Grandmothers most commonly bathed the baby immediately after birth , whereas mothers and health workers were common decision makers regarding breastfeeding initiation. Predictors for a mother being the key decision maker on the place of birth included: Mother having a secondary education (AOR 1.9: 95% C.I (1.0–3.6)) and mother being formally employed (AOR 2.0: 95% (1.5–2.9)). Mothers, fathers, grandmothers, health-workers, and traditional birth attendants were the most influential in the selected newborn care practices. Programs that aim to promote newborn care need to involve husbands, grandmothers, and health workers in addition to mothers.Knowledge of key decision makers and actors in newborn care is necessary to ensure that health interventions are targeted at the right people. This was a cross-sectional study carried out in Lira district, Northern Uganda. Multivariable logistic regression was used to determine factors associated with mothers being the key decision maker regarding where to give birth from and when to initiate breastfeeding. Fathers were the key decision makers on the place of birth (54.3%,  Uganda has unacceptably high under-5-year child mortality; 62.4 deaths per 1000 live births compared to the worldwide estimate of 38.4 deaths per 1000 live births [One under-recognized sociocultural aspect of newborn care is the primary caretakers of newborns in the perinatal period . TraditiIn this study, we aimed to quantitatively determine key decision makers and actors in newborn care among mothers with children under the age of 2 years in the Lira district, Northern Uganda.This was a cross-sectional study that involved mothers who had a child below the age of 2 years.The study was carried out between August and November 2016 in the Lira district, Northern Uganda. The district has 13 subcounties, 1 municipality, and 751 villages . The majAll villages in the 3 selected subcounties were listed, and 30 villages were selected by probability proportional to size. A sampling frame of all households in the village obtained from the village leaders was used to randomly select a household to begin with in each village. After identifying the first household, the next household was chosen based on proximity to the first. The nearest household was defined as one whose door was closest to the prior household. Only one mother–child pair would be chosen from each household. This process would continue until 31 mother–child pairs had been interviewed in each village. If the mother had more than one child born within the previous 2 years, questions were addressed to the most recent pregnancy and therefore the youngest child. If the mother had twins, questions were aimed at the younger twin.We recruited mothers with children born within the last 2 years. Mothers were recruited only if they were residents in the respective village as determined by the village leadership and were mentally and physically able to complete the interview.https://opendatakit.org/) software installed on mobile phones. The questionnaires were translated to the local language and back-translated to English for accuracy. Village health team members and local area leaders acted as our guides. Households whose members were absent at the time of the visit were revisited later on the same day, prior to exiting the village. Those that were still absent were revisited the following day and, if not found, were declared missing and replaced. Interviews were carried out at the mothers’ homesteads, preferably at a private place away from distraction, e.g., under a tree at the periphery of the compound. The interviews lasted between 45 and 60 min. The questions asked were close-ended questions.Data collection was carried out with the help of 30 research assistants who were recruited from Lira district and were well conversant with the local languages. Data were collected using Open Data Kit . We defined key decision makers and actors as the most frequently cited decision maker or actor, and common decision makers and actors as the two most frequently cited decision makers and actors. The newborn care practices studied were obtained from lists of recommended newborn care practices ,16,17,18www.openepi.com), assuming that a proportion of 50% of women decide where they should give birth. We also assumed precision of 5% and a design effect of 2. This gave us a sample size of 768 participants. Assuming a nonresponse of 15%, we came up with a minimum sample size of 904 mothers. To achieve an equal enumeration in all the 30 villages, we purposed to enroll at least 31 mothers from each village. The sample size calculated was sufficient to determine the different proportions of decision makers, influencers, and actors, assuming that 50% of mothers were the key decision makers concerning all practices.The sample size was calculated for the primary objective of this study. A total of 930 mothers were enrolled in the study. This was calculated by Open-epi software , place of birth , and parity. We used these strata because qualitative literature suggested there could be differences in key decision makers in these strata [The analysis strategy considered the fact that we used multistage sampling. Descriptive variables were presented as means and proportions for continuous and categorical variables, respectively. Factors with a e strata ,20,21,22We obtained ethical approval from the Makerere University School of Medicine Research and Ethics committee (SOMREC number: 2015-121) and from the Uganda National Council of Science and Technology. District and local area leaders gave us permission to perform the study. Written informed consent was obtained from the participants prior to participation in the study. For participants who could not write, a thumbprint was obtained.A total of 930 mother–child pairs were considered for the analysis. The response rate was 93%. The majority (>95%) of non-respondents were mothers who were absent from their homestead. The mean child age was 11.2 months, with a standard deviation of 7.7 months. The mean maternal age was 25.8, with a standard deviation of 5.9 years. The majority of mothers  and fathers  had less than 7 years of formal education. The rest of the participant characteristics are presented in n = 505), but the mothers  were also common decision makers. Fathers also most commonly decided on whether to seek care for a sick newborn child . Grandmothers most commonly bathed the baby immediately after birth , whereas mothers most commonly decided on when to initiate breastfeeding  and on whether to bottle-feed   . Health n = 684) . The disn = 684) . Table AAt multivariable analysis, the factors associated with the mother being the key decision maker for where she gave birth included: Mother having a secondary education compared to no education ), mother having parity of 4 or more compared to one ), and mother being employed ) . MothersRegarding the decision to initiate breastfeeding, older mothers were more likely to decide on when to initiate breastfeeding ), whereas mothers in rural areas were less likely to make this decision ) .This study showed that mothers, fathers, grandmothers, health workers, and traditional birth attendants were the key decision makers and actors in newborn care, albeit for different practices. This finding is not surprising and has been reported by qualitative findings in other African countries ,8,23. ThWe found that in the majority of cases, fathers were the key decision makers concerning where the mother gave birth from and whether care was sought for sick newborns. Similar findings were observed in a study in Eastern Uganda  and in ZGrandmothers most commonly performed the first neonatal bath, whereas traditional birth attendants most commonly applied substances to the umbilical cord of the child in the period immediately after birth. In the period immediately after birth, mothers are (or perceived to be) exhausted , and theMothers were key decision makers relating to practices concerning breastfeeding. Since mothers spend the most time with the babies in the post-immediate period after birth, they are most likely to be the key decision makers concerning practices related to infant feeding. However, health workers were also common key decision makers in breastfeeding practices. Similar findings were seen in Malawi, where mothers were the most often-cited decision makers in breastfeeding, but grandmothers were also involved . A studyRegarding the decision on place of birth, mothers earning money and more educated mothers were more likely to decide on the place of birth. Similar findings have been observed in Ghana , and thiIn this study, we observed that men were not commonly involved in the selected newborn care practices, apart from deciding the place of birth and seeking care for sick newborns. Prior research has shown that improved male involvement is associated with improved maternal and newborn health through the promotion of skilled birth attendance, postpartum care, complications preparedness, and maternal nutrition . As suchThis study had some limitations; the long recall period (11 months on average) could have introduced a measurement/information bias. We used this long recall window to enable us to obtain sufficient mothers using multistage sampling . To miniFathers, grandmothers, health-workers, and traditional birth attendants were influential in newborn care. Programs that aim to promote newborn care, especially programs that encourage facility births, and immediate newborn care, need to involve persons other than the mother; particularly husbands and grandmothers. Health workers should be involved in programs that promote timely initiation of breastfeeding, especially in rural areas. This study also highlights the importance of context-specific formative research prior to interventional studies being rolled out, to ensure that interventions are directed at the right people.


