work with: Alina Rodriguez, Marjo-Riitta Järvelin

    Affiliations Department of Epidemiology and Biostatistics, Medical Research Council Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom, Medical Research Council Social Genetic Developmental Psychiatry Centre, King’s College London, London, United Kingdom, Department of Social Sciences –Psychology, Mid Sweden University, Östersund, Sweden

  • Sylvain Sebert,

    Affiliations Department of Epidemiology and Biostatistics, Medical Research Council Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom, Institute of Health Sciences, University of Oulu, Oulu, Finland

  • Marika Kaakinen,

    Affiliations Institute of Health Sciences, University of Oulu, Oulu, Finland, Biocenter Oulu, University of Oulu, Oulu, Finland

  • Stéphane Cauchi,

    Affiliation Unités Mixte de Recherche 8199, Centre National de la Recherche Scientifique, Institut de Biologie de Lille, Université Lille 2, Institut Pasteur, Lille, France

  • Philippe Froguel,

    Affiliations Unités Mixte de Recherche 8199, Centre National de la Recherche Scientifique, Institut de Biologie de Lille, Université Lille 2, Institut Pasteur, Lille, France, Genomic Medicine, Hammersmith Hospital, Imperial College London, London, United Kingdom

  • Anna-Liisa Hartikainen,

    Affiliation Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, Oulu, Finland

  • Anneli Pouta,

    Affiliations Department of Clinical Sciences/Obstetrics and Gynecology, University of Oulu, Oulu, Finland, National Institute for Health and Welfare, Oulu, Finland

  • Marjo-Riitta Järvelin

    Contributed equally to this work with: Alina Rodriguez, Marjo-Riitta Järvelin

    Affiliations Department of Epidemiology and Biostatistics, Medical Research Council Health Protection Agency Centre for Environment and Health, Imperial College London, London, United Kingdom, Institute of Health Sciences, University of Oulu, Oulu, Finland, Biocenter Oulu, University of Oulu, Oulu, Finland, National Institute for Health and Welfare, Oulu, Finland

The Interplay of Variants Near LEKR and CCNL1 and Social Stress in Relation to Birth Size

  • Anokhi Ali Khan, 
  • Alina Rodriguez, 
  • Sylvain Sebert, 
  • Marika Kaakinen, 
  • Stéphane Cauchi, 
  • Philippe Froguel, 
  • Anna-Liisa Hartikainen, 
  • Anneli Pouta, 
  • Marjo-Riitta Järvelin



We previously identified via a genome wide association study variants near LEKR and CCNL1 and in the ADCY5 genes lead to lower birthweight. Here, we study the impact of these variants and social stress during pregnancy, defined as social adversity and neighborhood disparity, on infant birth size. We aimed to determine whether the addition of genetic variance magnified the observed associations.

Methodology/Principal Findings

We analyzed data from the Northern Finland Birth Cohort 1986 (n = 5369). Social adversity was defined by young maternal age (<20 years), low maternal education (<11 years), and/or single marital status. Neighborhood social disparity was assessed by discrepancy between neighborhoods relative to personal socio-economic status. These variables are indicative of social and socioeconomic stress, but also of biological risk. The adjusted multiple regression analysis showed smaller birth size in both infants of mothers who experienced social adversity (birthweight by −40.4 g, 95%CI −61.4, −19.5; birth length −0.14 cm, 95%CI −0.23, −0.05; head circumference −0.09 cm 95%CI −0.15, −0.02) and neighborhood disparity (birthweight −28.8 g, 95%CI −47.7, −10.0; birth length −0.12 cm, 95%CI −0.20, −0.05). The birthweight-lowering risk allele (SNP rs900400 near LEKR and CCNL1) magnified this association in an additive manner. However, likely due to sample size restriction, this association was not significant for the SNP rs9883204 in ADCY5. Birth size difference due to social stress was greater in the presence of birthweight-lowering alleles.


Social adversity, neighborhood disparity, and genetic variants have independent associations with infant birth size in the mutually adjusted analyses. If the newborn carried a risk allele rs900400 near LEKR/CCNL1, the impact of stress on birth size was stronger. These observations give support to the hypothesis that individuals with genetic or other biological risk are more vulnerable to environmental influences. Our study indicates the need for further research to understand the mechanisms by which genes impact individual vulnerability to environmental insults.


Being born small is associated with increased risk of perinatal morbidity and hospitalization [1], [2], poorer developmental and cognitive outcomes in childhood [3], [4], as well as cardiovascular disease, non-insulin dependent diabetes and intermediate risk factors for chronic diseases in adulthood [5][11]. Examining factors associated with small birth size is important to improve our understanding concerning the links between disturbed fetal growth and the development of disease later in life.

We recently identified in a large-scale genome wide association study (GWAS) that variants near LEKR and CCNL1 and in the ADCY5 are associated with birthweight [12]. Better understanding of the interplay between genetics and environmental factors would strengthen our ability to predict outcomes. Various environmental factors including social stress characterized by low social class, social adversity, or social disparity, have been linked to small birth size, though results are inconsistent [13][17]. This inconsistency may be due to methodological differences across studies concerning measurement of social factors, insufficient statistical power, and inclusion of covariates. Whether genetic variance adds to the association between social stress and birth size has not been previously studied.

