VDR rs2228570 and GC rs4588, were significantly associated with birth weight and head circumference, respectively; and (3) a potential interaction was observed between maternal VDR rs2228570 with maternal vitamin D deficiency on head circumference. These findings suggest that the underlying mechanisms of vitamin D on foetal growth are likely to be localised in the maternal compartment, mediated through the placenta, rather than through cellular mechanisms within the foetus. Further large-scale studies are warranted to validate and extend these findings.Vitamin D deficiency during pregnancy has been associated with poor foetal growth and neonatal birth anthropometry. However, the associations were inconsistent and could be confounded by neonatal vitamin D status and genetic factors. Until recently, limited studies have concomitantly examined the effect of maternal and neonatal vitamin D deficiency and vitamin D-related single nucleotide polymorphisms (SNPs) on neonatal birth anthropometry. This study aims to examine the independent and combined effects of maternal and neonatal vitamin D deficiency and vitamin-D-related SNPs on neonatal birth anthropometry. This cross-sectional study included 217 mother–neonate dyads recruited from Hospital Serdang, Selangor, Malaysia, between 2015 and 2017. Plasma 25-hydroxyvitamin D (25OHD) concentration was measured in maternal and umbilical cord blood using ultra-high-performance liquid chromatography (UHPLC). Maternal and neonatal vitamin D Receptor (VDR) SNP (rs2228570) genotypes were determined using high-resolution melting (HRM). Group-specific component (GC) SNPs (rs4588 and rs7041) genotypes were determined using restriction fragment length polymorphism. Our results showed that: (1) maternal vitamin D deficiency (25OHD < 30 nmol/L) was inversely associated with birth weight, head circumference and crown–heel length; (2) neonatal SNPs,  Within the paradigm of developmental origins of health and disease, the intrauterine environment plays an important role in programming the risk of disease later in life ,2. A pooVitamin D, a secosteroid hormone, has important functions in calcium homeostasis and bone metabolism. Plasma or serum 25-hydroxyvitamin D (25OHD) has been widely used as a biomarker of vitamin D status ,7, and iObservational studies have shown mixed results on the associations between maternal 25OHD concentrations and neonatal birth anthropometry. While several studies have reported positive linear associations between maternal 25OHD concentrations with birth weight ,14,15,16VDR rs2228570 with birth weight and SGA, respectively. In a more recent study, Barchitta et al. [VDR rs228570 with birth weight. In other studies, vitamin-D-related SNPs were shown to interact or confound the effect of maternal vitamin D deficiency on birth anthropometry [Despite the growing body of evidence on the association between maternal vitamin D deficiency and neonatal birth anthropometry, the mechanism underlying the association remains to be explored. There are many plausible ways for maternal vitamin D deficiency to impact neonatal birth anthropometry. Maternal vitamin D deficiency can impact foetal growth directly or indirectly through reducing foetal 25OHD availability. Previous studies that examined the associations assessed either maternal or neonatal 25OHD concentrations. Given that maternal and neonatal 25OHD concentrations are highly correlated (correlations coefficients ranged from 0.3 to 0.9) , both vaa et al.  reportedopometry ,32. Althopometry . This suData were analysed from a cross-sectional study that examined the risk factors of maternal vitamin D deficiency . The detThe following data were obtained from the interview-administered questionnaire: educational level, ethnicity and vitamin D supplementation during pregnancy. Information on maternal height, pre-pregnancy weight and weight at birth for pre-pregnancy body mass index (BMI) and gestational weight gain calculations, last menstrual period (LMP), and parity were drawn from the electronic medical record and antenatal records. Gestational age at birth was determined by LMP and confirmed by the first dating scan. Birth data included neonatal date of birth, sex, birth weight, head circumference and crown–heel length were extracted from Child Health Record Book.Blood samples were collected from pregnant women before delivery and mixed arterial/venous umbilical cord after delivery. All collected blood samples were processed within the collection day and plasma was stored at −80 °C until analysis. Plasma 25OHD concentrations were analysed using an in-house validated ultra-high-performance liquid chromatography (UHPLC) method as described earlier . The met5 M−1 and 7 × 108 M−1, respectively. Bioavailable 25OHD was calculated as the sum of free 25OHD and albumin-bound 25OHD using Equation (2) [Plasma albumin was measured by using an immunoturbidimetric assay . VDBP was measured using the Quantikine Human Vitamin D Binding Protein Immunoassay Kit , following the manufacturer’s protocol. The manufacturer claimed the mean CV for the VDBP assay was 6%. The concentration of free 25OHD was calculated by using the equation from Powe et al.  (Equatiotion (2) .(1)FreeVDR rs2228570, GC rs7041 and GC rs4588 were selected. They were well-studied missense SNPs , whose variation in a nucleotide resulted in the change in amino acid and protein structure and/or function: VDR rs2228570, A-to-G allele transversion creates an alternative translation start site, resulting in a VDR protein that is 3 amino acids shorter than the wild type; GC rs7041, A-to-C allele transversion causes the change of amino acid aspartic acid (CTA) to glutamic acid (CTC); rs4588, G-to-T allele transversion causes the change of amino acid threonine (TGC) to lysine (TTC). These SNPs have been frequently reported to be associated with 25OHD concentrations [DNA was extracted from the buffy coat using the QIAamp DNA Blood Mini Kit , following the manufacturer’s protocol. Based on the previous literature, three SNPs, trations ,38,39 antrations  in genomtrations .HaeIII (for rs7041) and StyI (for rs4588) . The digested products were then evaluated by electrophoresis on 1.5% agarose gel stained with ethidium bromide. The genotypes of rs2228570 were determined using LightCycler® 480 High-Resolution Melting Master , performed on the LightCycler 480 Instrument . Melting curve analysis was performed for genotyping: the heterozygous genotype was displayed with a difference in the shape of the melting curve, whereas changes in melting temperature discriminated the wild type and the mutant genotypes.The restriction fragment length polymorphism (RFLP) method was performed to determine the genotypes of rs4588 and rs7041 . In brieStatistical analysis was performed using SPSS version 26.0 . Hardy–Weinberg Equilibrium (HWE) for each SNP was examined using the chi-square test.VDR rs2228570, GC rs4588 and GC rs7041 with birth outcomes; (3) the interactions of maternal and neonatal vitamin D deficiency and SNPs by including the interaction term in the adjusted GLM models; and (4) the combined effect of maternal and neonatal vitamin D deficiency and SNPs on the birth anthropometry. To determine the combined effect, variables that had a p-value < 0.25 in the group-specific associations were included as a base model. The backward stepwise method was used to generate the final model of factors associated with the birth anthropometry. When required, the data and p-values were adjusted for maternal pre-pregnancy BMI, infant sex, gestational weight gain, parity, and maternal height.Birth weight, head circumference and crown–heel length were the three neonatal birth anthropometries examined in the study. The associations between maternal and neonatal plasma 25OHD and SNPs with these outcomes were analysed using uni- and multivariate general linear models (GLM). The associations were performed in four analyses, as follows: (1) the separate association between maternal and neonatal vitamin D deficiency and maternal bioavailable 25OHD with the birth anthropometry; (2) the separate association of maternal and neonatal 2 .This study included a total of 217 mother–neonate dyads. The mean maternal age at delivery was 28.9 ± 4.2 years, and the mean pre-pregnancy maternal BMI was 23.7 ± 5.1 kg/m2 . Half ofMaternal vitamin D deficiency (plasma 25OHD < 30 nmol/L) was associated with birth weights, head circumference and crown–heel length at birth after adjustment . Low matVDR rs2228570 with birth weight was near significant (p = 0.064)  larger in head circumference than other genotypes. In multivariate analyses, no significant associations were found between maternal and neonatal SNPs with crown–heel length.The association of neonatal = 0.064) . There wVDR rs2228570 and maternal vitamin D deficiency on head circumferences . There was no significant difference in head circumference in neonates whose maternal 25OHD concentrations were ≥30 nmol/L when compared according to maternal SNPs. After the adjustment, maternal vitamin D deficiency and low maternal bioavailable 25OHD (<1.6 μmol/L) were significantly associated with head circumference . In addiIn the final model, maternal vitamin D deficiency was significantly associated with crown–heel length, whereas maternal and neonatal SNPs examined were not associated with crown–heel length .VDR rs2228570, GC rs4588 and GC rs7041) with birth outcomes. This study showed a consistent pattern effect of vitamin D deficiency and SNPs on birth anthropometry: (1) maternal vitamin D deficiency (25OHD <30 nmol/L) demonstrated consistent associations on birth weight, head circumference and crown–heel length at birth in all the analyses; (2) neonatal SNPs, VDR rs2228570 and GC rs4588, demonstrated significant associations with birth weight and head circumference, respectively; and (3) a potential interaction was observed between maternal VDR rs2228570 with maternal vitamin D deficiency on head circumference.This study examined the individual and combined effect of maternal and neonatal vitamin D deficiency and vitamin D-related SNPs 2D for bone development and mineral homeostasis as the foetus completely depends on the maternal transfer of calcium [Despite the growing evidence on the effect of vitamin D on foetal growth, the mechanistic pathways remain to be explored. By its classical role in calcium homeostasis and bone mineralisation, maternal vitamin D deficiency affects foetal growth by reducing foetal 25OHD availability and decreasing the local generation of 1,25(OH) calcium .VDR rs2228570 [GC rs7041 [Previous studies have demonstrated the potential interaction effect between neonatal s2228570  and mateC rs7041  with vitC rs7041 , more paC rs7041 . MoreoveC rs7041 ,26,51.This study has several limitations. First, the vitamin D level assessed at the delivery might not represent the concentrations for the entire course of pregnancy. Second, the sample size was small and not powered to show the significant interaction effect of less frequent SNPs, but it was sufficiently powered to show the interaction effect of common SNPs examined in the present study. Third, the adjustment for multiple testing was not performed. This limitation potentially increases the likelihood of a false-positive result in the bivariate and multivariate association analyses. However, the variables included in the present study were selected based on previous studies.VDR rs2228570 and head circumference. Taken together, these findings demonstrate the complex interplay between maternal and neonatal vitamin D deficiency and SNP on foetal growth and suggest that the underlying mechanisms of vitamin D on foetal growth are likely localized in the maternal compartment, mediated through the placenta, rather than through cellular mechanisms within the foetus. Further large-scale studies are warranted to validate and extend our findings and understand the onset of the inverse associations between vitamin D and birth anthropometry.In conclusion, our findings indicate that maternal vitamin D deficiency (<30 nmol/L) was inversely associated with all the neonatal birth anthropometry, while neonatal VDRrs2228570 and GCrs4588 were significantly associated with birth weight and head circumference, respectively. There are potential interactions between maternal vitamin D deficiency and maternal


