Bacilli, but diminished and converged in later samples, as bifidobacteria started to prevail. The first month proportions of enterococci were associated with maternity hospital locality and supplementation of breastfeeding with formulae, while Enterococcus faecalis proportion reflected the mode of delivery. Group Bacteroides-Prevotella proportion was associated with infant weight and ponderal index at first month. Infant mixed feeding pattern and health issues within the first month revealed the most profound and extended microbial perturbations. Our findings raise concerns over the ability of the early feeding supplementation to emulate and support the gut microbiota in a way similar to the exclusively breastfed infants. Additionally, practicing supplementation beyond the first month also manifested in higher first year weight and weight gain Z-score.Sound and timely microbial gut colonization completes newborn’s healthy metabolic programming and manifests in infant appropriate growth and weight development. Feces, collected at 3, 30, and 90 days after birth from 60 breastfed Slovenian newborns, was submitted to microbial DNA extraction and qPCR quantification of selected gut associated taxa. Multivariate regression analysis was applied to evaluate microbial dynamics with respect to infant demographic, environmental, clinical characteristics and first year growth data. Early microbial variability was marked by the proportion of  Sound and timely microbial stimuli during a nonrandom colonization process were addressed for their importance in the intestinal barrier development, activation and maturation of baby’s immune system, central nervous system development, metabolic system programming, and were finally linked to infant’s growth velocity and later life weight development7. Various adverse life events, however, may interrupt the appointed infant’s growth trajectory. Regardless of an individual’s birth weight status, the accelerated postnatal growth was reported to unfavorably contribute to individual’s metabolic programming by subjecting him to an increased adiposity and to a risk for later life obesity8. Namely, enhanced, unbalanced energy expenditure on growth at the cost of other developmental activities in their critical periods, was associated with later life chronic diseases5. During the postnatal maturation of various organ systems , in the neonate, brief reorganizational periods – windows, were suggested to overlap and interact with the time-specific and even species-specific microbial stimuli10. Alongside of diet- or gene-induced obesity, the microbe-induced obesity, driven by a compositional underrepresentation of the key protective intestinal taxa in the early life, was proposed2.Fetal course of growth, set by genetic and environmental determinants at an early developmental phase, finally, results in the size at birth11. In European infants, differential environmental circumstances were suggested to affect gut microbiota development in compositional shifts following ‘north-south’ geographic gradient12.The susceptibility of the early microbial succession pattern to perinatal clinical and environmental circumstances was suggested to hold a remarkable potential to determine infant’s developmental fate and future well-being13. Multivariate approach was suggested for the integrated investigation of the interactions among the microbial community members, environmental variables and individuals’ clinical aspects14. Moreover, longitudinal studies were reported to introduce the temporal developmental dynamics15.Unique and immensely complex mature gastrointestinal tract (GIT) microbial communities result from the intrinsic microbial interactions and their developmental, host-associated determinants, which are still not satisfactorily understoodOur longitudinal exploratory study aims to investigate the hypothesis that early and time-framed perinatal clinical events and environmental circumstances alter infant microbial gut colonization by affecting the abundances of specific microbiota members, which introduces changes in infant metabolic programming and reflects in later infant growth and development. We aimed to identify the factors that contributed substantially to infant microbiota shifts in Slovenian geographic locality, to relate them to infant growth and development during the first year, and finally, to determine the microbial shifts directly associated with infant growth and development at first year.16.A comprehensive knowledge on the fecal microbial experience in the period of the microbiota acquisition and its relevance to the multiple early life exposures and clinical and developmental outcomes, would not only enable to identify the critical determinants of an ‘aberrantly’ colonized gut, but would also lead to the appropriate tailoring of the consortia in its initial, confined momentum. The clinical benefits of the microbiota targeted interventions during the colonization onset, however, could extend well beyond infancy, reducing the immense future health burdenThe early life determinants for our study group are presented in details in Table Bifidobacterium during the first three months, while relative abundances of staphylococci and enterococci simultaneously decreased  was characterized by an increased relative abundance of genus sed Fig. .Figure 1Bacilli , prevailed 3 days after birth (55%), while in the majority of later samples, Bifidobacterium group was recognized as the most abundant among the microbial groups surveyed.Samples, dominant in the class et al.11, are depicted in Table Bacilli, B- Enterobacteriaceae, C- Bifidobacterium and D- cumulative relative abundance of Bacteroides-Prevotella and Clostridium cluster XIV, IV) in the sample at specific time intersection. During the three months period the population drift towards the microbial profile C is discerned: 8.9% (5/56) of individuals reached profile C at the third day, 50.9% (29/57) by the end of the first month, and finally, 18.5% (10/54) only, by the end of the third month. By the end of the first month, the majority of subjects at third day placed in group A (21), and half of the subjects classified in group D due to highly abundant Bacteroides-Prevotella members (6), were caught in group C, rich in bifidobacteria while most of the subjects based in group B (6) and C (4) still lingered there. Four subjects stayed in group C for the whole trimester, and the additional 21 subjects joined in for the investigated last two months. During the last two months, however, limited number of individuals from the profiles A (1), B (4) and C (5) also progressed toward the profile D.Infants, classified on the basis of fecal microbiota composition into four groups as suggested by Dogra Enterococcus faecalis (explaining the highest variability of the PC1 (0.93)) and genus Staphylococcus (explaining the highest data variation of the PC2 (0.93)). A loading plot is presented in Fig. Enterobacteriaceae, Bacteroides-Prevotella and Bifidobacterium. Both principal components best explained the variability in initial microbial communities in subjects 3 days after birth, while in later sampling, this variability diminished , two components were extracted, together explaining 49.0% (32.8% + 16.2%) of sample variance at all three collection time points. Bacterial groups described in Table hed Fig. .Table 3SBivariate analyses of the associations of microbial abundances with clinical, environmental, anthropometrical variables, and finally, with infant’s growth deviation scores have been performed. Multiple linear regression followed on the constricted set of the associated (p < 0.1) independent variables. The established associations are presented Table .Table 4M−1, range (r): 9.2 × 10−4–7.7 vs. m: 7.8 × 10−2, r: 1.6 × 10−4–2.2 × 101; p = 0.008). Relative abundance of Enterococcus faecalis at first month was higher in infants born via C-section (9) compared to those vaginally delivered (49) . The comparison of absolute and relative abundances of microbial representatives in fecal samples from partially (18) versus exclusively (40) breastfed infants at first