Neighborhood environment is also known to be associated with lower birthweight and influence morbidity and mortality [18][20], independent of socio-economic status (SES). Neighborhood social disparity, i.e. living in areas where neighborhood financial capacity differs from individual SES, is associated with higher all-cause mortality [17]. This disparity may be explained partly through differences in access to care and amenities between neighborhoods as well as social stress. However, previous studies, examining the association between neighborhood social disparity and birth size, have not been able to consider important covariates such as smoking, maternal pre-pregnancy BMI, and ethnicity, thus potentially biasing results [18][20]. The interplay of genetic determinants with regard to neighborhood social disparity and birth size is unknown.

Our primary objective was to examine whether social stress and variance in the previously identified birthweight-lowering alleles would contribute in an additive manner to birth size (birthweight, birth length, head circumference and ponderal index). We used data from the Northern Finland Birth Cohort 1986 (NFBC 1986) and operationalized social adversity as the presence of at least one known environmental factor associated with stress at the individual level [21][24]. We used young maternal age, an indicator of poor social conditions and behavioral risk factors [25]; low education, an index of social class [26]; and single parenthood, associated with low household income and lack of social support [27]. These three indicators of social adversity have each been previously associated with low birthweight [25], [27][40] and poor developmental outcomes [24]. We hypothesized, based on potential biological vulnerability, that there is an association between social stress (individual social adversity or neighborhood social disparity) during pregnancy and smaller infant size at birth. In addition we hypothesized that this association will be magnified in individuals carrying birthweight-lowering alleles near LEKR and CCNL1 or in ADCY5.

Materials and Methods

Study Cohort

The current study is based on data from the NFBC 1986 cohort, which comprises 9362 pregnant women (99% of pregnant population) and 9203 live-born singletons with expected date of birth between July 1985 and June 1986 from the provinces of Lapland and Oulu in Finland [41], [42].

Data concerning maternal health and social-demographics were collected via medical records, examinations/interviews by midwives, and data from a self-report questionnaire administered to pregnant women at the first visit to maternity health centers, approximately gestational week 12 and returned by gestational week 24 if still pregnant [41]. This study was approved by the ethics committee of the University of Oulu in accordance with the Declaration of Helsinki.

We had information on maternal social adversity for 9106 mothers and genotype for 5369 children [12].

Assessment of Social Stress

Social stress at the individual level consisted of the sum of three stressors, thus scores ranged between 0 and 3. Young maternal age was defined as being <20 years at time of birth and coded as 1, otherwise as 0 [25]. Low education was coded as 1 when maternal education was <11 years or 0 if higher [26]. Unmarried maternal marital status was coded as 1 if single, divorced or widowed and 0 if married or cohabiting with the expectant father [27].

Social stress at the neighborhood level, i.e. neighborhood social disparity, was defined as a discrepancy between the family SES and the neighborhood financial estimate. A disparity score was created by comparing family SES (highest maternal or paternal occupation) with neighborhood financial estimate. Maternal and paternal occupation were categorized as 1 = professional, 2 = upper white collar, 3 = lower white collar, 4 = unskilled worker, 5 = farmer/farmer’s wife owning >8 hectares of land, 6 = farmer/farmer’s wife owning <8 hectares of land. Neighborhood financial estimate was based on financial capacity category (FCC) of the neighborhood for the 1982–92 classification by the National Finnish KOUTA database and rated from one (deprived) to six (affluent) [41], [43]. FCC takes into account factors such as density and age distribution in the population, income, expenditure on social and health care, education, net total expenditure, capital liabilities and industry [43]. Neighborhood social disparity was coded as 1 when participants lived in a deprived environment with a low FCC score relative to their own SES. Disparity was coded as 0 when participants lived in an environment matching with their individual SES (i.e. the neighborhood had a high FCC score and individual had high SES or reversed, low FCC and low SES).

Genotyping and Genetic Risk Scores

Blood samples were taken when adolescents were 16 years old. The DNA extractions, sample quality controls, biobank up-keeping and aliquotting were performed in the National Public Health Institute, Biomedicum Helsinki, Finland. The rs900400 and rs9883204 single nucleotide polymorphisms (SNPs) near LEKR and CCNL1 and in the ADCY5 were genotyped (n = 5369) by Taqman allelic discrimination. No deviation (p≥0.05) from Hardy-Weinberg equilibrium was observed [12]. Success rate in genotyping was 0.96 for both SNPs. For the analyses, we categorized the genetic variants into two classes: 0 and at least 1 risk allele.

Outcome Measures

Data on infant birth size, i.e. birthweight (in kg), head circumference (in cm) and birth length (in cm), were collected at birth by trained medical staff according to standardized procedure, entered into the medical records, and transferred onto the study forms. Ponderal index was calculated using the standard formula [birth weight (kg)/birth length (m3)].


