Observational epidemiological studies indicate that maternal height is associated with gestational age at birth and fetal growth measures . Different mechanisms have been postulated to explain these associations. This study aimed to investigate the casual relationships behind the strong association of maternal height with fetal growth measures  and gestational age by a Mendelian randomization approach.p = 6.31 × 10−9), birth weight (p = 2.19 × 10−15), and gestational age (p = 1.51 × 10−7). Our parental-specific haplotype score association analysis revealed that birth length and birth weight were significantly associated with the maternal transmitted haplotype score as well as the paternal transmitted haplotype score. Their association with the maternal nontransmitted haplotype score was far less significant, indicating a major fetal genetic influence on these fetal growth measures. In contrast, gestational age was significantly associated with the nontransmitted haplotype score (p = 0.0424) and demonstrated a significant (p = 0.0234) causal effect of every 1 cm increase in maternal height resulting in ~0.4 more gestational d. Limitations of this study include potential influences in causal inference by biological pleiotropy, assortative mating, and the nonrandom sampling of study subjects.We conducted a Mendelian randomization analysis using phenotype and genome-wide single nucleotide polymorphism (SNP) data of 3,485 mother/infant pairs from birth cohorts collected from three Nordic countries . We constructed a genetic score based on 697 SNPs known to be associated with adult height to index maternal height. To avoid confounding due to genetic sharing between mother and infant, we inferred parental transmission of the height-associated SNPs and utilized the haplotype genetic score derived from nontransmitted alleles as a valid genetic instrument for maternal height. In observational analysis, maternal height was significantly associated with birth length  is mainly defined by fetal genetics. In contrast, the association between maternal height and gestational age is more likely to be causal. In addition, our approach that utilizes the genetic score derived from the nontransmitted maternal haplotype as a genetic instrument is a novel extension to the Mendelian randomization methodology in casual inference between parental phenotype (or exposure) and outcomes in offspring. Using a Mendelian randomization approach, Ge Zhang and colleagues examine the causal relationship between maternal height, birth size, and gestational age at birth. Soon after the birth of a baby, doting parents send messages to friends and relatives or post information on social media sites to let everyone know when their new baby boy or girl was born. They may also post information about how heavy he/she was at birth and his/her length. These pregnancy outcomes, together with gestational age at birth (the length of time that a baby has spent developing in its mother’s womb), affect the baby’s immediate health and survival. Importantly, however, these pregnancy outcomes are also associated with the risk of long-term adverse health outcomes such as obesity, cardiometabolic disorders (heart disease and conditions such as diabetes that affect how the body makes energy from food), and neuropsychiatric conditions . For example, some studies have shown an association between low birth weight and an increased risk of type 2 diabetes later in life.Identification of the environmental and genetic factors that causally influence gestational age, length, and weight at birth would improve our understanding of why these pregnancy outcomes are associated with disease during adulthood and could help in the design of strategies to prevent these diseases. Epidemiological studies (investigations that examine disease patterns in populations) suggest that, compared to tall mothers, short mothers tend to deliver their babies at earlier gestational ages, with lower birth weights and lengths. Epidemiological studies cannot show, however, whether variations in maternal height cause variations in pregnancy outcomes. Other characteristics shared by tall mothers might actually determine the size and gestational age of their offspring (confounding). Here, the researchers use “Mendelian randomization” to assess the causal effect of maternal height on the size and gestational age at birth of babies. Because gene variants are inherited randomly, they are not prone to confounding. So, if maternal height actually affects gestational age and size at birth, genetic variants (instruments) that affect maternal height should be associated with differences in gestational age and size at birth, provided confounding due to the transmission of parental alleles  is avoided by adjusting for the baby’s genotype.The researchers used phenotype data  and single nucleotide polymorphism  data obtained from 3,486 Nordic mother/baby pairs. Analysis of the phenotype data indicated that maternal height was significantly associated with length, weight, and gestational age at birth (a significant association is unlikely to have arisen by chance). For their Mendelian randomization analysis, the researchers constructed a genetic score based on 697 SNPs known to be associated with adult height. To avoid confounding due to genetic sharing between the mother and baby, they determined which of the height-associated alleles each baby had inherited from its mother and used the nontransmitted haplotype score as a genetic instrument for maternal height (a haplotype is a set of DNA variations that are inherited together). Birth length and weight were significantly associated with both the maternal and paternal transmitted haplotype scores but not with the maternal nontransmitted haplotype score. However, gestational age was significantly but modestly associated with the maternal nontransmitted haplotype score.The validity of the assumptions that underlie the Mendelian randomization approach and the design of the studies supplying the data for this analysis may affect the accuracy of the findings reported here. Nevertheless, these findings suggest that the observed association between maternal height and fetal growth measurements is mainly determined by the genetics of the baby. That is, differences in maternal height do not cause differences in birth weight or length. Rather, some of the gene variants that the baby inherits from its mother determine both its size and its mother’s height. These findings also provide weak evidence that the association between maternal height and gestational age is causal. Maternal height might, for example, causally influence gestational age by limiting the space available for the baby’s growth before birth. Finally, these findings introduce an extension to the Mendelian randomization approach that can be used to investigate causal associations between parental characteristics and offspring outcomes.http://dx.doi.org/10.1371/journal.pmed.1001865.This list of resources contains links that can be accessed when viewing the PDF on a device or via the online version of the article at low birth weight and its consequencesThe March of Dimes, a not-for-profit organization for pregnancy and baby health, provides information about weight of newborn babies (in English and Spanish)Nemours, a not-for-profit organization for child health, provides information about the birth weight, gestational age, and Mendelian randomization Wikipedia has pages on birth weight and a brief explanation of gestational age (in English and Spanish)MedlinePlus provides information and links to additional resources about  Gestational age at birth , birth length, and birth weight are pregnancy outcomes associated not only with perinatal morbidity and mortality but also with long-term adverse health outcomes, such as obesity , cardiomObservational epidemiological studies have revealed several factors that are associated with birth length, birth weight, and gestational age. Examples include maternal age, height, and weight, offspring sex , maternaThe associations between maternal height and pregnancy outcomes have been interpreted based upon a mechanistic assumption—that maternal height sets a physical constraint on the intrauterine environment   to examine the causal effect of an exposure or an intermediate phenotype (X) on an outcome (Y). In our study, we used the height genetic score as the genetic instrument (G) to probe the causal relationship between maternal height (X) and various pregnancy outcomes (Y). The validity of this approach requires the following assumptions to be met:G is associated with X with enough strength, which is called the instrument relevance in statistical terms.G is related to Y only through X, not through other environmental confounding factors or biological pleiotropy. This assumption is also referred to as instrument exogeneity.Mendelian randomization is a type of instrumental variable analysis that uses a genetic instrumental variable .We used the phenotype and genome-wide SNP data of up to 3,485 mother/infant pairs from birth cohorts collected from three Nordic countries .The Finnish cohort (FIN) was collected for a genetic study of preterm birth . BrieflyThe Mother Child Cohort of Norway (MoBa) is a nationwide Norwegian pregnancy cohort study administered by the Norwegian Institute of Public Health. The study includes more than 107,000 pregnancies recruited from 1999 through 2008. Gestational age was estimated by ultrasound at gestational weeks 17–19. In the few participants without ultrasound dating, gestational age was estimated using the date of the last menstrual period. For the current study, we used the mother-child pairs that were selected from version 4 of the MoBa cohort, which included a total of 71,669 pregnancies. Singleton live-born spontaneous pregnancies with mothers in the age group 20–34 y were selected. Pregnancies involving preexisting medical conditions and pregnancies with complications, as well as pregnancies conceived by in vitro fertilization, were excluded from the study. Random sampling was done from two gestational age ranges of 154–258 d (cases) and 273–286 d (controls). In total, 3,121 mothers and children were genotyped . Of the http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000103.v1.p1), which contains data from a genome-wide case/control study using approximately 1,000 preterm mother-child pairs  from the DNBC, most with spontaneous onset of labor or preterm premature rupture of membranes (PPROM), along with 1,000 control pairs in which the child was born at ~40 wk gestation. Gestational age in this cohort was estimated by combining all available information from multiple sources: self-reported date of last menstrual period, self-reported delivery date, and gestational age at birth registered in the Medical Birth Register and the National Patient Register. Of the 3,838 DNBC samples with genotype data, 3,712 samples passed the genotype QC, from which we identified 1,684 mother/child pairs and used them in the current study.The Danish National Birth Cohort (DNBC) followed over 100,000 pregnancies between 1997 and 2002, with extensive epidemiologic data recorded on health outcomes in both mother and child . The curAll the preterm births included in this study were spontaneous. Obstetrical induction of labor, placental abnormalities, preeclampsia, congenital malformations, and multiple births were excluded. Pregnancies involving preexisting medical conditions known to be associated with preterm birth and pregnancies with complications were also