Preterm birth affects the gut microbiota, metabolome and health outcomes of twins at 12 months of age: a case control study

Preterm birth affects the gut microbiota, metabolome and health outcomes of twins at 12 months of age: a case control study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Preterm birth affects the gut microbiota, metabolome and health outcomes of twins at 12 months of age: a case control study Hong Mei, Liqin Hu, Meng Yang, Feiyan Xiang, Hao Zheng, Xiaonan Cai, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4381172/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Perinatal factors can influence gut microbiota, adversely impacting infant health outcomes. However, little is known about the combined effect of preterm birth and chorionicity on gut microbiota, metabolism, physical and neurobehavioral development for twin infants. In this study, we profiled and compared the gut microbial colonization of 350 twins aged 12 months. Twins were divided into four groups based on their gestational age at birth and chorionicity as dichorionic-diamniotic full-term birth group, dichorionic-diamniotic preterm-birth group, monochorionic-diamniotic full-term birth group, and monochorionic-diamniotic preterm birth group. Gut microbiota diversity and fecal metabolic alterations at 12 months old were determined by 16S rDNA sequencing and untargeted metabolomics, respectively. Wilcoxon's rank-sum tests were used to compare alpha diversity between the four groups. The general linear models were applied to identify microbiota species that were differentially abundant among the four groups and the health effects of gut microbiota on physical and neurobehavioral development conducted at 12 months of age. In addition, the twin-based ACE model was used to evaluate the contribution of genetic and environmental effects on the composition and function of the gut microbiota. We found that preterm birth and chorionicity dominated genetics in altering the composition of gut microbiota and concentration of metabolites over 12 months of age. The influence of genetic factors differed between preterm and full-term births. There were 16 gestational age and chorionicity specified gut microbiota genera and 285 group-specified metabolites. Association analysis filtered 7 microbiota genera and 19 metabolites associated with twins' physical and neurobehavioral development. Three metabolites, N-Oleoyl dopamine, Ecgonine, and Methyl jasmonate participated in the neuroactive ligand-receptor interaction pathway, tropane, piperidine, and pyridine alkaloid biosynthesis pathway, and alpha-Linolenic acid metabolism and biosynthesis of secondary metabolites, respectively. We concluded that preterm birth is associated with dysbiotic microbiota profiles and significant metabolic alterations, which may eventually influence physical and neurobehavioral development. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Preterm birth (PT) generally refers to birth with a gestational age below 37 weeks. Preterm birth affects approximately 10% of babies worldwide (1), and preterm birth rate in twin pregnancies can reach 35% or higher (2). Preterm birth has short and long-term health consequences on the human body. For example, almost 25% of preterm births have adverse neurodevelopmental outcomes at birth (3). Furthermore, premature birth is an independent predictor of short- and long-term respiratory and cardiovascular risks (4, 5). For children at an early age, preterm birth has a widely spanned impact on anthropometric growth, cognitive development, and behavior (6-8). As a research topic of utmost interest in recent years, the human microbiota has been documented to play non-negligible roles in the gut-systemic metabolic interplay, which may influence immune and metabolic development in early life and alter offspring cognitive development and behavior through the microbiota-gut-brain axis (9-11). Many studies indicate that host genetics and preterm birth can shape the microbiome at birth and persistently influence the pace of microbial acquisition before getting an adult-like gut microbiome construction (12-14). However, studies about gene and preterm birth's combined or interactive effect on the gut microbiota are scarce. Twins have high genetic and environmental resemblance as monozygotic twins share 100% of their genes compared to dizygotic twins with 50% shared genes, and they are characterized by the common environment of parents and family, like parenting style, education, and so on (15). These similarities make twins an excellent model for disentangling the effect of genetic and environmental factors on shaping gut microbiota. However, most twin studies on the genetic and environmental determinants of the gut microbiota have focused on genetic effects; twin studies on the association between genetic and environmental factors and the gut microbiota are rare (16, 17). Apart from the zygosity characteristics, twins have chorionicity, which can be classified as dichorionic-diamniotic and monochorionic-diamniotic twins. Approximately 20% twins are monochorionic-diamniotic, and most of them are monozygotic. Several studies have found that adverse perinatal outcomes among twins differ significantly depending on chorionicity (18-20), among which monochorionic-diamniotic imparts a greater risk of preterm birth than dichorionic-diamniotic twins (21). Preterm birth in dichorionic-diamniotic twins could be attributed to various genetic or/and environmental factors, including genetic dissimilarity and differences in placental mass, placental insufficiency, umbilical venous diameter, and flow (22-24). On the contrary, monochorionic-diamniotic twins share more genetic resemblance as most of them are monozygosity pairs, and the particular challenges of preterm birth in monochorionic-diamniotic twins arise mainly from the shared placenta and the placental vascular anastomoses, which are almost universal (25). The correlation between chorionicity and preterm birth makes twin pairs ideal for investigating placental-related long-term growth and development in children and how genetic and environmental factors shape the gut microbiome and metabolism. Therefore, well-designed twin studies are required to elucidate the impact of genetic and intrauterine environmental factors on gut microbiota. Based on the Wuhan Twin Birth Cohort study, we collected stool samples for twin pairs at 12 months of age and used them for 16S rDNA sequencing in 268 twins and for untargeted metabolomics in 138 twins. Then we classified the twins into four groups based on their gestational age at birth and chorionicity: dichorionic-diamniotic full-term birth (DCFT) group, dichorionic-diamniotic preterm birth (DCPT) group, monochorionic-diamniotic full-term birth (MCFT) group, and monochorionic-diamniotic preterm birth (MCPT). The first goal was to determine the effect of gestational age at birth and chorionicity on the gut microbiota and metabolism of twins at 12 months of age. Then, to evaluate the effect of microbiota and metabolites on physical and neurobehavioral development at 12 months of age. Materials and Methods Ethics approval and consent to participate This study was approved by the Ethics Committee of Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital) (IRB number: WHFE2016050) and performed according to relevant guidelines and regulations. Informed consent to infants’ parents was conducted before the investigation. Subject recruitment This study was conducted based on the ongoing Wuhan Twin Birth Cohort study. Mothers were approached for informed