The ratio of “rich bifidobacteria” infants was 20/31, 34/36, and 30/30 in groups N, H, and L, respectively. In group N, the “rich bifidobacteria” group received first colostrum significantly earlier than the “poor bifidobacteria” group . Compared with the N group, both groups H and L had a significantly high proportion of “rich bifidobacteria” infants (P < 0.05). Bifidobacterial colonization was poor in premature infants at 1 month compared with term infants, and the level of colonization was associated with the timing of initial provision of colostrum. Providing probiotics to premature infants can improve bifidobacterial colonization.This study aimed to evaluate the association between bifidobacterial colonization in low birth weight infants and perinatal factors, including the timing of initial colostrum and the effect of probiotics on this colonization. In this non-randomized controlled trial, we enrolled 98 low-birth-weight infants from a neonatal intensive care unit (NICU) in Japan. Infants were divided into three groups: group N (no intervention), group H (received non-live bifidobacteria), and group L (received live bifidobacteria). The number of bifidobacteria in the infants’ stools at 1 month of age was measured using real-time polymerase chain reaction (PCR). We divided infants into “rich bifidobacteria” (≥10 Bifidobacteria are commonly found in the intestine of breast-fed term infants; they are initially found in feces on days 2–3 in breast-fed term infants, and then become predominant within the first week of life . Live biBifidobacterium bifidum OLB6378 (OLB6378), a strain derived from the intestine of human infants, reduced the incidence of late-onset sepsis in very low birth weight (VLBW) infants.In premature infants, the intestinal barrier system is immature . The estBifidobacterial colonization in premature infants is delayed compared with that in term infants . It has Colostrum, the first milk produced by mothers after delivery, contains high concentrations of immunoprotective agents compared with mature human milk . One stuIn this study, we evaluated bifidobacterial colonization and related perinatal factors, such as the timing of first colostrum, in low birth weight (LBW) infants at 1 month of age and we examined the influence of the administration of live OLB6378 on the bifidobacterial colonization levels in these infants. In addition, we examined whether partially similar effects would be observed with the administration of non-live OLB6378.This study was registered in the UMIN  Clinical Trial Registry (UMIN000020520) and was conducted in accordance with the Declaration of Helsinki. We used fecal samples collected for our previously reported study  after obThe subject and protocol have been described previously , and thiWe determined the infants to the study groups based on entry order to prevent cross colonization because cross contamination of the control group infants with the probiotic administration to the study group was observed in studies of probiotic use . The subWe used a lyophilized probiotic powder containing OLB6378 , which contains 0.5 g/g of live OLB6378 concentrate, 0.25 g/g of sucrose, and 0.25 g/g of trehalose, as previously reported [9 live cells) and 480 mg of dextrin , which is the same as the dose used in our previous multicenter study [9 non-live cells) and 480 mg of dextrin. Each mixture (500 mg) was diluted in 1 mL of warm water, mixed with either breast milk or infant formula (depending on the extent of lactation in the mothers), and divided into two 250-mg doses. A 250-mg dose was administered to subjects in each group twice daily. The interventions were started within 48 h after birth and continued for at least 1 month after birth. Prior to discharge from the NICU, the parents of the infants in groups L and H were instructed on how to administer the trial compound to their infants at home and were requested to perform ongoing intervention. Furthermore, after discharge from the NICU, compliance with the use of the trial compound was assessed by interviewing the parents.Group N received no intervention. Group L received a mixture of 20 mg of live OLB6378 powder , and 1 μL DNA solution in a total volume of 10 μL. The PCR amplification protocol included initial denaturation for 1 min at 95 °C followed by 40 cycles of melting, annealing, and extension at 95 °C for 15 s and 60 °C for 20 s, respectively. The bacterial DNA from stool samples that were too small to be measured were excluded from this analysis.For measuring the number of bifidobacteria, the subjects’ stools were collected at 1 month of age. The collected stool samples were immediately frozen at −20 °C. We collected stool samples of the infants on admission in the hospital. In cases of the infants after discharge from the hospital at 1 month of age, we asked the parents to collect their infants’ stool sample and bring them to the hospital at 1 month. There were 14 infants on admission in the hospital and 17 infants were discharged at 1 month of age. Then, bacterial genomic DNA was extracted from stool samples using a commercial extraction kit  as previously described . Real-tiBackground information, including gestational age, birth weight, and body weight at 1 month of age, Apgar score, sex, mode of delivery, multiple birth ratio, and antibiotic use during the first month, was collected from the case report forms. The data of initiation time of colostrum intake and breast milk intake rate  was also collected from the case report forms. We assessed the data that we could confirm exactly when colostrum intake began. As accurate measurements of the feeding methods were not possible during the observation period post-NICU discharge, data were only collected while the infant was in the NICU. We provided formula, not donor milk, to the infants who were unable to obtain their own mother’s milk in the NICU.t-test, Fisher exact test, or Mann–Whitney U test using the Bell Curve for Excel  and by multiple comparisons using Ryan’s method [P value of <0.05 indicated a significant difference.Non-normally distributed data were subjected to log transformation before analysis. Differences were evaluated using the Student’s s method . A P valn = 31; group H; n = 37; and group L, n = 30, and these infants were evaluated as an intent-to-treat population. As described previously [As shown in eviously , no sign8 cells, and six cases had a count of approximately 104 cells. In five cases, no bifidobacteria were detected. For comparison with the bifidobacterial colonization level in term infants, we set the lower limit of normal bifidobacterial counts in term infants at 104.8 cells/g feces, according to the mean value −2 SD  for Japanese term infants in the report by Tsuji et al. [In the no intervention control group at 1 month of age, who did not receive OLB6378, 20 cases had a bifidobacterial count of >10i et al. . As showp = 0.04). An average of breast milk intake rate  during the observation period was determined. For all periods, the average of breast milk intake rate was lower in the “poor bifidobacteria” subgroup than in the “rich bifidobacteria” subgroup.n = 31; group H, n = 36; and group L, n = 30. One sample from the H group was excluded owing to the lack of bacterial DNA from the stool sample. As the probiotic interventions were started within 48 h after the infants’ birth, we divided infants into one group who were first provided colostrum earlier than 48 h after birth and another group who were first provided colostrum later than 48 h after birth. The solid dots (●) indicate infants who ingested colostrum within 48 h of birth, and circles (O) indicate infants that did so after 48 h since birth. We also set the lower limit of normal bifidobacterial counts in term infants at 104.8 cells/g feces. The ratios of infants above the lower limit were 20/31, 34/36, and 30/30, in group N, H, and L, respectively. Furthermore, compared with the N group, both the H and L groups had a significantly high number of infants with bifidobacterial counts above the lower limit set in this study . In addition, the proportion of infants provided colostrum earlier than 48 h among the three groups was not significantly different.In this study, we found that bifidobacterial colonization in LBW infants at 1 month of age was poor compared with that in term infants, and the level of that colonization was associated with the timing of initial colostrum provision to LBW infants. Next, we found that providing OLB6378 to LBW infants improved the bifidobacterial colonization to the same level as that in the term infants.First, the bifidobacterial colonization in LBW infants at 1 month was poor compared with that in the term infants in a previous report . In VLBWIn addition to the poor bifidobacterial colonization in LBW infants, we also found that providing live OLB6378 to LBW infants improved the bifidobacterial colonization to the same level as that in term infants . The curAs shown in There were some limitations in our study. First, the data on bifidobacterial colonization in term infants were quoted data that were not measured under the same conditions as those in this study. Thus, the results of the comparison between LBW infants and term infants may be inaccurate. Second, this study could not employ multiple regression analysis to identify other confounding factors due to the small sample size. However, there have been no reports showing that the timing of initiation of colostrum feeding affects bifidobacterial colonization in LBW infants. Thus, further investigation regarding the effects of the timing of initial colostrum feeding on bifidobacterial colonization may contribute to the understanding of the intestinal flora in premature infants. Additionally, as we had not given dextrin as placebo to infants in N group, we could not conclude that dextrin had any effects on bifidobacterial colonization.We showed that bifidobacterial colonization of LBW infants at 1 month was poor compared with that of the term infants, and the level of that colonization was associated with the timing of initial colostrum in LBW infants. We also showed that providing OLB6378 to LBW infants increased the bifidobacterial colonization to the same level as that in term infants. Further studies on bifidobacterial colonization in premature infants are required for the neonatal healthy growth during hospitalization in the NICU.