month across the three time intersections are presented in Supplementary Fig. After adjustment for potential confounders, maternity hospital  vs. main 44)) reflected in higher relative abundance (%) of fecal enterococci at first month (median (m): 6.0 × 104) reflecStaphylococcus epidermidis  at the end of the first month, distinguished babies consuming probiotic preparation (14) from those, who did not (44). This bacterial group was in starting samples also inversely correlated with maternal age (Spearman’s rho (rs): −0.328; p = 0.016).Lower proportions of Bifidobacterium  and Streptococcus  at one month, and Clostridium cluster XIV  at third month.Subjects experiencing health complications at birth (3) diverged from the majority of healthy individuals (54) by relative abundance of Streptococcus at third month , and finally, seven babies with health issues recorded at third month compared to the healthy subpopulation (50), were associated with higher proportion (%) of Bacteroides-Prevotella  in samples at third month.Health complications reported for five infants at the end of the first month, were in comparison to healthy subjects (52) associated with a shift in Lactobacillus . Higher relative abundance of lactobacilli at the end of the infants’ first month was associated with rural maternal residence (18) compared to those from the urban background (39) . Urban maternal background was associated with higher initial enterococci ratio (%), compared to infants born to mothers from rural settings . Maternal smoking prior pregnancy (7) was associated with higher proportion of streptococci in the offspring samples three days after birth compared to samples from babies born to non-smokers (50)  and lower proportion of Bacteroides-Prevotella in the samples obtained at the end of the first month .Maternal antibiotic treatments after birth characterized six infants at the end of their third month, compared to those, whose mothers were not exposed to antibiotics (51), by lower proportion (%) of Bacteroides-Prevotella group in the samples at 3, 30, and 90 days were associated with ponderal index (PI) at one month . Finally, first month Bacteroides-Prevotella sample content was also inversely associated with infant’s first month weight . The opposite direction of the observed associations with PI and weight was attributed to the negative associations of this bacterial group with height. It should also be noted here that no association with early BMI was found. However, none of the studied microbial groups was associated with growth indicator deviation scores.Relative abundances of (12–3)) was lower (odds ratio (OR) = 0.30, p = 0.032), if maternal pre-pregnancy body mass index (BMI) was higher , and finally, if infant% body fat (BF%) at 1 year age was higher , probability for Clostridium cluster XIV detection in babies at one month of age was higher. Also, likelihood for Lactobacillus gasseri detection in stools of 3 months old babies was higher, if maternal BMI was higher .Additionally, bivariate associations established for the bacterial groups with low sample prevalence (<40%) Table  were inv3i-Z0i; Z12i-Z0i) and BF% at first year. Compromised infants’ health at birth was associated with the WG Z(12–3), while health related issues at 1 month of age were associated with higher weight (g) at 1 year and higher WG Z(12–1) and WG Z(12–3). Subjects on supplemental formula feeding after the first month were positively associated with WG Z(12–1) and weight at 1 year compared to fully breastfed, while negative associations were established for duration of breastfeeding with WG Z(12–1) and WG Z(12–3). Infants whose pregnant mothers experienced health issues compared to healthy mothers had lower PI at third month PP (PI 90), while antibiotic treatment of mothers after birth was related with lower WG Z(1–0) and higher WG Z(3–1) compared to healthy subpopulation. Infants born to prior smokers had higher BMI at birth and were negatively associated with BMI Z score change from birth to 1 month (BMI Z1i - Z0i) and positively associated with WG Z(12–3) compared to non-smokers. Infants born to elder mothers had lower PI 90 and lower BF% at first year.Limited associations found between bacterial proportions and anthropometrics with growth related data led us to investigate the presumptive perinatal growth confounders  with an increased Gram-positive initial bacterial load  was reported to reflect the increased hygiene in the Western countries18. Cross-sectional study by Fallani et al.12 exposed higher fecal proportions of bifidobacteria in six weeks old infants and lower proportions of Bacteroides, enterobacteria and lactobacilli to be typically associated with the North European countries. Term, breastfed Slovenian infants reached approximately 1–2 logs higher average counts of fecal bifidobacteria compared to twenty term, exclusively breastfed infants from Northern Spain19. Our results are in line, though, with the prevailing bifidobacterial counts detected in one month old infants from KOALA Birth Cohort Study, conducted a decade ago in Netherlands20. During the first three months, Bacteroides-Prevotella group was detected only in one third of samples from Slovenian infants, but the average counts were higher 1–2 logs compared to the study by Arboleya et al.19. Average values for the lactobacilli at first month and enterobacteria during the period of three months, in our study, were comparable to Spanish infants.The successional evolution of microbes residing in the gut of neonates, surveyed in our study, generally resembled the colonization stages proposed by Dogra Bifidobacterium and Collinsella, was found associated with typical adiposity at 18 months of age21. In our study, however, direct links between the proportions of surveyed microbes in the samples during the first three months and infant adiposity (measured as BF% at first month and at first year) were not established. But, BF% at the end of the first year was negatively associated with male gender (p < 0.001), maternal age  and gestational age . Even though gestational age was proposed to chiefly dictate the tempo of microbial development in the gut of premature infants22, within our set of gestational periods, no divergences in the proportions of the acquired early microbes were established in multivariate analysis. Nevertheless, higher maternal age was associated (p = 0.046) with lower relative abundance of Staphylococcus epidermidis in infant’s feces 3 days after birth, and apart from lower BF% at first year, also with lower PI 90 . Our observations on an early infant phenotype support the recent study on the socio-economic homogenous group of participants, linking higher maternal age at birth with taller size and the reduced abdominal fat in the respective pre-pubertal children23. The reported associations could originate from various prenatal factors , or variation in colostrum/milk composition26.Within the first six months of life, an earlier acquisition of an anaerobic microbiome, dominated by 18. While reports indicated that meconium samples harbored also many Enterococcus faecalis isolates, some of these early colonizers were even found bacteriocin producers and were suggested as probiotic candidates28. In our analysis, higher relative abundance (p = 0.020) of Enterococcus faecalis one month after birth only, marked samples of C-section infants, though, this difference dissipated in later samples. Except for this variation, other examined ‘developmental taxa’ remained quantitatively unaffected. Similarly, regional maternity hospitals induced shifts towards higher enterococci (p = 0.008), but transiently, only in samples one month after birth.Enterococci colonize vaginally and C-section delivered infants equally earlyet al.29, however, observed C-section to be associated with delayed colonization, lower abundance and diversity of Bacteroidetes phylum in infant intestinal microbiota during the first two years of life. Recent meta-analysis30, founded on the reviewed epidemiological evidences, supported a higher risk of developing childhood obesity for children