excluded.Genome-wide SNP genotyping was conducted using different SNP arrays . Specifip < 1 × 10−4) were excluded.Similar QC procedures were used across the three studies. Briefly, individuals with incorrect sex assignment, excessive heterozygosity, or low call rate (<98%) were excluded from further analysis. Incorrect mother-child relationships and cryptic relatedness were detected by identity-by-descent (IBD) sharing estimated from genome-wide SNPs. Non-European samples were identified and excluded using principal component analysis (PCA) anchored with 1000 Genomes or HapMap reference samples. At marker level, SNPs with low call rate (<98%), low minor allele frequency (<0.03), or significant deviation from Hardy-Weinberg equilibrium  . A standhttp://www.broadinstitute.org/collaboration/giant) to construct a weighted genetic score and use it as a genetic instrument of height. The list of height-associated SNPs and their effect size estimates were extracted from the supplementary table of reference [S = ∑biGi, where Gi = 0, 1 or 2 (for genotype-based genetic score) or Gi = 0 or 1 , which indicates the number of reference alleles for a specific SNP, and bi is the estimated allelic effect.We selected the 697 SNPs associated with adult height from the stage II meta-analysis  of GIANTeference . The repeference . The weiAi and Bi to denote the transmitted and nontransmitted alleles of SNP i in a mother, the maternal transmitted haplotype score (M1) was calculated as ∑biAi and the maternal nontransmitted haplotype score (M2) was ∑biBi. Obviously, the genotype score equals ∑biGi = ∑biAi + ∑biBi, since the genotype is the summation of the two alleles (Gi = Ai + Bi). The two fetal haplotype scores (C1 and C2) were calculated in the same way, and C1 = M1. Association between the maternal transmitted haplotype score (M1/C1) and a fetal phenotype can be explained as either intrauterine effects  and pregnancy outcomes  using linear regression. Associations between various height genetic scores and maternal phenotype as well as pregnancy outcomes were examined by regression analysis. In the regression analyses of birth length and birth weight, maternal age, fetal gender, and gestational age were included as covariates. In the analysis of gestational age, only maternal age and fetal gender were included. To estimate the magnitude of causal effects of maternal height on pregnancy outcomes, we performed instrumental variable analysis using the two-stage least squares approach . Three gp-values were 6.31 × 10−9 (birth length), 2.19 × 10−15 (birth weight) and 1.51 × 10−7 .Summary statistics of the phenotype data of the three cohorts are provided in p = 2.75 × 10−189) and could explain a large fraction (~20%) of the observed variance in maternal height in all of the three cohorts (p < 2 × 10−16).As expected, the maternal genetic score was significantly associated with maternal height (meta  cohorts . The tra cohorts . At singp = 3.04 × 10−5) and birth weight (meta p = 1.19 × 10−7) in the studied cohorts. The fetal height genetic score was even more significantly associated with birth length (meta p = 5.40 × 10−9) and birth weight (meta p = 1.84 × 10−12), which indicated direct fetal genetic influence of height-associated SNPs on fetal growth. After adjustment for fetal genetic score, the associations between maternal genetic score and birth length or birth weight were no longer significant . However, the correlation of the estimated effect size of these SNPs on birth weight and the reported effect size on adult height was not significant .Given the highly significant association between fetal genetic score based on adult height-associated SNPs with both birth length and birth weight, we further examined single SNP associations of these adult height-associated SNPs with both birth length and birth weight in the infants. As indicated by the quantile-quantile (Q-Q) plots , there wp = 0.077), preferably in the DNBC cohort (p = 0.047). However, the fetal height genetic score was not significantly associated with gestational age. After adjustment for fetal genetic score, the maternal height genetic score was still marginally associated with gestational age (meta p = 0.072). Further single marker association  analysis also showed some inflation of association test statistics   , indicatp = 0.0405 and 0.404 for birth length and birth weight, respectively). In addition, although both were significantly associated with birth weight, the effect of maternally transmitted alleles was significantly larger than paternally transmitted alleles (p = 0.0053). This difference of effect was not observed for birth length. In contrast, the maternal nontransmitted haplotype score (M2), but not the parental transmitted score (C2), showed significant associations with gestational age (meta p = 0.0424), which suggests cross-generational effect of maternal height SNPs on gestational age through an intermediate phenotype  but not through genetic inheritance.Our parental haplotype genetic score analysis revealed significant associations of both maternal transmitted haplotype score and paternal transmitted haplotype score with birth length and birth weight . HoweverAlthough not a primary objective of this current study, we also examined the pairwise correlations between the various genetic scores . In addip = 0.0336). In contrast, all three methods revealed consistent and significant causal effect of maternal height on gestational age, with every 1 cm increase in maternal height resulting in ~0.4 more gestational d. To exclude the possible bias introduced by assortative mating , we performed instrumental variable analysis with maternal and paternal transmitted scores (M1/C1 and C2) adjusted as covariates (method 4). The results were similar to those obtained by method 3.To estimate the causal effect of maternal height on the pregnancy outcomes, we performed instrumental variable analysis . For comWhile the extremes of birth size, particularly birth weight, and gestational age at birth are important global contributors to infant mortality, more modest deviations in these parameters have been repeatedly linked to risk for adult disorders such as type 2 diabetes mellitus, hypertension, and cardiovascular disease –60. ThusIn this study, we observed the consistent associations between maternal height and birth length, birth weight, and gestational age in each of the three study cohorts. The estimated effect sizes of the observed associations were respectively ~0.05 cm/cm for birth length, ~10 g/cm for birth weight, and ~0.34 d/cm for gestational age per 1 cm change in maternal height, which are in the same range as previous reports for fetal growth  and gestn = 697) reported by a recent large GWA meta-analysis [Based on an extensive list of height-associated SNPs  and maternal nontransmitted haplotype score (method 3). The results strongly suggested that the observed correlations between maternal height and birth length or birth weight were mainly due to fetal genetic effects but not due to nongenetic intrauterine effect . Multiple lines of evidence consistently supported this conclusion.First, we observed significant association of fetal genetic score with birth length and birth weight, and after controlling for fetal genetic score, the associations between maternal genetic score with birth length and weight were no longer significant, which suggests substantial fetal genetic influence. This observation was in line with previous studies, which showed that the magnitudes of associations between paternal height with either birth length or birth weight were comparable to maternal height ,21,64. GSecond, our haplotype-based genetic score analysis showed that both maternal transmitted genetic score and paternal transmitted genetic score were highly significantly associated with birth length and birth weight, but the nontransmitted score was not (particularly for birth weight), which again indicated that maternal height has relatively minimal effect on birth length or birth weight if not transmitted genetically. The nontransmitted score was marginally associated with birth length, which suggests some direct causal effect of maternal height on birth length, and the estimated causal effect size was similar to the observational effect .Thirdly, our single marker analysis indicated that those adult height-associated SNPs were excessively associated with birth length and birth weight in fetus, but the estimated effect sizes of these SNPs on birth length and birth weight were not well correlated with their effect on maternal height, which indicated direct but different functional effects of these SNPs on fetal growth rather than a common causal effect mediated through maternal height. The inference of the parental transmission in mother-child pairs also allows us to examine parent-of-origin effects of those height-associated SNPs on birth length and weight. Our results showed excessive differences between the maternal and paternal transmitted alleles. For birth weight, the effect sizes of the maternal transmitted alleles were generally larger than paternal transmitted ones.We also performed the analysis using gestation-adjusted birthweight percentiles (converted to z-scores) as the outcome and obtained similar results . This evIn contrast with the findings in birth length and birth weight, we observed evidence that suggested gestational age at birth might be causally influenced by maternal height, in which case maternal height might operate as an important factor that shapes the intrauterine environment and influence the gestational age. Again, this conclusion was supported by multiple lines of evidence: (i) gestational age was  associated with maternal height genetic score but not with fetal height genetic score, and after adjustment for fetal genetics, the association between gestational age and maternal height genetic score were still significant; (ii) gestational age was associated with maternal nontransmitted haplotype genetic score, which indicated the effect of maternal height on gestational age was transmitted through phenotypic causal mechanism rather than genetic inheritance ; (iii) the estimated causal effect of 0.4 d/cm of maternal height on gestational age was similar to the size from observational association; and (iv) single-marker analysis indicated that those adult height SNPs were excessively associated with gestational age, and the estimated effect sizes of these SNPs (in mothers) on gestational age, although relatively low, were correlated with their effect on maternal height, which suggested the effect of these SNPs is projected through a common intermediate  rather than through SNP-specific pathways .p = 3.77 × 10−6, with an estimated 3% increase in risk if the mothers are 1 cm shorter). This result was consistent with a recent epidemiology study [p = 0.0456), which suggests that maternal height causally influences preterm birth risk.We also performed analysis using preterm status as a dichotomous outcome, and the results  indicategy study . Geneticp = 0.0424), and it was mainly driven by the DNBC cohort (p = 0.018) and was not significant in the other two cohorts. Also, gestational age in the DNBC cohort was estimated by combining information from self-reported data and gestational age reported in birth registry, while it was estimated by ultrasound in the other two cohorts. However, our meta-analysis did not reveal any sign of inconsistency—the confidence intervals of the estimates from different cohorts overlap and the p-value for the heterogeneity test was