Japan has an exceptionally high proportion of low-weight births and underweight women. It has been suggested that an appropriate increase in gestational weight gain (GWG) for underweight women will help to prevent low birth weight. The current strategy aims to raise the desired value of GWG equally for all pregnant women within the underweight category. However, it remains elusive whether or not the relationship between GWG and birth weight for gestational age (BW/GA) are uniformly equivalent for all the women.n = 75). Second, we dichotomized the main cohort (n = 1114) according to BW/GA to verify our hypothesis that the correlation between GWG and BW/GA differs depending on the size of the newborn.We performed a retrospective cohort analysis of women who delivered their newborns at Tokyo Medical and Dental University Hospital from 2013 to 2017. First, in order to examine the direct effect of an increase or decrease in GWG on BW/GA, we analyzed the correlation between inter-pregnancy differences in GWG and BW/GA using a sub-cohort of women who experienced two deliveries during the study period (p = 1.9 × 10− 9). The correlation between GWG and BW/GA in women who delivered larger-sized newborns was higher  than that in women who delivered smaller-sized newborns . This disparity did not change after adjustment for pre-pregnancy BMI. The mean birth weight in the dichotomized groups corresponded to percentile 52.0 and 13.4 of the international newborn size assessed by INTERGROWTH-21st standards.The inter-pregnancy difference in BW/GA was not correlated with that of GWG. However, the correlation between BW/GA of siblings was high (r = 0.63, In our study, GWG was positively correlated with BW/GA for heavier neonates whose birth weights were similar to the average neonatal weight according to world standards. However, caution might be required for low-birth-weight neonates because increased GWG does not always result in increased birth weight. Weight gain management during pregnancy is important for the future health of pregnant women and their newborns. In Japan, the high proportion of newborns with low birth weight and the high prevalence of underweight women are major problems. Low birth weight increases the risks of metabolic disorders, coronary heart disease, and other non-communicable diseases. Due to the exceptionally high proportion of low birth weight infants, there has been much focus on the establishment of a strategy to increase birth weight in Japan. It has been argued that the recommended weight gain limits during pregnancy might be too strict . One proBirth weight is a multifactorial phenotype and individuals have different multifactorial situations. Furthermore, GWG could interact with multiple other factors. Theoretically, pure GWG effects on BW/GA can be estimated by the relationship between GWG and BW/GA for the same mother. Observationally, the effects of a GWG difference on BW/GA difference between siblings could provide useful information on the direct effect of GWG , 6. For Moreover, raising the limit of GWG with the aim to reduce low-weight births has been considered. However, if population heterogeneity exists, in which an increase in GWG may be ineffective in women who will deliver small neonates, this aim may be invalid. Therefore, the purpose of this study was to present evidence that an increase in GWG does not always contribute to an increase in BW/GA and to prompt a reconsideration of the risks and appropriateness of a uniform recommendation for GWG increase. This was done with the assumption that there are two types of fetuses: those that will increase in birth weight in response to maternal weight gain (GWG responders) and those that will not increase in birth weight, even if the mother has a substantial gain in weight (non-responders). Our hypothesis was that larger-sized newborns would be GWG responders whereas smaller-sized newborns would be non-responders.n = 2238). For all patients, maternal and neonatal information were recorded and routinely validated in the standard electronic prenatal medical records by trained medical personnel. From these electronic prenatal medical records, all data were anonymously collected, including the following information: maternal height, maternal age, pre-pregnancy weight (self-reported), parity, gestational weight, obstetrics history (preterm birth and stillbirth), present pregnancy complications (such as hypertensive disorder of pregnancy and preterm labor), history of non-communicable diseases , child’s sex, gestational age at delivery, and neonatal body size. Gestational age was determined according to the date of the mother’s last menstrual period and confirmed via measurement of the fetal crown-rump length on a first-trimester scan. Maternal weight measurements during gestation were performed 11.3 ± 3.1 times for each woman. Institutional review board approval was obtained from Tokyo Medical and Dental University . The requirement for individual informed consent was waived by the institutional review board because of the retrospective nature of the study and because the data of the involved participants were anonymized. All methods were performed in accordance with relevant guidelines and regulations.We performed a retrospective cohort analysis of pregnant women who delivered their newborns at Tokyo Medical and Dental University Hospital from 2013 to 2017 . This comparison was performed to assess prenatal factors that affect neonatal health to eliminate potential confounding factors such as genetics, sociodemographic factors, and other unmeasured factors. The sibling comparison was restricted to women who were primiparous at the time of first delivery during the study period. The second analysis focused on the general population to investigate the heterogeneity in terms of GWG responsiveness. Unique (non-overlapping) individuals were included in the study by selecting only one delivery for each woman with multiple deliveries during the study period. The number of women with sufficient weight data to calculate GWG was 1440; therefore, 1440 women were eligible for this study , was estimated using the Pearson correlation coefficient (r). A minimum sample size of 32 was estimated for the correlation analysis using Sample Size Calculators (https://www2.ccrb.cuhk.edu.hk/stat/epistudies/reg1.htm), with an alpha value of 0.05, power of 0.80, and Pearson correlation coefficient of 0.48. A linear regression analysis of BW/GA percentile with respect to pre-pregnancy BMI was performed for women (n = 1114), with a BMI of 17 to 23 kg/m2. A minimum sample size of 404 was estimated for the simple linear regression analysis using Sample Size Calculators (https://www2.ccrb.cuhk.edu.hk/stat/epistudies/reg1.htm) with an alpha value of 0.05, power of 0.80, and effect size of 2.86. To analyze the heterogeneity of neonatal birth weight response to GWG, we dichotomized the women according to their BW/GA percentile: group 1 comprised women with larger-sized newborns , whereas group 2 comprised women with smaller-sized newborns . Since the BW/GA reference was available for only gestational ages less than 42 weeks, two post-term deliveries were excluded in this analysis, which resulted in the main sample size of 1114. The difference in the correlation between GWG and BW/GA between large and small newborns was explored, both unadjusted and adjusted for pre-pregnancy BMI.All calculations and graphical presentations were performed using R software . Sibling correlation, which compared the first and second pregnancies for the same mother (The characteristics of our sibling cohort are shown in Table p < 2.2 × 10− 16), suggesting that the inter-pregnancy difference in BMI was negligible. In contrast, the correlation between the first and second GWG values was modest , although the sibling correlation was high for the BW/GA percentile , indicating that BW/GA may not be correlated with GWG. Subsequently, we investigated how GWG changes between the two pregnancies influenced differences in birth outcomes. In Fig. p = 3.8 × 10− 1)   were too small for a statistically meaningful analysis of the variables . The GWG distribution was quite similar among the six pre-pregnancy BMI ranges  . To explore the cryptic heterogeneity of the GWG-BW/GA relationship, we hypothesized that the correlation between GWG and BW/GA differed according to newborn size. We then dichotomized the women into two groups according to the BW/GA percentile. Birth weight in Japan is generally low compared to world standards. Therefore, we also calculated the mean birthweight (SD) and the mean (SD) values of the international newborn size percentile for large and small sized newborns, according to INTERGROWTH-21st standards , 52.0 (19.0); smaller-sized, 2719 (326), 13.4 (8.6)), suggesting that BW levels in the large half were equivalent to the world average levels but those in the small half were considerably low. A correlation between GWG and BW/GA in larger newborns was measured as r = 0.17 (p = 4.1 × 10− 5). In contrast, in smaller newborns, r = 0.099 (p = 1.9 × 10− 2). After adjustment for pre-pregnancy BMI, r = 0.18 (p = 1.2 × 10− 5) in larger newborns, whereas r = 0.092 (p = 2.9 × 10− 2) in smaller newborns. The disparity of the GWG and BW/GA correlation between large and small newborns indicated potential heterogeneity between them. Notably, there was a weaker correlation between GWG and BW/GA in women who delivered smaller newborns. We then further stratified women according to pre-pregnancy BMI   individuals n = 140 (Fig. In this study, using sibling comparison, we found that an increase in GWG does not always contribute to an increase in BW/GA. Rather, BW/GA may be largely preset by familial factors before the perinatal period because the sibling correlation for BW/GA was found to be high. In addition, we found that the correlation between GWG and BW/GA was low in the women who delivered smaller children.The overweight/obesity and excessive GWG significantly increase the risk of high birth weight, whereas underweight and inadequate GWG increase the incidence of low birth weight –18. The All the above studies basically used the linear-regression method to explore the effects of different maternal factors on birth weight. However, Koenker and Hallock  and AbreOur findings have indicated the importance of considering neonatal size when attempting to determine the optimal GWG to reduce low birth weight. For example, if the child was healthy and large during the first pregnancy, and GWG during the second pregnancy is lower than expected, it might be possible to encourage an increase in weight gain within the recommended range. However, if the first child was healthy and small, careful consideration of whether to reinforce weight gain during the second pregnancy is necessary because GWG increase may not contribute to an increase in BW/GA. It would be safe to perform GWG management in consideration of the individual circumstances such as whether the women would probably deliver a large or small child, rather than uniformly recommending GWG increase. We have presented basic data for the above idea.Our study has some strengths. The study population was well characterized and calculation of GWG was performed using the standard methodology. In addition, to the best of our knowledge, this is the first study to explore heterogeneity in the population in terms of a correlation between GWG and BW/GA. The prevention of low birth weight by uniform GWG increase is not yet an established method. The results of our study will be useful for devising more appropriate GWG recommendations.Our study had several limitations. First, we dichotomized women according to BW/GA percentile to demonstrate heterogeneity: that some fetuses will respond to maternal weight gain and accordingly increase in weight while others will not respond. We hypothesized that heterogeneity exists between women who deliver large and small newborns; however, the true nature of heterogeneity is unknown. Since genetic polymorphisms , 29 and Our study provides evidence that the correlation between GWG and BW/GA is dependent on neonatal size in Japan. Particularly, for low-birth-weight babies, it is notable that GWG increments do not always contribute to birth weight increases. However, we reproduced the positive association between GWG and birth weight for newborns whose weights were similar to the average neonatal birth weights based on world standards.Additional file 1: Table S1. Distribution of child sex between the first and second deliveriesAdditional file 2: Table S2. Distribution of small for gestational age, appropriate for gestational age, and large for gestational age between the first and second deliveriesAdditional file 3: Table S3. Characteristics of the study population with unique individualsAdditional file 4: Figure S1. Inter-pregnancy difference in the BW/GA percentileAdditional file 5: Figure S2. Relationship between GWG and BW/GA percentile in the subgroups by pre-pregnancy BMI and BW/GAAdditional file 6: Figure S3. Graded effect of GWG on birth weight shown by quantile regression analysis.