consent between March 2016 and December 2020 at Wuhan Children's Hospital in China. The inclusion criteria for this sub-study contained live neonates from dichorionic-diamniotic and monochorionic-diamniotic twin pregnancies who were followed up at 12 months of age with anthropometric measurements, stool samples, and neurobehavioral estimates. The exclusion criteria included mothers with gestational diabetes mellitus, gestational hypertension during pregnancy, fetus with twin to twin transfusion syndrome, and neonates with birth defect. Data collection Twins' gestational weeks at delivery and chorionicity of placenta were collected from the birth record in the electronic medical record system. Gestational age at delivery was calculated as the difference between the twins' birth date and the maternal last menstrual date. Twins can be classified as PT for gestational age < 37 gestational weeks, FT for gestational age ≥ 37 gestational weeks, dichorionic-diamniotic and monochorionic-diamniotic twins. We further calculated the twins as DCPT, DCFT, MCPT, and MCFT according to their gestational age at birth and chorionicity. Twins' sex, weight, length, and neurobehavior were measured at 12 months of age. The twins' zygosity was also detected using neonatal blood spot samples by short tandem repeats genetic typing and categorized as mono-zygosity (MZ) or di-zygosity (DZ). Twins' sex was set as boy or girl. Body mass index (BMI) was calculated as weight (kg) divided by the square of the height (m 2 ). Sex and age-specified weight, length, and BMI were generated as the weight for age z score (WFA), length for age z score (LFA), and BMI z score (BMI_Z) according to the WHO Child Growth Standards (2006) (26). Twins' neurobehavioural developments at 12 months of age were evaluated using Age and Stage Questionnaire, 3 rd version (ASQ-3) (27). ASQ-3 contained five development domains, including communication, gross motor, fine motor, problem-solving, and personal social, with 60 points possible in the combined evaluation for each infant. For each domain of the ASQ-3, infants' neurobehavioral development was set as normal development, suspected development delay, or development delay. Any domain screened > -1 SD was defined as normal development (ND), screened between -2 SD and -1 SD was defined as suspected development delay, and < -2 SD was defined as developmental delay. As the proportion of development delay domains in our participants was small, we merged suspected development delay and development day as development delay (DD). To fully assess the neurocognitive development, we generated a variable as a neurocognitive problem, set as yes or no. The answer yes was defined as at least one of the five development domains being DD, and the answer no was defined as all the five development domains being ND. Apart from these leading indicators, we also collected parental BMI, maternal educational level, maternal age at delivery, delivery mode, whether used assisted reproductive technology (ART), twins' birth weight, feeding patterns at 1 st and 6 th months, and antibiotics used during the first 6 and 12 months of life. Stool sampling, DNA extraction, and high-throughtput sequencing Fresh stool samples were collected in a sterile plastic container by caregivers 1 or 2 days before the interview and immediately frozen at -20℃ at home before being transferred to the -80℃ freezer within 1 hour of arriving at the study hospital. DNA extraction was conducted using the QIAamp PowerFecal DNA Kit (QIAGEN, Germantown, MD, United States), and high-throughput sequencing was provided by Novogene Co. Ltd (Beijing, China). DNA extraction and 16S rDNA high-throughput sequencing in V3-V4 region details were illustrated in one of our previous studies (28). Microbial community analysis For microbiota community analyses, alpha diversity, including Shannon and Chao1 index, was calculated using QIIME (version 1.9.1, http://qiime.org/scripts/split_libraries_fastq.html). Bray-Curtis distance was used to evaluate the species complexity differences of samples. The Wilcoxon rank sum test performed statistically significant differences in Shannon, Chao1 indexes, and species complexity differences between DCPT, DCFT, MCPT, and MCFT groups. PCoA was used to discriminate the signatures among the four groups. For taxonomic diversity analysis, the genera taxa were filtered and transformed as follows: (1) only genera taxa presented in > 10% of stool samples at 12 months old were used; (2) genus-level relative abundance should be normal distribution after log2 transformed. Zero values were disposed of by plus one before log2-transformation. We used the general linear model (GLM) with Tukey's honestly significant difference comparisons to identify microbiota species that were differentially abundant among the four groups, DCPT, DCFT, MCPT, and MCFT, after controlling for twins' sex, delivery model, and birth weight. Means of log-transformed relative abundance for differential genus taxa in the four groups were clustered using GraphPad Prism 8.0.1. Heritability of the microbiome The twin-based ACE model was used to evaluate the contribution of genetic and environmental effects on the composition and function of the gut microbiota according to methods from Jing Yang et al (29). The ACE model partitions the total variance of the gut microbiota into three components: A for additive genetic effect, C for common/shared environment effect, and E for unique environment effect by comparing trait variability in MZ twin pairs versus DZ twin pairs. Before calculating the microbial heritability, raw count tables were filtered and transformed as follows. First, data in the ACE models were collected from paired MZ and DZ twins. Furthermore, only genus-level taxa with significant differences between DCPT, DCFT, MCPT, and MCFT groups analyzed by GLM were considered in the ACE models. Finally, each taxon's heritability estimation was calculated using the ACElong package in Stata 15.1. Untargeted metabolomics analysis of stool samples Metabolite analysis of stool samples of 12 months old twins, including metabolite extraction, data preprocessing and statistical analysis, were performed by Novogene Co, Ltd (Beijing, China). Detailed methods were attached in the supplementary. Statistical analysis Intergroup comparisons, including for microbial alpha and beta diversity, between DCPT, DCFT, MCPT, and MCFT groups, between dichorionic-diamniotic and monochorionic-diamniotic groups, and between FT and PT groups, were conducted with Wilcoxon's rank-sum test with multiple testing correction. Association between groups and log-transformed relative abundance differences for specific taxa, between groups and physical development indicators, WFA, LFA, and BMI_Z, and between log-transformed relative abundance differences for specific taxa and physical development indicators were conducted using GLMs. Association between groups and indicators for neurobehavioural developments were analyzed using multiple logistic regression models with twins' sex, birth weight, and delivery mode. False discovery rate was used for p values in multiple testing. Results We included 350 twin pregnancy cases and tracked them for 12 months, collecting data on their gut microbiome, metabolome, anthropometrics, and neurobehavior. As a result, 119 twin pairs had all data on gut microbiota, metabolites, anthropometrics and neurobehaviors. Details of the main variables collected werer included in Table 1. According to Table 2, maternal pre-pregnancy Body mass index (BMI) was significantly higher in the DCPT and MCPT