In sub-Saharan Africa (SSA), every 1 in 12 children under five dies every year compared with 1 in 147 children in the high-income regions. Studies have shown an association between birth intervals and pregnancy outcomes such as low birth weight, preterm birth, and intrauterine growth restriction. In this study, we examined the association between birth interval and under-five mortality in eight countries in West Africa. A secondary analysis of the Demographic and Health Survey (DHS) data from eight West African countries was carried out. The sample size for this study comprised 52,877 childbearing women (15-49 years). A bivariate logistic regression analysis was carried out and the results were presented as crude odds ratio (cOR) and adjusted odds ratios (aOR) at 95% confidence interval (CI). Birth interval had a statistically significant independent association with under-five mortality, with children born to mothers who had >2 years birth interval less likely to die before their fifth birthday compared to mothers with ≤2 years birth interval , and this persisted after controlling for the covariates . The country-specific results showed that children born to mothers who had >2 years birth interval were less likely to die before the age of five compared to mothers with ≤2 years birth interval in all the eight countries. In terms of the covariates, wealth quintile, mother's age, mother's age at first birth, partner's age, employment status, current pregnancy intention, sex of child, size of child at birth, birth order, type of birth, and contraceptive use also had associations with under-five mortality. We conclude that shorter birth intervals are associated with higher under-five mortality. Other maternal and child characteristics also have associations with under-five mortality. Reproductive health interventions aimed at reducing under-five mortality should focus on lengthening birth intervals. Such interventions should be implemented, taking into consideration the characteristics of women and their children. Under-five mortality is a global indicator of the overall development children's health, and nations' socioeconomic and environmental conditions for both children, household, and community . To dealIn SSA, every 1 in 12 children under five years dies every year compared with 1 in 147 children in the HICs . NotableHowever, literature has shown that in LMICs, education, household size, and age of women are essential determinants of under-five mortality . For inshttps://dhsprogram.com/data/available-datasets.cfm.A secondary analysis of the Demographic and Health Survey (DHS) data on women in West Africa was done from eight West African countries in Burkina Faso (2010), Benin (2018-2018), Cote D'Ivorie (2011-2012), Gambia (2013), Mali (2018), Nigeria (2018), Sierra Leone (2019), and Togo (2013-2014). The DHS is a nationally representative survey designed to collect data on various health topics such as nutrition, mortality, domestic violence, and female genital mutilation, access to mass media, fertility, young child development, breastfeeding and food intake, vaccinations, and treatment of diseases. A stratified dual-stage sampling approach was employed, and the same questions were posed to women of all these countries and thus make it feasible for multicountry study. The study involved a multistage sampling process , followed by systematic household sampling within the selected EAs. The sample size for this study comprised 52,877 childbearing women see  between The study used under-five mortality as the outcome variable. Under-five mortality, also known as child mortality, refers to the death of infants and children under the age of five. This variable took a binary form; such that under-five mortality will be regarded as a success (1 = if death occurs in the specified age period) or failure .The study used birth interval as the main independent variable. This was derived from asking the women the time preceding birth interval. Responses to this were stated in months. For the purpose of this study, this variable was categorised into 1 = less or equal to 2 years and 2 = more than 2 years.Twenty-one (21) control variables were considered for our study. These variables were made up of maternal factors, child factors, and contextual factors. The maternal factors were mother's age, mother's age at birth, husband/partner's age, mother's religion, mother's educational level, mother's employment status, father's employment status, sexual autonomy, contraceptive use, mother's experience of IPV, mother's exposure to mass media, and desire for pregnancy. The child factors were the sex of the child, size of the child at birth, birth order, the type of delivery, delivery assistance, place of delivery, and antenatal care. Place of residence, wealth quintile, and country were the contextual factors. These variables were not determined a priori; instead, based on parsimony, theoretical relevance, and practical significance with under-five mortality –18.p value less than 0.05 and odds ratios at 95 percent confidence intervals were determined. This was followed by a multivariable binary logistic regression. Statistical significance was considered at a p value less than 0.05 and odds ratios at 95 percent confidence intervals were determined. All frequency distributions were weighted, while the survey command (SVY) in Stata was used to adjust for the complex sampling structure of the data in the regression analyses.Analysis for the study was done using Stata version 16.0. Univariate, bivariate, and multilevel and binary logistic regression analyses were conducted. Frequencies and proportions were first conducted, followed by a distribution of under-five mortality per the explanatory variables considered in this study, with their respective confidence intervals (CIs). After this, we carried out a multilevel logistic regression analysis to examine the factors associated with under-five mortality using five models. Model 0 showed the variance in under-five mortality attributed to the clustering of the primary sampling units (PSUs) without the explanatory variables. Model 1 contained the main independent variable  and under-five mortality. Model 2 and 3 contained the child and maternal factors, respectively, and under-five mortality, while Model 4 contained the contextual factors. Model 5 contained all the independent and control variables. The Stata command “melogit” was used in fitting these models. We used Akaike's Information Criterion (AIC) tests for model comparison. Finally, we stratified our analysis by country to examine the heterogeneity in under-five mortality in all countries using binary logistic regression. Statistical significance was considered at a As shown in The country-specific results showed that children born to mothers who had >2 years birth interval were less likely to die before the age of five compared to mothers with ≤2 years birth interval in all the eight countries .The focus of this study was to assess the association between birth interval and under-five mortality in eight West African countries, using data from the DHS. From the study, the odds of children dying before age 5 was higher among children born to mothers who had more than a 2-year space between the previous birth compared to children born to mothers who had less or equal to 2 years interval. This confirms previous studies on the association between birth interval and under-five mortality  such as th birthday. This finding contradicts the finding of previous studies [Children from the richest households, those whose mothers age were 20-29, those whose mothers gave birth by age 20-29, and those whose mothers said their current pregnancy were mistimed and unwanted had a lower risk of under-five mortality. On the contrary, children whose fathers were 50 years and above, whose size at birth were smaller than average and very small, and those whose mothers had caesarean section have a higher risk of under-five mortality. These findings are consistent with previous reports –28. From studies –29.th birthday, compared to male children. Several studies confirm this finding [th birthday compared to children who are firstborns. This finding contradicts the findings of previous studies which state that the higher birth orders the higher the likelihood of under-five mortality [The study further established that under-five mortality varied by child sex, with female children having smaller odds of dying before their 5 finding , 30–32.  finding , 31, 33. finding , 31, 33.ortality . The finOur study's strength lies in the use of nationally representative datasets from eight countries in West Africa under the DHS program. This fortifies the generalizability of our study findings to the general population of young women. Moreover, the use of binary logistics regression and a large sample size ensured the reliability and replicability of the study to other populations. Notwithstanding the apparent strengths of our study, we caution that our findings be interpreted in the light of some limitations. Given that the dataset used employed cross-sectional designs, it limits the possibility of causal inferences primarily because of the snapshot nature of such design. The data used in this study is a retrospective self-reporting data, and as such, it may be biased by the mother's perception or feeling about pregnancy which might change over time.We conclude that shorter birth intervals are associated with higher under-five mortality. Wealth quintile, mother's age, mother's age at first birth, partner's age, employment status, current pregnancy intention, sex of child, size of child at birth, birth order, type of birth, and contraceptive use also had associations with under-five mortality. Reproductive health interventions aimed at reducing under-five mortality should focus on lengthening birth intervals. Such interventions should be implemented taking into consideration the characteristics of women and their children.