not significant. Nevertheless, the error-free validity of this association needs to be confirmed by further replication studies in independent cohorts.We recognize that the association between maternal non-transmitted haplotype score and gestational age was only modestly significant  built on multiple associated SNPs, while capturing more variance in maternal height, is not a valid instrument (method 1) unless the genetic effect of those SNPs in offspring can be appropriately adjusted for. However, complete adjustment for the fetal genetic effect of multiple SNPs could be difficult to achieve. We recognized that the weighted height genetic score in infants might not correctly summarize the genetic effect of the height-associated SNPs on pregnancy outcomes, since the weights of the SNPs were based on their effect sizes on adult height. As indicated by our single SNP analyses, the estimated effect sizes of these SNPs on birth length and birth weight were poorly correlated with the reported effect sizes on adult height. Considering this fact, using maternal height genetic score as genetic instrument, even adjusted for offspring genotype by fetal genetic score (method 2), might be inappropriate and could lead to biased estimate in causal inference. In contrast, our approach that used the nontransmitted haplotype genetic score (method 3) did not suffer from this problem.There are several caveats for our analytical approach: (1) high-density SNP data are required to construct long-range haplotypes in mothers and children when the allele transmission cannot be unambiguously determined from allelic states when both mother and child are heterozygote, and (2) in certain scenarios, the mutual independence between the transmitted and nontransmitted allele(s) cannot be assumed, e.g., in case of assortative mating dependent on the phenotype under study. The first limitation will require genotype data on additional SNPs around the SNPs used as genetic instrument, which is not a problem when genome-wide SNP data are available. The second limitation might introduce bias in causal inference, since the nontransmitted haplotype genetic score could correlate with the transmitted one. Intuitively, the extent of the bias due to assortative mating should be proportional to the correlation between haplotype genetic scores and the correlation between the  transmitted haplotype genetic scores and the pregnancy outcome, and therefore, it would be small because the correlation due to assortative mating is generally low.Biological pleiotropy is always a potential issue that could undermine the validity of Mendelian randomization causal inference . Completp = 0.0053). In addition, we performed the same analysis only in term mother/child pairs  and obtained similar results .From a study design perspective, there are number of limitations of our study. We tried to infer a mechanistic causal relationship between maternal height and several pregnancy outcomes. For this type of study, a random sample from target population is usually preferred. The three included studies were all case/control studies, in which the samples were enriched for mother/child pairs with short gestational age (<37 wk). In addition, the case/control sampling also broke down the normal distributions of the traits, with the distribution of the gestational age broken down into two segments (one for controls and one for cases). Similarly, the distributions of birth length and birth weight have two modes. However, after adjustment for gestational age, birth weight and length followed normal distributions. Yet, there is no easy method to transform the two-segmented distribution of gestational age into normal. This problem might undermine the validity of our analysis of gestational age, although regression analysis is robust to the normality assumption . To inveAnother potential problem is the differences in phenotype data collection and availability among the three studies. For example, gestational age at birth was estimated by last menstrual period in DNBC but by ultrasound in the MoBa and FIN studies, and birth length was unavailable in DNBC. In addition, the high missing rates in some of participant studies also prevented us from including some known important covariates like maternal BMI, smoking, and parity in the regression models. However, in the individual studies in which these variables were available, the analytical results from models with or without these covariates did not show significant differences. Therefore, we believe the major findings of our study are solid and robust to these limitations.In summary, this study defines the causal relationship for the strong association of maternal height with fetal growth measures  and gestational age at birth. The observed association between maternal height and fetal growth parameters is mainly defined by fetal genetics, and many of the adult height-associated SNPs also influence fetal growth. In contrast, the association between maternal height and gestational age more likely reflects the maternal height phenotype, and the resulting fetal growth environment it shapes, as being deterministic. In addition, our approach that utilizes the genetic score derived from the parental nontransmitted haplotype as a genetic instrument is a novel extension to the Mendelian randomization methodology in casual inference between parental exposure and outcomes in offspring.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S9 Table(PDF)Click here for additional data file.


Aboriginal populations are at substantially higher risks of adverse birth outcomes, perinatal and infant mortality than their non-Aboriginal counterparts even in developed countries including Australia, U.S. and Canada. There is a lack of data on recent trends in Canada.We conducted a population-based retrospective cohort study  using the linked vital events registry databases for singleton births in Quebec 1996–2010. Aboriginal  births were identified by mother tongue, place of residence and Indian Registration System membership. Outcomes included preterm birth, small-for-gestational-age, large-for-gestational-age, low birth weight, high birth weight, stillbirth, neonatal death, postneonatal death, perinatal death and infant death.Perinatal and infant mortality rates were 1.47 and 1.80 times higher in First Nations , and 2.37 and 4.46 times higher in Inuit  relative to non-Aboriginal  births . Compared to non-Aboriginal births, preterm birth rates were persistently (1.7–1.8 times) higher in Inuit, large-for-gestational-age birth rates were persistently (2.7–3.0 times) higher in First Nations births over the study period. Between 1996–2000 and 2006–2010, as compared to non-Aboriginal infants, the relative risk disparities increased for infant mortality (from 4.10 to 5.19 times) in Inuit, and for postneonatal mortality in Inuit (from 6.97 to 12.33 times) or First Nations (from 3.76 to 4.25 times) infants. Adjusting for maternal characteristics  attenuated the risk differences, but significantly elevated risks remained in both Inuit and First Nations births for the risks of perinatal mortality , infant mortality  and postneonatal mortality  in Inuit and First Nations infants .Aboriginal vs. non-Aboriginal disparities in adverse birth outcomes, perinatal and infant mortality are persistent or worsening over the recent decade in Quebec, strongly suggesting the needs for interventions to improve perinatal and infant health in Aboriginal populations, and for monitoring the trends in other regions in Canada. Aboriginal populations are at higher risks of adverse birth outcomes (e.g. preterm birth), perinatal and infant mortality than their non-Aboriginal counterparts in many countries including Australia, the U.S., and Canada –14. SuchThere are three groups of Aboriginal peoples in Canada: First Nations (North American Indians), Inuit and Metis. In most Canadian provinces, there is a lack or poor-quality of data on Aboriginal birth outcomes, perinatal and infant mortality due to the lack or poor-quality of Aboriginal birth identifiers on birth registrations . There rAboriginal singleton births, but did not permit the inclusion of all non-Aboriginal singleton births and recommended a 20% random sample of non-Aboriginal births for inclusion in the study cohort since the total number of non-Aboriginal singleton births was huge (about 1.15 million). The representativeness of the 20% sampled non-Aboriginal singleton birth cohort was excellent: identical rates of preterm birth, low birth weight, stillbirth and infant death, and identical mean maternal age and year of education were observed in the sampled singleton non-Aboriginal birth cohort vs. the total singleton non-Aboriginal birth cohort. In respecting the ISQ data reporting confidentiality rules, birth numbers are reported to the nearest 10. The final study birth cohort included 20,190 First Nations, 4,260 Inuit and 229,960 non-Aboriginal singleton births in Quebec, 1996–2010.This was a population-based retrospective birth cohort study, based on the linked vital events registry databases in Quebec, Canada. In Quebec, all vital events  are required to be registered by law using standard registration forms, and are routinely collected through the Ministère de la Santé et des Services Sociaux (MSSS) registry system. Vital events are reviewed and captured into electronic databases at the Institut de la Statistique du Québec (ISQ). The study birth cohort was constituted based on all singleton births to Quebec residents in 1996–2010, the most recent databases available at the time of the approval of the project by the Commission for Access to Information (CAI) in Quebec in 2013. The initial research protocol proposed to include all singleton births  in Quebec 1996–2010 in the study cohort. However, the CAI granted the inclusion of all identified The study was approved by the Research Ethics Board of Sainte-Justine hospital research center. Informed consent was waivered because the study was based on administrative health databases. Patient records/information was anonymized and de-identified prior to data analysis. Approvals of the study were also obtained from the ISQ, the CAI, the Commission for Access to Information of the Aboriginal Affairs and Northern Development Canada, and three major Aboriginal community health organizations in Quebec—the Cree Board of Health and Social Services of James Bay, the First Nations of Quebec and Labrador Health and Social Service Commission, and the Nunavik Regional Board of Health and Social Services.Linkage of live births (1996–2010) to infant death records (1996–2011) were based on infant’s sex, first name, last name, date of birth and birth weight, residential municipality and postal code, and mother’s/father’s first name, last name, mother’s residential municipality and postal code. The record linkage used probabilistic linkage technique, and the validity of the methodology has been well documented in Canadian vital events data linkage . ExternaAboriginal births were identified using three sources of information: 1) mother tongue–a birth was considered a First Nation or Inuit if the mother or father reported a First Nation or Inuit mother tongue on the birth registration, respectively; 2) residential postal code and municipality name–a birth was considered a First Nation if the mother’s residential postal code and/or municipality name corresponded to a First Nation community/reserve  in Quebec, while a birth was considered an Inuit if the mother’s residential postal code / municipality name