Elevated lipoprotein(a) (Lp(a)), a low‐density lipoprotein‐like particle bound to the polymorphic apolipoprotein(a) (apo(a)), may be causal for cardiovascular disease. However, the metabolism of Lp(a) in humans is poorly understood.FCR) and production rate PR) were studied. Plasma apo(a) concentration was significantly and inversely associated with apo(a) isoform size  and apo(a) FCR , and positively with apo(a) PR . There were no significant associations between the FCRs of apo(a) and low‐density lipoprotein‐apoB‐100. Subjects with smaller apo(a) isoform sizes (≤22 kringle IV repeats) had significantly higher apo(a) PR (P<0.05) and lower apo(a) FCR (P<0.01) than those with larger sizes. Plasma apo(a) concentration was significantly associated with apo(a) PR , but not with FCR  in subjects with smaller apo(a) isoform size. In contrast, both apo(a) PR and FCR were significantly associated with plasma apo(a) concentrations  in subjects with larger isoforms. In multiple regression analysis, apo(a) PR and apo(a) isoform size were significant predictors of plasma apo(a) concentration independent of low‐density lipoprotein‐apoB‐100 FCR and background therapy with atorvastatin and evolocumab.We investigated the kinetics of Lp(a)‐apo(a) and low‐density lipoprotein‐apoB‐100 in 63 normolipidemic men. The fractional catabolic rate  concentration is predominantly determined by the rate of production of Lp(a) particles, irrespective of apo(a) isoform size and background therapy with a statin and a proprotein convertase subtilisin‐kexin type 9 inhibitor. Our findings underscore the importance of therapeutic targeting of the hepatic synthesis and secretion of Lp(a) particles. Lp(a) particle catabolism may only play a modest role in determining Lp(a) concentration in subjects with larger apo(a) isoform size.URL: http://www.clinicaltrials.gov. Unique identifier: NCT02189837. The present study shows that plasma lipoprotein(a) (Lp(a)) concentration is predominantly determined by the rate of production of Lp(a) particles independent of apolipoprotein(a) isoform size and treatment with atorvastatin and evolocumab.Lp(a) particle catabolism only plays a modest role in determining Lp(a) concentration in subjects with larger apolipoprotein(a) isoform size.The present study advances knowledge of the in vivo physiology of Lp(a) particles, suggesting that therapies for lowering plasma Lp(a) concentration should target the hepatic synthesis and secretion of apolipoprotein (a).Classical and Mendelian epidemiological studies demonstrate that elevated plasma concentrations of lipoprotein(a) (Lp(a)) may be related to the development of atherosclerotic cardiovascular disease (ASCVD).Lp(a) is a polymorphic lipoprotein synthesized by the liver and comprises 1 molecule of the glycoprotein apolipoprotein(a) (apo(a)) covalently bound to apoB‐100‐containing low‐density lipoprotein (LDL)‐like particles.While apoB is essential for the binding of LDL particles to the LDL receptor, necessary for cellular uptake and degradation by the liver, the role of the LDL receptor in Lp(a) particle catabolism remains unclear.To gain a better understanding of the association between apo(a) isoform size and Lp(a) kinetics in humans, we performed a kinetic study of Lp(a) particles using stable isotope labeling in a larger study of 63 apparently healthy individuals. The primary objective of the present study was to investigate the extent to which the plasma concentration of Lp(a) is determined by the production and catabolism of Lp(a) particles, as reflected by the kinetics of apo(a), under normal physiological conditions. The secondary objective was to examine the dependence of these associations on apo(a) isoform size and background therapy with atorvastatin and/or evolocumab therapy. To investigate the role of LDL receptor and related receptors in the catabolism of Lp(a) particles, we also examined associations between the catabolism of Lp(a) and the catabolism of other apoB‐100 containing lipoproteins, principally LDL particles also under normal physiological conditions.http://www.clinicaltrials.gov; NCT02189837).www.amgen.com) upon reasonable request. Pre‐intervention (baseline) data were used to examine the association between the plasma concentration, kinetics, and isoform size of apo(a) in available subjects. Postintervention data from the same cohort were also used to verify the association observed in the baseline data and to investigate these associations of apo(a) metabolism against background treatment with atorvastatin and/or evolocumab. Full details of the recruitment of subjects were published previously.2, fasting plasma LDL‐cholesterol of ≥2.5 and <4.9 mmol/L and triglycerides of <1.7 mmol/L, . None of the subjects had familial hypercholesterolemia or secondary hyperlipidemias ; further exclusion criteria were published previously.The present investigation was part of a larger tracer study examining the kinetic effects of atorvastatin and evolocumab on lipoprotein metabolism in healthy normolipidemic men  was administered intravenously within a 2‐minute period into an antecubital vein. Blood samples were taken at baseline and at 5, 10, 20, 30, and 40 minutes and at 1, 1.5, 2, 2.5, 3, 4, 5, 6, 8, and 10 hours after injection of the isotope. Subjects were then given a snack and discharged home. Additional fasting blood samples were collected in the morning on the following 4 days of the same week . All the procedures were repeated after the 8‐week intervention period  as previously described.Full details of methods, including quantification of very‐low density lipoprotein (VLDL)‐B‐100, intermediate‐density lipoprotein (IDL)‐apoB‐100, and LDL‐apoB‐100 and isolation and measurement of isotopic enrichment of apo(a), VLDL‐apoB‐100, IDL‐apoB‐100, and LDL‐apoB‐100 have been published elsewhere.r=0.945 and 0.967, respectively, P<0.001 for both), because they both measure apo(a) without the impact of apo(a) size polymorphism. Variability in the difference between the 2 assays is because of the differences in the analytical principles used and/or use of different assay calibrators.Plasma apo(a) concentration was determined following a standardized sample trypsin digestion procedure and liquid chromatography–mass spectrometry with a specific target peptide, LFLEPTQADIALL.Apo(a) isoform size was determined by a high‐sensitive SDS‐agarose gel electrophoresis followed by immunoblotting  and the apo(a) isoforms are expressed in terms of the respective number of KIV repeats as previously reported.Compartmental analysis, using the SAAM II program , was used to fit a model to the isotopic enrichment data for apo(a), VLDL‐apoB‐100, IDL‐apoB‐100, and LDL‐apoB‐100.t tests, after logarithmic transformation of skewed variables where appropriate. The subjects were also grouped according to apo(a) isoform sizes with reference to a cut‐off of KIV repeats ≤22; subjects with ≤22 KIV repeats  isoform sizes) have been shown to be associated with an increased risk for ASCVD.All analyses were performed using SPSS 21 . Group characteristics were compared by The anthropometric, biochemical, and lipoprotein kinetic characteristics of the 63 white male subjects at baseline  are summarized in Table r=−0.536, P<0.001) and apo(a) FCR , and positively with apo(a) PR   and inversely with apo(a) PR . In stepwise regression including apo(a) PR and apo(a) FCR, apo(a) PR was the best predictor in determining apo(a) concentration  in the 63 subjects. As seen in Table P>0.05 for all). There was also no significant association between the FCR of apo(a) and LDL‐apoB‐100 . Apo(a) PR was not significantly associated with the PR of LDL‐apoB‐100 . There was no significant association between apo(a) FCR and the FCRs of VLDL‐apoB‐100  and IDL‐apoB‐100 .Table P<0.001) and apo(a) isoform size  were significant predictors of the plasma concentration of apo(a) after adjusting for LDL‐apoB‐100 FCR . In multiple regression analysis including apo(a) FCR, apo(a) isoform size and LDL‐apoB‐100 FCR (Table P=0.01) was the only significant predictor of the plasma concentration of apo(a) . The data shown in Tables Table CR Table , apo(a) P<0.001) in subjects with small apo(a) isoforms . Additionally, subjects with smaller apo(a) isoforms had significantly higher apo(a) PR (P<0.05) and reduced apo(a) FCR (P<0.01) compared with those with larger apo(a) isoform sizes. The concentration, FCR, and PR of LDL‐apoB‐100 were not significantly different between the 2 groups . Similar differences were observed when the subjects were grouped according to apo(a) concentration at a geometric mean cut‐off of 22 nmol/L , but not with apo(a) FCR  . In subjects with larger apo(a) isoform sizes, plasma apo(a) concentration was significantly and positively correlated with apo(a) PR , and inversely with apo(a) FCR  , but this association did not reach statistical significance (P=0.07) in these subjects. There were no significant associations of the concentration, FCR, and PR of apo(a) with the kinetics of LDL‐apoB‐100 in subjects with smaller or larger apo(a) isoform sizes (data not shown).Table /L Table . In subjThe full set of results of the effects of atorvastatin and evolocumab, alone and in combination on plasma lipid and lipoprotein concentrations, and on the kinetics of LDL‐apoB‐100 and Lp(a)‐apo(a) kinetics have been previously reported.r=−0.681, P<0.001; Figure r=−0.561, P<0.001; Figure r=0.811, P<0.001; Figure In a pooled analysis using post‐treatment data (n=47), plasma apo(a) concentration was significantly and inversely associated with apo(a) isoform size (P<0.001 for both). Apo(a) isoform size, but not apo(a)‐FCR, was a significant predictor of the plasma apo(a) concentration after adjusting for LDL‐apoB‐100 FCR, type of treatment (Table P<0.01). The results in Tables In multiple regression analyses Table , apo(a) P<0.001 for both), evolocumab , and atorvastatin plus evolocumab treatment groups . Apo(a) isoform size was a negative predictor of plasma apo(a) concentration (Table P<0.05), evolocumab , and atorvastatin plus evolocumab treatment groups ; however, the P value did not reach statistical significance in the evolocumab alone group. The lack of statistical significance in this analysis reflects the small sample size of this treatment group. Accordingly, in post‐treatment data pooled from the atorvastatin (n=17) and evolocumab alone (n=14) groups, apo(a) isoform size was a statistically significant predictor of plasma apo(a) concentration .As seen in Table on Table  after adOur major finding was that in healthy normolipidemic white men, plasma apo(a) concentration, which reflects Lp(a) particle number, was chiefly determined by the rate of production of lipoprotein particles independent of apo(a) isoform size and treatment with atorvastatin and evolocumab. Subjects with smaller apo(a) isoform sizes had significantly elevated apo(a) concentration because of elevated production and reduced catabolism of apo(a). Apo(a) PR was, however, more strongly associated with apo(a) concentration than the apo(a) fractional catabolic rate. We also found no significant association under normal physiological conditions between the FCRs of apo(a) and LDL‐apoB‐100 in normolipidemic white men.Early radioisotopic studies in normal individuals and familial hypercholesterolemia patients have suggested that Lp(a) concentration is largely determined by the PR of Lp(a) with no significant association with FCR.It is generally considered that plasma apo(a) concentration is mainly determined by the production of apo(a), which is in turn determined by apo(a) isoform size.The cellular and molecular mechanisms responsible for Lp(a) catabolism are not fully known. Previous studies in transgenic mice have demonstrated that apo(a) is an important ligand for Lp(a) uptake by the liver.The role of the LDL receptor in Lp(a) clearance remains controversial. Early radioisotopic studies showed that the LDL receptor is not required for normal catabolism of Lp(a).A strength of our study is that we used a well‐validated compartment model to describe the kinetics of apo(a) and LDL‐apoB‐100 in a large sample of normolipidemic men. The selection of healthy, insulin sensitive, normolipidemic subjects ensured that the LDL receptor (or other related receptor) pathway was fully functional and plasma Lp(a) transport physiological. Our on‐treatment observation confirmed and reinforced our baseline findings on apo(a) metabolism. Although strictly exploratory, the importance of the on‐treatment analyses relate to improved knowledge of the kinetic determinants of Lp(a) metabolism in the setting of future add‐on therapies that target the production of Lp(a) particles.We did not measure the kinetics of both apo(a) isoforms in all our subjects partly because the gel separation method we use does not provide enough concentration of the minor apo(a) isoform to be able to precisely measure the corresponding isotopic enrichment; this especially applies to the majority of the subjects who had low total plasma apo(a) concentrations. The estimation of Lp(a) kinetics based on the predominant isoform may be particularly confounded in subjects with widely different isoform sizes . However, we consider that our kinetic data would reasonably reflect the “true” metabolism of Lp(a) in subjects who had a predominant apo(a) isoform that contributed substantially to total plasma apo(a) concentration , or in subjects with 2 apo(a) isoforms of comparable size . We identified 9 of the 63 subjects who did not meet the above criteria  isoform with concentration >70% and to the difference in KIV repeats ≤8 KIV repeats). However, removal of these 9 subjects from statistical analyses did not alter the principal findings of the study, implying that our results were not significantly confounded by reliance of our kinetic analyses on the predominant apo(a) isoform alone. However, a more detailed study is required to examine the kinetics of 2 different apo(a) isoforms within the same individual.r=0.982 and 0.995, respectively). This suggests that the metabolism of both protein components of the Lp(a) particle are tightly coupled across a wide spectrum of different apo(a) isoform sizes. These findings were also observed in interventions that alter or do not alter the plasma concentration of Lp(a).Measurement of Lp(a)‐apoB‐100 kinetics also could provide additional information for more fully addressing the transport of Lp(a) particles. In a substudy of our cohort,Our kinetic study advances knowledge of the in vivo physiology of Lp(a) particles and identifies the rate of apo(a) production as the principal determinant of apo(a) concentration in the circulation of normolipidemic, white males. That PR determines apo(a) concentration against background statin and evolocumab suggests that therapies for effectively lowering plasma Lp(a) concentration could target the hepatic synthesis and secretion of apo(a). Although we only studied subjects under physiological condition, our findings may extend to subjects with elevated Lp(a) concentration. Our findings support use of apo(a) antisense oligonucleotides or silent RNAs for lowering Lp(a) by targeting apo(a) production.Amgen Inc funded this study.Watts has received honoraria for advisory boards and speakers bureau or research grants from Amgen Inc, Sanofi, Regeneron, Kowa, Gemphire, and Genfit. Coll and Wasserman are current employees of Amgen Inc and own Amgen stock/stock options. The remaining authors have no disclosures to report.