In vitro fertilization (IVF) births contribute to a considerable proportion of preterm birth (PTB) each year. However, there is no formal surveillance of adverse perinatal outcomes for less invasive fertility treatments. The study objective was to describe associations between fertility treatment , or ovulation drugs alone) and preterm birth, compared to no treatment in subfertile women.The Fertility Experiences Study (FES) is a retrospective cohort study conducted at the University of Utah between April 2010 and September 2012. Women with a history of primary subfertility self-reported treatment data via survey and interviews. Participant data were linked to birth certificates and fetal death records to asses for perinatal outcomes, particularly preterm birth.A total 487 birth certificates and 3 fetal death records were linked as first births for study participants who completed questionnaires. Among linked births, 19% had a PTB. After adjustment for maternal age, paternal age, maternal education, annual income, religious affiliation, female or male fertility diagnosis, and duration of subfertility, the odds ratios and 95% confidence intervals (CI) for PTB were 2.17  for births conceived using ovulation drugs, 3.17  for neonates conceived using IUI and 4.24  for neonates conceived by IVF, compared to women with subfertility who used no treatment during the month of conception. A reported diagnosis of female factor infertility increased the adjusted odds of having a PTB 2.99 . Duration of pregnancy attempt was not independently associated with PTB. In restricting analyses to singleton gestation, odds ratios were not significant for any type of treatment.IVF, IUI, and ovulation drugs were all associated with a higher incidence of preterm birth and low birth weight, predominantly related to multiple gestation births.The online version contains supplementary material available at 10.1186/s12978-022-01363-4. Infertility treatments such as in vitro fertilization are associated with preterm birth, but less is known about how other less invasive treatments contribute to preterm birth. This study compares different types of fertility treatments and rates of preterm birth with women who are also struggling with infertility but did not use fertility treatments at the time of their pregnancy. 490 women were recruited at the University of Utah between 2010 and 2012. Participants were asked to complete a survey and were linked to birth certificate and fetal death certificate data. Women who used in vitro fertilization were 4.24 times more likely to have a preterm birth than those who used no treatment. Use of intrauterine insemination were 3.17 times more likely to have a preterm birth than those who used no treatment at time of conception. Ovulation stimulating drugs were 2.17 times more likely to have a preterm birth. Having female factor infertility was also associated with higher odds of having preterm birth. For those who are having trouble conceiving, trying less invasive treatments to achieve pregnancy might reduce their risk of preterm birth.The online version contains supplementary material available at 10.1186/s12978-022-01363-4. Approximately 9% of couples worldwide experience difficulty conceiving or maintaining a pregnancy; this prolonged duration of non-conception is referred to as subfertility –5. SubfeAbout half of subfertile couples seek medical treatment . Common In the United States, the Society of Assisted Reproductive Technology (SART) and the Center for Disease Control conduct fertility clinic level surveillance with the goal of tracking IVF procedures and outcomes , 15. TheThis research aims to provide insight into the relationship between fertility treatments  and preterm birth among women with primary subfertility, compared to subfertile women who conceived without fertility treatment. We used data from parallel clinic and population-based cohorts, and examined the contribution of fertility-related diagnoses, as well as the role of multiple gestation.The Fertility Experiences Study (FES) is a retrospective cohort study conducted at the University of Utah between April 2010 and September 2012. Two parallel cohorts were recruited. For the clinic-based cohort, participants were recruited from female patients seen for a new consultation for subfertility and/or treatment at one of the two specialty fertility clinics in Utah between 2000 and 2009. For the population-based cohort, two period-based cohorts were recruited using the Utah Population Database (UPDB) to identify and recruit potentially eligible women of reproductive age range who were married as of 2002 or 2006 but had not yet had a live birth as of the end of 2004 or 2008 (index dates). For both clinic and population-based cohorts, the final eligibility criteria were as follows: Between 20 and 35 years of age at the index date, no pregnancies prior to index date, at least 1 year of regular intercourse without contraception with a male partner at the index date, and a Utah resident during 3 years following the index date. The inclusion of the population cohort allows inclusion of women with subfertility who never receive specialty fertility treatment. Full details of study design and recruitment have been published previously . All parThe key exposure measure is the type of fertility treatment received during the month of conception that resulted in the first live birth or fetal death. Treatment groups are defined based on the most invasive medical treatment used during the cycle of conception. For the purposes of this study, the most invasive treatment is IVF. IVF includes all procedures that involve manipulating both sperm and eggs outside the body. The next most invasive treatment was considered to be IUI. Women were categorized as using IUI during the cycle of conception, regardless of if they were also using OS. If women only reported medication to stimulate or enhance ovulation during the conception cycle then they were classified as using OS. Women who did not receive any medication or procedure during the cycle of conception were classified as having no treatment, even if they receive medical treatment during previous cycles, or reported alternative non-medical treatment (such as acupuncture or herbs). This group of untreated subfertile women was used as the control for the analysis.We assessed the duration of pregnancy attempt, which provides an indicator of severity of subfertility. During the structured telephone interview, trained study staff asked each woman about specific dates when she was at risk for pregnancy. Pregnancy attempt duration was calculated as the interval between the date the participant reported her attempt began and the estimated date of conception. We estimated the date of conception by subtracting the clinical gestational age at birth and date of birth as reported on the birth certificate.Fertility-related diagnoses were obtained through the self-administered online questionnaire. The question asked “have you or your partner ever been told or suspect that you have any of the following diagnosis?” Answers were yes, no, and unsure. Women who answered “no” or they were “unsure” were considered to have a negative answer. For this analysis, diagnoses were grouped into the Society for Assisted Reproductive Technology Clinical Outcomes Reporting System (SARTCORS) categories. SARTCORS categories are tubal factor, endometriosis, ovulation dysfunction, uterine factor, male factor, or unexplained. If women had more than one female factor diagnosed then they were categorized as multiple female factors. If a couple has a female contributor and a male factor issue then they are categorized as combined male and female factor. For this analysis, any female factor infertility was collapsed into a dichotomous variable, and any male factor was considered a separate dichotomous variable.The primary outcome measure was preterm birth as determined by gestational age reported on the birth