corresponded to an Inuit village / municipality in Nunavik ; 3) Indian Registration System (IRS) membership–a birth was considered a First Nation if either the mother or father was identified as an IRS member, according to the IRS membership database for all members (including deceased) up to December 2012 in Canada. The IRS membership database was provided by Aboriginal Affairs and Northern Development Canada which routinely collects and updates the IRS membership database every year. The linkage of births to IRS records was based on first name, last name, sex and date of birth of the infant, mother and father.We created a composite Aboriginal birth identifier using all the three sources of information. A birth was considered a First Nation if the birth was identified as a First Nation by anyone of the three sources of information . There were 7,020 singleton births identified as First Nations by mother tongue, 16,150 by residential postal code / municipality name, and 17,790 by IRS membership, respectively, while the total number of First Nations singleton births identified by anyone of the three methods was 20,190 . This total birth number is close to the estimated total number of births (n = 20700) based on self-reported First Nations children under 5 years of age in Quebec according to the 2006 Census. A birth was considered an Inuit if the birth was identified as an Inuit by mother tongue or postal code. There were 3,560 singleton births identified as Inuit by mother tongue, 4,190 singleton births identified as Inuit by residential postal code / municipality name, while the total number of Inuit singleton births identified by either method was 4,260. Exploratory analyses were conducted to examine Aboriginal birth outcomes by different Aboriginal birth identifiers.Too few stillbirths and infant deaths could be identified through the linkage to the IRS membership database. Therefore, the IRS identifier was used in the analyses on birth outcomes, but was not used in the analyses on perinatal and infant mortality in First Nations.Based on geocoding postal code and municipality name  of the mth percentile, based on the Canadian fetal growth standards , and rural vs. urban residence. All data analyses were carried out using SAS, Version 9.2.Table 1). First Nations and Inuit mothers were much younger than non-Aboriginal mothers. The proportion of mothers under 20 years of age was substantially higher for Inuit (22.5%) or First Nations (18.3%) vs. non-Aboriginal (3.4%) mothers. First Nations and Inuit mothers had much lower educational attainment. Over 40% of Inuit and First Nations mothers had completed education less than 11 years (high school in Quebec)—over 4 times higher than non-Aboriginal mothers. The proportion of single, divorced or widowed mothers was over 3 times higher in First Nations (28.7%) or Inuit (34.2%) versus non-Aboriginal (8.8%) mothers. The proportion of primiparous women was lower in First Nations (34.0%) or Inuit (28.5%) vs non-Aboriginal (46.3%) mothers. A much higher percentage of births was from rural areas in First Nations (84.5%) or Inuit (97.5%) vs. non-Aboriginal (22.5%) births.Maternal characteristics  differed significantly between Aboriginal and non-Aboriginal births (Table 2). Preterm birth rates were 1.72 times higher for Inuit and 1.11 times higher for First Nations relative to non-Aboriginal births during the study period. Compared First Nations vs. non-Aboriginal births, LBW (RR = 0.76) and SGA (RR = 0.45) birth rates were substantially lower, while HBW (RR = 2.27) and LGA (RR = 2.80) birth rates were substantially higher. Perinatal and infant mortality rates were 1.47 and 1.80 times higher for First Nations , and 2.37 and 4.46 times higher for Inuit  births as compared to non-Aboriginal  births. Large relative risk elevations were observed in neonatal mortality among Inuit infants (2.94 times), and in postneonatal mortality among First Nations (3.71 times) or Inuit (8.98 times) infants.In general, the risks of adverse birth outcomes were much higher in First Nations or Inuit vs. non-Aboriginal births (Table 2). Compared to non-Aboriginal infants, large relative risk elevations were observed for infant mortality due to infections for First Nation (RR = 8.76) or Inuit (RR = 11.35), due to SIDS for First Nations (RR = 3.97) or Inuit (RR = 18.51), and due to injuries/external causes for First Nations (RR = 4.00) or Inuit (RR = 9.73) infants.Analyses of cause-specific infant mortality rates revealed significant risk elevations for infant death due to congenital anomalies, SIDS, infections and external causes among First Nations or Inuit vs. non-Aboriginal infants, and due to asphyxia or immaturity-related conditions among Inuit vs. non-Aboriginal infants  higher in Inuit, LGA rates were persistently (2.7–3.0 times) higher in First Nations births. SGA rates were persistently lower for First Nations or Inuit vs. non-Aboriginal births, while SGA rates trended higher for Inuit births (increased from 4.7% to 6.4%) only. The disparities in perinatal mortality comparing First Nations or Inuit vs. non-Aboriginal births fluctuated and showed no apparent trends between 1996–2000 and 2006–2010. Surprisingly, the relative risk disparities in infant mortality comparing Inuit vs. non-Aboriginal infants increased from 1996–2000 to 2006–2010 (from 4.1 to 5.2 times). The First Nations vs. non-Aboriginal relative risk disparities in infant mortality fluctuated, narrowing from 1996–2000 (RR = 2.09) to 2001–2005 (RR = 1.36), but widening in the most recent period (RR = 1.93 in 2006–2010). Increasing relative risk disparities were observed in neonatal mortality comparing Inuit vs. non-Aboriginal infants (from 2.76 to 3.29 times), and in postneonatal mortality comparing Inuit (from 6.97 to 12.33 times) or First Nations (from 3.76 to 4.25 times) to non-Aboriginal infants.The trends in disparities in birth outcomes, perinatal and infant mortality are presented in Table 4. The adjustments for maternal characteristic  attenuated the risk differences in perinatal, infant and postneonatal mortality comparing First Nations or Inuit vs. non-Aboriginal births. However, significantly elevated risks remained in perinatal, infant and postneonatal mortality comparing First Nations  or Inuit  to non-Aboriginal births. Significantly elevated risks of preterm birth (adjusted OR = 1.33) and neonatal mortality (adjusted OR = 2.44) remained for Inuit infants, while the lower risk of SGA and higher risk of LGA for First Nations and Inuit births were even more striking after the adjustments.The crude and adjusted ORs of adverse outcomes comparing First Nations and Inuit vs. non-Aboriginal births are presented in Similar risk patterns and risk differences were observed if the comparisons of First Nations and Inuit vs. non-Aboriginal birth outcomes, perinatal and infant mortality were restricted to rural areas (data not shown).Table 5 presents the outcome rates for Aboriginal groups by different Aboriginal birth identifiers. Preterm birth rates were lower for First Nations births identified by IRS membership (6.6%) than those identified by mother tongue (7.7%) or residential postal code (7.1%). SGA birth rates were identical (3.6%) for First Nations births identified by residential postal code vs. IRS membership, and both were higher than the SGA rate for First Nations births identified by mother tongue (2.8%). LGA birth rates were similar for First Nations births identified by residential postal code / municipality name (27.2%) and IRS membership (26.4%), and both were higher than the rate for First Nations births identified by mother tongue (29.7%). Stillbirth, perinatal, infant, neonatal and postneonatal mortality rates were all higher for First Nations births identified by mother tongue than those First Nations births identified by residential postal code / municipality name; the relative risk differences were in the range of 15–25%. The relative risk differences in adverse birth outcomes were in the range of 1–12% for Inuit births identified by mother tongue vs residential postal code and municipality name.We found persistently higher rates of preterm birth in Inuit vs. non-Aboriginal births, and of LGA birth in First Nations vs. non-Aboriginal births, and widening disparities in neonatal mortality and infant mortality rates comparing Inuit vs. non-Aboriginal infants, and in postneonatal mortality rates comparing First Nations or Inuit vs. non-Aboriginal infants over the recent decade (between 1996–2000 and 2006–2010) in Quebec. Maternal characteristics could only partly explain the higher risks of perinatal and infant mortality among First Nations and Inuit populations.1–14 Our results confirmed the elevated risks of perinatal and infant mortality among First Nations and Inuit in Quebec [22 These disadvantages are likely the root causes of poor perinatal and infant health.Aboriginal peoples are a potent mirror of human multiplicity of culture, language, and spirit, yet they are frequently marginalized , and sufn Quebec ,4,11. Mon Quebec . More won Quebec . The obsn Quebec ,24. AborLarge risk elevations were observed for infant death due to preventable causes including infections, SIDS and injuries among First Nations and Inuit infants in Quebec. SIDS was a leading cause of infant mortality among both First Nations and Inuit infants in Quebec. Unsafe sleep environment  has been recognized as a major risk factor of SIDS ,26. EffeConsistent with the findings from previous studies, we observed substantially higher rates of preterm birth in Inuit, and of LGA birth in First Nations and Inuit vs. non-Aboriginal births ,11. In cThe main strengths are the large population-based birth cohort, and the use of multiple sources of information to identify Aboriginal births allowing more complete identification of Aboriginal births than previous studies. However, misclassifications may occur as births to Aboriginal women  who neither reported an Aboriginal mother tongue, nor resided in an Aboriginal community or registered as a First Nation would have been classified as “non-Aboriginal” births. Nevertheless, such misclassifications are likely a very small number of the “non-Aboriginal group”, and would only tend to deflate the observed risk disparities. Similarly, some births to non-Aboriginal women in Aboriginal communities might have been misclassified as “Aboriginal”—this would also attenuate the observed risk differences. Also, we had limited information on maternal characteristics, with no information on many risk factors of adverse birth outcomes such as smoking, alcohol and substance use. However, most of these factors may be considered as mediating risk factors for the poorer outcomes in Aboriginal populations, and thus should not be adjusted for in quantifying the risk disparities.The persistent and even widening disparities in perinatal and infant health indicators comparing First Nations and Inuit to non-Aboriginal births in Quebec over the recent decade strongly call for effective intervention programs to improve Aboriginal perinatal and infant health, and the need for timely monitoring the trends in other regions in Canada. Possible interventions may include governmental investments for improving socioeconomic conditions and accessibility to high-quality perinatal and infant health care, along with promotion of safe motherhood and infant care for Aboriginal parents and communities. Concerted efforts from all levels of government policy makers, Aboriginal authority and health organizations are needed to make a difference .