delivered by caesarean section, even after the adjustment for maternal pre-pregnancy weight as a potential confounder. The bias for the residual confounders  on the obtained results, however, could not have been excluded. In our study, delivery mode did not induce any variations associated with the inspected growth and anthropometric parameters within the infants’ first year of life. Yet, Bacteroides-Prevotella relative abundance in one month old infants was found inversely associated with infant’s first month weight (p = 0.028), formula supplementation (p = 0.004), and prior maternal smoking (p = 0.031), while positive association for this bacterial group was demonstrated with PI at first month (PI 30) in samples from all three time intersections . During the period of the first three months, however, this microbial member was detected in only small percentage of samples, therefore, further investigation on its potential to uphold a protective role in the newborns’ gut is warranted. Riva et al.31 explained that lower levels of Bacteroidetes taxa typically found in the gut microbiota of obese children may stem from the phylum ‘intra-family’ ecological cohesion, involving similar niche responses of microbiota members which positively correlate with one another, and thus, make this phylum a better forecaster of obese phenotype than Firmicutes lineage31. Though, certain Firmicutes populations on the lower taxonomic scale were reported to be associated with childhood obesity.Jakobsson et al.32 examined the detection of the probe signals in the fecal samples of term, vaginally born Norwegian infants, initially not exposed to antibiotics. Among different microbial groups and time points surveyed within the first four months of life, growth outcomes were associated with staphylococci, Escherichia coli and Bacteroides species. Following, it was also reported that the presence of Bacteroides fragilis group in stools of 1 month old infants was likely related to higher BMI later in childhood33. Altogether, based on the reviewed literature data, involvement of staphylococci, higher lactobacilli, lower Bacteroides spp. and bifidobacteria in gut colonization within the 3 months of birth, were hypothesized to be predictive for infant and child overweight34. In our study, after a multiple linear regression, except for the relative abundance of Bacteroides-Prevotella, other bacterial groups were not found directly associated with any of the investigated infant anthropometric measurements nor growth deviation scores during the first year. Though, the most versatile microbial associations with the external parameters, related to the pace of the ‘evolutionary taxa colonization’, in our study, were found with respect to infant early feeding pattern and early health.In search for a time-dependent early gut stimuli associated with the expected growth in infants, White Bacteroides and higher Firmicutes 35. Mixed feeding pattern, in the study by Penders at al.20, did not affect fecal bifidobacteria at first month. Still, report scarcity on the early mixed-feeding practices and the related infants’ clinical outcomes36 make our contribution a valuable extension. Our data on the mixed-feeding practice in one month old infants were associated with lower fecal relative abundance of Bacteroides-Prevotella group (p = 0.004) and higher enterococci (p = 0.036), while delayed associations extended also further to samples from three months old infants having lower relative abundances of bifidobacteria (p = 0.011) and higher relative abundances of Enterobacteriaceae group (p = 0.039), Enterococcus faecalis (p = 0.002), and both investigated clostridia groups (p < 0.01). As suggested35, these differences most likely result from the capability of bifidobacteria and Bacteroides-Prevotella group to utilize human milk oligosaccharides (HMO), while also being capable to metabolically cross-feed other gut members from the Firmicutes lineage. Moreover, the breast milk oligosaccharide array – sialylated HMO, were recently reported to correlate with infant growth outcomes via the microbiota-dependent metabolic route and also promoted phenotypes with an improved usage of dietary components37. However, no associations were found on the microbes in the feces at the age of three months and the relevant feeding data for the corresponding two-month period, which could reflect the importance of timing on the feeding variable to exert its influence on the microbiota. Additionally, the fact that some mothers one month PP reversed the type of baby’s feeding - either they started to nourish their neonate solely on breastmilk (five) or began to supplement their nutrition with formula (nine), may have blurred the differences in the microbial representatives between both investigated groups. Practiced feeding type, however, was not submitted to prior infant growth and anthropometrics as affirmed by lack of the established associations. As reported previously, initial breastfeeding led to faster weight gain in the first few months compared to formula feeding, whereas only later on, breastfed infants demonstrated lower weights38. In accordance, in our study, exclusive breastfeeding pattern only after first month was found associated with lower WG Z(12–1) (p = 0.043) and weight at first year (p = 0.041). In addition, duration of breastfeeding was negatively associated with the WG Z(12–1) and WG Z(12–3) . Bäckhed et al.39 emphasized also the role of nutrition in the first year microbiota maturation and function and exposed the cessation of breastfeeding rather than the dietary integration of solid foods to be associated with microbial configuration shifts. We would like to note here, that the duration of breastfeeding in our population was also associated with infant feeding pattern at third month (p < 0.001). Namely, exclusively breastfed infants were breastfed for a longer period during the first year than partially breastfed peers. Additionally, lower birth weight was associated with higher BMI Z1i-Z0i, BMI Z3i-Z0i, and BMI Z12i-Z0i (p < 0.01), but not with WG Z-scores.Relative abundances of bacterial genera in the three months old infants were reported to be affected by formula type of feeding, resulting in a lower 41. Moreover, it was suggested40 that the far-reaching repercussions of the antibiotics on the fecal microbial composition in the pre-school children could linger for up to two years after refraining from the antibiotic usage. Though, higher relative abundance of Bacteroides-Prevotella group (p = 0.025) established in the ‘unhealthy’ seven babies at three months of age, did not mirror any growth deviations in the first year of life.In our study, infants experiencing health issues at birth and during first month, manifested also higher WG Z-scores (p < 0.05). Even though relative abundances of single microbes were not directly associated with growth parameters, the health variables at the observed time-intersections were found extendedly associated with differential proportions in some microbial groups. The associations were mostly discerned with a lag, at a later time intersections. Similarly, in these subjects the time lag in differential weight gain also manifested later, during the period of the first year. The ‘growth stimulating’ effect associated with the health status at birth and at first month of age could have resulted from the antibiotic treatments, which is in accordance with the recent publications43. Moreover, lactobacilli and bifidobacteria were less frequently found in the breast milk of women on prior antibiotic therapy during pregnancy and lactation44. While in our study, maternal postnatal antibiotic consumption similarly reflected in lower relative abundance of fecal lactobacilli in 3 months aged infants (p = 0.031), also infant WG Z(1–0) and WG Z(3–1) during the lactation period were affected . Smoking cessation was proposed to induce changes in the composition of the intestinal microbiota similar to the differences reported between obese and lean humans and was also proposed to influence the individual’s weight gain45. Even though all mothers