certificate. PTB is defined as any pregnancy that ended in a live birth or fetal death at less than 37 completed weeks of gestation as reported on the state birth certificate . Birth c2) and Overweight/Obese BMI (≥ 25 kg/m2). While low-BMI is a known risk factor for PTB we collapsed with normal BMI as numbers of low-BMI participants were insufficient (n = 21), we also address this in the sensitivity analysis. Education, income, and LDS religious affiliation were obtained from the FEQ survey. Education was dichotomized as less than college graduation and college graduation or more. Income was grouped as annual household income of less than $50,000, $50,000–$99,000, and $100,000.Covariates for the analysis were based on known risk factors for fertility treatments and for preterm birth. Variables considered in the analysis include age of mother at delivery, age of male partner, race, maternal education, prepregnancy BMI, annual income, and religious affiliation. Religious affiliation with the Church of Jesus Christ of Latter-day Saints was identified because of its strong association with avoiding behavioral risk factors for preterm birth, including tobacco use, alcohol use, and drug use . MaternaThe frequency of PTB was compared across participant characteristics and exposure variables. Crude and adjusted odds ratios and 95% confidence intervals for each exposure (treatment category) and each birth outcome were estimated using simple and multivariable logistic regression. Parental age, prepregnancy BMI, education, income, and LDS religious affiliation, and treatment received during the cycle of conception were included as potential confounders in the base model. Subsequent models assessed additional potential confounders including extended duration of pregnancy attempt (less than 24 months vs 24 months or more), self-reported diagnosis categories , and multiple gestation dichotomized. We repeated the analyses restricted to singleton births. Data were analyzed using Stata14 or higher .A sensitivity analysis was conducted using BMI, and duration of attempt as continuous variables. We performed a logistic regression removing participants who reported a fertility related diagnosis related to tubal factors, as these women would not have been able to conceive without treatment by IVF. Additional sensitivity analysis was conducted for women who were subfertile using the screening question but may have had intervening breaks in their pregnancy attempt  so that their cumulative time at risk of pregnancy was found to be less than 12 months. A flow diagram describing exclusion and loss to follow up has been published previously .Study participants reported a total of 492 first births in the FEQ telephone interview. Of these, 491 were linked to state vital records—488 came from birth certificates and 3 came from fetal death records. One participant was excluded from the analysis due to an unintended pregnancy that occurred while not actively trying to get pregnant. There were some notable differences in attempt duration, treatment and diagnosis patterns between individuals recruited from the population cohort and those who had been recruited for the general population. Specifically, in the population cohort, over half 53.5% had had a live birth by the time we interviewed them for the study, close to a third (31.3%) had never received any type of fertility treatment, 5% reported trying alternative treatments, 29% had used fertility drugs, 20% had used artificial insemination, and 14% had used IVF. Among the participants from the clinic cohort, 58% had had a live birth by the time we interviewed them for the study, 6.5% had never received any type of fertility treatment and 1.7% reported trying alternative treatments, 15.9% had used fertility drugs, 29% had used artificial insemination, and 47.3% had used IVF. Of the 490 linked live births while intending to get pregnant, 19% were preterm. Table In the 490 subfertile women, 41% reported having unexplained infertility, 40% reported male factor infertility, 54% reported a diagnosis of ovulation dysfunction, 27% endometriosis, 16% a tubal factor, 13% uterine factor infertility, 28% multiple female factors, and 12% blocked or damaged fallopian tubes . Overall during the cycle of conception, 44% had no infertility treatment, 16% used OS, 13% had IUI, and 28% had IVF  for women who conceived using ovulation drugs, 3.17 times higher  for women who conceived using IUI and 4.24 times higher  for women who conceived by IVF, compared to women with subfertility who used no treatment during the month of conception. Duration of pregnancy attempt was not independently associated with PTB. A reported diagnosis of female factor infertility increased the adjusted odds of having a PTB was 2.99 times higher  compared to women who did not report any female factor infertility. In sensitivity analyses excluding women with tubal factor infertility, the odds of PTB were about the same for most invasive treatment during cycle of conception, and still significant  for women with any female factor infertility. Only 6.6% of the births conceived without any treatment during the month of conception were twins, for OS this increased to 19% twins and 6% triplets, IUI births were 10.9% twins and 5% triplets, and IVF births were 30% twins and 2% triplets. Accordingly, when multiple gestation was added to the model, it had the highest association with PTB . Table When compared to subfertile women who did not use any fertility treatments during the cycle of conception, women who used any kind of fertility treatment were more significantly more likely to deliver preterm. As the invasiveness of treatment increased, so did both the incidence of multiple gestation and the incidence of PTB. Women who used OS to conceive were more than twice as likely to deliver preterm compared to women who used no treatment, while women who used IVF were about four times as likely. A large body of research has previously established this relationship for IVF, and some studies have also found it for IUI , 27, 28.Few studies have compared birth outcomes of subfertile women conceiving with fertility treatments with subfertile women who conceive spontaneously . Our popWe validated our questionnaire for the woman’s report of treatment ; other rGeneralizability of findings may also be limited by the geographic location with a relatively homogenous racial and ethnic population, and a relatively lower prevalence of smoking, alcohol, and drug use. However, this population may also for a more direct effect of the effect of treatment to be evaluated, as Utah was noted to have the highest proportion of women giving birth from fertility treatments of 38 states examined from birth certificate data  . AdditioPTB is a significant public health issue worldwide. In the United States, more than 11% of live born infant are born at gestational ages < 37 weeks. PTB contributes largely to infant and child morbidity and mortality , 44. MorWe suggest that individuals who are experiencing difficulty conceiving should consider first the opportunity for conception with less invasive treatments or no treatment. Although the time to conception may be longer, the potential for improved optimal birth outcomes should be weighed strongly against the desire to conceive faster. Additional research needs to be conducted to assess time to live birth in subfertile populations using a variety of fertility treatments . The risOur findings support efforts to encourage women to give an adequate trial of the least invasive fertility treatment that may work for them, and to modify the practice of all fertility treatments to minimize incidence of multiple gestation. Future research should consider interventions that may prevent preterm birth among these higher risk populations of subfertile women, regardless of type of treatment received.Additional file 1: Table S1. Most invasive treatment used during the month of conception and ever used.