Pentalogy of Cantrell (PC) is a malformation characterized by defects in the ventral abdominal wall, lower sternum, diaphragmatic pericardium, anterior diaphragm associated with omphalocele, thoraco-abdominal ectopia cordis, diaphragmatic hernia, and intracardiac abnormalities. PC is stratified as complete or incomplete and we present both the complete and incomplete forms. Pentalogy of Cantrell (PC) is a malformation characterized by defects in the ventral abdominal wall, lower sternum, diaphragmatic pericardium and anterior diaphragm associated with omphalocele, thoraco-abdominal ectopia cordis, diaphragmatic hernia, and intracardiac abnormalities, all of which were present below in Case 1  [Stenotrophomonas and methicillin-sensitive Staphylococcus aureus. She needed escalation of her respiratory support and was hemodynamically very unstable needing to be resuscitated twice for severe decompensation. At 19 days, the parents elected to withdraw critical care support with demise soon afterwards.A 39-week gestation infant was born to a 20-year-old primigravida whose pregnancy was complicated by pre-gestational diabetes mellitus and late prenatal care starting at 31 weeks, when the fetus was diagnosed with PC. Prenatal ultrasound showed a large omphalocele with herniation of liver, anterior diaphragmatic hernia, absence of lower sternum, ectopia cordis, and a ventricular septal defect (VSD). Maternal laboratory tests were all normal and amniocentesis revealed 46 XX on karyotype. She was on prenatal vitamins, insulin, and metformin, which was discontinued at the time of conception. The mother had no history of teratogen exposure, smoking, alcohol or illicit drugs. Apart from a maternal aunt with Holt-Oram syndrome there was no other relevant family history. There was no history of consanguinity and the father was 22 years old. The infant was born via vaginal delivery with APGAR of five and eight at one and five minutes and required mechanical ventilation for respiratory distress. The infant’s birth weight was 2790 g (25th percentile), length was 46 cm (25th percentile), head circumference was 33.5 cm (50th to 75th percentile). Examination revealed a term infant with a small abdomen and thorax with the internal organs herniated into a large omphalocele with bowel and liver visible through the sac, a pulsatile ectopia cordis with visible pericardium and heart outside the thorax. Chest radiographs showed absent middle and lower sternum, and bowel in the anterior part of the chest . EchocarStaphylococcus aureus, Stenotrophomonas (Xanthomonas) maltophilia, and Enterococcus fecalis. An acute organizing pericarditis and serous fluid in both pleural cavities were identified. The heart was grossly elongated. There were large atrial and ventricular septal defects, and a large patent ductus arteriosus was identified. The liver showed centrilobular congestion along with extra-medullary hematopoiesis and multiple abscesses. The findings of PC after Gore-Tex closure of sternal and abdominal wall defect, ectopia cordis and omphalocele along with absence of distal sternum, anterior diaphragm, and diaphragmatic pericardium were confirmed. Autopsy revealed that the organs of the thorax and abdomen were not in their normal anatomic relationships with the heart protruding beneath the rib cage and sternum, with the liver and intestines protruding from the abdominal cavity, but the large abdominal and thoracic wall defect were covered by surgical Gore-Tex. The underlying liver and abdominal organs were severely adhered with abscesses below and above the liver with culture confirming the growth of A male infant born at 38 weeks was transferred soon after birth for multiple anomalies. His mother, a 22-year-old, had prenatal care and was not aware of any abnormalities prior to delivery. There was no relevant family history and no exposure to teratogens or a history of consanguinity. The weight at birth was 2400 g and anomalies were noted necessitating the transfer. Physical examination revealed ectopia cordis and umbilical hernia with no other external anomalies or dysmorphism . The cheProcedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the Helsinki Declaration. The authors retain an informed consent from parents for the autopsy performance, and for publication and the study was approved by the institutional ethics research committee.Pentalogy of Cantrell (PC) is an extremely rare association of congenital anomalies originally described in 1958 ,3,4.It is more commonly found in males and has multifactorial inheritance. The embryologic basis is thought to be a defective formation, differentiation and migration of mesoderm in the early embryonic period. There is a failure in the folding mechanism of the lateral body folds and cranio-caudal folds that converts the flat trilaminar germ disc into a tubular structure starting at about 5 weeks gestation and which normally should converge and contract at the umbilical ring, thereby closing the ventral abdominal wall . This coA wide range of intracardiac anomalies have been associated with PC including septal defects, tetralogy/pentalogy of Fallot, total anomalous pulmonary venous drainage, and left ventricular diverticulum ,9,10,11.A variety of mutations including deletions or duplications of the PORCN, thoraco-abdominal syndrome (TAS), ALDH1A2, and teneurin-4 (TENM4) genes have been identified ,13,14,15PC is rare association of anomalies and can be challenging to manage. Prenatal diagnosis and careful assessment is essential for appropriate postnatal management ,21. A mu


Numerous studies have examined the association between air pollution and preterm birth (< 37 weeks gestation) but findings have been inconsistent. These associations may be more difficult to detect than associations with other adverse birth outcomes because of the different duration of exposure in preterm vs. term births, and the existence of seasonal cycles in incidence of preterm birth.We analyzed data pertaining to 1,001,700 singleton births occurring between 1999 and 2008 in 24 Canadian cities where daily air pollution data were available from government monitoring sites. In the first stage, data were analyzed in each city employing Cox proportional hazards models using gestational age in days as the time scale, obtaining city-specific hazard ratios (HRs) with their 95% confidence intervals (CIs) expressed per interquartile range (IQR) of each air pollutant. Effects were examined using distributed lag functions for lags of 0–6 days prior to delivery, as well as cumulative lags from two to six days. We accounted for the potential nonlinear effect of daily mean ambient temperature using a cubic B-spline with three internal knots. In the second stage, we pooled the estimated city-specific hazard ratios using a random effects model.3, 13.3 ppb) 0–3 days prior to delivery was associated with a hazard ratio of 1.036  for preterm birth, adjusting for infant sex, maternal age, marital status and country of birth, neighbourhood socioeconomic status (SES) and visible minority, temperature, year and season of birth, and a natural spline function of day of year. There was some evidence of effect modification by gestational age and season. Associations with carbon monoxide, nitrogen dioxide, particulate matter, and sulphur dioxide were inconsistent.Pooled estimates across 24 cities indicated that an IQR increase in ozone  contains supplementary material, which is available to authorized users. Numer in 2010 , while i in 2010 , 13. Mos in 2010 , while a in 2010 –20, case in 2010  or time  in 2010 –26. It h in 2010 , 27, 28.2 and 1.27 km2 respectively.We employed data from the Canadian births database. Live birth events are reported to Statistics Canada by the provincial and territorial Vital Statistics Registries in Canada. For this study, singleton live births between 1999 and 2008 in 24 cities with daily air pollution data were eligible. Data include more than one birth to the same mother, but these could not be identified due to data limitations. Preterm births were those occurring at less than 37 weeks gestation, which were further categorized as 20–27, 28–31, 32–33 and 34–36 weeks gestation . Informa2.5) as well as carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2). Daily temperature data were obtained from Environment and Climate Change Canada’s meteorology data archive. Where data were available from multiple monitors, they were averaged.Daily air pollution data were obtained from the National Air Pollution Surveillance (NAPS) monitoring network for particulate matter of median aerodynamic diameter less than 2.5 μm , Edmonton (north) and Vancouver . Three degrees of freedom in the natural spline of both O3 and temperature exhibited the lowest AIC for all three cities. We therefore employed this lag structure specification in all 24 cities. Potential non-linearity in associations with air pollution was assessed by specifying air pollution as a natural spline with 3 degrees of freedom. We accounted for the potential nonlinear effect of daily mean ambient temperature using a cubic B-spline with 3 internal knots, placement of which was evaluated based on model AIC and guided by recent literature [p-values< 0.05 were considered statistically significant. Analyses were performed with R version 3.4, using dlnm package, version 2.3.2 and metafor, version 2.0.Statistical analysis was conducted in two stages. In the first stage, data were analyzed in each city employing Cox proportional hazards models using gestational age in days as the time scale, obtaining city-specific hazard ratios (HRs) with their 95% confidence intervals (CIs) expressed per interquartile range (IQR) of each air pollutant. We tested the proportional hazards assumption using the cox.zph function in R, which evaluates the significance of the interaction between the scaled Schoenfeld residuals for the air pollution term(s) and time, and found no evidence of violation of the proportional hazards assumption. Effects of air pollution were examined using distributed lag functions , 35 for terature . We compDuring the study period there were 1,248,240 singleton births in the 24 cities. Frequency and prevalence of preterm and term birth by maternal and infant characteristics, city, season and year are shown in Table 2.5 concentrations were highest in Montreal, Hamilton and Windsor in relation to traffic and industrial activity, while maxima were highest in Kamloops and Kelowna due to wildfire smoke. Mean NO2 concentrations, an indicator of traffic pollution, were greatest in Vancouver, Calgary and Toronto, while mean and maximum ozone concentrations were generally highest in the southwestern