Gestational age was calculated from the date of the last menstrual period (in 16%) or ultrasound examination (in 84% of the pregnant women). Maternal pre-pregnancy BMI was calculated using standard formula [kg/m2]. Information on smoking (nonsmoker = 0; smoker = 1), alcohol consumption (no alcohol consumed = 0; alcohol consumed = 1) and parity were taken from the self-report questionnaires during pregnancy. Blood pressure (BP) during pregnancy was classified as gestational hypertension (BP≥140/90 in the absence of proteinurea after the 20th gestational week), pre-eclampsia (BP≥140/90 with proteinurea after the 20th gestational week), chronic hypertension (on anti-hypertensive medication due to pre-existing hypertensive disorder or blood pressure of ≥140/90 before the 20th week of gestation), superimposed pre-eclampsia (chronic hypertension with proteinurea), proteinuria (BP≥140, diastolic <90 with proteinurea, or diastolic ≥90 and systolic<140 with proteinurea), and normotensive. Protein urea was tested using a urinary dip-stick test (≥0.3 g/L). Oral glucose tolerance test (OGTT) was used as a method of screening mothers for gestational diabetes mellitus according to Finnish national guidelines, between the 26 and 28 gestational weeks. Screening was indicated in the case of glucosuria, prior gestational diabetes mellitus, suspected fetal macrosomia, previous macrosomic infant (birthweight >4500 g), maternal pre-pregnancy body mass index greater than 25 kg/m2 and age greater than 40 years. OGTT was performed using oral glucose load of 75 g after overnight fasting; upper ranges were 5.5, 11.0, and 8.0 mmol/L at fasting, 1 hour and 2 hours post glucose load. A single abnormal value in the OGTT was considered pathological and diagnoses of gestational diabetes mellitus made [44].

Statistical Analysis

We calculated frequencies and percentages for descriptive analysis of the maternal and infant demographic and anthropometric measures. Using graphical tools we examined the distributions for normality and linearity, and used Pearson and Spearman correlations for continuous and categorical data, respectively, to examine multicollinearity. We used Chi square test statistics to test for unadjusted associations between social stress and categorized birth outcomes and maternal factors (as shown in Table S1).

To determine the adjusted association between social stress and birth size we conducted multiple linear regression analysis and adjusted the model for several a priori selected well known predictors of birth size and exposures that may confound the associations i.e. gestational age, maternal pre-pregnancy body mass index (BMI), smoking, alcohol consumption, parity, gestational diabetes and hypertensive disorders during pregnancy. The analyses were performed for males and females together (adjusted for sex) and separately by sex. We examined the additive effect of social stress and carrying at least one risk allele by comparing the reference group who had neither social stress or risk allele (coded as 0) with the following groups who either: 1) carried at least one risk allele only, 2) experienced social stress only, and 3) had both social stress and at least one risk allele. The P-value for trend across the exposure categories was calculated.

Tests were two-tailed and the level of significance set at 0.05. We did not use correction for multiple testing due to a priori set hypotheses, and analytical strategies. We used the version 9.1 of the SAS system for windows (SAS Institute Inc, Cary, NC) for statistical analyses.


Cohort Characteristics

In the NFBC 1986 cohort 24.5% of mothers experienced at least one type of adversity during pregnancy, 3.6% two and 0.3% three adversities. Of the women experiencing at least one adversity, 36% had low education (<11years), 5% were single parents, and 4% were young (<20 years) at the time of delivery. Table S1 shows the association analyses of social stress (composite variables) with birth outcomes and maternal characteristics. Mothers experiencing any social adversity were more likely to deliver before the 37th gestational week (P<0.0001), to be smokers (P<0.0001), and had higher maternal pre-pregnancy BMI (P<0.0001). Mothers from lower SES were also more likely to be smokers (P<0.0001) to be multipara (P<0.0001), have a high pre-pregnancy BMI (P<0.0001) and report at least one adversity (P<0.0001) [data not shown]. Single-parenthood was the component of the social adversity composite that showed the strongest association with birth size in comparison to infants from two-parent families in a mutually adjusted analysis (data not shown). This association between single parenthood and birth size was significant in males but not in females (males: birthweight −114 g (95%CI = −178.08, −50.13), birth length −0.40 cm (95%CI = −0.67, −0.14), head circumference −0.34 cm (95%CI = −0.53, −0.15); females: birthweight −16.87 g (95%CI = −4.15, 76.89) birth length 0.10 cm (95%CI = −0.16, 0.35 ), head circumference 0.03 cm (95%CI = −0.15, 0.21). Table S2 provides the distribution of the birth size outcomes according to genotype; with a frequency of ca 1582 (29.9%) for at least one risk allele in rs900400, and 4668 (96.6%) in rs9883204.

Multiple Regression Analyses

Tables 1, 2, 3 and 4 present the results of the multiple regression analysis examining the association between social stress and birth size as well as the additive effects of the birthweight-lowering alleles.

Table 1. Multiple Linear Regression Analysis (GLM) of the association between social adversity and birth size [birth weight (g), length (cm), head circumference (cm) and ponderal index] in the whole NFBC86 Cohort (n = 9135) and stratified by sex.

Table 2. Mean differences (95% confidence intervals, CI) in birth size as predicted by the additive effects of social Adversity and at least one risk allele (CCNL1/LEKR1- rs900400).

Table 3. Multiple Linear Regression Analysis (GLM) of the association between neighborhood social disparity and birth size [birth weight (g), length (cm), head circumference (cm) and ponderal index] in the whole NFBC86 Cohort (n = 9135) and stratified by sex.

Table 4. Mean differences (95% confidence intervals, CI) in birth size as predicted by the additive effects of neighborhood social disparity and at least one risk allele (CCNL1/LEKR1- rs900400).

Table 1 shows the association between social adversity and birth size after adjusting for gestational age, maternal smoking, alcohol consumption, pre-pregnancy BMI, parity, gestational diabetes and hypertensive disorders during pregnancy. After adjustment infants of mothers who experienced at least one social adversity had smaller birth sizes as compared to infants of mothers with no adversity. Once stratified by sex, this difference remained statistically significant in males and females for birthweight, but only in females for birth length (−0.16 cm, 95%CI −0.29, −0.04) and in males for head circumference, (−0.09 cm, 95%CI −0.19, −0.007) respectively.