Electromagnetic recordings are useful for non-invasive measurement of human brain activity. They typically sample electric potentials on the scalp or the magnetic field outside the head using electroencephalography (EEG) or magnetoencephalography (MEG), respectively. EEG and MEG are not, however, symmetric counterparts: EEG samples a scalar field via a line integral over the electric field between two points, while MEG samples projections of a vector-valued field by small sensors. Here we present a unified mathematical formalism for electromagnetic measurements, leading to useful interpretations and signal processing methods for EEG and MEG.We represent electric and magnetic fields as solutions of Laplace’s equation under the quasi-static approximation, each field representable as an expansion of the same vector spherical harmonics (VSH) but differently weighted by electro- and magnetostatic multipole moments, respectively.We observe that the electric and the magnetic fields are mathematically symmetric but couple to the underlying electric source distribution in distinct ways via their corresponding multipole moments, which have concise mathematical forms. The VSH model also allows us to construct linear bases for MEG and EEG for signal processing and analysis, including interference suppression methods and system calibration.The VSH model is a powerful and simple approach for modeling quasi-static electromagnetic fields.Our formalism provides a unified framework for interpreting resolution questions, and paves the way for new processing and analysis methods. I.ELECTRIC properties of objects can be studied noninvasively by measuring relevant aspects of the associated electromagnetic (EM) field and by analyzing the recorded data with the aid of mathematical algorithms. Of particular interest to us are applications in neuroimaging where such non-invasive measurements facilitate interpretation of the dynamic brain functions while patients and subjects remain safe and comfortable. Related applications also arise in other scientific domains, e.g., in the study of geomagnetism.In neuroimaging, the most commonly applied EM-based measurement modalities are electroencephalography (EEG) and magnetoencephalography (MEG). The former is the recording of the electric potential differences on the scalp and the latter is the measurement of the magnetic field outside the head. Both EEG and MEG employ sensors in the form of a multi-channel array, providing a spatially discretized representation of the underlying field distribution. In MEG, in particular, the optimal spatial sampling of the field, in terms of capturing all relevant information, has been studied in detail ,2]. The. The2]. One of the most fundamental tasks in the interpretation of multi-channel EM signals is finding tractable mathematical expressions that connect the electric activity in the source space to the EM field in the sensor space. These expressions can be applied in inverse modeling, which is the task of reconstructing a plausible electric source distribution  given the measured data matrix. In EEG and MEG, the mathematical models are simplified to some extent due to the fact that we can apply the quasi-static approximation of Maxwell’ sequations . PioneerThe electric and magnetic fields are generated by the same source distribution, with the latter component being solely related to the movement of the electric charge, i.e., the magnetic field is generated by electric currents. However, mainly due to the differences in the significance of the volume current contributions, EEG and MEG signals are often considered to be quite different in terms of feasibility of reconstructing accurate source distributions via inverse modeling. This is due to the fact that the magnetic field appears to be insensitive to radially oriented currents, and, on the other hand, volume current contributions can be represented as current elements normally oriented to surfaces where electric conductivity changes. As the conductivity gradient surfaces in the head, related to, e.g., skull and scalp, are close to being spherically symmetric, the volume current contributions are close to radial and one can conclude that the majority of the MEG signal contribution arises from tangentially oriented primary currents . Thus, tNon-invasive study of the human brain based on electromagnetism started with early EEG recordings  and seveHere, we unify the mathematical expressions arising from the quasi-static Maxwell’s equation, which allow us to represent the full quasi-static EM field outside the volume containing the sources (electric charge or current) as a combination of vector-valued basis functions that are common to both the electric and the magnetic field. Then we show how the amplitude coefficients of these basis functions are related to the underlying source distribution in a way that makes the electric and magnetic fields distinct from one another. We then develop a linear basis model for the multi-channel EM recordings that allows us to predict the essential number of degrees of freedom of these measurements. Useful applications readily follow, such as sensor calibration and algorithmic interference suppression. While MEG and EEG measurements are the practical, existing modalities that are covered by our theoretical framework, our letter predicts potential benefits of measuring the brain-related electric field, rather than the electric potential, if such measurements become feasible.II.A.ρ indicates charge density, ε0 is the permittivity of vacuum, μ0 is the permeability of vacuum, J indicates current density, and A complete description of the electric and magnetic fields is given by Maxwell’s equations:e, e.g., ), which r is the three dimensional location where the field is evaluated and t indicates time. When evaluating the magnetic field in a volume free of electric currents (J = 0),Next we will investigate specific spatiotemporal properties of the electric and magnetic fields from the point of view of MEG and EEG measurements. First of all, we can ignore any time retardation effects due to the very short distance between the measurement sensors and the brain. Next, we investigate the relation between the physical dimensions of our measurement and the electromagnetic wavelength associated with the dominant time scale of the object of interest. If these physical dimensions are smaller, then the quasi-static approximation is justified . ). 12]). e.g., in  and 5]..∇⋅J=0 but|<<|σE| . This rethe head . With thf, we can express both E and B as a gradient of a harmonic scalar potential under the quasi-static approximation. As both fields have this fundamental mathematical property, let us now in place of E and B introduce a generic vector field F for which r is the location  in the spherical coordinate system, at which VH is evaluated, Yl,m is the spherical harmonic function of order {l,m}, and F is evaluated. Inside the head we would obviously have Consequently, since r = 0 or at r = ∞ depending on whether the source generating the potential is farther from or closer to origin than r is, respectively. If the source is located at r and we evaluate the potential at r with the relation r arbitrarily far from the origin. Conversely, if F is given byThe transition from the scalar field to the vector field rding to , 15], functions:Here Then, based on F represents the electric or magnetic field. The theory of the multipole expansions in the case of time-varying fields has been investigated in detail, e.g., in : and B is restricted to the integration kernels:So, the physical difference between E, the integration kernel for Note that if we were to observe the time derivative l = 0 term in l = 0 has no contribution to B.Note that the 0=0 (see ), which,C.E and B to the difference between the source-space kernels The electric field is in principle sensitive to any electric charge or electric current in the source volume.The magnetic field is only sensitive to a tangential projection of the electric current. It should be noted, however, that in this context the tangential and radial directions depend on the choice of the origin of the coordinate system. Thus, we cannot conclude that the magnetic field is physically insensitive to radial current segments with respect to any absolute frame of reference.Now we have reduced the difference between An interesting consequence of the second of these two principles is that with respect to a chosen origin, any radially oriented currents any where in the brain will correspond to  surface , and in  surface , 20]. T. Tklmb and B fields and shown that they are composed of the same VSH functions that are weighted differently by the associated multipole moments. However, EEG measures the distribution of the electric potential on the head surface, which corresponds to spatial sampling of the scalar field V  from which the electric field can be derived as So far, we have developed our mathematical theory for the E(rV would have to be derived from Poisson’s equation) is composed of scalar spherical harmonic functions weighted by the same electrostatic multipole moments as the VSH functions of the electric field. Thus, one could conclude that a measurement of the electric potential or the electric field would contain equal information. We will, however, show in Consequently, the component of the electric potential for which III.A.N sensors as follows:d(tj) is the N-dimensional signal vector measured at time instant tj, i.e., the data matrix D has dimensions of N × m. Let us now investigate any momentary signal vector d and attempt to construct a general signal space for it, i.e., express it as a linear combination of basis vectors in the same manner as a stationary temporal signal can be expressed as a linear combination of sine and cosine terms. Based on j will produce an output signal that is proportional to a superposition of termsfj is some function that transforms the value of the field into a measurable quantity. For example, in an MEG measurement this function typically corresponds to taking the flux of the magnetic field component through the area of the corresponding pick-up loop ) f) fLEEG. This can be thought of as equivalent to the variance explained by the expansion of the time courses of independent, identically distributed (IID) neural white noise sources placed at each location in the volumetric grid.LEEG = 4 explains more than 98% of the signal variance, which indicates that the EEG signals are limited to less than l (LEEG = 6) than in the case of MEG (LMEG = 8).Even for the most superficial sources, an order of pipeline  in Fieldl = 4 or higher was used.To look at an example of how expansion order relates to source localization, we took the trial-averaged data for the single adult subject in the MNE-Python sample dataset  under flm} weighted by the radial term over the length of the path from A to B, which could attenuate spatially complex features, corresponding to high orders of l, along the path. Of