Ontario cities of Brampton, Hamilton, St. Catharines and Kitchener, consistent with the most common location of summer regional smog events. Mean SO2 concentrations were highest in Saint John, Montreal, Hamilton and Windsor, reflecting local industrial activity, and CO concentrations were uniformly low. Mean temperatures were generally mildest and exhibited the narrowest ranges in the coastal British Columbia cities of Richmond, Vancouver, Victoria and Nanaimo.The combined population of the 24 cities was 11,522,776 in 2006. A descriptive summary of air pollution and temperature data is shown in Table 3 by lag day from distributed lag models are shown in Fig. 2, Cochrane’s Q and p-values of Q are shown in Table 3). As a sensitivity analysis, we specified O3 as a natural spline function with 3 degrees of freedom in four cities of varying sizes and climates  and found that in all cases models employing a linear O3 term had a lower AIC, indicating better fit.Pooled estimates of associations with O3 with preterm birth at lags 0 and 1 days in the 1st tertile, lag 0 days in the 2nd tertile and lag 6 days in the 3rd tertile of neighbourhood percent low income , Giorgis-Allemand et al. found no association of preterm birth with NO2, nitrogen oxides (NOx), PM2.5 and PM10 exposures over durations ranging from one week to the entire pregnancy [10 and NO2 in the week prior to delivery were positively associated with preterm birth in Barcelona and negatively associated with preterm birth in Rome, while ozone was positively associated with preterm birth in both cities [3 were comparable in magnitude to what we observed: 1.010  per 9.2 ppb in Barcelona and 1.025  per 15.3 ppb in Rome [10, O3 and NO2 lagged 0–2 days were not associated with preterm birth in the warm or cold season; only PM10 lagged 12–22 days in the warm season was significantly associated with preterm birth [10, NO2 and O3, with the peak magnitude of effect at 25 weeks , 26 weeks  and 28 weeks  gestation respectively [2.5 on the day of delivery was associated with preterm birth only among women assigned to the highest quartile of PM2.5 glutathione-related oxidative potential based on approximately 200,000 births among 31 cities in the province of Ontario, Canada [2.5 or NO2 and preterm birth in a discrete time survival analysis of 258,294 births in New York City [10 2 days and 5 days before birth (relative risk (RR) = 1.10; 95% CI, 1.00–1.21 per 50 μg/m3 and RR = 1.07; 95% CI, 0.98–1.18 per 50 μg/m3 respectively) [3 were not reported. In another time series analysis of 476,489 births in Atlanta, Darrow et al. observed mostly null associations with air pollution (including O3), but reported that preterm birth was associated with PM2.5 sulfate and PM2.5 water-soluble metal concentrations in the week preceding delivery [2.5 lagged 0–2 weeks before birth was associated with an increased risk of preterm birth in an analysis of nearly 1.8 million births in Ohio, Pennsylvania, and New Jersey [3 was included as a covariate but associations of preterm birth with O3 were not reported. A time series study in Ahvaz, Iran found no association between O3 in the two weeks prior to birth and preterm birth, although significant associations with CO, NO2 and PM10 were observed [3, PM10 or meteorological variables with preterm birth in a time series analysis in London examining exposures in the week prior to birth [3 in the twelfth week of gestation and preterm birth in a time-series analysis in Madrid [3 in weeks 1–5 and PM2.5 in weeks 4–22 were associated with increased risk of preterm birth in a study in eastern Texas [2.5 based on the meta-analysis by Sun et al. [Our analysis is one of a limited number which have examined these short term associations employing Cox proportional hazards models to account for the different exposure durations of preterm vs. term births (in contrast to studies based on exposure during the entire pregnancy or third trimester). Oregnancy . In an ah cities . The haz in Rome . In cont in Rome . An earlrm birth . In a stork City . Sagiv ectively)  . Associw Jersey . O3 was to birth . Arroyo n et al.  or Schif2.5 could also trigger preterm birth through cardiovascular mechanisms or effects on endothelial function [Mechanisms through which exposures in the days prior to delivery might trigger preterm birth are unknown, but may include non-specific processes such as inflammation or oxidative stress, which are known to be associated with both preterm birth –41 and afunction .3 concentrations would be expected to be relatively homogeneous over larger areas compared to pollutants such as NO2. Of four other studies with the same design, two with the same limitations with respect to relatively sparse fixed site monitoring data found consistent associations with O3 and inconsistent associations with NO2 and PM10 [10, NO2 and O3 [2, NOx, PM2.5 and PM10 [2 and PM2.5 [Strengths of our study include the large sample size distributed over multiple cities and utilization of Cox models which account for the differing length of exposure in preterm and term births, and distributed lag models which parsimoniously evaluate effects over multiple lags. We also assessed the shape of the exposure response relationship, examined effect modification by infant, maternal and other factors, and considered the effects of other pollutants. Limitations of our study include the lack of data on maternal behavioural risk factors and possible exposure measurement error owing to the limited number of monitors within each city. Since our analysis deals by design with short term temporal variability in air pollution exposure, observed associations are unlikely to be confounded by short-term time invariant risk factors such as smoking. In the only other study employing the same design which included data on maternal smoking, Giorgis-Allemand et al. found that results were not sensitive to inclusion of smoking and other individual characteristics as covariates . Exposurand PM10  and cons2 and O3 , while tand PM10  and NO2 nd PM2.5 , potentind PM2.5 , 24. Datnd PM2.5 . Differend PM2.5 .3 in the week prior to delivery and preterm birth, based on an analysis of approximately 1 million births over a ten year period. Given the mixed findings in other studies of this kind, additional studies are needed to determine whether the weight of evidence supports the existence of a causal association between preterm birth and air pollution exposure in the days preceding delivery.In this study, one of a small number employing time to event analysis, we observed significant associations between OAdditional file 1:Figure S1. Hazard Ratio by City per 13.3 ppb O3 lag 0 days. Figure S2. Pooled Hazard Ratio by Lag per 0.36 ppm CO. Figure S3. Pooled Hazard Ratio by Lag per 10.3 ppb NO2.Figure S4. Pooled Hazard Ratio by Lag per 7.4 μg/m3 PM2.5. Figure S5. Pooled Hazard Ratio by Lag per 2.9 ppb SO2. (PDF 119 kb)


The estimated association of maternal influenza vaccination and birth outcomes may be sensitive to methods used to define preterm birth or small‐for‐gestational age (SGA).In a cohort of pregnant women in Lao People's Democratic Republic, we estimated gestational age from: (a) date of last menstrual period (LMP), (b) any prenatal ultrasound, (c) first trimester ultrasound, (d) Ballard Score at delivery, and (e) an algorithm combining LMP and ultrasound. Infants were classified as SGA at birth using a Canadian, global, and equation‐based growth reference. We estimated the association of maternal influenza vaccination and birth outcomes, by influenza activity, using multivariable log‐binomial regression and Cox proportional hazards regression with vaccination as a time‐varying exposure.The frequency of preterm birth in the cohort varied by method to estimate gestational age, from 5% using Ballard Score to 15% using any ultrasound. Using LMP, any ultrasound, or the algorithm, we found statistically significant reductions in preterm birth among vaccinated women during periods of high influenza activity and statistically significant increases in SGA, using a Canadian growth reference. We did not find statistically significant associations with SGA when using global or equation‐based growth references.The association of maternal influenza vaccination and birth outcomes was most affected by the choice of a growth reference used to define SGA at birth. The association with pre‐term birth was present and consistent across multiple statistical approaches. Future studies of birth outcomes, specifically SGA, should carefully consider the potential for bias introduced by measurement choice. From randomized clinical trials, influenza vaccination reduces the incidence of influenza among pregnant women and their infants.Gestational age is estimated in several ways: reported date of last menstrual period (LMP), measurements of fetal size by prenatal ultrasound, or assessment of developmental features of the newborn shortly after birth. Discussions on the best methods for determining gestational age are not new, and many papers describe the differences and biases of various methods and algorithms.Furthermore, several recent discussions have suggested that uncontrolled biases  in observational studies of influenza vaccination during pregnancy may account for some or all of the observed associations with birth outcomes.2We re‐analyzed a retrospective cohort of pregnant women in Lao People's Democratic Republic (Lao PDR) aimed at estimating the association between influenza vaccination and birth outcomes; methods were described previously.We estimated gestational age using five methods: (a) date of last menstrual period (LMP) as recalled by women at her first ANC visit, (b) first ultrasound conducted at any time during pregnancy, (c) first ultrasound conducted before 14 weeks of gestation, (d) Ballard Score from examination of neuromuscular and physical characteristics of the infant at birth,We considered babies’ SGA when the infant's birth weight was in the lowest 10th percentile of a reference. Three reference growth charts were used: (a) a Canadian reference,We examined the association between influenza vaccination during pregnancy and preterm birth and SGA using log‐binomial models adjusted for mother's age, parity, education, residential province, ethnic group, household income, number of antenatal care visits, number of people residing in the household, distance of the home to the hospital, and level of influenza activity at time of delivery. For the model of preterm birth, we further restricted vaccine exposure to women who were vaccinated at least 2 weeks prior to term . We considered a woman unvaccinated for the analysis of preterm birth if she was vaccinated after 35 weeks’ gestation.To examine uncontrolled bias from time‐varying exposure to influenza vaccination, we constructed adjusted Cox proportional hazards models considering vaccination as a time‐varying covariate. We included the same covariates in the Cox regression as for the log‐binomial regression. For the Cox regression analysis, we truncated observation time at 37 weeks’ gestation.We estimated the association of maternal influenza vaccination with birth outcomes for the full cohort overall and then stratified by whether the delivery date  or pregnancy (for Cox models) was within a period of low or high influenza activity. We used sentinel surveillance data for influenza‐like illness and influenza testing to define periods of low influenza activity (<15% of samples were influenza‐positive by reverse transcriptase polymerase chain reaction [RT‐PCR]) and high influenza activity (≥15% of samples were influenza‐positive by RT‐PCR).