but one ceased smoking during pregnancy, the extended associations found in the initial microbial colonizers in their offsprings could have been introduced via the entero-mammary pathway46 or enhanced via the epigenetically modified compounds in breast milk48. More importantly, however, seven infants born to prior-smokers, also demonstrated higher BMI at birth (p = 0.022), lower BMI Z1i-Z0i (p = 0.030) and higher WG Z(12–3) (p = 0.032), which even exceeded + 0.67 cut-off in five infants and thus may predispose them to later obesity risk8. No pre-/during-/post-pregnancy maternal BMI associations were established, however, between priorly smoking and non-smoking mothers.Maternal lifestyle concerning her residential environment and health also reflected in several compositional shifts. We argue these associations with the infant fecal microbiota could have been introduced through differential composition of the maternal colostrum and breast milk. Inclinations for higher median counts of total cultivable lactobacilli in breast milk of women living in the rural areas compared with those from urban areas were also reported previouslyLimited population size, low sampling intensity and the restricted microbial survey may have affected the resolution potential of our research, presenting the colonization progression in the gut of mature Slovenian infants in association with the related confounders. We also realize, early compositional deviations found in some subjects may have originated from the unpredictable events or sudden shifts. We would like to remind, therefore, to keep the results bearing the unproportionate and small groups of individuals, especially those concerning health variables, in perspective.Bacilli, which was thought to be a common characteristic for the premature babies. Bifidobacteria, however, became dominant in the majority of samples later, at first and third month. Also, the variability in microbial proportions was noteworthy in these early, initial samples, especially for the early proportional content of Enterococcus faecalis and Staphylococcus groups. While the perinatal determinants surveyed in our multivariate approach explained only little about the staphylococcal load variability, we propose the maternal pre- and probiotic components in colostrum and breast milk to have been involved in the discerned differences. Moreover, the variability in starting proportions of fecal enterococci was associated with maternal urban residence, while the first month proportions, only, were related to the regional maternity hospitals and partial breastfeeding. Mode of delivery, however, reflected only in the first month Enterococcus faecalis proportions.Fecal microbiota at third day postpartum in term Slovenian breastfed infants prevailed in bacterial groups belonging to the class Bacteroides-Prevotella, which could suggest its protective role in infant development.Infant early phenotypes - higher weight and lower ponderal index at first month, were associated with decreased proportions of group Chiefly, we would like to expose here the early mixed feeding practices and antibiotic treated health issues, for the manifestation of the most diverse and even extended shifts in microbial proportions related to the ‘evolutionary’ important taxa in infant gut. Later, during the first year, these two factors were further associated with higher weight gain deviation scores.https://clinicaltrials.gov/ct2/show/record/NCT01548313?term=MY+MILK&rank=1. The protocol was approved by the National Medical Ethics Committee of the Republic of Slovenia . Written informed consent was obtained from all participating mothers. The recruitment proceeded in the years 2010–2011.The study was conducted in accordance with the Helsinki declaration and was registered at In this observational clinical study, healthy women in the first two trimesters of pregnancy (maximum 24 weeks gestation) were recruited during medical checks at their gynaecologists. We included expecting mothers from three Slovenian regions , who planned to breast-feed their babies for at least 6 weeks PP. Women suffering from acute or chronic infections or with an increased risk of a premature delivery were excluded from the study enrollment. Anthropometric data, demographic, lifestyle and clinical characteristics of mother-infant pairs were collected. Microbial analyses were performed on the DNA extracted from fecal samples from singleton infants.et al.49. Infant’s expected growth was defined using the World Health Organisation’s (WHO) Child Growth Standards (2006)50. Sex standardized BMI-for-age Z-scores  were calculated by lmsGrowth Excel add-in51 based on the captured dataset . The difference between BMI Z-score at the specific time point and at birth (BMI Z-score change) was estimated for each individual and served as an indicator of infant’s growth in the investigated time frame. Namely, according to WHO growth curves, theoretical, that is, anticipated child’s growth is submitted to its birthweight percentile (Z-score). Similarly, sex standardized WG Z-scores for an infant were calculated between the measured weights at the two selected time points. The percentage of newborns, following the expected growth, based on the Z-score change within the proposed cut-off values of −0.67 and + 0.6752, was also calculated.The information on mother-infant demographic, clinical and environmental background was gathered from medical records and through interviews with research personnel at University Medical Centre Ljubljana (UMCL) and the attending physicians. Data on consumption of probiotics were obtained via self-administered questionnaire. Anthropometric data included maternal prepregnancy BMI  and measurements of infant weight, length, and subscapular and triceps skinfolds. Data were acquired 0 to 3 days PP (3 days) at maternity hospitals and during UMCL visits at 4–5 weeks PP (30 days), 12–14 weeks PP (90 days), and at 12 months PP (1 year). BMI, PI, and BF% were calculated according to Slaughter Infant fecal samples (approximately 2 g) were obtained at the maternity hospitals 2 to 4 days PP (3 days) and were immediately frozen at −20 °C. In the periods 4–5 weeks (30 days) and 12–14 weeks (90 days) PP mothers were instructed to collect fresh infant’s feces from a nappy in a provided sterile sampling container with a spoon attached to the lid. Until the ambulatory visit, fresh samples were kept refrigerated (4 °C) for not more than 24 hours and were immediately frozen (−20 °C) upon the arrival to UMCL. Within a month after the collection, fecal samples were transferred to the laboratory and were stored at −80 °C for up to a few months, until the DNA extraction and qPCR analyses.53. Briefly, after homogenization, lysis and sonication, samples were transferred to Maxwell 16 Tissue DNA Purification Kit (Promega) and the obtained 167 community DNA extracts served as a template for qPCR amplification of the following microbial groups: all bacteria, Bacteroides-Prevotella group, Bifidobacterium group, Clostridium leptum group, Clostridium coccoides group, Enterobacteriaceae group, Enterococcus group, Enterococcus faecalis, Lactobacillus group, Lactobacillus gasseri, Staphylococcus group, Staphylococcus epidermidis and Streptococcus group.The protocol for fecal sample preparation and microbial DNA extraction has been in detail described elsewhereet al.17, but with the admixed treated fecal matrix, served as a basis for standard curve construction. QPCR analyses of fecal microbiota were performed by Mx3000 P instrument . For a single target gene, 6 assays were performed in a 96-well thermal block. Each assay included reactions with standard DNA dilutions (1:2) as well as reactions with 10-fold diluted sample DNA and non-template controls (NTCs), run in duplicates. Samples were distributed on plates randomly. The reactions were performed in a total volume of 20 µl, and the PCR mix consisted of KAPA SYBR Fast Master Mix (2x) Universal , 0.2 μM of each of the two oligonucleotide primers and 1 μL of DNA extract. Primers used were as listed by Obermajer et al.17 and Supplementary