Numerous studies have examined associations between air pollution and pregnancy outcomes, but most have been restricted to urban populations living near monitors.We examined the association between pregnancy outcomes and fine particulate matter in a large national study including urban and rural areas.2.5 (particles of median aerodynamic diameter ≤ 2.5 μm) were assigned by mapping the mother’s postal code to a monthly surface based on a national land use regression model that incorporated observations from fixed-site monitoring stations and satellite-derived estimates of PM2.5. Generalized estimating equations were used to examine the association between PM2.5 and preterm birth , term low birth weight , small for gestational age , and term birth weight, adjusting for individual covariates and neighborhood socioeconomic status (SES).Analyses were based on approximately 3 million singleton live births in Canada between 1999 and 2008. Exposures to PM3 increase in PM2.5 over the entire pregnancy was associated with SGA  and reduced term birth weight . Associations varied across subgroups based on maternal place of birth and period (1999–2003 vs. 2004–2008).In fully adjusted models, a 10-μg/m2.5 estimates from a national spatiotemporal model, provides further evidence linking PM2.5 and pregnancy outcomes.This study, based on approximately 3 million births across Canada and employing PM2.5 in a National Canadian Study. Environ Health Perspect 124:243–249; http://dx.doi.org/10.1289/ehp.1408995Stieb DM, Chen L, Beckerman BS, Jerrett M, Crouse DL, Omariba DW, Peters PA, van Donkelaar A, Martin RV, Burnett RT, Gilbert NL, Tjepkema M, Liu S, Dugandzic RM. 2016. Associations of pregnancy outcomes and PM Numerous studies have examined the association between air pollution and pregnancy outcomes. Meta-analyses and pooled multi-center analyses suggest that particulate matter is associated with low birth weight and preterm birth, although there is heterogeneity among centers . Most st2.5) from a spatiotemporal model using ground measurements and satellite-based estimates and applied these to approximately 3 million births across Canada between 1999 and 2008. We examined preterm birth, term low birth weight (LBW), and small for gestational age (SGA) as binary variables, and term birth weight as a continuous variable.In this study we employed estimates of fine particulate matter   was used to geocode birth records using the maternal postal code in order to obtain Statistics Canada standard geographic identifiers. In urban Canada (75–80% of the population), postal codes generally refer to a small geographic area containing on average 30 people. Each postal code is represented spatially by a representative point or points. In urban areas, it is most often located at the mid-point along a block-face portion that generally corresponds to one side of a road. For apartment buildings it is often the location of the building. For rural Canada, postal codes can cover a large geographic area with as many as 1,100 people, encompassing more than one census dissemination area. For these cases, postal code representative points are randomly allocated using a population-weighted file from Statistics Canada , such that the probability of a given dissemination area (DA) centroid being used reflects the spatial distribution of the underlying population. Postal codes were considered rural if the second character was zero. Using geocoded birth records, neighborhood-level socioeconomic status (SES) variables were calculated at the DA level using census data, including proportion of individuals ≥ 15 years of age who were unemployed, proportion of individuals ≥ 15 years of age in the lowest income quintile, and proportion of females ≥ 25 years of age with postsecondary education  model that incorporated observations from fixed-site monitoring stations and satellite-derived estimates of PM2.5. Exposures were estimated for the entire duration of pregnancy, by trimester, and by gestational month.2.5 for Canada by combining observed PM2.5 levels with observations from the contiguous United States. These observations were combined to help stabilize the variability likely to be predicted by the small Canadian data set. In Canada, there were 241 sites over a very large landmass, whereas the U.S. data set had a total of 1,464 sites. There was a concern that the limited density of observations in Canada would reduce our ability to generate defensible predictive estimates. Given the adjacency of the observations in the United Staters, we determined that they could bolster the predictive capacity of a Canadian model.Using the same two-stage methods as those described by 2.5 concentration estimated from remote sensing (squared and cubed) were chosen by the DSA algorithm, for the LUR model as the most predictive variables, using cross-validation selection techniques. Additionally, an indicator for the Canadian data set was interacted with the remote sensing variable to provide a small marginal adjustment to the remote sensing contribution to the prediction. The polynomial terms served to reduce bias in the remote sensing estimates because they did not use any ground data to calibrate the predicted levels. The polynomial term on open space was selected by DSA because it predicted best and described the functional form of the relationship with observed PM2.5 levels. The prediction results were very similar to those reported by R2 based on v-fold CV prediction error. However, there was significant residual variability as the non-normalized CV pseudo-R2 was 26%. In the second stage, the Bayesian maximum entropy interpolation method  randomly selected leave-out observations from 22 monitoring sites. This model appeared less predictive than the U.S. model (CV R2 = 0.79); however, the poor fit was partly driven by a small number of outlying observations  and removing them improved the model prediction (CV R2 = 0.53).During the first stage of modeling, a machine learning method, known as the deletion/substitution/addition (DSA) algorithm, was implemented to create a LUR model, as described by n method  was usedn method , produceAs a sensitivity analysis, we also employed monthly average concentrations from ground-based monitors for 24 cities with at least 85% complete monthly data. The total population of these cities based on the 2006 census was approximately 11,500,000, or about one-third of the Canadian population.Statistical analysis. We used a similar approach to that employed in the recent International Collaboration on Air Pollution and Pregnancy Outcomes (ICAPPO) multi-center analysis .analysis , reportianalysis . Because2.5 exposures, of which 2,969,380 were singletons . After further excluding births with missing covariate data, analyses of preterm birth, term LBW and birth weight, and SGA were based on up to 2,966,705, 2,781,940, and 2,965,440 births, respectively. The overall prevalence of preterm birth was 6.23%, of term LBW 1.57%, and SGA 8.31%. Mean assigned PM2.5 exposure over the entire pregnancy was 8.4 μg/m3 and interquartile range (IQR) was 3.6 μg/m3  . Prevalence of each outcome and mean PM2.5 exposure by infant, maternal, and neighborhood characteristics are shown in 2.5, but not of pregnancy outcome, between 1999 and 2008. There was also a consistent trend of increased prevalence of adverse pregnancy outcome in neighborhoods in the lowest tertile of SES indicators, but there was no consistent trend in PM2.5 exposure in relation to SES: There was no trend in PM2.5 exposure by tertile of percent unemployed; PM2.5 exposure was higher in the highest tertile of percent low income; and there was a trend of higher PM2.5 exposure with increasing percent of females who completed postsecondary education. Gradients in neighborhood SES variables were consistent with those in individual-level maternal education in Quebec, where individual level data on maternal education were available: Compared with mothers who had lower educational attainment, a larger percentage of mothers with higher educational attainment lived in DAs containing the highest percentage of females who had completed postsecondary education, the lowest percentage of individuals in the lowest income quintile, and the lowest percentage of unemployed individuals. Conversely, compared with mothers with higher educational attainment, a larger percentage of mothers with lower educational attainment lived in DAs containing the lowest percentage of females who had completed postsecondary education, the highest percentage of individuals in the lowest income quintile, and the highest percentage of unemployed individuals .During the study period there were 3,104,090 live births. Of these, 3,061,155 (98.6%) could be mapped to PM.6 μg/m3 . Mean ex2.5 and pregnancy outcomes based on exposures averaged over the entire pregnancy are shown in 2.5 exhibited a significant negative association with preterm birth in both unadjusted and fully adjusted models. SGA and term LBW exhibited significant positive associations, and term birth weight a significant negative association with PM2.5 in unadjusted models. Associations of PM2.5 with term LBW, SGA, and term birth weight were not sensitive to adjustment for neighborhood SES; however, they were all reduced substantially after adjustment for infant and maternal characteristics. Analysis by exposure period : –74.6, –18.0] for a small number of births  with gestational age > 9 months. We did not include this in the figure because it obscured the results for all other exposure periods. The association with term birth weight based on exposure over the entire pregnancy was not sensitive to exclusion of these births .Associations between PMe period  revealedn = 681,915 singleton live births), where we were able to adjust for individual-level maternal education as a covariate. Significant negative associations were observed with preterm birth in both instances  based on 1,140,920 singleton live births, and employing data from Quebec only  = 1.04; 95% CI: 1.01, 1.07] and reduced term birth weight . Expressed per IQR (3.58 μg/m3) increase, these values are 1.014  and –7.4 g .We employed estimates of PM2.5 exposure to examine pregnancy outcomes in large studies examining both urban and rural areas. 3 increase in PM2.5 was associated with a decrease in term birth weight of –13.8 g  and an odds ratio for preterm birth of 1.06  from a study in Massachusetts, adjusting for infant and maternal characteristics including smoking. They found a positive, significant association with preterm birth based on entire pregnancy exposure, and a null association based on exposure during the last month. 