When adding genetic variance into the analysis (Table 2); compared to the reference category zero, i.e. no social adversity nor birthweight-lowering risk allele at rs900400, belonging to categories one, two or three (as described above) was associated with smaller birth size. The association with birth size was magnified in category three containing both social adversity and risk allele at rs900400 compared to the results for social adversity only and carrying one risk allele only; with reduction in birthweight by a total of −118 g (95%CI = −156.9, −79.9), birth length of −0.30 cm (95%CI =  −0.46, −0.14), head circumference by −0.23 cm (95%CI =  −0.35, −0.11), and ponderal index of −0.47 kg/m3(95%CI =  −0.67, −0.26). The P-value for trend for effect sizes by exposures categories was significant for all birth size outcomes. When stratified by sex (Table S3), this difference in birth size though not always reaching statistical significance due to reduced sample size, was more prominent in females. The results for rs9883204 (Table S4) showed no association across the categories, which may be attributed to insufficient sample size in the fully adjusted analyses.

Neighborhood social disparity was associated with smaller birth size with a difference in birthweight of −28.8 g (95% CI −47.7, −10.0) and birth length of −0.12 cm (95% CI −0.20, −0.05) in the adjusted model (Table 3). In the stratified model, the association of neighborhood social disparity with birth size was statistically significant in males only. When examining the association of neighborhood social disparity and genetic vulnerability (rs900400), compared to the reference group i.e. category zero, belonging to categories one, two or three was associated with smaller birth size (Table 4). However, the results did not reach statistical significance in all categories, though the P-values for trend for effect sizes by exposures categories were significant for all birth size outcomes. Here again, carrying at least one rs900400 birth weight lowering risk allele magnified the association with smaller birth size and neighborhood disparity. The association analyses for rs9883204 were non-significant.


Many human diseases stem from complex interplay between environmental and individual susceptibility. In our study we examined how and whether specific genetic susceptibility modulates the association with adverse outcomes from environmental exposure such as social stress. Our results show that maternal social stress during pregnancy, both at the individual and neighborhood levels, was associated with smaller infant birth size and that carrying the birthweight-lowering rs900400 C allele located near CCNL1/LEKR magnified this association. These results provide support for the hypothesis that an individual with a genetic or other biological risk is more vulnerable to environmental adversity. Though the magnitude of the reduction in birth size attributable to variants in genotype is of significant proportion (44 g) we can only stipulate a trend from these results. Though both social stress and the birth weight lowering allele rs9883204 at ADCY5 [12] (in larger samples) are associated with smaller birth size, we were not able to report any significant additive effects on birth size. This is most likely due to limitations due to reduced sample size in our cohort as we performed complete case analysis. Larger sample studies are needed in order to determine the underlying social and biological pathways for the additive effect, genetic liability and environmental adversity have on fetal development.

Though variants near CCNL1/LEKR are linked to lower birth weight, the biology behind this association is still unclear [12]. Insulin is one of the most important fetal growth hormone, and the fetal insulin hypothesis suggests that genetic variants in glucose and insulin metabolism may affect fetal growth [45], [46]. However the CCNL1/LEKR has not yet been linked to either with type 2 diabetes or adult glycemic traits. On the contrary, a recent study has shown an association between the C-allele of the rs900400 located near CCNL1/LEKR1 and increased insulin response to oral glucose stimulation in non-diabetic individual [47].

The impact of maternal social stress had on infant birthweight is comparable to mothers smoking two cigarettes per day during pregnancy [48], [49]. This highlights the importance of maternal social stress, both at individual and neighborhood levels, as an indicator for increased risk for lower infant birth size and consequent development of disease later in life.

Small head circumference is linked with poor developmental and cognitive outcomes in the offspring [3], [50], 1 cm increase in head circumference represents11% brain volume at term [51][53]. In our study maternal social stress was associated with both smaller infant head circumference and birth length; however, the differences were small. Measurement error cannot be excluded, e.g. head circumference is a measure of occipital-frontal circumference and is subject to some degree of unreliability. However, measurement error is unlikely to be systematically associated with social stress, thus should be considered as random variance and evenly distributed by exposure status.

Previous studies have shown male fetuses are more vulnerable to intrauterine insults [54]. The sexual dimorphic association with birth size we report here indicates differential response to fetal environmental factors between the sexes. This may help explain some of the gender differences involved in the cascade of development of diseases as seen later in life.

We hypothesize that maternal perceived stress as one of the biological pathways through which social stress affects birth size. Perceived stress has been linked to changes in maternal stress hormone levels (i.e. cortisol) during pregnancy [55]. Social adversity as defined here is a measure of stress related to biological and individual adversity, rather than just as financial and economic adversity. Maternal stress during pregnancy is known to be associated with physiological and cognitive outcomes in offspring [56][58]. Animal studies have shown that increased levels of maternal glucocorticoids, which are a major component of the stress response, are associated with smaller birth size in offspring [59], [60], [60][65]. The fetus may react to stress in an analogous manner as adults, i.e. with increased levels of fetal cord cortisol during late pregnancy [61][63]. However, studies have yet to show an increase in fetal cortisol levels due to maternal stress [66]. Other biological pathways that may link maternal stress to birth size, reduced placental blood flow and maternal diet (over and under nutrition), have yet to be tested [67][69].