groups compared to the DCFT and MCFT groups ( p < 0.05). Maternal educational level and delivery age were also significantly different among the four groups, with ART significantly higher in the dichorionic-diamniotic groups than in the monochorionic-diamniotic groups ( p 0.05). Preterm birth outweighs genetics in shaping twins ' gut microbiota at 12 months old Based on the group rule, we first examined whether preterm birth and chorionicity could affect microbial communities at twins 12 months of age. We observed that the Chao1 index in alpha diversity of the gut microbiota in the MCPT group was significantly higher than that in the MCFT group ( p = 0.03) (Figure 1A). However, no significant difference was found in the Shannon index among the four groups (Figure 1B). Subgroup analysis for PT and full-term birth (FT) groups showed a significantly higher Chao1 index for PT group twins (Supplemental Figure 1A), while no significant difference was found in the Shannon index between the two groups (Supplemental Figure 1B). Subgroup analysis in chorionicity found no significant difference in Chao1 and Shannon index (Supplemental Figure 1C and 1D). These results suggested that preterm birth outweighs genetics in shaping gut microbiota richness in 12 months old twins. Next, we calculated the Bray-Curtis of the microbial community between individuals of each group (Figure 1C). The Bray-Curtis distances between samples in the MCPT group were significantly higher than those in the MCFT group ( p < 0.001). However, for the dichorionic-diamniotic twins, the Bray-Curtis distances were higher in the FT twins than in the PT twins ( p = 0.002). A similar trend of the Bray-Curtis distances was found between DCFT versus MCFT (DCFT > MCFT, p < 0.001) and between DCPT versus MCPT (DCPT < MCPT, p < 0.001). Finally, subgroup analysis was conducted to confirm the difference according to gestational age at birth and chorionicity. The Bray-Curtis distances between samples in the PT group were significantly higher than those in the FT group ( p < 0.001) (Supplemental Figure 2A). In addition, the Bray-Curtis distances between samples in the dichorionic-diamniotic group were higher than those in the monochorionic-diamniotic group ( p < 0.001) (Supplemental Figure 2B). These results indicated that preterm birth and dichorionic-diamniotic twins might have higher heterogeneity between individuals. Principal coordinate analysis (PCoA) was used to further investigate the clustering effect of microbial communities among the four groups. The samples were similar within each of the four groups and separated between different groups ( p < 0.001, Adonis test), with the distribution of DCFT and DCPT samples relatively more dispersed than that of MCFT and MCPT samples (Figure 1D). Subgroup analysis found that the bacterial signatures in PT twins were significantly distinct from that in the FT twins ( p < 0.001) (Supplemental Figure 2C), while no significant difference was found between dichorionic-diamniotic and monochorionic-diamniotic groups ( p =0.2) (Supplemental Figure 2D). These results indicated that preterm birth might lead to the distinctions among individuals outweighing genetic effects. By conducting a clustering analysis of microbial classification and abundance in each group, we identified 16 genera in four groups with different relative abundances. Log transformed means and standard deviation are shown in Figure 1E. The cumulative frequency of the 16 discriminatory genera in the four groups was displayed in Figure 1F. Figure 1F shows that the genus Anaerostipes , Kosakonia, Parabacteroides , Alistipes , Anaerotruncus , Dialister , Subdoligranulum and Acinetobacter were among the microbiota with highest cumulative frequency of log transformed RA, and the cumulative frequency of the DCFT groups was significantly larger than that in the MCFT groups. These results further confirmed that chorionicity significantly shapes the gut microbiota in 12 months old twins. Specific taxa correspond to the environment or genetic factors In order to determine how the environment and genetic factors impact the 16 specific genera, apart from the total sample size, 119 twin pairs were further separated into full-term and preterm twins (Figure 2). We observed that the effect of environmental and genetic factors differed among the 16 specific taxa in total sample size and between twins with full-term and preterm birth. From the total sample size, in most genera, the effect of unique environment effect (E) and common/shared environment effect (C) was larger than that of additive genetic effect (A), apart from genera Hydrogenophaga , and Finegoldia . Compared to the effects of specific taxa between preterm and full-term twins, the effect of A was larger in full-term twins for genera Hydrogenophaga , Finegoldia , Negativicoccus, and Anaerostipes , and the effect of C versus E were specifically different for most taxa. For example, for genera Serratia , the effect of C and E in the full-term twins was 6.3% and 81.1%, while in the preterm twins, it was 77.67% and 22.32%, respectively (Supplemental Table 1). These results demonstrated that the effect of genetic and environmental factors on the microbiota was influenced by preterm birth. Altered fecal metabolites co-occurred with distinguished gut microbiota in preterm births Untargeted metabolomics analysis was simultaneously applied to compare the metabolic signatures among the four groups. Based on generalized linear models (GLMs), we compared the metabolic differences among the four groups for all the 2721 metabolites (1765 positive and 956 negative metabolites) with confounding factors controlled. The concentration of 285 metabolites significantly differed among the four groups (Supplemental Table 2). Among the 285 metabolites, multiple metabolites displayed enrichment or depletion among the four groups, as shown in supplemental table 2. These altered metabolites were mainly involved in histidine metabolism, the Phosphotransferase system, isoflavonoid biosynthesis, valine, leucine, and isoleucine degradation in up-regulated metabolites, and the degradation of aromatic compounds, flavonoid biosynthesis, and aminobenzoate degradation in down-regulated metabolites (Figure 3). We explored the potential correlations of abundances of the 16 differential bacterial species and 285 fecal metabolites. Overall, the co-occurrence analysis revealed that the genera Anaerotruncus and Escherichia Shigella were correlated with the greatest number of metabolites, with 53 and 41 metabolites having correlation values greater than 0.2. Furthermore, the top two metabolites correlated with most microbiota genera were Benzophenone (Com_12390_pos) and 5-Methyl-2'-deoxycytidine (Com_2803_pos) (Supplementary table 3). Gut microbiota and metabolites correlated with twins' physical and neurocognitive development in preterm births To explore the correlation between gut microbiota, metabolites, and twins' physical and neurocognitive development, we first performed Analysis of Variance (ANOVA) and chi-square tests to explore the differences between WFA, LFA, and BMI_Z, and differences of the five neurocognitive development domains, communication, gross motor, fine motor, problem-solving and personal social, among the four groups. No significant difference was found in WFA among the four groups (Figure 4A). We found that twins in the DCPT group have the smallest LFA compared with the other three groups (Figure 4B), and the BMI_Z in the DCPT twins was larger than that in the DCFT twins (Figure 4B). Results from chi-square tests showed that