3 increase in PM2.5 was associated with decreases in birth weight ranging from –24.9 g  to –78.8 g  depending on the method of exposure assignment; odds ratios for term LBW ranged from 1.04  to 1.38 , for SGA ranging from 1.13  to 1.38 ; and for preterm birth they ranged from 1.00  to 0.96 . Associations were adjusted for infant and maternal characteristics (including smoking) and were consistently larger based on satellite-derived exposures than those based on ground-based monitoring, except for associations with preterm birth, which were consistently null. Greater exposure misclassification using ground-based monitoring versus satellite observations was identified as a possible explanation. 2.5 and preterm birth and LBW  using the World Health Organization Global Survey on Maternal and Perinatal Health in Africa, Asia, and Latin America. PM2.5 was not associated with either preterm birth or LBW across the entire sample, but the highest quartiles of exposure were associated with LBW, and in China, the highest quartiles were associated with both preterm birth and LBW. Maternal smoking data were not available in this study.To our knowledge, only three previous studies have employed model/satellite-based estimates of PM3 increase in PM2.5 averaged over the entire pregnancy was associated with a summary odds ratio of 1.05  for LBW  as well as a summary –23.4 g  reduction in birth weight . An increase of 10 μg/m3 PM2.5 averaged over the entire pregnancy was also associated with a summary odds ratio of 1.16  for preterm birth (n = 4 studies). 3 PM2.5 was associated with an odds ratio of 1.04  for LBW and an –8.9 g reduction in birth weight . They also found that associations were sensitive to adjustment for SES and maternal and infant characteristics.Our results are consistent with those observed in a recent meta-analysis , as well2.5 on risk of adverse pregnancy outcomes in population subgroups remain to be identified. Most other studies have examined effect modification by race rather than maternal place of birth, and results have been inconsistent , and a nonsignificant summary odds ratio > 1 was reported for PM10 and preterm birth (n = 3 primary studies), both based on entire pregnancy exposure  were not sensitive to adjustment for maternal smoking . They proposed a method for upward adjustment of air pollution relative risks derived from studies lacking individual data on smoking. Smoking during pregnancy was also strongly associated with maternal education in Quebec  in 2003 in Los Angeles County . In a na smoking . Villenen Quebec  and neign Quebec ; thus, i2.5 estimates from a national spatiotemporal model provides further evidence linking PM2.5 and pregnancy outcomes. Associations between PM2.5 and pregnancy outcomes were sensitive to adjustment for individual covariates, but not neighborhood SES. In fully adjusted models, PM2.5 was associated with SGA and term birth weight. These associations varied across subgroups based on maternal place of birth and time period. Further study to identify population groups at greater risk and to examine mechanisms that could account for increased vulnerability would be desirable.This study based on approximately 3 million births across Canada and employing PM(4.1 MB) PDFClick here for additional data file.


Recently, a new technique was developed for non-catalytically mixing microdroplets. In this method, an alternating-current (AC) electric field is used to promote the antigen–antibody reaction within the microdroplet. Previously, this technique has only been applied to histological examinations of flat structures, such as surgical specimens. In this study, we applied this technique for the first time to immunofluorescence staining of three-dimensional structures, specifically, mammalian eggs. We diluted an antibody against microtubules from 1:1,000 to 1:16,000, and compared the chromatic degree and extent of fading across dilutions. In addition, we varied the frequency of AC electric-field mixing from 5 Hz to 46 Hz and evaluated the effect on microtubule staining. Microtubules were more strongly stained after AC electric-field mixing for only 5 minutes, even when the concentration of primary antibody was 10 times lower than in conventional methods. AC electric-field mixing also alleviated microtubule fading. At all frequencies tested, AC electric-field mixing resulted in stronger microtubule staining than in controls. There was no clear difference in a microtubule staining between frequencies. These results suggest that the novel method could reduce antibody consumption and shorten immunofluorescence staining time. Immunofluorescence staining is a very important procedure for evaluating the functions of cells, including human and murine eggs124678101178Previous reports have also suggested that pH level, the presence of oxygen, and time are associated with fading of immunofluorescence staining13In this study, we investigated whether immunofluorescence staining of murine eggs by AC electric-field mixing could have the potential leading to reduce consumption of antibody and shorten staining time. We also assessed whether there was a difference in microtubule immunofluorescence staining in the presence or absence of the AC electric field.Following AC electric-field mixing, there were no clear differences in microtubule morphology between the control group and the AC electric-field mixing group . SimilarImages obtained with a high-speed camera revealed the changes in the form of the microdroplet at each frequency . There wWe show the results of immunofluorescence staining at each antibody concentration in We showed the Relative Intensity (RI) for each concentraion of primary antibody and each frequency as a box-and-whisker plot in We showed that AC electric-field mixing increased the intensity of immunofluorescence staining of microtubules. Of course, in such an evaluation, it is necessary to consider individual differences in the eggs. Also, microtubules were stained to some extent in only 5 minutes when eggs were simply left to rest in droplets containing antibody. At a high antibody concentration  , the antibody reacted sufficiently with antigen even in the control group; therefore, there was no apparent difference between the conditions. RI tended to increase in the mixing group at the first observation stating at a dilution of 1:2,000. When the antibody concentration was low, staining after 5 minutes was weak, and the degree of fading was strong in the control group. However, when antibody was used at a concentration 10-fold lower than usual, microtubule staining could be achieved after 5 minutes of AC field mixing. Moreover, fading was minimized in samples subjected to AC electric-field mixing, facilitating the second observation (conducted 48 hours after the first). Various frequencies resulted in different degrees of transformation of the droplet, as shown in Transformation of microdroplets has been attempted previously using vibration, acoustic actuation, microwave irradiation, and physical methods10111278As shown in 7816It is also important to consider the effect of electroporation, in which an aperture in the cell membrane is created by application of an electric current. This technique has been applied to introduction of mRNAFinally, we considered whether this method of immunofluorescence staining with mixing leads to reduce costs relative to conventional staining methods. To date, several methods for effective immunostaining have been used, including microwave15In summary, we applied AC electric-field mixing for the first time to murine eggs. Because the antigen–antibody reaction was promoted by mixing, staining was intense and fading was minimal; consequently, observation was possible even at very low concentrations of antibody. These findings indicated that this method may has the potential to reduce the quantiy of antibody used and shorten the staining time. We predict that this technique could be applied in multiple ways to studies of eggs and other cell types.All procedures for murine care and use were carried out in accordance with the guidelines and approved by the Animal Research Committee of the Akita University, Japan (Permit number: 26-1-40).Murine eggs were obtained from imprinting control region (ICR) mice at 4–7 weeks of age. Mice were stimulated for egg collection according to a previous report5Eggs were subjected to immunofluorescence staining to detect microtubules, as previously described5AC electric-field mixing was performed using a Histo-Tek® R-IHC® , shown in 678After a 5-min period of electric field mixing in the presence of primary antibody at room temperature, we washed the eggs in KB buffer for 1 minute, moved each egg to a droplet containing secondary antibody, and performed electric field mixing for an additional 5 minutes. The AC electric field mixing took place at 5 Hz for dilution of 1:8,000 to 1:1,000. In 1:16,000 the least dense, 21 Hz, 32 Hz, 46 Hz underwent AC field mixing in addition to 5 Hz order to investigate the influence of different frequencies. In the control group, eggs were left to rest in primary antibody for 5 minutes without AC field mixing. It was assumed that the secondary antibody only left an egg at rest similarly in the control group for 5 minutes.Eggs were observed and analyzed on a DP73 microscope  equipped with a digital color and monochrome camera. The objective lens was a 100 × oil-immersion lens. The imaging software was cellSens® Standard (Olympus). We limited observation of each egg to <1 minute to prevent fluorescence fading. Focus matches the whole and we only adjusted gain so that microtubule contrast became clear; otherwise, we did not perform any type of image editing. For the first observation, eggs were observed immediately after mounting in VECTASHIELD® Mounting Medium with DAPI. The second observation, to determine the degree of fading, was carried out 48 hours after first observation. During the first and second observations, we kept eggs in the dark at 4 °C. The immunofluorescence staining protocol is summarized in To evaluate the intensity of microtubules staining quantitatively, we used the ImageJ software (version 1.48 for Mac OS X)2424Data were analyzed by two-sample t-test or Welch’s t-test with Levene’s test using the SPSS software version 21.0 . A value of probability (P) < 0.05 or <0.01 was considered statistically significant. We applied Bonferroni correction for multiple comparisons between groups.How to cite this article: Hiromitsu, S. et al. Novel method for immunofluorescence staining of mammalian eggs using non-contact alternating-current electric-field mixing of microdroplets. Sci. Rep.5, 15371; doi: 10.1038/srep15371 (2015).