Our study also highlights the importance of using three indicators of social adversity in the clinical setting in identifying pregnant women at risk for poor birth outcomes. The factors used to define social adversity are indicative of potential economic hardship and biological risk [24]. We showed that women experiencing some social adversity were more likely to smoke and deliver prematurely, risk factors known to predict small birth size [48], [49]. Importantly, the presence of only a single index of adversity was enough to result in lower birthweight, with maternal single marital status having the strongest association. Social adversity is also important as it may not only affect fetal development, but also influence postnatal development in terms of poor maternal resources.

The association of neighborhood social disparity with smaller birth size is likely to be explained by stress related to access to amenities. In Finland, despite uniformly distributed tax-paid health care, many individuals in rural areas, may require long distance travel to access e.g. health care and other amenities. Interestingly, though these types of neighborhood deprivations are small as compared to other industrialized nations, they still accounted for clinically significant differences in birth size.

Strengths of this study include the prospective data collection with extensive maternal and infant demographic and medical information. The study population is known to be genetically homogenous consisting of white Caucasians, therefore, reducing bias introduced by ethnicity. Moreover, we were able to adjust for major confounders, which has not been possible at this scale in previous studies. The study had, however, limited statistical power to report the additive effects of genetic variants on birth size with higher precision, or to test for any interactions between the genetic variants and social stress. These aspects should ideally be addressed in larger meta-analyses combining several studies, but the availability of such data has become a key issue. Another limitation is that we hypothesized that the objective measures of social stress employed in this study correspond to the biological stress response e.g. in hypothalamic pituitary adrenal-axis functioning. In this regard, it would have been a strength to have maternal blood cortisol samples available. However, cortisol in relation to perceptions of stress is fraught with measurement difficulties [70]. Nonetheless, the indices that we used have been previously correlated with perceived stress [71][74].

This study shows that genetic susceptibility magnified the association between social stress, (both at the individual and neighborhood levels) and birth size. The fact that social stress was associated with smaller birth size even in a society where there is relatively little social inequality as compared with other high-income countries and where tax–paid health care is universally available, is alarming and highlights the strength of the association. Moreover, social stress was a stronger predictor of birth size than having birthweight-lowering alleles, which emphasizes the use of indicators of social stress in clinical settings. It is promising that the addition of the genetic variants made a significant additional contribution which calls for further work in identifying groups of genetic variants and their interaction with environment.

Supporting Information

Table S1.

Chi-square tests of associations between maternal characteristics, birth outcomes and social stress in the whole NFBC86 Cohort.


Table S2.

Genotype frequency by birth size outcomes including means (±SD).


Table S3.

Mean differences (95% confidence intervals, CI) in birth size as predicted by the additive effects of social adversity and at least one risk allele (CCNL1/LEKR1- rs900400) stratified by sex.


Table S4.

Mean difference (95% confidence intervals, CI) in birth size as predicted by the additive affects of maternal social adversity and at least one risk allele (ADCY5-rs9883204).



The authors wish to Professor Paula Rantakallio (launch of 1986), Ms Outi Tornwall and Ms Minttu Jussila (DNA biobanking).

Author Contributions

Conceived and designed the experiments: AAK AR MRJ. Performed the experiments: AAK AR MRJ. Analyzed the data: AAK. Contributed reagents/materials/analysis tools: AAK MK. Wrote the paper: AAK AR MRJ. Interpretation of results, draft revising and final approval: AAK AR SS MK SC PF AH AP MRJ.