twins in the MCFT group have the highest risks for communication problems; no significant difference was found in the other four domains among the four groups (Table 3). Next, we used GLMs to evaluate the association between the 16 specific gut microbiota genera and WFA, LFA, and BMI_Z with twins' sex, birthweight, delivery mode, and antibiotic used within the first 12 months of life adjusted for. Finally, only seven microbiota genera, Parabacteroides , Subdoligranulum , Alistipes , Kosakonia , Negativicoccus , Acinetobacter, and Finegoldia , were found to be associated with WFA or BMI_Z (Table 4). We adopted multiple logistic regression models to evaluate the association between the 16 specific gut microbiota genera and the six neurocognitive development variables. We found Parabacteroides and Finegoldia were significantly associated with communication in 12 months old twins. Acinetobacter was associated with gross motor, Subdoligranulum was associated with problem-solving, and Negativicoccus was associated with personal social and neurocognitive problems (Supplementary table 4). Thus, the seven microbiota genera as the candidate microbiota shaped by the gestational age and chorionicity might influence twins' physical and neurocognitive development at 12 months of age. Then, we used similar statistical strategies to screen the candidate 285 metabolites significantly different between the four groups and associated with the seven microbiota genera and the physical and neurocognitive development variables. Finally, 19 metabolites were filtered out, as shown in Figure 4D. Finally, based on the KEGG database, we searched the functional pathway of the 19 metabolites, and only three were found in three pathways. Among them, N-Oleoyl dopamine functioned in the neuroactive ligand-receptor interaction pathway (Supplemental figure 3). Ecgonine functioned in the tropane, piperidine, and pyridine alkaloid biosynthesis pathway (Supplemental figure 4). Methyl jasmonate functioned in the alpha-Linolenic acid metabolism and biosynthesis of secondary metabolites (Supplemental figure 5). Discussion To our knowledge, this is the first study exploring the relationship between gestational age and chorionicity and 12 months old gut microbiota, metabolic, and physical and neurobehavioral development in twins. We found a significant difference in the relative abundance of 16 microbiota genera and the concentration of 285 metabolites among the gestational age and chorionicity specified four group twins. The top two active gut microbiota and metabolites were Anaerotruncus and Escherichia Shigella in genera and Benzophenone and 5-Methyl-2'-deoxycytidine, respectively. In addition , Parabacteroides , Subdoligranulum , Negativicoccus , Finegoldia , Acinetobacter, Alistipes , and Kosakonia were associated with WFA or BMI_Z. Furthermore, Parabacteroides , Finegoldia, Acinetobacter, and Subdoligranulum were associated with infant neurocognitive development in some aspects. For metabolism, 19 metabolites were filtered to be significantly associated with infants' physical and neurocognitive development. They were mainly involved in the neuroactive ligand-receptor interaction pathway, tropane, piperidine, and pyridine alkaloid biosynthesis pathway, alpha-Linolenic acid metabolism, and biosynthesis of secondary metabolites. Gestational age at birth and chorionicity are two main signature features of twins. Their combination may lead to different maternal and neonatal birth outcomes and influence infants' bacterial colonization (18). For instance, studies on preterm birth reported that the preterm microbiota displays delayed maturity with prolonged membership of facultative anaerobic bacteria compared to that of the predominantly strict anaerobic community of term infants (30, 31). For another, the difference in chorionicity may lead to the difference in fetus gut microbiota establishment, thus influencing microbiota assembly after birth (29). This study found a significantly higher richness of relative abundance in the MCPT group than in the MCFT and DCFT groups. In addition, higher Bray-Curtis distances were found in preterm birth and dichorionic-diamniotic twins. Results from PCoA also displayed similar results. All these results reflected that preterm birth might influence gut microbiota structure at 12 months of age after controlling for the effect of genetic influence (adjusting for chorionicity) (12, 32). We also filtered 16 specific gut microbiota genera for the four groups by comparison analyses. Among the 16 genera, the cumulative frequency in the relative abundance of the genus Anaerostipes , Kosakonia, Parabacteroides , Alistipes , Anaerotruncus , Dialister , Subdoligranulum and Acinetobacter was significantly higher in the DCFT groups than that in the MCFT groups. These results indicated that chorionicity plays a significant role in shaping the gut microbiota in 12 months old twins, consistent with the results of Yang et al. for twin neonates who suffered from fetal growth restrictions (29). To further confirm the effects of genes and environment on gut microbiota, we assessed the influence of genetic and environmental factors on the 16 distinct microbiota. The effect of E and C was larger than that of A for most genera. At the same time, there was a trend that the effect rationale of E and C was inconsistent between full-term and preterm twins for each genus. These results reflected that environmental factors, including preterm birth, may impact gut microbiota more at 12 months of age (33, 34). This study also detected fecal metabolites as a functional readout of gut microbiota. We identified 285 metabolites that significantly differed in concentration among the four groups. With KEGG analyses, the 285 metabolites were categorized into 9 main metabolism pathways. Four of the 9 main metabolism pathways were essential amino acids, such as histidine metabolism, valine, leucine, and isoleucine degradation. This finding was consistent with Nilsson et al.'s association between serum metabolomics and highly premature infants (35). Apart from the essential amino acid pathways, we also detected up-regulated phosphotransferase systems in preterm births' fecal samples at 12 months of age. Furthermore, the phosphotransferase system participated in coupled transport and phosphorylation of sugars (36), demonstrating a gestational age and chorionicity dependence in sugar transport and phosphorylation. Flavonoids and their secondary metabolite, isoflavonoids, are plant-derived natural products that have been shown to improve human health (37, 38). In this study, the isoflavonoid biosynthesis was up-regulated, and flavonoid biosynthesis was down-regulated in twins' fecal samples, suggesting the gestational age and chorionicity difference in fecal metabolisms. Apart from the reported pathways, we also found decreased metabolic levels in aromatic compounds and aminobenzoate degradation. More research is needed to understand the mechanisms underlying these correlations. In addition to the metabolic analysis for twins in the four groups, we found correlations between the specified gut microbiota genera and the distinct metabolites. Our results indicated that as one of the top four distinct community states of preterm birth microbiota genera, Escherichia Shigella was responsible for the metabolism for preterm birth as it correlated with most of the distinct metabolites (31, 39). Besides, we also detected Anaerotruncus as another top active genus in our study, and the top two active metabolites are Benzophenone and 5-Methyl-2'-deoxycytidine, while