course, it is in principle possible to arrange the EEG electrodes in such a way that a distribution of estimated values of the electric field could be obtained due to the relation ex, ey, and ez are the unit vectors pointing in the x-, y-, and z-directions. Without loss of generality, let us assume that we picked a pair of electrodes that are both located on the x-axis, i.e., rref corresponds to the location of a common reference electrode. Now Our experiments suggest that the MEG signal (or the corresponding electric field measurement), when considering a typical sensor arrangement based on SQUID technology, generally contains about 80 degrees of freedom while the EEG signals usually contain less than 40 degrees of freedom. It may seem surprising that the measurement of the electric potential contains significantly less spatial information about the underlying source than a measurement of the corresponding electric or magnetic field, even when reducing the smoothing effects of the conductor model. However, even without considering any effects caused by conductivity boundaries, the limited spatial information in the measured EEG signal can be understood by the fact that the electric potential is always a proportional quantity between two points. Mathematically, the potential difference between points A and B is the negative of the line integral of E(r) between these points:dl = exdx in this case. Thus, based on the measurement of V (r), our estimate of the component Ex reduces to its mean value over the interval from x1 to x2:Ex is. Note that for simplicity we dropped the time variable from Because field could yield more informative data than the present measurement of the corresponding on-scalp potential distribution, unless high-density EEG arrays can be applied that contain similar information as E(r) in terms of Our theory and simulation results indicate that a measurement of the brain-induced electric B.Sin and Sout, respectively. Then, for the multi-channel measurements of the magnetic and electric fields we havex(t) consists of the corresponding S = [SinSout] to be smaller than the number of channels and the angle between subspaces Sin and Sout is large enough to guarantee a favorable condition number of S, we can decompose the data uniquely to the componentsn components of the vector In MEG and EEG in particular, the space can be divided into an enclosed volume containing the object of interest and an open volume outside of it that extends to infinity  determine whether the basis functions describe the electric or magnetic counterpart of the EM field. Our main focus is on the modeling and measurement of the EM fields produced by the brain, but rather than deriving the formalism for the common modality pair MEG and EEG, we investigate MEG and the associated electric field, as opposed to the electric potential that EEG measures. This approach provides us with a symmetric mathematical formalism for the magnetic and electric interactions from which EEG’s properties can be easily derived assuming that the EEG signal can be modeled accurately enough with Laplace’s equation. Our formulae describe the relationship between current distributions giving rise to quasistatic EM fields in a manner that has no specific assumptions regarding the distribution of electric conductivity or spatial profile of the currents. Previous work on the unification of the magnetic and electric interactions in neuroimaging either investigates mathematical properties of the source space in detail , or is bDue to the strict hierarchical organization of the VSH functions in terms of their amplitude decay as a function of measurement distance, our formalism can be used to study the essential number of spatial degrees of freedom contained in an EM field. As the electric and magnetic field components are composed of the same VSH functions, they have similar convergence properties as a function of distance, indicating that their dimensionality and information contents are also similar as the first approximation. On the other hand, the electric potential is proportional to a line integral of the electric field between two points, making it a scalar-valued field that is a spatially averaged outcome of a projection of the vector-valued electric field. This could smooth out some of the spatial features of the electric field, which decreases the effective dimensionality of the measurement. Although propagation through different head tissues is typically cited as the critical source of signal smoothing that reduces the resolution of EEG compared to MEG, our mathematical derivations suggest that the effective computation of a line integral over the electric field already acts as an important source of smoothing regardless of the conductivity profile of the head. In practice, EEG is the dominating measurement of the electric properties produced by the brain, but these results indicate that spatially more informative data could possibly be obtained by measuring the vector-valued electric field rather than the scalar electric potential. Previous simulation studies are in agreement with this conclusion . One canThe feasibility of directly measuring the electric fields generated by neural sources remains to be explored. There have been recent advancements in the detection of weak electric fields but major challenges remain in the construction of such instruments. For example, SQUID-based electrometeters are sensitive to charges instead of the flux of the actual electric field. This is a complication as compared to the measurement of the magnetic flux through a loop . ). 38]). l = 4, corresponding to 25 components). We also observe a similar trend for ECD localization bias in one example dataset. Consistent with existing work investigating the lower bounds for uncertainty for source localization in EEG [Looking at the variance explained as a function of expansion order, we find that in EEG most of the data variance can be explained with a relatively low order expansion . It remBy applying the VSH model that provides us with a compact representation of the multi-channel data, we demonstrate linear algebra methods for decomposing the measured data into basic components that can be used for signal standardization across measurement geometries, channel interpolation, calibration, and suppression of external interference. For MEG, this is basically just a review of previously demonstrated methods , but by total electric current distribution only, with the tangential direction being relative to the chosen origin of the forward model. This result is independent of the geometry of the current distribution and conductor volume.In terms of source distribution, a particularly interesting outcome of the VSH methodology shown in this article is the fact that the forward models of the quasi-static magnetic field always reflect the tangential projection of the V.In this article, we have reviewed the previously developed VSH theory for quasi-static magnetic fields and extended it to cover the full quasi-static electromagnetic field in a unified manner. The VSH model represents the continuous magnetic and electric vector fields as a set of hierarchically organized basis functions and their amplitude coefficients, which can be used as a standard representation of spatially sampled MEG and EEG measurements. The former measurement is a result of sampling the vector-valued magnetic field while the latter modality corresponds to sampling the electric potential, which is a scalar field. Our theory, however, unifies the mathematical representation of the electric and magnetic fields and we show that sampling of the electric field could have benefits over EEG and MEG in non-invasive brain imaging. The possible benefit over MEG can be accounted for the fact that the electric field is less restrictive to the direction of the underlying electric current. We also demonstrated practical applications in terms of signal processing and calibration that immediately follow from our theoretical framework.


Birth weight, Head circumference (HC), and Length are important clinical indicators for evaluation of prenatal growth and identification of neonates requiring detail assessment and monitoring. Gestational age-specific percentile charts are essential tool for both obstetricians and pediatricians in their day to day practice. This study aimed to develop gestational age specific percentile chart of Birth weight, Length and HC for neonates.In this Cross sectional observational study, HC, Birth weight and Length of live singleton neonates from 28 to 42 weeks of gestation fulfilling the inclusion criteria were measured over a period of one year. Mean, standard deviation, and percentiles values for different gestational age were calculated. Graphs were constructed using two way graph and Lowess smoothening method.th, 50th and 90th percentile at 40 weeks for Birth weight being 2550gm, 3100gm and 3750gm respectively. The gestational age specific percentile chart and growth curve are appropriately placed in the manuscript.Of total 2662 neonates, male: female ratio was 1.3:1 with maximum neonates in 40 weeks of gestation. The mean Birth weight, HC and Length was 2852.02 gm, 33.6 and 48.42 cm respectively. Overall males have more mean weight than females by 46.35gms. However, mean HC of male and female were similar 33.6 and 33.61 cm respectively and on average males were 0.27 cm longer compared to female. The mean Birth weight, HC and Length at 40 weeks was 3123.43gm (± 427.82), 34.249 cm (± 0.87) and 49.61 cm(± 1.85) respectively. The 10The percentile charts in this study may be used as reference for local population and similar data from various parts of the nation can provide a national reference curve for healthy neonates. The neonatal anthropometric indicators - birth weight, length and head circumference(HC) are of utmost importance for evaluation of prenatal growth and identification of infants that require thorough assessment and close monitoring postnatally.Birth weight is also a valuable indicator of maternal health, nutrition and quality of antenatal services, and for the monitoring of epidemiological outcomes and public health care policies , 2. NeonSimilarly, long infants are at higher risk of perinatal mortality, and excessive variation in HC can denote malformation of the central nervous system secondary to genetic or chromosomal abnormalities or teratogenic insults , 5.Based on ethnically mixed population in Colorado, the first gestational age specific anthropometric reference chart was developed by Lubchenco, et al. in 1963, which is still used in many centers . HoweverSimilar growth charts have been published by various authors representing growth patterns of diverse population in different parts of the world. The objective of present study is to construct percentile charts for birth weight, length and head circumference for infants born from 28 to 42 weeks of gestation to suit the neonatal population of Nepal.This study was a