  1. 1. Tapia-Rombo CA, Ugarte-Torres RG, Alvarez-Vázquez E, Salazar-Acuna AH (2001) Risk factors for intrahospital infection in newborns. Archives of Medical Research 4: 304–311. 10.1016/S0188–4409(01)00281-8 .
  2. 2. Vik T, Vatten L, Markestad T, Ahlsten G, Jacobsen G, et al. (1996) Morbidity during the first year of life in small for gestational age infants. Archives of Disease in Childhood 1: 33F–37F.
  3. 3. Lou HC (1994) Prenatal stressors of human life affect fetal brain development. Developmental medicine and child neurology 9: 826–832.
  4. 4. Peterson J, Taylor HG, Minich N, Klein N, Hack M (2005) Subnormal head circumference in very low birth weight children: Neonatal correlates and school-age consequences. Journal of Early Human Developement 5: 325–334.10.1016/ j.earlhumdev.2005.09.014:
  5. 5. Barker DJP (1995) Fetal origins of coronary heart disease. BMJ 6998: 171–174. 10.1136/bmj.311.6998.171.
  6. 6. Gillman MW (2005) Developmental origins of health and disease. The New England Journal of Medicine 17: 1849–1850.10.1056/ NEJMe058187:
  7. 7. Gluckman PD, Hanson MA, Cooper C, Thornburg KL (2008) Effect of in utero and early-life conditions on adult health and disease. The New England Journal of Medicine 1: 61–73.10.1056/ NEJMra0708473:
  8. 8. Law CM, Shiell AW (1996) Is blood pressure inversely related to birth weight? The strength of evidence from a systematic review of the literature. Journal of Hypertension 8: 935–941.
  9. 9. Leon DA, Lithell HO, Vagero D, Koupilova I, Mohsen R, et al. (1998) Reduced fetal growth rate and increased risk of death from ischaemic heart disease: cohort study of 15’000 Swedish men and women born 1915–29. BMJ 7153: 241–245. 10.1136/bmj.317.7153.241.
  10. 10. Martyn CN, Barker DJP, Osmond C (1996) Mothers’ pelvic size, fetal growth, and death from stroke and coronary heart disease in men in the UK. Lancet 9037: 1264–1268.10.1016/ S0140–6736(96)04257-2).
  11. 11. Rich-Edwards JW, Stampfer MJ, Manson JE, Rosner B, Hankinson SE, et al. (1997) Birth weight and risk of cardiovascular disease in a cohort of women followed up since 1976. BMJ 7105: 396–400.10.1136/.
  12. 12. Freathy RM, Mook-Kanamori DO, Sovio U, Prokopenko I, Timpson NJ, et al. (2010) Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight. Nat Genet 5: 430–435.10.1038/ ng.567:
  13. 13. Fairley L, Leyland AH (2006) Social class inequalities in perinatal outcomes: Scotland 1980–2000. Journal of Epidemiology and Community Health 1: 31–36.10.1136/ jech.2005.038380:
  14. 14. Nobile C, Raffaele G, Altomare C, Pavia M (2007) Influence of maternal and social factors as predictors of low birth weight in Italy. BMC Public Health 1: 192.10.1186/. 1471–2458–7-192.
  15. 15. Pearl M, Braveman P, Abrams B (2001) The relationship of neighborhood socioeconomic characteristics to birthweight among 5 ethnic groups in California. American Journal of Public Health 11: 1808–1814.10.2105/. AJPH.91.11.1808.
  16. 16. Torres-Arreola L, Constantino-Casas P, Flores-Hernandez S, Villa-Barragan J, Rendon-Macias E (2005) Socioeconomic factors and low birth weight in Mexico. BMC Public Health 1: 20.10.1186/. 1471-2458-5-20.
  17. 17. Bosma H, Dike van de Mheen H, Borsboom GJJM, Mackenbach JP (2001) Neighborhood socioeconomic status and all-cause mortality. American Journal of Epidemiology 4: 363–371.10.1093/aje/. 153.4.363.
  18. 18. Sellström E, Arnoldsson G, Bremberg S, Hjern A (2007) Are there differences in birth weight between neighbourhoods in a Nordic welfare state? BMC Public Health 1: 267.10.1186/. 1471-2458-7-267.
  19. 19. Gudmundsson S, Björgvinsdóttir L, Molin J, Gunnarsson G, Marsal K (1997) Socioeconomic status and perinatal outcome according to residence area in the city of Malmö. Acta Obstetricia et Gynecologica Scandinavica 4: 318–323.10.1111/j.1600–0412.1997.tb07985 x.
  20. 20. Sundquist J, Sundquist K, Johansson SE, Li X, Winkleby M (2011) Mothers, places and small for gestational age births: a cohort study. Archives of Disease in Childhood 4: 380–385.10.1136/. adc.2009.180042.
  21. 21. Chandola T, Clarke P, Morris JN, Blane D (2006) Pathways between education and health: a causal modelling approach. Journal of the Royal Statistical Society: Series A (Statistics in Society) 2: 337–359.10.1111/j.1467–985X.2006.00411 x p..
  22. 22. Englert RC, Dauser D, Gilchrist A, Samociuk HA, Singh RJ, et al. (2007) Marital status and variability in cortisol excretion in postmenopausal women. Biological Psychology 1: 32–38.10.1016/. j.biopsycho.2007.08.011.
  23. 23. Fiocco AJ, Joober R, Lupien SJ (2007) Education modulates cortisol reactivity to the Trier Social Stress Test in middle-aged adults. Psychoneuroendocrinology 8–10: 1158–1163.10.1016/. j.psyneuen.2007.08.008.
  24. 24. Rodriguez A, Olsen J, Kotimaa A, Kaakinen M, Moilanen I, et al. (2009) Is prenatal alcohol exposure related to inattention and hyperactivity symptoms in children? Disentangling the effects of social adversity. Journal of Child Psychology and Psychiatry 9: 1073–1083.10.1111/j.1469–7610.2009.02071:x.
  25. 25. Chen XK, Wen SW, Fleming N, Demissie K, Rhoads GG, et al. (2007) Teenage pregnancy and adverse birth outcomes: a large population based retrospective cohort study. International Journal of Epidemiology 368–373.10.1093/ije/. dyl284.