there is a lack of studies to report the possible mechanisms among them. The topics of preterm birth, gut microbiota and metabolism, and child growth are not uncommon, but studies linking all of these topics together while accounting for genetic effects are lacking. Twins are a unique population in research on environmental and genetic effects. Jing Yang et al. conducted the only twins study on this topic. They focused on fetal growth restriction (FGR) twins and discussed the twin neonates' gut microbiota and metabolism and their effects on physical and neurobehavioral developments. They found that twins with FGR have dysbiotic gut microbiota and metabolic alterations, and the altered fecal cysteine level is positively correlated with the physical and neurocognitive developments of twins at 2 or 3 years of age (29). We used this study design to investigate the relationship between preterm birth and chorionicity distinct gut microbiota and metabolites and twins' physical and neurobehavioral growth at 12 months of age. We found 7 gut microbiota genera were associated with physical growth, and four also had a relationship with neurocognitive development. Among the 7 gut microbiota, Parabacteroides (40, 41) , Subdoligranulum (42, 43), and Alistipes (44, 45) were reported to be functioned in obesity and mental health, while the other four genera Negativicoccus , Finegoldia , Acinetobacter, and Kosakonia were not found to be discussed on health and disease. We also identified 19 metabolites significantly associated with twins' physical and neurocognitive development. Our results revealed that N-Oleoyl dopamine might influence vas interplay's physical and neurobehavioral development in the neuroactive ligand-receptor interaction pathway, as reported in a mice model (46). Ecgonine was reportedly involved in energy metabolism in an animal experiment (47). Our study discovered that it was related to tropane, piperidine, and pyridine alkaloid biosynthesis. Recently, Methyl jasmonate was found to influence the neuroprotective active and stress-induced behavioral in animal models (48, 49). We demonstrated that Methyl jasmonate might play a role in twins' growth by functioning in the alpha-Linolenic acid metabolism and biosynthesis of secondary metabolites. Apart from the advantages we have, there are several limitations in our research. Firstly, as it showed in our study, genetic effects functioned on infants’ gut microbiota. However, we did not evaluate the exact gene or locus that may influence gut microbiota. Further studies are needed in this area. Secondly, the number of infants in MCFT and MCPT groups are relatively small, which may reduce the power to identify some more gut microbiota and metabolites that are correlated with environmental effects. Thirdly, almost all twins were delivered by Cesarean section, which may lack generalizability to twins born vaginally. Fourthly, we did not collect data about antibiotics revived at birth, which may influence the initial gut microbiota. Lastly, although we found the correlations between group specified gut microbiota and metabolites, we did not conduct animal experiments to confirm and explain the correlations. Declarations Funding This study was supported by the National Natural Science Foundation of China grant No. 81903332, the Applied Frontier Project of Wuhan Municipal Science and Technology Bureau grant 2019020701011488 and 2020020601012307, and the Medical Young Talents (2019) of Hubei Province. Acknowledgements We are grateful to all the families who took part in this study. We also thank the Wuhan Twin Birth Cohort study team members, which included interviewers, nurses, computer and laboratory technicians, volunteers, managers and receptionists. We also thank Home for Researchers editorial team (www.home-for-researchers.com) for language editing service. Author’s contributions HM, FYX, HZ, RZL, and ANP coordinated the Wuhan Twin Cohort Study and collected the data. HM, JDZ, AFZ, and HX designed the study and obtained funding; HM, XNC, and GLH analyzed the data and wrote the manuscript; LQH and MY finalized the tables and figures in the manuscript. All authors critically reviewed the manuscript and approved the final version for submission. Data availability statement The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA007379) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa. Competing interests The authors declare no competing interests. References Huang K, Waken RJ, Luke AA, Carter EB, Lindley KJ, Joynt Maddox KE. Risk of delivery complications among pregnant people experiencing housing insecurity. American journal of obstetrics & gynecology MFM. 2023;5(2):100819. 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Sample information at 12 months old DCFT group (n) DCPT Group (n) MCFT Group (n) MCPT Group (n) Twin pairs （n） Total sample size (n) Gut microbiome 136 60 41 31 125 268 Gut metabolome 74 30 18 16 67 138 Anthropometric measurements 162 79 50 33 160 324 Neurobehavior 125 70 40 25 131 260 Note: n refers to number of twins; DCFT refers to dichorionic-diamniotic full-term twins; DCPT refers to dichorionic-diamniotic preterm term twins; MCFT refers to monochorionic-diamniotic full-term twins; and MCPT refers to monochorionic-diamniotic preterm term twins; preterm term was defined as delivery with a gestational age less than 37 weeks. Table 2 Basic characteristics of participants in the four groups DCFT DCPT MCFT MCPT P value Maternal pre-pregnant BMI (kg/m 2 ); mean (SD) 21.01 (2.67) 22.11 (2.95) 21.50 (2.71) 23.57 (4.45) 0.001 Paternal BMI (kg/m 2 ); mean (SD) 24.02 (4.39) 24.34 (4.81) 24.87 (3.70) 22.56 (3.00) 0.203 Maternal educational level; n (%) < 0.001 Middle school or less 64 (53.33) 16 (39.02) 5 (13.51) 10 (40.00) High school 46 (38.33) 13 (31.71) 24 (64.86) 12 (48.00) College or above 10 (8.33) 12 (29.27) 8 (21.62) 3 (12.00) Maternal age at delivery (year) ; mean (SD) 31.34 (3.31) 31.26 (3.18) 29.63 (5.04) 28.90 (3.05) 0.003 ART; n (%) 55 (45.83) 22 (53.66) 2 (5.41) 2 (8.00) < 0.001 C-section delivery rate; n (%) 118 (98.33) 39 (95.12) 37 (100.00) 23 (92.00) 0.17 Child sex; n (%) 0.90 Boy 58 (48.33) 22 (53.66) 20 (54.05) 13 (52.00) Girl 62 (51.67) 19 (46.34) 17 (45.95) 12 (48.00) Gestational age (week) ; mean (SD) 37.50 (0.41) 35.60 (1.34) 37.41 (0.41) 35.56 (1.34) < 0.001 Birth weight (g) ; mean (SD) 2636.25 (307.21) 2394.15 (435.87) 2657.84 (365.05) 2226.80 (479.53) < 0.001 Feeding pattern at 1 st month old; n (%) 0.63 Exclusive breastfeeding 10 (8.47) 3 (7.69) 3 (8.57) 5 (21.74) Mixed feeding 95 (80.51) 31 (79.49) 28 (80.00) 16 (69.57) Formula feeding 13 (11.02) 5 (12.82) 4 (11.43) 2 (8.70) Feeding pattern at 6 th months old; n (%) 0.41 Exclusive breastfeeding 2 (1.82) 2 (5.13) 0 (0.00) 0 (0.00) Mixed feeding 60 (54.55) 18 (46.15) 23 (67.65) 14 (60.87) Formula feeding 48 (43.64) 19 (48.72) 11 (32.35) 9 (39.13) Antibiotic used at 6 th months old; n (%) 61 (50.83) 20 (48.78) 16 (43.24) 15 (60.00) 0.63 Antibiotic used at 12 th months old; n (%) 63 (52.50) 23 (56.10) 18 (48.65) 15 (60.00) 0.82 Note: DCFT refers to dichorionic-diamniotic full-term twins; DCPT refers to dichorionic-diamniotic preterm term twins; MCFT refers to monochorionic-diamniotic full-term twins; and MCPT refers to monochorionic-diamniotic preterm term twins; preterm term was defined as delivery with a gestational age less than 37 weeks; BMI: body mass index; ART: assisted reproductive technology. Table 3. Neurocognitive development for twins with different gestational age and chorionicity (number of ND/DD) DCFT group DCPT group MCFT group MCPT group P value