Table 55. The standard correlation chart between the observed cycles at the threshold fluorescence (Cq) and the estimated numbers of bacterial copies in the standard samples was processed by Mx3000 P software algorithms. Software settings based on the adaptive baseline correction and the amplification based threshold, as recommended by the manufacturer. The correlation coefficients (R2) and the amplification efficiencies (E) were calculated for different target reactions as presented in Table Enterobacteriaceae group and Staphylococcus group, where the difference in the reaction E was 5.7% and 5.1%, respectively. Sample concentrations (copies per gram of feces) were calculated from the sample Cq interpolated into the standard plot. Limit of detection (LOD) for a single assay run was calculated from the equation of the standard curve while the last cycle number served as the cutoff point. Bacterial quantities extending below LOD for the specific assay were set to half of LOD value. The mean LODs for 6 assay runs, expressed as copies per reaction mix, are indicated in Table Maxwell DNA extracts from the respective microscopically enumerated pure-culture bacteria, as specified by Obermajer For the majority of samples, the difference between Cq values of technical replicates in different target reactions was ≤0.5. In the instances, where only one of the two replicates succeeded, a single value was used. For ‘all bacteria’ primer pair only, the NTCs crossed the threshold before the last cycle (30); except for four samples at day 3 and one sample at day 90, which were detected in the 10-fold NTC concentration area, quantification was conducted reliably. Melting curve analysis was performed at the end of each run to examine the curve profiles of sample replicates, standards and NTCs. At least five standard dilutions were included in each run, in different target reactions; the standards were covering a linear range from at least 2 to 5 logs.1i-Z0i), 3 months and birth (Z3i-Z0i), and between 12 months and birth (Z12i-Z0i); and as WG Z-scores for time periods between birth (0), 1, 3, and 12 months after birth as WG Z(1–0), WG Z(3–0), WG Z(12–0), WG Z(3–1), WG Z(12–1), and WG Z(12–3). Associations of the selected variables (p < 0.1) with relative microbial abundances (logit transformed) at specific time-point were then further evaluated by a multiple linear regression. Standardized effect size (differences in logit relative abundances) and significance (p < 0.05) for individual predictors were reported for the prevalent bacterial groups (detected in at least 30% of the samples). For the low prevalent bacterial groups (detected in less than 40% of the samples) multivariate logistic regression analysis was used on the dichotomized (detected/undetected) microbial abundance at specified time point. Associations between microbial groups in the forming gut communities were further investigated through the principal component analysis (PCA) of the relative abundance of 12 targeted microbial groups at 3 time points. Dimensionality of the problem was assessed by a scree plot. The results were presented as microbial group loadings on principal components (loading plot) and individual sample scores according to sampling time (scores plot).Statistical analysis was carried out using SPSS version 23 . For descriptive purposes, continuous variables were presented as medians (ranges), and frequencies (percentages) were used to describe categorical variables. Significance level was set at p < 0.05. The relative abundance of each bacterial group was determined as ratio between each group-specific qPCR assay (copy number estimate per gram) and all bacteria assay in the respective sample. Nonparametric Friedman test was used to assess longitudinal differences in absolute and relative abundances of microbial groups. Wilcoxon’s signed rank test was used in post hoc pair-wise comparisons and to assess the influence of feeding type . Holm’s method was used for control of type I error in post hoc tests. Associations of relative microbial abundances with clinical, environmental, anthropometric variables, and growth deviation scores were initially screened by bivariate analyses applying non-parametric Spearman’s rank correlation (continuous data) or Mann-Whitney U test (dichotomous data) to select predictor variables. The same approach was used to test for associations between various growth parameters and growth confounders. Growth deviation scores were calculated as BMI Z-score changes in individuals (i) between 1 month and birth (ZAll relevant data are within the paper and its Supplementary information file.Supplementary information file


No significant associations were observed between Cd levels from any trimester and birth size in boys. Maternal Cd exposure during earlier periods of pregnancy may have a larger impact on delayed fetal growth.Prenatal cadmium (Cd) exposure has been associated with adverse birth outcomes, but the findings of previous studies are inconsistent. We measured Cd concentrations in urine samples at or near 13, 24, and 35 gestational weeks from 282 women in Wuhan, China. We used generalized estimating equation models to assess the associations between maternal creatinine adjusted urinary Cd concentrations at each trimester and birth size. A significant inverse association was observed between higher maternal Cd levels measured during the 1st trimester and birth size in girls. For each log unit increase in Cd (µg/g creatinine) levels from the 1st trimester, there was a decrease in birth weight by 116.99 g : −208.87, −25.11 g). The Cd levels from the 1st and 2nd trimesters were also borderline significantly associated with ponderal index in girls. Joint estimation of trimester-specific effects suggested that associations with Cd levels for ponderal index ( Cadmium (Cd) is a toxic heavy metal that is ubiquitously distributed in the environment, mainly as a result of pollution from many modern industrial processes. Environmental exposure to Cd in humans mainly results from smoking, ambient air, and the ingestion of contaminated food  . In adulCd is primarily stored in the liver and kidneys, however, the placenta also accumulates Cd , which mThe fetal period is considered to be a sensitive stage of human development to environmental exposures, and the vulnerability of the fetus to environmental exposures in different pregnancy stages may be considerably different . ExposurIn this cohort study, we assessed the trimester-specific effects and the sex difference of prenatal exposure to Cd in relation to size at birth using maternal urinary Cd levels measured at each pregnancy trimester in 282 singleton pregnant women from Wuhan, China.The present study was carried out between October 2013 and October 2014 at the Wuhan Women and Children Medical Care Center, a major maternity hospital in Wuhan, China. A total of 282 pregnant women who satisfied the following conditions were recruited: (1) aged 20 years or older; (2) coming to the hospital for their first prenatal examination; (3) less than 16 weeks pregnant with a singleton gestation at the time of enrollment; (4) residence in Wuhan City at the time of the recruitment period; and (5) willingness to have prenatal care and give birth in the study hospital. All participants in this study provided written informed consent at enrollment. The research protocol was approved by the ethics committee of the Tongji Medical College, Huazhong University of Science and Technology (No. (2012)07), and the Wuhan Women and Children Medical Care Center (No. 2012003).Maternal urine samples were collected during prenatal visits to the hospital at each trimester. On average (±SD), the 1st, 2nd, and 3rd trimester gestational ages at urine collection were 13 ± 1.2, 24 ± 2.9, and 35 ± 4.4 weeks. Among the 282 women who were enrolled in the study, all of the women donated at least two urine samples, and 238 (84%) donated one urine sample at each trimester. The number of women who did not donated urine samples during the first, second, and third trimester was 3, 36, and 6, respectively. The reason that