  26. 26. Mortensen LH, Diderichsen F, Arntzen A, Gissler M, Cnattingius S, et al. (2008) Social inequality in fetal growth: a comparative study of Denmark, Finland, Norway and Sweden in the period 1981–2000. Journal of Epidemiology and Community Health 4: 325–331.10.1136/. jech.2007.061473.
  27. 27. Raatikainen K, Heiskanen N, Heinonen S (2005) Marriage still protects pregnancy. British Journal of Obstetrics and Gynaecology 10: 1411–1416.10.1111/j.1471–0528.2005.00667:x.
  28. 28. Briggs MM, Hopman WM, Jamieson MA (2007) Comparing pregnancy in adolescents and adults:obstetric outcomes and prevalence of anemia. Jornal of Obstetrics and Gynaecology Canada 7: 546–555.
  29. 29. Chandra PC, Schiavello HJ, Ravi B, Weinstein AG, Hook FB (2002) Pregnancy outcomes in urban teenagers. International Journal of Gynecology & Obstetrics 2: 117–122.10.1016/ S0020–7292(02).
  30. 30. de Vienne CM, Creveuil C, Dreyfus M (2009) Does young maternal age increase the risk of adverse obstetric, fetal and neonatal outcomes: A cohort study. European Journal of Obstetrics & Gynecology and Reproductive Biology 2: 151–156.10.1016/. j.ejogrb.2009.08.006.
  31. 31. Haldre K, Rahu K, Karro H, Rahu M (2006) Is a poor pregnancy outcome related to young maternal age? A study of teenagers in Estonia during the period of major socio-economic changes (from 1992 to 2002). European Journal of Obstetrics & Gynecology and Reproductive Biology 1: 45–51.10.1016/. j.ejogrb.2006.05.002.
  32. 32. Kongnyuy E (2008) Adverse perinatal outcomes of adolescent pregnancies in Cameroon. Maternal and Child Health Journal 2: 149–154.10.1007/. s10995–007–0235-y.
  33. 33. Sawchuk LA, Burke SDA, Benady S (1997) Assessing the impact of adolescent pregnancy and the premarital conception stress complex on birth weight among young mothers in Gibraltar’s civilian community. Journal of Adolescent Health 4: 259–266.10.1016/. (97). S1054–139X.
  34. 34. Strobino DM, Ensminger ME, Kim YJ, Nanda J (1995) Mechanisms for maternal age differences in birth weight. American Journal of Epidemiology 5: 504–514.
  35. 35. Auger N, Luo ZC, Platt RW, Daniel M (2007) Do mothers education and foreign born status interact to influence birth outcomes? Clarifying the epidemiological paradox and the healthy migrant effect. Journal of Epidemiology and Community Health 5: 402–409.10.1136/. jech.2007.064535.
  36. 36. Halileh S, Abu-Rmeileh N, Watt G, Spencer N, Gordon N (2008) Determinants of birthweight; Gender based analysis. Maternal and Child Health Journal 5: 606–612.10.1007/. s10995–007–0226-z.
  37. 37. Bird ST, Chandra A, Bennett T, Harvey SM (2000) Beyond marital status: Relationship type and duration and the risk of low birth weight. Family Planning Perspectives 6: 281–287.
  38. 38. Holt V, Danoff N, Mueller B, Swanson M (2003) The association of change in maternal marital status between births and adverse pregnancy outcomes in the second birth. Paediatric and Perinatal Epidemiology S1: 31–40.
  39. 39. Luo ZC, Wilkins R, Kramer MS, the Fetal and Infant Health Study Group of the Canadian Perinatal Surveillance System (2004) Disparities in pregnancy outcomes according to marital and cohabitation status. Obstetrics & Gynecology 6:
  40. 40. Shoham- Yakubovich I, Barel V (1988) Maternal education as a modifier of the association between low birthweight and infant mortality. International Journal of Epidemiology 2: 370–377.10.1093/ije/. 17.2.370.
  41. 41. Järvelin MR, Elliott P, Kleinschmidt I, Martuzzi M, Grundy C, et al. (1997) Ecological and individual predictors of birthweight in a northern Finland birth cohort 1986. Paediatric and Perinatal Epidemiology 3: 298.
  42. 42. Järvelin MR, Hartikainen A-L, Rantakallio P (1993) Labour induction policy in hospitals of different levels of specialisation. British Journal of Obstetrics and Gynaecology 4: 310–315.
  43. 43. KOUTA-database (koulun tilastoaineisto) (1993) kuntien Talous. Helsinki: Tilastokeskus.
  44. 44. Vääräsmäki M, Pouta A, Elliot P, Tapanainen P, Sovio U, et al. (2009) Adolescent Manifestations of Metabolic Syndrome Among Children Born to Women With Gestational Diabetes in a General-Population Birth Cohort. American Journal of Epidemiology 10: 1209–1215.10.1093/aje/. kwp020.
  45. 45. Hattersley AT, Tooke JE (1999) The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. The Lancet 9166: 1789–1792.10.1016/ S0140–6736(98).
  46. 46. Mook-Kanamori DO, Steegers EAP, Eilers PH, Raat H, Hofman A, et al. (2010) Risk factors and outcomes associated with first-trimester fetal growth restriction. JAMA: The Journal of the American Medical Association 6: 527–534.10.1001/. jama.2010.78.
  47. 47. Andersson EA, Harder MN, Pilgaard K, Pisinger C, Stancáková A, et al. (11 A.D.) The birth weight lowering C-Allele of rs900400 Near LEKR1 and CCNL 1 associates with elevated insulin release following an oral glucose challenge. PloS One 11: 10.1371/journal.pone.0027096.
  48. 48. Andres RL, Day MC (2000) Perinatal complications associated with maternal tobacco use. Seminars in Neonatology 3: 231–241. 10.1053/siny.2000.0025.
  49. 49. Bernstein IM, Mongeon JA, Badgers GJ, Solomon L, Heil SH, et al. (2005) Maternal smoking and its association with birth weight. Obstetrics and Gynecology 5: 986–991.