Communication 97/7 32/5 22/9 15/0 0.01 Gross motor 95/9 32/5 24/7 14/1 0.35 Fine motor 96/8 32/5 29/2 15/0 1.00 Problem solving 93/11 29/8 26/5 15/0 0.16 Personal social 90/0 33/0 24/0 14/0 1.00 Neurocognitive problem 99/5 33/4 31/0 15/0 0.23 Note: ND/DD refers to normal development/development delay; DCFT refers to dichorionic-diamniotic full-term twins; DCPT refers to dichorionic-diamniotic preterm term twins; MCFT refers to monochorionic-diamniotic full-term twins; and MCPT refers to monochorionic-diamniotic preterm term twins; preterm term was defined as delivery with a gestational age less than 37 weeks; false discovery rate was used for p values. Table 4. Associations between the gestational age and chorionicity specific gut microbiota genera and physical development WFA LFA BMI_Z Co-efficient P value Co-efficient P value Co-efficient P value Anaerostipes -0.04 0.11 -0.02 0.82 -0.04 0.45 Kosakonia -0.01 1 0.02 0.09 -0.03 0.03 Acinetobacter -0.01 0.26 0.01 1 -0.02 0.08 Bilophila -0.01 0.33 0.01 1 -0.02 0.11 Finegoldia 0.01 0.21 -0.01 0.57 0.02 0.01 Negativicoccus 0.01 1 0.01 0.94 -0.01 1 Escherichia_Shigella -0.01 1 -0.06 0.14 0.04 0.93 Odoribacter 0.01 1 0.01 1 0.01 1 Hydrogenophaga 0.01 1 -0.01 1 0.01 1 Flavobacterium -0.01 1 -0.01 1 -0.01 1 Massilia 0.02 0.29 -0.01 0.42 0.03 0.01 Anaerostipes -0.04 0.11 -0.02 0.82 -0.04 0.45 Kosakonia -0.01 1 0.02 0.09 -0.03 0.03 Parabacteroides 0.01 0.61 -0.01 0.68 0.03 0.13 Alistipes 0.02 0.02 0.01 1 0.03 0.01 Anaerotruncus -0.01 0.17 -0.01 0.38 -0.01 1 Note: WFA -weight for age z score, LFA - length for age z score, and BMI_Z - BMI z score. GLM models were used with twins' sex, birthweight, delivery mode and antibiotic used within the first 12 months of life adjusted; false discovery rate was used for p values. Additional Declarations No competing interests reported. Supplementary Files Supplementarytablesandfigures.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4381172","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":303245269,"identity":"9dac74fc-c6bd-4dc1-8702-9eb0f0f7a094","order_by":0,"name":"Hong Mei","email":"","orcid":"","institution":"Wuhan Children’s Hospital (Wuhan Maternal and Child Healthcare Hospital, Huazhong University of Science and Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Mei","suffix":""},{"id":303245270,"identity":"a73225ac-2efe-404c-8a14-2460ac5d76b2","order_by":1,"name":"Liqin 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monochorionic-diamniotic preterm term twins. (A) The comparison of Chao1 index of gut microbiota among DCFT, DCPT, MCFT and MCPT groups. * refers to significant difference between groups, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. (B) The comparison of Shannon index of gut microbiota among the four groups. (C) Bray-Curtis distances were calculated and compared among the four groups. * refers to significant difference between groups, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05. (D) Principal component analysis plot of gut microbiota for twins in the four groups. Ellipses represent a 95% CI. (E) The average of log transformed relative abundance for the 16 specified gut microbiota of the four groups. (F) The cumulative frequency of log transformed relative abundance for the 16 specified gut microbiota of the four groups. RA refers to relative abundance.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4381172/v1/667088b846c96a7d4683759b.jpg"},{"id":57034545,"identity":"f9d4a7c2-672a-4ccc-84e0-93bbebf84327","added_by":"auto","created_at":"2024-05-23 18:28:57","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1208771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContribution of genetic and environmental factors on 12 months old twins’ gut microbiota. \u003c/strong\u003eThe x-axis refers to the 16 groups specified gut microbiota at the genus level. The y-axis represents the proportion of genetic effects (green), common environment effects (red), and unique environment (blue) for each genus.\u003c/p\u003e","description":"","filename":"Figure2ACE.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4381172/v1/7fb1bffb64143397c39bb174.jpg"},{"id":57034547,"identity":"3e26351e-59e4-454b-98f4-8e8e90858376","added_by":"auto","created_at":"2024-05-23 18:28:57","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":339638,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe KEGG enrichment pathways metabolites mainly involved in.\u003c/strong\u003e Abscissa variations indicate ratio of the number of differential metabolites in the corresponding pathway to the total number of determined metabolites in that pathway. The bigger the ratio is, the higher enrichment of metabolites are in the pathway. The color of the dots represents the \u003cem\u003ep\u003c/em\u003evalue of the hypergeometric test. The smaller the value is, the more reliable the test is. The size of the dots represents the number of differential metabolites in the corresponding pathway. The bigger the size of the dots are, the more differential metabolites are enriched in the pathway.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4381172/v1/b04a49b156c1f23bc704ba72.jpg"},{"id":57034546,"identity":"4992fe4a-4ceb-4768-99dd-ac49bad76d19","added_by":"auto","created_at":"2024-05-23 18:28:57","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355120,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe comparison of physical indicators among the four groups and correlations between metabolites and developmental indicators.\u003c/strong\u003e DCFT refers to dichorionic-diamniotic full-term twins; DCPT refers to dichorionic-diamniotic preterm term twins; MCFT refers to monochorionic-diamniotic full-term twins; and MCPT refers to monochorionic-diamniotic preterm term twins. LFA refers to length for age z score; WFA refers to weight for age z score; BMI_Z refers to body mass index for age z score.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4381172/v1/e1d898a86817d9198919a908.jpg"},{"id":90571206,"identity":"2d1b1d72-5311-4910-8416-d7a0ca05336e","added_by":"auto","created_at":"2025-09-04 08:24:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3726839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4381172/v1/a5760a65-7d0f-4533-b5b3-6d21ced9b58b.pdf"},{"id":57034548,"identity":"e3a2b9db-72fc-4ebe-8663-206011517a0d","added_by":"auto","created_at":"2024-05-23 18:28:57","extension":"doc","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1952768,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytablesandfigures.doc","url":"https://assets-eu.researchsquare.com/files/rs-4381172/v1/8fce2aea0b6165d064241d51.