pregnant women did not provide urine samples was they did not go to the hospital for prenatal care within the specified time. There was no difference between pregnant women who provided urine samples and those who did not.3 was added to the final volume of 2.5 mL for overnight nitrification. The resulting sample was digested at 40 °C for 1 h and then analyzed for Cd. Cd in urine was measured by inductively coupled plasma mass spectrometry (ICP-MS)  in helium mode. The operation conditions of ICP-MS were as follows: RF power 1550 w, plasma gas flow 15.00 L/min, auxiliary gas flow 0.9 L/min, carrier gas flow 0.25 L/min, resolution (peak high 10%) 0.65~0.80 atomic mass unit (amu), improve quantity of samples 0.4 mL/min, unimodal residence time 0.1 s.Urine samples were collected in polyethylene containers and stored at −20 °C until further analysis. The sample preparation and analytical methods have been described elsewhere . BrieflyQuality control urine samples were incorporated in each batch of 20 samples and each quality control sample was measured in duplicate. All available urine samples from each mother were measured in the same batch. National Institute of Standards and Technology  Standard reference material was used as an external control in each batch, and the concentrations measured were within the range recommended by the manufacture (5%). The limit of detection was 0.002 µg/L, and no samples were below it. The spiked recovery of the quality control standard was 103%. The between-assay coefficient of variation (CV) was 2.2% and the within-assay CV was 1.1%.Urinary creatinine, determined by a creatinine kit , was used to control for variations in urine dilution in spot urine specimens. The adjusted urinary Cd concentration was expressed as µg/g creatinine.3 in centimeters) × 100 [The following measures of size at birth were obtained: birth weight, birth length, and ponderal index. Birth weight (in grams) and birth length (in centimeters) were retrieved from medical records. The measurements of birth weight and birth length in the hospital were conducted within one hour after birth by experienced obstetric nurses using standardized procedures. Ponderal index, an indicator of fetal growth status, especially to assess asymmetrical intrauterine growth retardation, was calculated using the following formula: (weight in grams/lengths) × 100 .Covariate data including demographic , socioeconomic , and lifestyle factors  were obtained through face-to-face interviews with each mother. Maternal smoking was defined as any smoking while pregnant based on self-report. Passive smoking was defined as exposure of a nonsmoker to tobacco smoke more than 30 min per day during pregnancy. Maternal drinking was defined as a frequency of drinking of more than one time per week during pregnancy. The interviews were conducted in the hospital by specially trained nurses within three days before or after delivery. Maternal weight before pregnancy and at delivery, maternal height, and information concerning the infants’ birth date, gender, and gestational age were all retrieved from medical records. Gestational age was calculated by subtracting the date of the last menstrual period (LMP) from the date of birth. For women who had a regular menstrual cycle and could report the accurate date of the LMP, the reported LMP was used when the difference between the reported gestational age and ultrasound-based gestational age was within 7 days. For women who had an irregular menstrual cycle and could not report the accurate date of the LMP, the ultrasound-based LMP estimated by experienced obstetricians was used.Descriptive statistics were performed prior to bivariate analyses. Urinary Cd levels in the three trimesters were compared using the Kruskal-Wallis test, and the correlations between Cd levels in different pregnancy stages were determined by Spearman correlation tests. The Wilcoxon rank sum test was used to compare distributions of Cd levels between mothers of boys and girls. Bivariate analyses among exposures, outcomes, and covariates were assessed using Spearman correlation coefficients.p < 0.1) associated with either urinary Cd or birth size in the bivariate analyses. Maternal education and household income were correlated (p < 0.001). Therefore, we adjusted for maternal education only because it was associated with birth size more strongly than household income. As most of the women have never given birth before  and parity was not associated with urinary Cd or birth size, we did not adjust for parity. Based on these criteria, confounders that were included in the subsequent GEE models included newborns’ sex, gestational age, maternal age at delivery, maternal BMI before pregnancy, net weight gain during pregnancy , maternal education level, and passive smoking. Maternal height was used instead of BMI before pregnancy for the birth length analysis, because it has been reported to be strongly associated with birth length [We assessed the associations between urinary Cd concentrations and birth outcomes  using a generalized estimating equation (GEE) model with a linear link function . The GEEh length .p-value < 0.05 was considered statistically significant.A series of sensitivity analyses were conducted. We first repeated analyses with the exclusion of women whose gestational age was less than 37 weeks. Then, we assessed whether including calcium supplementation into the models modified the associations between Cd and birth size. In addition, we ran models that incorporated log-transformed creatinine concentrations as a covariate rather than using creatinine-adjusted Cd concentrations. All the statistical analyses were performed with SAS . A The mean age of the women at delivery was 28.8 years, with a range from 20–45 years . The avep < 0.001) and creatinine adjusted (p = 0.006) urinary Cd concentrations were significantly different across the three pregnancy trimesters. The unadjusted median urinary Cd concentrations decreased between the 1st and 2nd trimesters, while the median in the 2nd and 3rd trimester levels were similar. In contrast, the creatinine adjusted urinary Cd concentrations increased between the 1st and 2nd trimester of pregnancy and were similar in the 2nd and 3rd trimester , 2nd and 3rd trimesters (r = 0.55), and 1st and 3rd trimesters (r = 0.43) . There were no significant differences in each trimester between the creatinine adjusted Cd levels in maternal urine samples from the women who delivered male or female infants .e-unit increase in creatinine adjusted Cd levels. Creatinine adjusted Cd levels at the 2nd trimester were also negatively associated with birth weight in girls and the magnitude of this effect was similar to that of the 1st trimester , although not statistically significant. There was no significant association between the 3rd trimester creatinine adjusted Cd levels and birth weight in girls. No significant associations were found between birth length in girls and maternal Cd levels measured during any of the trimesters. Results in girls for associations of ponderal index with creatinine adjusted Cd levels measured during the 1st  and the 2nd  trimester showed similar patterns to those observed for birth weight, with no association apparent for exposures measured at the 3rd trimester (pint = 0.02), and borderline significantly different for birth weight (pint = 0.08).We then conducted stratified analyses by sex . In girlrimester . Tests oIn boys, there was no evidence of an association between creatinine adjusted maternal urinary Cd levels and any birth size parameter, including for levels measured at any trimester .In sensitivity analyses, we first excluded five women whose gestational age was less than 37 weeks, but it did not markedly change any of the effect estimates (change < 5%) for creatinine adjusted maternal Cd on birth size see . We thenIn this cohort study, we evaluated associations between birth size and Cd exposure measured at three trimesters of pregnancy. We found that maternal urine Cd levels varied moderately over pregnancy, it increased between the 1st and 2nd trimester of