  50. 50. Engel SM (2005) Psychological trauma associated with the World Trade Center attacks and its effect on pregnancy outcome. Paediatric and Perinatal Epidemiology 5: 334.10.1111/j.1365–3016.2005.00676:x.
  51. 51. Carlson BM (2004) Fetal period and growth. 18: 477–496.
  52. 52. Cheong JLY, Hunt RW, Anderson PJ, Howard K, Thompson DK, et al. (2008) Head Growth in Preterm Infants: Correlation With Magnetic Resonance Imaging and Neurodevelopmental Outcome. Pediatrics 6: e1534-e1540.10.1542/ peds.2007–2671:
  53. 53. Cooke RW, Lucas A, Yudkin PL, Pryse-Davies J (1977) Head circumference as an index of brain weight in the fetus and newborn. Early Human Development 145–149:
  54. 54. Salzman BE, Wender RC (2006) Male sex: a major health disparity. Primary Care: Clinics in Office Practice 1: 1–16.10.1016/ j.pop.2005.11.014:
  55. 55. Obel C, Hedegaard M, Henriksen TB, Secher NJ, Olsen J, et al. (2005) Stress and salivary cortisol during pregnancy. Psychoneuroendocrinology 7: 647–656.10.1016/ j.psyneuen.2004.11.006:
  56. 56. Entringer S (2010) Prenatal stress and developmental programming of human health and disease risk: concepts and integration of empirical findings. Current opinion in endocrinology and diabetes 6: 507.10.1097/. MED.0b013e3283405921.
  57. 57. Koubovec D, Geerts L, Odendaal H, Stein D, Vythilingum B (2005) Effects of psychologic stress on fetal development and pregnancy outcome. Current Psychiatry Reports 4: 274–280.10.1007/. s11920–005–0081–9.
  58. 58. Rodriguez A, Bohlin G (2005) Are maternal smoking and stress during pregnancy related to ADHD symptoms in children? Journal of Child Psychology and Psychiatry 3: 246–254.10.1111/j.1469–7610.2004.00359:x.
  59. 59. Cotterrell M, Balazs R, Johnson AL (1972) Effects of corticosteroids on the biochemical maturation of rat brain: postnatal cell formation. Journal of Neurochemistry 9: 2151–2167.10.1111/j.1471–4159.1972.tb05124:x.
  60. 60. Scheepens A, van den Waarenburg M, van den Hove D, Blanco CE (2003) A single course of prenatal betamethasone in the rat alters postnatal brain cell proliferation but not apoptosis. The Journal of physiology 1: 163–175.10.1113/. jphysiol.2003.043414.
  61. 61. Jobe AH, Wada N, Berry LM, Ikegami M, Ervin MG (1997) Single and repetitive maternal glucocorticoid exposures reduce fetal growth in sheep. American Journal of Obstetrics and Gynecology 5: 880–885.10.1016/ S0002–9378(98).
  62. 62. Dunlop SA, Archer MA, Quinlivan JA, Beazley LD, Newnham JP (1997) Repeated prenatal corticosteroids delay myelination in the ovine central nervous system. The Journal of maternal-fetal medicine 6: 309–313.10.1002/(SICI)1520–6661(199711/12)6:6<309::AID-MFM1>3.0.CO; 2-S:
  63. 63. Moss TJM, Doherty DA, Nitsos I, Sloboda D, Harding R, et al. (2005) Effects into adulthood of single or repeated antenatal corticosteroids in sheep. American Journal of Obstetrics and Gynecology 1: 146–152.10.1016/. j.ajog.2004.06.065.
  64. 64. Huang WL, Beazley LD, Quilivan JA, Evans SF, Newnham JP, et al. (1999) Effect of corticosteroids on brain growth in fetal sheep. Obstetrics and gynecology 2: 213–218.
  65. 65. Aghajafari F (2002) Repeated doses of antenatal corticosteroids in animals: a systematic review. American Journal of Obstetrics and Gynecology 4: 843.
  66. 66. Glover V, Bergman K, Sarkar P, O’Connor TG (2009) Association between maternal and amniotic fluid cortisol is moderated by maternal anxiety. Psychoneuroendocrinology 3: 430.10.1016/. j.psyneuen.2008.10.005.
  67. 67. Harville EW, Savitz DA, Dole N, Herring AH, Thorp JM, et al. (2008) Stress and placental resistance measured by Doppler ultrasound in early and mid-pregnancy. Ultrasound in Obstetrics and Gynecology 1: 23–30.10.1002/. uog.5344.
  68. 68. Chadio SE, Kotsampasi B, Papadomichelakis G, Deligeorgis S, Kalogiannis D, et al. (2007) Impact of maternal undernutrition on the hypothalamic-pituitary-adrenal axis responsiveness in sheep at different ages postnatal. Journal of Endocrinology 3: 495–503.10.1677/. JOE-06–0172.
  69. 69. Belkacemi L, Nelson DM, Desai M, Ross MG (2010) Maternal undernutrition influences placental-fetal development. Biology of Reproduction 3: 325–331.10.1095/. biolreprod.110.084517.
  70. 70. Hansen AM, Larsen AD, Rugulies R, Garde AH, Knudsen LE (2009) A review of the effect of the psychosocial working environment on physiological changes in blood and urine. Basic & Clinical Pharmacology & Toxicology 2: 73–83.10.1111/j.1742–7843.2009.00444:x.
  71. 71. Avison WR (1997) Single motherhood and mental health: implications for primary prevention. Canadian Medical Association Journal 5: 661–663.
  72. 72. Cairney J, Boyle M, Offord DR, Racine Y (2003) Stress, social support and depression in single and married mothers. Social Psychiatry and Psychiatric Epidemiology 8: 442–449.10.1007/. s00127–003–0661–0.
  73. 73. Mollborn S, Morningstar E (2009) Investigating the relationship between teenage childbearing and psycholocial distress using longitudinal evidence. Journal of Health and Social Behavior 3: 310–326.
  74. 74. Miech R, Power C, Eaton WW (2006) Disparities in psychological distress across education and sex: A longitudinal analysis of their persistence within a cohort over 19 years. Annals of Epidemiology 289–295.10.1016/. j.annepidem.2006.07.015.