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preterm birth affects the gut microbiota, metabolome and health outcomes of twins at 12 months of age: a case control study","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreterm birth (PT) generally refers to birth with a gestational age below 37 weeks. Preterm birth affects approximately 10% of babies worldwide\u0026nbsp;(1), and preterm birth rate in twin pregnancies can reach 35% or higher\u0026nbsp;(2). Preterm birth has short and long-term health consequences on the human body. For example, almost 25% of preterm births have adverse neurodevelopmental outcomes at birth\u0026nbsp;(3). Furthermore, premature birth is an independent predictor of short- and long-term respiratory and cardiovascular risks\u0026nbsp;(4, 5). For children at an early age, preterm birth has a widely spanned impact on anthropometric growth, cognitive development, and behavior\u0026nbsp;(6-8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs a research topic of utmost interest in recent years, the human microbiota has been documented to play non-negligible roles in the gut-systemic metabolic interplay, which may influence immune and metabolic development in early life and alter offspring cognitive development and behavior through the microbiota-gut-brain axis\u0026nbsp;(9-11). Many studies indicate that host genetics and preterm birth can shape the microbiome at birth and persistently influence the pace of microbial acquisition before getting an adult-like gut microbiome construction\u0026nbsp;(12-14). However, studies about gene and preterm birth\u0026apos;s combined or interactive effect on the gut microbiota are scarce. Twins have high genetic and environmental resemblance as monozygotic twins share 100% of their genes compared to dizygotic twins with 50% shared genes, and they are characterized by the common environment of parents and family, like parenting style, education, and so on\u0026nbsp;(15). These similarities make twins an excellent model for disentangling the effect of genetic and environmental factors on shaping gut microbiota. However, most twin studies on the genetic and environmental determinants of the gut microbiota have focused on genetic effects; twin studies on the association between genetic and environmental factors and the gut microbiota are rare\u0026nbsp;(16, 17).\u003c/p\u003e\n\u003cp\u003eApart from the zygosity characteristics, twins have chorionicity, which can be classified as dichorionic-diamniotic and monochorionic-diamniotic twins. Approximately 20% twins are monochorionic-diamniotic, and most of them are monozygotic. Several studies have found that adverse perinatal outcomes among twins differ significantly depending on chorionicity\u0026nbsp;(18-20), among which monochorionic-diamniotic imparts a greater risk of preterm birth than dichorionic-diamniotic twins\u0026nbsp;(21). Preterm birth in \u0026nbsp;dichorionic-diamniotic twins could be attributed to various genetic or/and environmental factors, including genetic dissimilarity and differences in placental mass, placental insufficiency, umbilical venous diameter, and flow\u0026nbsp;(22-24). On the contrary, monochorionic-diamniotic twins share more genetic resemblance as most of them are monozygosity pairs, and the particular challenges of preterm birth in monochorionic-diamniotic twins arise mainly from the shared placenta and the placental vascular anastomoses, which are almost universal\u0026nbsp;(25). The correlation between chorionicity and preterm birth makes twin pairs ideal for investigating placental-related long-term growth and development in children and how genetic and environmental factors shape the gut microbiome and metabolism. Therefore, well-designed twin studies are required to elucidate the impact of\u0026nbsp;genetic and intrauterine environmental factors on gut microbiota.\u003c/p\u003e\n\u003cp\u003eBased on the Wuhan Twin Birth Cohort study, we collected stool samples for twin pairs at 12 months of age and used them for 16S rDNA sequencing in 268 twins and for untargeted metabolomics in 138 twins. Then we classified the twins into four groups based on their gestational age at birth and chorionicity: dichorionic-diamniotic full-term birth (DCFT) group, dichorionic-diamniotic preterm birth (DCPT) group, monochorionic-diamniotic full-term birth (MCFT) group, and monochorionic-diamniotic preterm birth (MCPT). The first goal was to determine the effect of gestational age at birth and chorionicity on the gut microbiota and metabolism of twins at 12 months of age. Then, to evaluate the effect of microbiota and metabolites on physical and neurobehavioral development at 12 months of age.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Wuhan Children\u0026rsquo;s Hospital (Wuhan Maternal and Child Healthcare Hospital) (IRB number: WHFE2016050) and performed according to relevant guidelines and regulations. Informed consent to infants\u0026rsquo; parents was conducted before the investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSubject recruitment\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted based on the ongoing Wuhan Twin Birth Cohort study. Mothers were approached for informed consent between March 2016 and December 2020 at Wuhan Children\u0026apos;s Hospital in China. The inclusion criteria for this sub-study contained live neonates from dichorionic-diamniotic and monochorionic-diamniotic twin pregnancies who were followed up at 12 months of age with anthropometric measurements, stool samples, and neurobehavioral estimates. The exclusion criteria included mothers with gestational diabetes mellitus, gestational hypertension during pregnancy, fetus with twin to twin transfusion syndrome, and neonates with birth defect.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eData collection\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwins\u0026apos; gestational weeks at delivery and chorionicity of placenta were collected from the birth record in the electronic medical record system. Gestational age at delivery was calculated as the difference between the twins\u0026apos; birth date and the maternal last menstrual date.\u0026nbsp;Twins can be classified as PT for gestational age \u0026lt; 37 gestational weeks, FT for gestational age \u0026ge; 37 gestational weeks, dichorionic-diamniotic and monochorionic-diamniotic twins. We further calculated the twins as DCPT, DCFT, MCPT, and MCFT according to their gestational age at birth and\u0026nbsp;chorionicity. Twins\u0026apos; sex, weight, length, and neurobehavior were measured at 12 months of age.\u0026nbsp;The twins\u0026apos; zygosity was also detected using neonatal blood spot samples by short tandem repeats genetic typing and categorized as mono-zygosity (MZ) or di-zygosity (DZ). Twins\u0026apos; sex was set as boy or girl. Body mass index (BMI) was calculated as weight (kg) divided by the square of the height (m\u003csup\u003e2\u003c/sup\u003e). Sex and age-specified weight, length, and BMI were generated as the weight for age z score (WFA), length for age z score (LFA), and BMI z score (BMI_Z) according to the WHO Child Growth Standards (2006)\u0026nbsp;(26). Twins\u0026apos; neurobehavioural developments\u0026nbsp;at 12 months of age were evaluated using Age and Stage Questionnaire, 3\u003csup\u003erd\u003c/sup\u003e version (ASQ-3)\u0026nbsp;(27). ASQ-3 contained five development domains, including communication, gross motor, fine motor, problem-solving, and personal social, with 60 points possible in the combined evaluation for each infant. For each domain of the ASQ-3, infants\u0026apos; neurobehavioral development was set as normal development, suspected development delay, or development delay. Any domain screened \u0026gt; -1 SD was defined as normal development (ND), screened between -2 SD and -1 SD was defined as suspected development delay, and \u0026lt; -2 SD was defined as developmental delay. As the proportion of development delay domains in our participants was small, we merged suspected development delay and development day as development delay (DD). To fully assess the neurocognitive development, we generated a variable as a neurocognitive problem, set as yes or no. The answer yes was defined as at least one of the five development domains being DD, and the answer no was defined as all the five development domains being ND. Apart from these leading indicators, we also collected parental BMI, maternal educational level, maternal age at delivery, delivery mode, whether used assisted reproductive technology (ART), twins\u0026apos; birth weight, feeding patterns at 1\u003csup\u003est\u003c/sup\u003e and 6\u003csup\u003eth\u003c/sup\u003e months, and antibiotics used during the first 6 and 12 months of life.