pregnancy and were similar in the 2nd and 3rd trimester. There were no apparent associations for boys alone, but among girls, we observed inverse associations between first trimester creatinine adjusted urinary Cd levels and birth weight and ponderal index.Blood Cd levels and urinary cadmium are the most commonly used biomarkers of exposure. It is generally considered that blood Cd levels are indicative of recent exposure rather than whole-body burdens, and urine Cd levels may reflect 6–38 years status of exposure. However, our findings showed that the levels of urinary Cd were variable throughout the three trimesters.Many studies have assessed the effects of maternal Cd exposure, but typically have relied on only one biosample measurement collected at varying times during pregnancy, at delivery, or on measurements collected from cord blood or placenta. There are few prior studies that have measured Cd exposure more than once during gestation. A recent study  of 1835 The fetus has different rates of growth at different stages of gestation, and environmental exposures occurring at different stages of pregnancy may have very different impacts on fetal development and birth outcomes. Although there have been few studies that have assessed longitudinal measurements of Cd over the course of pregnancy and birth outcomes, the plausibility that early pregnancy in particular may be a period of heightened susceptibility and a critical window has been noted for other environmental exposures, such as polycyclic aromatic hydrocarbons (PAHs). PAH exposure has been found to exert the greatest adverse effect on fetal growth during the first trimester, with inverse associations observed for the fetal growth ratio, birth weight, and birth length . An animp-trend = 0.08; males: low = reference, middle = 0.18 cm , high = 0.78 cm , p-trend = 0.07; p for interaction = 0.03). Caroline M. Taylor [We found a sex difference in the associations between creatinine adjusted maternal urinary Cd levels and birth weight and ponderal index, which were only apparent in girls. This is consistent with previous research that has suggested sex differences in the disposition and patterns of toxicity for a variety of different metals . Signifi. Taylor  also fou. Taylor , and evi. Taylor . The sex. Taylor ,27. It hPrevious study ,29,30,31The main strengths of our study include the prospective study design and the assessment of maternal Cd levels in three urine samples collected from different trimesters, which enabled us to evaluate trimester-specific associations with birth size and evaluate critical windows of susceptibility during the pregnancy. In addition, we collected data on a broad array of potential confounders, including maternal BMI before pregnancy, net weight gain during pregnancy, and passive smoking. Our study also had some limitations. First, the sample size was modest, which was due to labor and cost limitations. A second limitation was that we did not adjust for dietary factors. However, we have controlled for calcium supplementation, and found it has almost no effect on the pattern of associations of maternal Cd concentrations and birth size.We observed that maternal Cd exposure during the earlier stage of pregnancy was inversely associated with birth weight and ponderal index, which was apparent in girls but not boys. While these findings will need verification in other populations, our data suggest that cadmium exposure among young women and women of childbearing age should be as low as possible in order to keep the body burden as low as possible.


The objective was to assess the prevalence of selected major birth defects, based on data from two medical registries in Murmansk County, and compare the observed rates with those available for Norway and Arkhangelsk County, Northwest Russia. It included all newborns (≥22 completed weeks of gestation) registered in the Murmansk County Birth Registry (MCBR) and born between 1 January 2006 and 31 December 2009 . The infants were followed-up post-partum for 2 years through direct linkage to the Murmansk Regional Congenital Defects Registry (MRCDR). Birth defects identified and confirmed in both registries constituted the “cases” and corresponded to one or more of the 21 birth defect types reportable to health authorities in Moscow. The overall prevalence of major birth defects recorded in the MRCDR was 50/10,000 before linkage and 77/10,000 after linkage with the MCBR. Routine under-reporting to the MRCDR of 40% cases was evident. This study demonstrates that birth registry data improved case ascertainment and official prevalence assessments and reduced the potential of under-reporting by physicians. The direct linkage of the two registries revealed that hypospadias cases were the most prevalent among the major birth defects in Murmansk County.Abbreviations: ICD-10, International Classification of Diseases, 10th revision; MCBR, Murmansk County Birth Registry; MRCDR, Murmansk Regional Congenital Defects Registry; MGC, Murmansk Genetics Center Congenital anomalies  are structural or functional anomalies that exist at or before birth, although some become evident during infancy. Based on EUROCAT data, the total prevalence of all birth defects diagnosed at birth in Europe is about 2.5%  and its Acquisition of data from population-based registries of birth defects constitutes an important information source . Since nThe Murmansk County Birth Registry (MCBR) is based on the format used in the Nordic countries and was established in 2006. Pertinent information was systematically and routinely collected from the 15 county maternity clinics, each of which deliver 1–4 neonates per day . In 2010, it was the only operational birth registry in Russia . There aRecent studies demonstrate the effectiveness of using secondary databases to improve the quality of registry data . One stuAccording to the 2010 Census, Murmansk County in Northwest Russia had 795,409 inhabitants, with a population density of 6.2 per square kilometre ,17. The We obtained detailed information on mothers and their newly born babies from the MCBR, as well as for birth defects diagnosed  during the perinatal period, namely from ≥22 weeks of gestation to the hospital discharge 7–12 days post-partum, as appropriate for the type of delivery  or any complications. The data in the MCBR derived from the mothers’ medical and obstetric records, the neonatal delivery records and from interviews with the mothers. The same physician or midwife who gathered the required information from medical and obstetric records conducted the interview and completed a two-page birth registry form comprised of 54 major fields of detailed medical and personal information about the mother and her baby/babies and father as well .We extracted details about cases of major birth defects from the MRCDR, which included information on all birth defects diagnosed between birth (≥22 weeks of gestation and birth weight >500 grams) up to 2 years of age. The MCBR was a passive registry with its main sources of information being the maternity hospitals, children’s polyclinics (primary care), children’s hospitals and pathology departments and other medical institutions. On diagnosis of a birth defect, the physician completed a notice form and submitted it to the local Medical Analytic Information Centre for registration. The pertinent information was recorded in the MRCDR only after its confirmation by a medical institution. One exception were the notice forms issued by children’s polyclinics, which were exempt from the confirmation requirement. The MRCDR includes information on birth date, weight, vital status, whether multiple delivery, birth defect diagnosis, gender, gestational age, place of delivery, mother’s age, mother’s parity and mother’s place of residence at the time of delivery. Subsequently we selected all cases born within the study period 1 January 2006 to 31 December 2009. During the study period, 234 neonates registered in the MRCDR had major birth defects see . Of thesFor the linkage procedure, we selected all cases from the MRCDR with major birth defects for