The economic stress hypothesis (ESH) predicts decreases in the sex ratio at birth (SRB) following economic decline. However, as many factors influence the SRB, this hypothesis is difficult to test empirically. Thus, researchers make use of quasi-experiments such as German reunification: The economy in East, but not in West Germany, underwent a rapid decline in 1991. A co-occurrence of a decline in the East German SRB in 1991 has been interpreted by some as support for the ESH. However, another explanation might be that the low SRB in 1991 stems from increased random variation in the East German SRB due to a drastically reduced number of births during the crisis. We look into this alternative random variation hypothesis (RVH) by re-examining the German case with more detailed data.Our analysis has two parts. First, using aggregate-level birth register data for all births in the period between 1946 and 2011, we plot the quantum and variance of the SRB and the number of births and unemployment rates, separately for East and West Germany, and conduct a time series analysis on the East German SRB over time. Second, we model the odds for a male birth at the individual level in a multiple logistic regression . Explanatory variables are related to the level of the individual birth, the mother of the child born, and the regional economic context.The aggregate-level analysis reveals a higher degree of variation of the SRB in East Germany. Deviations from the time trend occur in several years, seemingly unrelated to economic development, and the deviation in 1991 is not statistically significant. The individual-level analysis confirms that the 1991-drop in the East German SRB cannot directly be attributed to economic development and that there is no statistically significant effect of economic development on sex determination in East or West Germany.Outcomes support the RVH but not the ESH. Furthermore, our results speak against a statistically significant effect of the reunification event itself on the East German SRB. We discuss the relative importance of behavioral and physiological responses to macro-level stressors, a distinction that may help integrate previously mixed findings. For human populations, a sex ratio at birth (SRB) of about 105 boys to 100 girls is seen as "natural", 3. ThisHere we are specifically concerned with the effects of economic contraction on the SRB. Also with regard to economic contraction, empirical results are mixed: Catalano and Bruckner, for insThe mixed empirical pattern has led researchers to ponder the potential mechanisms at play in linking economic development to the SRB. Recently, Grant and collaborators proposed a model that integrates divergent explanations into a combined framework and may help to explain why the SRB tends to increase after some types of crises, but to decrease after other ones–26. The The timing of stress exposure at conception and during pregnancy plays an important role in this framework, and allows us to make rough predictions about the direction of the expected SRB bias in a population. If stress occurs around the time of conception, more males will be conceived; but if stress persists during pregnancy, more males will die in utero, and the SRB might be normal. If stress occurs around the time of conception but subsides during pregnancy, more males will be conceived and survive pregnancy, resulting in a male-biased SRB. Finally, under normal conditions, there will be no overproduction of male fetuses. If stress occurs during pregnancy and more males die in utero, the SRB in a population may decrease relative to normal conditions. These two mechanisms, pre-conception testosterone and glucose levels and in-utero male vulnerability, together explain some of the inconsistencies in the results of previous studies. StudiesIn addition to the challenge of measurement timing, researchers are faced with the problem of confounding by additional influences on the SRB. A large number of potential correlates with the SRB have been suggested, including birth order, parental age, coital frequency, parental hormone levels, genetic effects, birth registration changes, and exposure to certain toxins, 38, 40.Against this background, the case of German reunification becomes interesting as a natural experiment to test the influence of economic stress on the SRB: In this case, we have two very similar populations with a common institutional framework following reunification. Yet, post-reunification economic decline selectively affected East Germany in 1991 and in subsequent years. Making Our study is motivated by the recent emphasis on statistical challenges in research on the SRB, 45. GivTo determine if the deviation from the time trend in 1991 is a real outlier and not just a random deviation from the time trend requires careful statistical modeling that takes the different degree of volatility in the SRB over time and between the two parts of Germany into account. Therefore, we revisit the case of German reunification by methods allowing to control for these aspects and analyzing a longer time series for East and West Germany up to and including the year 2011 and by drawing on individual-level data on births that occurred in East and West Germany between 1991 and 2010. This allows us to test the ESH against the RVH: First, and in line with the argument made by Catalano, the SRBOur aggregate-level analysis is based on yearly SRB data for Germany covering the period between 1946 and 2011. This data we obtained from the Human Mortality Database. Our anaSpecifically, we compare five models in the Results section, which are described in detail in the statistical appendix: Model 1 is a simple linear regression model in which time is entered as a continuous covariate. Model 2 accounts for temporal autocorrelation with an autoregression component (AR1) and additionally includes a moving average (MA1). The "1" indicates that we are working with lags of one year. We chose the combination of AR1 and MA1 as it provided the best goodness of fit as compared to a range of alternative AR and MA specifications. Whereas Model 2 adjusts for temporal autocorrelation, it doesn’t correct for the considerable degree of heteroskedasticity present in the data. In Models 3–5 we therefore accounted for heteroskedasticity by implementing different weighting procedures in weighted least squares regressions. In Model 3, weights are based on standard errors obtained from an autoregressive conditional heteroskedasticity (ARCH) model. Since ta. We excluded multiple births and births that occurred in Berlin, as the data did not allow us to distinguish between former East and West Berlin. This left a total of close to 13.9 million births which we considered in our analysis. For these, in addition to information on the month of birth, we also had a number of individual-level attributes available. Furthermore, we could link the birth data to unemployment data at the level of the federal states. The added variation of unemployment rates through the monthly and more fine-grained regional specification allowed to control for spatial and temporal variation in the magnitude of the economic transition crises in East Germany following reunification. Our analytical strategy in this part of the analysis was to model sex determination at birth as a binary random process and to use logistic regression to analyze how offspring sex is associated with changes in unemployment rates[According to the theory by Valerie Grant, outlined above, the different timing of stress exposure may lead to diametrical effects on sex determination that could neutralize each other in annual aggregate data. We therefore amended the aggregate-level analysis with an individual-level analysis that allowed a more time-sensitive examination of the potential effect of economic contraction on sex determination. This was achieved by using individual-level data from the German birth register that includes information on the month of birth. To this data we obtained access through the Data Research Center of the German statistical offices. 100%-sant rates.0). And in response to the theoretical arguments outlined above about expected differences in the strength and direction of stress effects at different times between conception and pregnancy, we first conducted a sensitivity analysis to determine at which time and in which direction unemployment change affects the sex of the respective child most strongly. To this purpose we used different time lags of unemployment change, measured at the level of 15 of the 16 federal states of Germany (excluding Berlin)b. For example, a lag of four would indicate unemployment change between the seventh (t-7) and fourth month (t-4) prior to the respective birth (t0). And a lag of eight would measure unemployment change between the eleventh and eighth month prior to a birth (t-11:t-8).Consistent with previous research from animal experiments, which showed that the effect of changes in conditions are more influential on the SRB than absolute resource levels, we operc. Our data were also limited with regard to parental socioeconomic status. We therefore could not control for a possible Trivers-Willard effect, which would predict a biased probability of having a male or a female birth depending on socioeconomic status[Given the large amount of cases in our sample and limitations in computing power, we conducted the sensitivity analysis on appropriate unemployment change lags on the full sample but without any additional covariates. Based on the results of the sensitivity analysis, we proceeded in our analysis with the time lag of unemployment change that showed the highest association with the odds for a male birth (lag 4). In a series of multiple logistic regression models, we tested whether unemployment change and the occurrence of a birth in East Germany in 1991 showed a statistically significant and robust effect on the odds for a male birth. In a first step, we extended the bivariate version of the model to include a dummy variable for the year in which a respective birth occurred (1991 vs. any other year), a dummy for the region of birth (West or East Germany), and an interaction between the two. This allowed us to check whether the SRB in 1991 deviated from the trend in East but not in West Germany. In a second step, we added additional control variables on the level of the individual birth: maternal characteristics at the time of birth , and month of birth to adjust for seasonality effects. Althougc status, 57–59. In the course of the modeling process, we also tested interaction effects. In one case, this served to test if unemployment change had a stronger effect in 1991, that is, at the beginning of a multi-year increase in unemployment in East Germany after reunification (acute stress), than in any other year (chronic stress). And, in another case, this served to test if the effect of unemployment change on sex determination is moderated by mother’s employment status.Visual inspection of the SRB in East and West Germany over time  and for SRB seasonality (month of birth) . We rather find support for our random variation hypothesis (RVH) which postulates that the low SRB in East Germany in 1991 stems from higher annual random fluctuations of the SRB as a result of a substantial reduction in birth counts after 1990. Against the background of other findings that do show associations between other types of stressors and even economic stress with SRB fluctuations, 11, 17,Another explanation for not finding a significant effect might be that the stress induced by the economic and political transformation in East Germany after 1991 rather affected fertility decisions than the SRB: Transformation in East Germany and in other East and Central European countries led women to postpone fertility. In EastaFor 14 of the 16 German states, the birth register covers all years from 1991–2010. We lack the years 1991–1994 for the East German state of Mecklenburg-Vorpommern, and the year 1991 for the West German state of Saarland. The birth register data unfortunately do not allow us to distinguish between West and East Berlin which belonged to West and East Germany, respectively, before reunification.bSee Endnote a.cFor inclusion in the birth register, recording paternal age is mandatory for marital births only. While no information on the father was collected if the birth was non-marital until 1999, since 2000 the parents of non-marital children can choose whether they want the paternal age to be reported. Given that in East Germany in the 1990s and the first decade of the 2000s 40%-60% of the births were non-marital, we lack information on the paternal age for a substantial share of the births.dAs an artifact of the way the moving average is calculated, that is, as the average variance across the focal year and the four subsequent years, this rise slightly predates reunification.eThe odds ratios for the main effects of birth year (2005) and birth region (East Germany) were .990  and .996 , respectively.fAccording to the source, the number of people affected includes people injured, left homeless, and those "requiring immediate assistance during a period of emergency; it can also include displaced or evacuated people."West), and a dummy variable indicating whether the year is 1991 or not (DUMMY91). Thus the following part is fixed in each model:Every model in Table Model 1 is an ordinary least squares regression:Model 2 is an ARMA  time series regression. This indicates that we included an autoregressive component (AR) and a moving average (MA), both at a time lag of one year.2. Instead, the variance of the errors (ht) is allowed to change over time depending on the volatility of errors in the previous two years (lag two).Model 3 is an ARMA, ARCH(2) model where ARCH stands for "autoregressive conditional heteroskedasticity" and the number stands for a time lag of two years. That is, as opposed to Models 1 and 2, here errors are not assumed to have constant variance σt, the parameters ω, α1, and α2 are to be estimated based on previous errors with a time lag of two years. For the estimation we used the garchFit function in the R add-on package fGarch. The function did not allow to estimate the regression and variance parameters in one step. We thus used a three-step procedure as recommended in the statistical literature[t as weight:In Model 3, to obtain the error variance hterature. First, tEast), divided by the total number of births in East Germany between 1946 and 2011 (NEast).Models 4 and 5 are replicates of Model 1, but with weighted least squares to adjust for heteroskedasticity in the data. In Model 4, weights are based on the number of births in East Germany in a particular year , or GARCH, model turned out to be the best one according to the AIC value.