cross-sectional observational study conducted over a period of one year (August 2015 to July 2016) at a tertiary center, Bishweshwar Prasad Koirala Institute of Health Sciences (BPKIHS), Dharan, Nepal. All the singleton live birth from 28 to 42 weeks of gestation within 24 h of birth, delivered during the study period were included in the study by consecutive non- purposive sampling method. The ethical approval was taken from the Institutional Review Committee (IRC) of BP Koirala Institute of Health Sciences.Gestational age was estimated by first day of the last menstrual period (LMP). In cases where LMP was unknown or in clinically discrepant cases, it was confirmed by clinical assessment using New Ballard’s scoring system or first trimester ultrasonography (USG). The gestational age estimated by scoring was included if the difference between LMP and scoring was more than two weeks.Birth weight was measured within 24 h of birth after drying without clothing on the electronic weighing machine EBSA-20 to the nearest ±5 g with maximum up to 20 kg and calibrated before each measurement.Length was recorded placing the child supine to the nearest 0.1 cm using infantometer. The head was held firmly in position against a fixed upright head board by one person. Legs were kept straight, keeping feet at right angles brought into firm contact with the child’s heels.Head Circumference was measured at 24 to 48 h of life with the locally available non-stretchable measuring tape, the maximum circumference of the head from the occipital protuberance to the supraorbital ridges on the forehead to the nearest 0.1 cm was recorded.Name of mother, birth weight, length and HC of enrolled newborn were recorded. Multiple birth, gross congenital malformations and hydrops, still birth, maternal complications , severe anemia, maternal medical disorders.), large caput succedaneum and cephalhaematoma were excluded.rd, 10th, 25th, 50th, 75th, 90th, 95th, and 97th ) for the birth weight, length and HC according to gestational age was calculated, and charts constructed using Stata IC 14.1 Growth curves were constructed using two way graph and Lowess smoothening method.Data entry was done in Microsoft 2007 Excel. Data analysis was performed in Microsoft 2007 Excel and Stata IC 14.1. The percentiles  were in 40 weeks of gestation . Mean birth weight of male was 2871.9gm (±600.78) and of female was 2825.55gm (±530.13). Similarly, mean length of male newborn was 48.52 cm (±2.64) and of female newborn was 48.25 cm (±2.25). On an average male newborn length was 0.27 cm more than female newborn. The mean head circumference of the newborn was 33.6 cm and both male and female newborn had the similar mean head circumference.According to gestational age of newborn, the mean ± SD of birth weight, head circumference and length were calculated Table .th percentile for birth weight, HC and length for neonates at 40 weeks of gestation were 3100gm, 34.2 and 49.8 cm respectively. The gestational age specific percentile of different anthropometric variables are shown in Tables The 50Growth curves are shown in Figs. BPKIHS is the tertiary care hospital in Eastern Nepal with highest number of deliveries. It includes mixed group of population that represents different ethnicity, races and castes. Data on birth weight indicates important role of geographic location as an environmental factor on fetal growth. This signifies that regional individualized growth charts for each population is an ideal method for evaluation , 10. HenA WHO multicenter study reported that the average birth weight of Nepalese newborns was 2780 gms . Our stuThe mean head circumference of neonates in our study was 33.6 ± 1.55 cm, with male and female neonates having HC of 33.6 ± 1.67 cm and 33.61 ± 1.38 cm respectively which is lower in overall (36.55 ± 1.189 cm) as well as both male (36.57 ± 4.60 cm) and female (36.54 ± 4.67 cm) compared to study done in Western Rajasthan . The neoBirth length at 40 weeks of gestation was found to be similar in our study (49.61 ± 1.85 cm) compared with that of Aryal et al. (49.22 ± 1.52 cm), Lubchenco et al. (49.4) and Manandhar et al. (49.2 ± 2.2 cm) , 6, 13. In comparison to our data with that of Lubchenco, et al. suggests that the 10th and 90th centiles of our neonates are lower. This resulted in overestimation of the incidence of SGA and underestimation of LGA babies, leading to many AGA neonates labelled as SGA, and LGA neonates being overlooked as they are falsely classified as appropriate for gestational age (AGA). This highlights the importance of population specific and updated growth charts.The limitations of the study are the gestational age of the neonates were not ultrasonography based but rather mostly based on LMP which may have error due to various factors like educational status of mother leading to under or over estimation of gestational age. The numbers of preterm infants were less in number similar to other studies. Factors other than ethnicity like altitude, maternal size, parity, smoking, parental social position which can affect fetal growth could confound the ethnic differences which has been observed in numerous other studies were not analyzed in detail and lastly it was a single centered study.The study concluded that the mean birth weight, head circumference and length of the single live neonates from 28 to 42 weeks of gestation was 2852.02 ± 571.89 gms, 33.6 ± 1.55 cm and 48.42 ± 2.48 cm respectively. In this study, we have established local relevant gestational age specific percentile chart and growth curves for Birth weight, Head circumference and length which might be appropriate for babies born in this region.


Background: Gestational age is associated with greater school achievement and variation in newborn metabolic markers. Whether metabolic markers are related to gestational age differences in achievement is unknown. This study examines whether newborn metabolic markers are associated with gestational age differences in performance on standardized school tests. Methods: This retrospective cohort study linked birth certificates of children born in Iowa between 2002 and 2010 to newborn screening records and school tests between 2009 and 2018. The analytical sample includes up to 229,679 children and 973,247 child-grade observations. Regression models estimate the associations between gestational age and 37 newborn metabolic markers with national percentile ranking (NPR) scores on math, reading comprehension, and science tests. Results: An additional gestational week is associated with 0.6 , 0.5 , and 0.4  higher NPRs on math, reading, and science, respectively. Compared to full term children (37–44 weeks), preterm children (32–36 weeks) have 2.2 , 1.5 , and 1.0  lower NPRs on math, reading comprehension, and science. Very preterm children (20–31 weeks) have 8.3 , 5.2 , and 4.7  lower NPRs than full term children on math, reading, and science. Metabolic markers are associated with 27%, 36%, and 45% of gestational age differences in math, reading, and science scores, respectively, and over half of the difference in test scores between preterm or very preterm and full term children. Conclusions: Newborn metabolic markers are strongly related to gestational age differences in school test scores, suggesting that early metabolic differences are important markers of long-term child development. Gestational age is positively associated with children’s academic performance , and preDespite the clinical and public health importance of understanding connections between newborn metabolic markers throughout their entire ranges including outside of thresholds clinically indicative of metabolic problems, there is little direct empirical evidence, especially from large-scale population-based studies. One study from Australia reported an association between differences in neonatal thyroid stimulating hormone (TSH) levels below the clinically used threshold for hypothyroidism and numeracy test scores . That stThis study examines the associations between gestational age and academic achievement measured via standardized school tests and the extent to which metabolic markers are related to gestational age differences in achievement. The study uses unique population-based data linkages in Iowa including birth certificates, school tests, and newborn screening program data.The study includes children born in Iowa between 2002 and 2010 who completed newborn screening and attended elementary school in Iowa between school years 2009/2010 and 2017/2018. The birth period begins in 2002 because this is the first year when the newborn screening marker panel was expanded to include multiple amino acid and fatty acid markers associated with gestational age. The study sample is from a unique linkage of three datasets: birth certificate data from the Iowa Department of Public Health, newborn screening data from the Iowa State Hygienic Laboratory, and standardized school test scores from the Iowa Testing Programs. The birth certificate data were independently matched to school tests based on child’s name and date of birth, and to the newborn screening data based on combinations of child’s name, date and time of birth, and sex, birth hospital, and mother’s name and date of birth. Between 2002 and 2010, there were 344,578 children born in Iowa based on birth certificates. Of those, 334,873 children (97.2% of population) were matched to newborn screening data. Of the matched children, 322,854 children had complete data on all 37 metabolic markers; of those, 320,395 (99.2%) had complete data on gestational age and demographic control variables from the birth certificate data.n = 320,395), 229,863 (71.7%) matched to at least one school test. Because testing increases with age (grade), only 18% of children born in 2010 and 57% of children born in 2009 were matched to a school test, compared to 82% of children born in 2002–2008. In sensitivity checks (discussed below), children born in 2009–2010 are excluded. In total, 67% of children born in Iowa between 2002 and 2010 (study population) were matched across the three datasets and had complete data on all metabolic markers, included birth certificate variables, and at least one test score between 1st grade and 10th grade. Comparisons between the included and excluded samples are discussed below.Of the sample with matched and complete birth certificate and metabolic marker data . Infants born at gestational age below 20 weeks or above 44 weeks are excluded to reduce the chance of recording errors. Gestational age in weeks and indicators for preterm birth (<37 weeks), preterm but not very preterm (32–36 weeks), and very preterm (20–31 weeks) birth, with full term birth (37–44 weeks) are examined.The newborn screening data includes 37 metabolic markers previously shown to correlate with gestational age . NewbornThe academic achievement data are from the Iowa Tests of Basic Skills, the Iowa Tests of Educational Development, and Iowa Assessments. These tests are nationally