babies born between 1 January 2006 and 31 December 2009. The MRCDR electronic platforms changed during the study period from Medmonitor to Microsoft Excel, and subsequently to Microsoft Access; they were thereby fragmentised. We received only paper printouts from The Ministry of Health Care located in Murmansk City and, thus, the linking of the MCBR and the MRCDR was manual.Based on place of delivery, date of birth of the mother and hospital ID file number in the MCBR, we requested all 210 original medical files from the maternity hospitals. Similarly, based on the same variables in the MRCDR, we requested 195 original medical files from maternity hospitals. After receiving these original files, we checked whether a case with a major birth defect had been in the MCBR, the MRCDR or in both. The 64 cases registered only in the MRCDR were combined with those in the MCBR using a manual (but direct) linkage algorithm, based on the original medical file and hospital ID number of the participant from the MCBR and the mother’s birthdate. Thus, the combined registry included 274 cases of major birth defects with the corresponding International Classification of Diseases, Revision 10 (ICD-10) code and date of diagnosis.We considered data on the 21 selected birth defects (referred to in this text as major birth defects), namely those included in the mandatory MRCDR annual report to the health authorities in Moscow. The statistical package SPSS version 21.0  was used to analyse and create descriptive statistics. We calculated confidence intervals based on the Wilson procedure, without correction for continuity.Prevalence rates were calculated separately for the MCBR, MRCDR and the combined registry. Furthermore, we compared rates of major birth defects with those reported for Arkhangelsk County and Norway.The Regional Health Administration of Murmansk Oblast, as well as the Ethics Committee of the Association of Gynecologists & Obstetricians of Murmansk Oblast approved this study. The Regional Ethics Committee (REK) in Norway also granted ethical approval. After linkage, all data from the MCBR and the MRCDR were de-identified. Both registries (MCBR and MRCDR) were obligatory parts of the healthcare system in the Murmansk County during the study period. All study participants signed an agreement form kept in their hospital medical files about their willingness to share future observations and possible use of personal data for further research. Within the study period, no one declined to complete the written consent form.Of the 35,417 neonates (live and stillborn) registered in the MCBR during the study period, 210 had major birth defects see .The characteristics of the study population summarised in We found 210 cases of major birth defects in the MCBR, compared to 195 in the MRCDR . Of the To the extent possible, the prevalence of major birth defects in Murmansk County were also compared with those in Arkhangelsk County –21 and iTo the authors’ knowledge, this is the first time that a birth registry and a birth defect registry have been combined in Russia to determine the prevalence of birth defects. We found that 79 of the 210 cases of major birth defects  registered in the MCBR were not included in the MRCDR. We, therefore, demonstrated a 40% increase in the overall prevalence of major birth defects after combining the two registries.Before 2006, there were no adequate mechanisms to estimate the completeness of the MRCDR and, consequently, there are no published reports regarding its quality. When comparing the MCBR and the MRCDR, we found that the former had better case ascertainment. The most likely explanation for this is that the registration routines were better in the MCBR, such as regular quality controls, having only one person responsible for registration in each maternity hospital, strict delivery of birth registry forms to the central office using courier services and in general having fewer individuals involved in the data chain. In contrast, the MRCDR draws upon all health institutions and, thereby, involves more people and fewer quality control routines. Furthermore, it covers the neonatal period and includes diagnoses for the child to 16 years of age.Our study is, therefore, an example of how useful registry linkage can be. It revealed significant under-reporting of some major birth defects in Murmansk County, which led to under-reporting of the overall rate of birth defects in this region. Our findings provide decision-makers with insight about a need for suitable and routine quality control measures to guarantee the quality of public health statistics.Certain population characteristics may influence the prevalence of birth defects and, therefore, it is important to compare them for the same period to those of neighbouring jurisdictions such as Arkhangelsk County and Norway. The average age of mothers (at the time of delivery) in Murmansk County was 26.5 years, which is lower than that in Norway, where it was 29.6 years during study period . The proFolic acid supplementation reduces the risks of spina bifida and some ano-rectal atresia, as well as of selected orofacial clefts in high doses –31. The More than 90% of pregnant women in Murmansk County undergo ultrasound examinations at least three times during their pregnancy, with the first one usually at about 12 (12.4 weeks on average) weeks of gestation, as required by Federal Order № 572 from the Ministry of Health Care of the Russian Federation . AccordiMaternal smoking is also associated with increased risk of birth defects, specifically missing or malformed limbs and facial disorders . In NorwGenerally speaking and based on the combined Murmansk County registries, the overall prevalence of major birth defects of 77 per 10,000 compared well with the 67 in Arkhangelsk County and 72 in Norway. Without linking the two registries, Murmansk County would have exhibited the lowest prevalence .For oesophageal atresia and ano-rectal atresia, the prevalence ranged from 1.5–2.5 per 10,000 in all three locations and was, thus, too rare to allow adequate comparisons. These two defects are easily recognisable at birth and require urgent surgical treatment.In Murmansk County, the prevalence of limb reduction defects was unexpectedly high (9.9), while in Arkhangelsk County it was 1.7 and in Norway 3.1 per 10,000. Detailed analysis revealed that 10 such cases were recorded with the same ICD-10 code and were all diagnosed in the military town of Gadzhiyevo, which has a population of about 11,000 and around 250 annual deliveries . All 10 We describe a successful linkage of records from a birth defects registry with those of a medical birth registry, based on original hospital data, hospital ID number and the last name of the mother. The established satisfactory quality of the MCBR constitutes a strength . AlthougThe dependence on the experience of the medical doctors to detect and correctly diagnose birth defects, especially in remote areas, may cause systematic errors such as under-reporting, over-reporting and misclassification. Another limitation is that elective abortions due to birth defects (<22 weeks of gestation) were not included in the Murmansk County and Arkhangelsk County registries. This hindered our attempts to acquire more accurate prevalence estimates. Moreover, differences in pre-natal diagnostics algorithms of birth defects and early terminations may also have contributed to rate differences in the regions compared. Information from the Murmansk Genetics Centre (MGC) could potentially include pregnancy terminations due to birth defects diagnosed pre-natally by the MGC. Although these data were available, they were not included in the MCBR. Another limitation is that some selected defects were so rare (as might be expected) that comparisons of rates lacked statistical power.A number of studies have indicated substantial under-reporting of birth defects based on statutory notifications of births compared with hospital records and this was the case in Murmansk County. A surveillance system solely based on notifications of births is not advocated . Under-r