Stata software version 12.2 was used. The Chi-square test was used inbivariate analysis and logistic regression was used in multivariate analysis, fromwhich Odds Ratios (OR) and Confidence Intervals (CI) of 95% were derived.  risk factors associated with premature birth were current twin pregnancy, inadequate prenatal care (< 6 controls) , absent prenatal care ,history of premature birth  and preeclampsia.  history of premature birth, preeclampsia, not receiving prenatal care andreceiving inadequate prenatal care were risk factors for premature birth. World Health Organization (WHO) defined premature birth or preterm birth as the birthoccurring after 20 weeks and before 37 weeks of gestation---,-,,,--,,-,Premature birth has been associated with several factors, such as history of pretermbirth,Moreover, although there are several risk factors associated with premature birth, itsetiology has not been fully determinedA retrospective unmatched case-control study was carried out with a sample of 600 babiesborn alive at the National Hospital Sergio E. Bernales (NHSEB), located in themunicipality of Lima, Peru. They were born between January 1 and December 31, 2011, withwhich two groups were formed:- A case group (premature babies), consisted of 298 newborns with gestational age (GA)less than 37 weeks at birth, out of a total of 422 premature babies born during thestudy period. Here, 34 medical records were excluded due to incomplete data  and 90 medical records thatwere not found in the Archive Department of the NHSEB (as shown in - A group of unmatched controls (full-term newborns), composed of 302 newborns with a GAgreater than or equal to 37 weeks and less than or equal to 42 weeks, out of a total of342 full-term newborns, selected through simple random sampling during the study period,from a total of non-cases (N=5020). Here, 12 medical records were excluded due toincomplete data and 28 medical records that were not found in the Archive Department of the NHSB. Simplerandom sampling was performed using RANDBETWEEN Function in Excel.It is worth mentioning that 268 medical records belonging to mothers who gave birth to apost term newborn were excluded. To determine the GA, it was taken into account the ageindicated in the record book of children born in 2011, of the Neonatology service of theNHSEB, confirmed by physical examination of the newborn or Capurro method, which wasregistered in each medical record.(r) 2010 database.The following data were obtained from medical records: maternal age, marital status,gestational age, number of prenatal controls Stata version 12.2 was used for data processing. In univariate analysis, proportions andmeasures of central tendency and dispersion were calculated according to the variabletype.In bivariate analysis, the Chi-square test was used to compare two categoricalvariables, based on previous specific assumptions, producing crude Odds Ratios and 95%Confidence Intervals (CI); and logistic regression was used in multivariable analysis,considering all the variables with p <0.20 in the bivariate analysis. Adjusted OddsRatios (OR) and 95% Confidence Intervals (CI) were estimated and Akaike and Bayesianinformation criteria were used for modelling; model goodness of fit was checked usingthe Hosmer-Lemeshow test. It was considered a level of significance p <0.05.Ethics Committee of the University and the Hospital approved the study. During the study period, 5,710 births were registered in the National Hospital Sergio E.Bernales, where the prevalence of premature birth in the population was 7.4%. It wasobserved that the average age was 26.2 years, in a sample of 600 pregnant women.According to As for the other factors investigated , no significant differences were found between thetwo groups.The most important findings of this study are: a) the prevalence of premature birth was7.4% and b) risk factors for prematurity were current twin pregnancy, history ofpremature birth, preeclampsia, inadequate prenatal care and lack of prenatal care.Before starting further discussion, the main limitations shall be mentioned: thelimitation regarding the sample is a possible selection bias, since 124 medical recordswere excluded from the cases group .In the control group, 40 medical records were excluded . However, there is no evidence that the associations investigated aredifferent in the samples not included in the cases and controls. Moreover, in theevaluation of the variables, it was not possible to measure the levels of catecholaminesor antecedents of HBP, because this was a retrospective study and such data were notregistered in the medical records.This study has as strengths the retrospective and unmatched design type of cases andcontrols with appropriate statistical analysis. In addition, it shows a representativesample of the study population, since it was used a census in the cases group, whereas asimple random sampling was used in the control group. On the other hand, this work is very important because there are scarce studies in LatinAmerica on premature birth. Amongst the factors investigated in this study, preeclampsia was a risk factor forpremature birth-As for the history of premature birth, this showed to be a risk factor for prematurebirth in a subsequent pregnancy, similar to the results reported by Gene Barrios, whichalso reported that prior premature birth is a risk factor for prematurity Another risk factor for premature birth was not receiving PNC or receiving an inadequatePNC, which is supported by the study of Sánchez and coworkers, who also indicated thatthe lack of prenatal care is related to prematurity, otherwise it would not be possibleto diagnose complications of pregnancy and provide appropriate treatmentIn addition, a study of Osorno and colleagues reported that the prevalence ofprematurity is higher when there is less than 6 prenatal controls , whereas in women without any prenatal control, the risk is 2.3 times higherIt is known that premature birth remains one of the most common problems in the Peruvianperinatology despite medical advances, as verified in the study hospital in North Lima,where the prevalence of prematurity in 2011 was 7,4% in every 100 births. This rateseems higher than the observed in previous years Therefore, this study is highly relevant to public health because the early detectionand control of the risks mentioned above could result in a reduction in prematurity.In future research, those interested in this subject are invited to conduct cohortstudies, in which catecholamine levels could be measured. The prevalence of premature birth was 7.4%. It was concluded that the risk factors forprematurity are not receiving prenatal care, receiving inadequate prenatal care,preeclampsia and history of premature birth.


To evaluate the growth patterns of infants born large-for-gestational-age (LGA) from birth to age 1 year compared to those born appropriate-for-gestational-age (AGA). In addition, we aimed to investigate possible epigenetic changes associated with being born LGA.Seventy-one singleton infants born at term were classified by birth weight as AGA  and as LGA . Post-natal follow-up until age 1 year was performed with clinical assessments at 3, 6, and 12 months. Assessments included measurement of infants’ weight, length, ponderal index, BMI, as well as head, chest, and abdominal circumference. A subgroup of 38 infants (17 AGA and 21 LGA) was selected for genome-wide DNA methylation analysis. Umbilical cord was collected at birth, and methylation profile on umbilical tissue was analysed using the Illumina Infinium 450K methylation array.At birth, the LGA group had greater birth weight (P<0.0001), length (P<0.0001), head circumference (P<0.0001), ponderal index (P=0.02), BMI (P<0.0001), chest and abdominal circumferences  than AGA newborns. At the age of 3 months, LGA infants still presented greater weight (P<0.0001), length (P=0.006), BMI (P=0.02), as well as head (P=0.004) and abdominal (P=0.04) circumferences than AGA peers. However, by age 6 months there were no more anthropometric differences between the two groups. This was due to higher length increment in AGA than LGA infants between 0-6 months , whereas length increment was identical in both groups between 6-12 months . For the genome-wide analysis, more than 485,000 DNA methylation sites covering 99% of human NCBI Reference Sequence (RefSeq) genes were examined at birth, but no differences were found between LGA and AGA infants.Despite being born oversized at birth, LGA infants displayed early catch-down growth (i.e. slower length velocity), so that by the age of 6 months LGA infants were of similar length and BMI as AGA infants. In addition, no epigenetic differences in genome-wide methylation were found in those born LGA.