® version 9.4 (SAS Institute) and R software (version 3.1.1).We conducted analyses using SAS3We have published a detailed description of the cohort elsewhere.P‐value < .001), had ≥4 ANC visits , high household income , and received influenza vaccination . There were similar differences between women who could and could not recall their LMP and for women who had an early ultrasound , the association was statistically significant. During periods of low influenza activity, none of the associations were statistically significant. When we restricted the cohort to women who had LMP, ultrasound, and Ballard Score available, the results were consistent with the findings from the full cohort Table .The frequency of infants born SGA varied considerably based on the growth reference used. Estimating gestational age using LMP, the Canadian reference resulted in the greatest frequencies of SGA 18%‐24%), followed by Intergrowth‐21st (10%‐15% SGA) and the equation‐based reference , as well as the statistical models applied. We found that the frequency of preterm birth varied, yet the measured association of maternal influenza vaccination and preterm birth was unaffected, by whether the date of last menstrual period (LMP) or ultrasound at any point in pregnancy was used to estimate gestational age. When classifying infants as SGA, the choice of the growth reference affected not only the frequency of SGA in the cohort but also the magnitude and statistical significance of the association with maternal influenza vaccination.Compared with fetal measurements by ultrasound, gestational age based on LMP tended to estimate a higher gestational age in our cohort and therefore a lower frequency of preterm birth. Others have also described this tendency and reflect that the timing of ovulation and menstruation is variable.Ultrasounds conducted early during pregnancy are the most accurate estimate of gestational age and are recommended for clinical practice.We found, along with others, that Ballard Score was an insensitive measure for preterm birth, as it classified half as many preterm births as LMP and ultrasound.Because not all women in our cohort had both a recorded LMP and an ultrasound, it was tempting to use whatever a woman had available to estimate gestational age in order to increase sample size and reduce possible selection bias. However, if vaccination status is associated with having an ultrasound or recalling LMP, as in our cohort, then the accuracy of the gestational age measurement would be associated with exposure, thereby introducing bias through classic differential misclassification. Because of this potential for bias, it is prudent to use a single measure for gestational age rather than a mixed approach for observational studies. However, we also recognize the potential for selection bias if analysis is confined to only those women who have the measured gestational age and remind the research community to be careful in designing cohort studies and use data sources with complete information.When we looked at the association with SGA, we saw slight differences in the results by gestational age measure; however, the growth reference used had a much greater effect on our estimate of the association. For initial publication, we compared birth weights to a Canadian growth reference and found that 24% of infants in the cohort were born SGA, basing gestational age on LMP.Others have commented on the wide variety of growth references available and how variable the frequency of SGA infants can be.We also explored whether the choice of analytic approach could be another source of variability, as has been suggested by others.In summary, through re‐analysis of a cohort of pregnant women, we found that the association of influenza vaccination during pregnancy with preterm birth and SGA was not sensitive to whether LMP or ultrasound was used to estimate gestational age, as long as the same method was used for all members of the cohort. The association with SGA was sensitive to the growth reference, and employing a global growth standard in future analyses would allow for cross‐country comparisons and may be more appropriate for countries that do not have an existing local growth reference. Furthermore, alternative analytic strategies did not change the associations with either pre‐term birth or SGA. Future analyses should also use sound statistical models to investigate the association of birth outcomes with maternal influenza vaccination. Although discussion of appropriate measures of gestational age and birth outcome is not new, during this formative time in the discussion of maternal influenza vaccination and its possible beneficial impact on birth outcomes it is important to maintain a critical eye on the methodological issues and potential biases in conducting and analyzing observational cohorts. Click here for additional data file.


Accelerated growth in early childhood is an established risk factor for later obesity and cardiometabolic disease, but the relative importance of fat mass (FM) and fat-free mass (FFM) accretion is not well understood. We aimed to study how FM and FFM at birth and their accretion during infancy were associated with body composition and cardiometabolic risk markers at 5 years.p < 0.001) and 367 g  greater FM at 5 years, respectively. Higher FM at birth and FM accretion from 0 to 3 months were associated with higher FFM and cholesterol concentrations at 5 years. Associations for cholesterol were strongest for low-density lipoprotein (LDL)–cholesterol, and remained significant after adjusting for current FM. A 100-g higher FM at birth and 100-g/month higher FM accretion from 0 to 3 months were associated with 0.16 mmol/l  and 0.06 mmol/l  higher LDL-cholesterol at 5 years, respectively. Higher FFM at birth and FFM accretion in infancy were associated with higher FM, FFM, waist circumference, and height at 5 years. For instance, 100-g/month higher FFM accretion in the periods 0–3 and 3–6 months was associated with 1,002 g  and 624 g  greater FFM at 5 years, respectively. We found no associations of FM and FFM growth with any of the other studied cardiometabolic markers including glucose, HbA1c, insulin, C-peptide, HOMA-IR, triglycerides, and blood pressure. Non-attendance at the 5-year follow-up visit was the main limitation of this study, which may have introduced selection bias and limited the power of the regression analyses.Healthy children born at term were enrolled in the Infant Anthropometry and Body Composition (iABC) birth cohort between December 2008 and October 2012 at Jimma University Specialized Hospital in the city of Jimma, Ethiopia. FM and FFM were assessed using air displacement plethysmography a median of 6 times between birth and 6 months of age. In 507 children, we estimated individual FM and FFM at birth and their accretion over 0–3 and 3–6 months of age using linear-spline mixed-effects modelling. We analysed associations of FM and FFM at birth and their accretion in infancy with height, waist circumference, FM, FFM, and cardiometabolic risk markers at 5 years using multiple linear regression analysis. A total of 340 children were studied at the 5-year follow-up . Higher FM accretion in infancy was associated with higher FM and waist circumference at 5 years. For instance, 100-g/month higher FM accretion in the periods 0–3 and 3–6 months was associated with 339 g (95% CI: 243–435 g, FM accretion in early life was positively associated with markers of adiposity and lipid metabolism, but not with blood pressure and cardiometabolic markers related to glucose homeostasis. FFM accretion was primarily related to linear growth and FFM at 5 years. Rasmus Wibaek and colleagues reveal associations between fat mass and fat free mass in the first 6 months of life and cardiometabolic markers and growth later in childhood. Suboptimal growth in fetal life and early childhood are important risk factors for later obesity, diabetes, and heart disease.Progression towards disease may start early in life, demonstrated by studies linking early growth with changes in fat mass, lean mass, and cardiometabolic risk markers in children as young as 4 years of age.In populations from low-income countries, currently experiencing a rapidly growing chronic disease burden, knowledge about these relationships is lacking.We followed a cohort of healthy preschool children from the city of Jimma, Ethiopia, for 5 years starting from birth.Using advanced statistical growth modelling and accurate assessment of body composition, we estimated individual fat and lean mass accretion in the periods 0–3 and 3–6 months of age in 507 children.We conducted regression analyses to test for associations of fat and lean mass growth in early infancy with body composition and cardiometabolic risk markers measured at 5 years of age. These analyses showed that children who gained more fat mass in early infancy had greater fat mass and waist circumference and higher concentrations of risk markers related to cholesterol metabolism at 5 years. Children who gained more lean mass in early infancy were taller and had more lean mass at 5 years.Fat and lean mass growth in early infancy were not associated with blood pressure and cardiometabolic markers related to glucose homeostasis at 5 years.This study showed that early infancy is an important developmental window for a child’s later body composition.This contemporary cohort of preschool children from a sub-Saharan African urban setting had markedly higher fat mass than similar-aged children from the UK and India, which may be a result of excess fat mass accretion during the first 6 months of life.Variable patterns of fat and lean mass growth did not appear to be consistently