recognized, standardized, and highly valid and reliable achievement tests that cover major academic subject domains. They have strong psychometrics and were standardized using a nationally representative student sample ,13. The The study sample includes observations with complete data on all included birth certificate variables (described below), all 37 metabolic markers, and at least one test score on one test domain. From the sample of 229,863 children with complete data on birth certificate and newborn screen variables who were matched to at least one school test, the analytical sample ranges between 222,439 and 229,679 unique children and between 909,454 and 973,247 child-grade observations depending on test domain . In general, science tests were less frequently administered at early grades than math and reading. The study was reviewed and approved by the University of Iowa Institutional Review Board.Regression analysis is employed to estimate: 1—the association between metabolic markers and gestational age; 2—the associations between gestational age and school test scores; and 3—the extent to which metabolic markers statistically account for the associations between gestational age and test scores. Three measures of gestational age are used. The first is a continuous measure of gestational age in weeks, which captures the whole variation in gestational age and allows estimating the association of an additional gestational week with test scores. Two indicators for preterm birth are also included to understand these associations specifically for preterm children. One measure is, a 0/1 indicator for preterm birth < 37 weeks, and another is 0/1 indicator very preterm birth < 32 weeks. To examine the association between metabolic markers and gestational age, three regression specifications are estimated. The first model regresses the three gestational age measures, one at a time, on sociodemographic variables as follows:i, GestationalAge is one of the three gestational age measures mentioned above. X includes the following variables from birth certificates: child’s gender and birth year 0/1 indicators, and maternal race, age, marital status, and education. In all equations (1 through 6 below), the last term designates the error .For child MetabolicMarkers) are added. Then, in a third regression, the metabolic marker squares and cubic terms to account for possible non-linearity in their associations with gestational age.Next, the 37 metabolic markers are added as covariates to this regression to examine the change in the R-squared compared to the first regression. That change in R-squared gives the association between these metabolic markers jointly and gestational age. First, the levels of metabolic markers  with complete data on sociodemographic and metabolic markers who were not matched to school tests to check for any sample selection bias from not matching to school tests.Equations (1) through (3) are estimated for the analytical sample with complete data on all included sociodemographic and metabolic markers, and math scores . These models are also estimated for children  as follows:i tested in school year t, TestScore is the child’s score on one of the three test domains . Grade includes fixed effects (0/1 indicators) for grade level, and Year includes fixed effects (0/1 indicators) for school year. 1β is the estimated association between gestational age and test scores. One model is estimated including gestational age in weeks, and another model including two indicators in GestationalAge for preterm but not very preterm (32–36 weeks), and very preterm (20–31 weeks), with full term birth (≥37 weeks) as the reference category.For child Next, the extent to which metabolic markers statistically account for the association between gestational age and school test scores is examined by adding these markers as covariates into the test score regression. Model (5) adds the levels of metabolic markers, while model (6) also adds their squared and cubic terms.β2 is the estimated association between each of the gestational age measures and test scores adjusting for metabolic marker levels (β3 is the same adjusting for non-linearity in metabolic marker effects). Then, β2 and β3 are compared with β1 to infer the extent to which metabolic markers are related to the association between gestational age and test scores; the difference from β1 is the part of association between gestational age and tests scores statistically accounted for by metabolic markers. All models are estimated using ordinary least squares. In models 4 through 6 with child-grade observations, standard errors are clustered at the child level and are robust to general heteroscedasticity.Additional models are estimated to evaluate the sensitivity of estimates to certain sample and model choices. The first check estimates the association between gestational age and school test scores excluding children with gestational age within 42–44 weeks to avoid possible data errors. The second sensitivity check estimates this association excluding children born in 2009–2010 since school testing is less common at early grades. Finally, as a more flexible way to account for possible non-linearity in the metabolic marker effects than adding their cubic and squared terms, the model includes instead 0/1 indicators for their 5-percentile ranges.n = 114,715) compared to the sample of children with complete data on birth certificate and newborn screen variables and at least one school test . Average gestational age was comparable between the two samples (38.5 versus 38.7), but preterm (32–36 weeks) and very preterm (20–31) birth rates were higher in the excluded sample . The excluded sample had a higher proportion of minority mothers (13.3% versus 7.7%), mothers who had not completed high school (17.5% versus 13.3%), and single mothers (35.9% versus 31.7%).n = 229,679 children). Results when adding squared and cubic terms of the metabolic markers (model 3) are in n = 90,532).There are similar associations between gestational age and school test scores when excluding gestational age above 41 weeks  online aGestational age is associated with academic achievement during childhood but also with early life metabolic markers. This study fills a major knowledge gap in the literature about the extent to which newborn metabolic markers are related to the association between gestational age and school test scores using a unique linkage of birth certificates, standardized school test scores, and newborn screening program for children from Iowa. Children born very preterm and preterm have lower scores than full-term infants by 0.2–0.3 SDs and 0.04–0.08 SDs, respectively, with larger differences for math than reading and science. Adjusting for newborn metabolic markers substantially reduces the association between gestational age and school test scores. For continuous gestational age, this adjustment reduces this association by one third (for math) to half (for science). Moreover, adjusting for metabolic markers reduces the difference in math scores between preterm or very preterm and full-term children by half, and nearly eliminates the difference in reading and science. To our knowledge, this is the first evidence on the relevance of multiple metabolic markers examined jointly for academic achievement using large-scale population-based data.The study includes 37 markers related to galactosemia, congenital hypothyroidism, adrenal hyperplasia, and amino acid and fatty acid metabolism disorders that statistically account for nearly half of the variation in gestational age. The levels of such endocrine and metabolic markers may reflect differences in fetal growth of certain body organs but also stress and developmental issues and pregnancy complications and maternal health. The evidence from this study suggests that these markers are broadly connected with fetal and child development throughout their entire ranges including levels considered normal and outside clinically used thresholds to indicate metabolic disorders. In that way, they might serve as early markers of long-term child development. This evidence also suggests that the relationship between gestational age and long-term development depends partly on early life endocrine and metabolic markers. Understanding the associations between newborn metabolic markers and achievement across gestational age ranges (including among preterm and full term children) and among children of the same gestational age warrants further investigation. Building on this work with additional research on the possible connections between neonatal metabolic markers and long-term child development outcomes could ultimately offer important knowledge on the role of specific markers and the information they might provide on developmental risks and the opportunities to address these risks early on.The study findings on the correlation of gestational age measures with metabolic markers are consistent with those from previous studies including a study from the same population but fewer birth years. The results on the associations between gestational age and school test scores are also consistent with the literature on this question and are similar to those from another population-based study of children in Florida . This coA key strength of this study is linking three population-based datasets  with data on gestational age and demographic variables (birth certificates), multiple newborn metabolic markers, and standardized school tests on three areas . The study has three limitations. First, the study only includes metabolic markers previously connected with gestational age in this population. It is possible that other metabolic markers can further explain the remaining difference in test scores by gestational age. Second, the data is from one state with little racial/ethnic variation (majority of population are non-Hispanic Whites); understanding the generalizability of findings in more diverse populations is important. Third, the analytical sample is approximately two thirds of the total population mostly due to not matching to school tests. The excluded sample has higher rates of very preterm and preterm births, which could elevate neonatal and infant mortality and developmental problems during childhood that may affect school attendance, particularly for very preterm birth ,18. HoweNewborn metabolic markers are associated with over half of the difference in school math scores between preterm or very preterm and full-term children and nearly the full difference in reading and science scores. This evidence from a population-based linkage of birth certificates, standardized school test scores, and newborn screening program data suggests that differences in newborn metabolic levels including in ranges considered normal and not indicative of metabolic disorders are important markers of long-term child development. Examining patterns of variation across various metabolic markers is an important future research direction.