associated with markers of cardiometabolic risk, other than markers of cholesterol metabolism at 5 years. Non-communicable diseases like type-2-diabetes and cardiovascular diseases are among the leading causes of death and disability worldwide ,2. ThereSimple anthropometry, such as weight or length, is commonly used to study associations of early growth with later health outcomes. However, as the 2 main components of the growing child, fat mass (FM) and fat-free mass (FFM), have different metabolic properties , they moIn the Infant Anthropometry and Body Composition (iABC) cohort of more than 500 Ethiopian children with repeated measurements of FM and FFM from birth to 6 months of age, we have previously reported different proportions of FM and FFM among newborns with similar body weights , and ideThis study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline . Data foThe iABC study is a prospective birth cohort study of the determinants and consequences of growth variability in early childhood ,15. The 3, while age- and sex-specific densities of FFM were used ) or median (interquartile range) for continuous variables and count (percentage) for categorical variables. Differences between groups were tested by 1-way ANOVA F-test for continuous variables and Pearson’s chi-squared test of independence or Fisher’s exact test of independence for categorical variables. Continuous variables with a right-skewed distribution were log-transformed  prior to regression analyses. Estimates from these models were back-transformed and presented as percentwise change. A significance level of 5% was used. All analyses were carried out in R version 3.4.1 .Linear-spline mixed-effects (LSME) modelling was used to approximate the non-linear relationship of age with FM and FFM by deriving a number of child-specific and average summary measures of growth over discrete time intervals from 0 to 6 months of age ,29. LSMEAssociations of estimated FM and FFM at birth and their growth velocity over the periods 0–3 months and 3–6 months with cardiometabolic markers and BC at 5 years were analysed in separate multiple regression models . Model 1 was adjusted for child’s sex, birth order, gestational age at birth, child’s exact age at the 5-year visit, maternal age at delivery, maternal postpartum height, maternal educational status, and IWI. Model 2 was additionally adjusted for FM at the 5-year visit. In the regression analyses of FM and waist circumference at 5 years as outcome, model 2 was adjusted for FFM at the 5-year visit instead of FM. We used a complete case approach, limiting the analyses to children with complete data on the included covariates. Covariates included in model 1 and 2 were identified a priori based on adjustment practices in similar studies and reported associations of the covariates with growth in early life and the outcomes studied –38. To oA total of 644 mother–child pairs attended the baseline examination at birth .n = 10) and mother–child pairs not residing in the city of Jimma who had only been to the hospital to give birth (n = 63) were excluded. Of the 571 children included in the follow-up study, 507 were eligible to be included in the growth modelling. Of these, 46, 56, 122, and 283 children had their FM and FFM assessed 3, 4, 5, and 6 times, respectively, during the first 6 months of follow-up (median number of measurements: 6). Thus, the LSME growth modelling included a total of 2,670 assessments of FM and FFM. The subsequent regression analyses included 340 children as 166 were not able to attend the 5-year visit or were lost to follow-up and 1 child did not have complete data on all covariates. Background characteristics, BC measures, and cardiometabolic markers of the mother–child pairs included in the regression analyses are presented in Tables Preterm births n = 0 and motThe vast majority of mothers were either exclusively or predominantly breastfeeding at 4.5 to 6 months postpartum, and 48% had completed primary school or a higher level of education. The average wealth status of the families was slightly lower than the national urban average, but considerably higher than the national rural average  ,41. At bThe LSME models estimated FM and FFM at birth and FM and FFM growth velocity over the periods 0–3 and 3–6 months. Examples of estimated growth velocity curves for 3 selected children are presented in During the first 6 months, the children had a much larger variation in FM growth velocity compared to FFM growth velocity  Figs. ThCorrelations between FM at birth and FM growth velocities as well as FFM at birth and FFM growth velocities are shown in pairs plots in Associations of estimated FM and FFM at birth and FM and FFM growth velocity in the periods 0–3 and 3–6 months with BC and cardiometabolic markers at 5 years are presented in p < 0.001) and 367 g  greater FM at 5 years, respectively (p < 0.001) greater FFM, and in the periods 0–3 and 3–6 months, 100-g/month higher FFM accretion was associated with 1,002 g  and 624 g  greater FFM at 5 years, respectively. Additionally, FFM at birth and accretion in the periods 0–3 and 3–6 months of age as well as FM accretion in the period 0–3 months were positively associated with height at 5 years. For instance, 100-g/month higher FFM accretion in the period 0–3 months was associated with 1.8 cm  greater height at 5 years, and 100-g/month higher FM accretion in the same period with 0.4 cm  greater height. FM velocity in the periods 0–3 and 3–6 months, and FFM at birth and velocity from 0–3 months, were positively associated with waist circumference at 5 years.Higher FFM, but not FM, at birth and higher FM and FFM accretion in the periods 0–3 and 3–6 months were associated with higher FM at 5 years. For instance, independent of current FFM and other covariates, 100-g/month higher FM accretion in the periods 0–3 and 3–6 months was associated with 339 g  greater LDL-cholesterol at 5 years, and 100-g/month greater FM accretion in the period 0–3 months with 0.06 mmol/l  greater LDL-cholesterol  at 5 years, and that higher FFM at birth and accretion in infancy were associated with FFM, FM, height, and waist circumference at 5 years. In addition, we found that higher FM accretion during fetal life, indexed by FM at birth, and higher FM accretion from 0 to 3 months predicted higher cholesterol concentrations at 5 years, with the strongest association for LDL-cholesterol. However, none of the other studied cardiometabolic markers were associated with FM or FFM at birth or their accretion during infancy.In high-income populations, fetal and postnatal growth have consistently been related to indices of obesity and markers of cardiometabolic disease risk ,6,45. ThThe mechanisms relating higher FFM accretion in infancy to higher FFM in childhood and higher FM accretion in infancy to higher FM in childhood remain elusive. Besides cellular and extracellular water, FFM consists primarily of protein followed by osseous minerals . In a laRegarding the cardiometabolic risk markers, we found that FM at birth and accretion from 0 to 3 months were positively associated with cholesterol concentrations at 5 years, with the largest estimates for LDL-cholesterol. Interestingly, the associations remained after controlling for current FM. While FM is important for brain development and resilience to infections in early life , excessiz-scores, that require a complete case analysis, we were able to reduce potential bias from selective dropout and increase the statistical power of the study considerably. This novel approach to growth modelling has been implemented successfully in previous studies [n = 571), 89% were included in the modelling of FM and FFM accretion in infancy, and 60% of the mother–child pairs were able to participate in the follow-up visit at 5 years, where data on the cardiometabolic and BC outcomes were collected.A major strength of this study was the detailed assessment of FM and FFM from birth to 6 months and again at 60 months, using accurate and validated equipment for estimation of BC. A high density of BC data during the first 6 months allowed us to robustly estimate the non-linear relationship of FM and FFM as a function of age in critical windows of development in early childhood using LSME modelling. This modelling approach provided easily interpretable growth coefficients from a complex data structure of repeated measures, and at the same time accounted for the dependencies of the child-specific BC measurements. Our growth models also allowed children to be included in the modelling even if they were not measured at the exact same time or had not attended all 6 follow-up visits during infancy. Thus, as opposed to more conventional growth modelling approaches, such as conditional growth modelling or tracing average  studies ,67,68. FAmong the limitations, we cannot rule out that the loss of mother–child pairs examined at birth but not able to attend the visit at 5 years could have introduced selection bias, although mother–child pairs lost to follow-up were generally similar to those included in the 5-year analyses . Also, aOur findings suggest that development of adiposity and changes in cardiometabolic risk markers related to lipid metabolism may occur already in early life and are likely to be driven by accelerated fetal and early postnatal FM accretion. In contrast, FFM accretion in early life is likely to improve cardiometabolic status through linear growth and continued accretion of lean tissue, even though we were not yet able to demonstrate any direct benefits in the cardiometabolic markers. Thus, based on existing evidence from the iABC cohort and the present findings, we stress the importance of promoting lean tissue accretion and linear growth without excess accumulation of adipose tissue in countries undergoing rapid economic and nutritional transitions, as children in such contexts are likely to be particularly exposed to an environment conducive to an unhealthy lifestyle. This may be done through public health policies that promote healthy diets and physical activity in preschools and schools as well as limit the exposure to obesogenic environments  within schools and in the community . Lastly,S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 FigAssociations of estimated fat mass and fat-free mass at birth and fat mass and fat-free mass growth velocity in the periods 0–3 and 3–6 months with body composition and cardiometabolic risk markers at 5 years .(PDF)Click here for additional data file.S6 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.S1 Text(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.S3 Text(PDF)Click here for additional data file.