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStool sampling, DNA extraction, and high-throughtput sequencing\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh stool samples were collected in a sterile plastic container by caregivers 1 or 2 days before the interview and immediately frozen at -20℃\u0026nbsp;at home before being transferred to the -80℃\u0026nbsp;freezer within 1 hour of arriving at the study hospital. DNA extraction was conducted using the QIAamp PowerFecal DNA Kit (QIAGEN, Germantown, MD, United States), and high-throughput sequencing was provided by Novogene Co. Ltd (Beijing, China). DNA extraction and 16S rDNA high-throughput sequencing in V3-V4 region details were illustrated in one of our previous studies\u0026nbsp;(28).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMicrobial community analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor microbiota community analyses, alpha diversity, including Shannon and Chao1 index, was calculated using QIIME (version 1.9.1, http://qiime.org/scripts/split_libraries_fastq.html). Bray-Curtis distance was used to evaluate the species complexity differences of samples. The Wilcoxon rank sum test performed statistically significant differences in Shannon, Chao1 indexes, and species complexity differences between DCPT, DCFT, MCPT, and MCFT groups. PCoA was used to discriminate the signatures among the four groups. For taxonomic diversity analysis, the genera taxa were filtered and transformed as follows: (1) only genera taxa presented in \u0026gt; 10% of stool samples at 12 months old were used; (2) genus-level relative abundance should be normal distribution after log2 transformed. Zero values were disposed of by plus one before log2-transformation. We used the general linear model (GLM) with Tukey\u0026apos;s honestly significant difference comparisons to identify microbiota species that were differentially abundant among the four groups, DCPT, DCFT, MCPT, and MCFT, after controlling for twins\u0026apos; sex, delivery model, and birth weight. Means of log-transformed relative abundance for differential genus taxa in the four groups were clustered using GraphPad Prism 8.0.1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHeritability of the microbiome\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe twin-based ACE model was used to evaluate the contribution of genetic and environmental effects on the composition and function of the gut microbiota according to methods from Jing Yang et al\u0026nbsp;(29). The ACE model partitions the total variance of the gut microbiota into three components: A for additive genetic effect, C for common/shared environment effect, and E for unique environment effect by comparing trait variability in MZ twin pairs versus DZ twin pairs. Before calculating the microbial heritability, raw count tables were filtered and transformed as follows. First, data in the ACE models were collected from paired MZ and DZ twins. Furthermore, only genus-level taxa with significant differences between DCPT, DCFT, MCPT, and MCFT groups analyzed by\u0026nbsp;GLM\u0026nbsp;were considered in the ACE models. Finally, each taxon\u0026apos;s heritability estimation was calculated using the ACElong package in Stata 15.1. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUntargeted metabolomics analysis of stool samples\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetabolite analysis of stool samples of 12 months old twins, including metabolite extraction, data preprocessing and statistical analysis, were performed by Novogene Co, Ltd (Beijing, China). Detailed methods were attached in the supplementary.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical analysis\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntergroup comparisons, including for microbial alpha and beta diversity, between DCPT, DCFT, MCPT, and MCFT groups, between dichorionic-diamniotic and monochorionic-diamniotic groups, and between FT and PT groups, were conducted with Wilcoxon\u0026apos;s rank-sum test with multiple testing correction. Association between groups and log-transformed relative abundance differences for specific taxa, between groups and physical development indicators, WFA, LFA, and BMI_Z, and between log-transformed relative abundance differences for specific taxa and physical development indicators were conducted using GLMs. Association between groups and indicators for neurobehavioural developments were analyzed using multiple logistic regression models with twins\u0026apos; sex, birth weight, and delivery mode. False discovery rate was used for \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalues in multiple testing.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eWe included 350 twin pregnancy cases and tracked them for 12 months, collecting data on their gut microbiome, metabolome, anthropometrics, and neurobehavior. As a result, 119 twin pairs had all data on gut microbiota, metabolites, anthropometrics and neurobehaviors. Details of the main variables collected werer included in Table 1. According to Table 2, maternal pre-pregnancy\u0026nbsp;Body mass index (BMI)\u0026nbsp;was significantly higher in the DCPT and MCPT groups compared to the DCFT and MCFT groups\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Maternal educational level and delivery age were also significantly different among the four groups, with ART significantly higher in the dichorionic-diamniotic groups than in the monochorionic-diamniotic groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). However, no significant difference was found in paternal BMI, delivery mode, child sex, feeding pattern at 1\u003csup\u003est\u003c/sup\u003e and 6\u003csup\u003eth\u003c/sup\u003e months, and antibiotics used at 6\u003csup\u003eth\u003c/sup\u003e and 12\u003csup\u003eth\u003c/sup\u003e months (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePreterm birth outweighs genetics in shaping twins\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026apos; gut microbiota at 12 months old\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the group rule, we first examined whether preterm birth and chorionicity could affect microbial communities at twins 12 months of age. We observed that the Chao1 index in alpha diversity of the gut microbiota in the MCPT group was significantly higher than that in the MCFT group (\u003cem\u003ep\u003c/em\u003e = 0.03) (Figure 1A). However, no significant difference was found in the Shannon index among the four groups (Figure 1B). Subgroup analysis for PT and full-term birth (FT) groups showed a significantly higher Chao1 index for PT group twins (Supplemental Figure 1A), while no significant difference was found in the Shannon index between the two groups (Supplemental Figure 1B). Subgroup analysis in chorionicity found no significant difference in Chao1 and Shannon index (Supplemental Figure 1C and 1D). These results suggested that preterm birth outweighs genetics in shaping gut microbiota richness in 12 months old twins. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNext, we calculated the\u0026nbsp;Bray-Curtis\u0026nbsp;of the microbial community between individuals of each group (Figure 1C). The\u0026nbsp;Bray-Curtis\u0026nbsp;distances between samples in the MCPT group were significantly higher than those in the MCFT group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). However, for the\u0026nbsp;dichorionic-diamniotic\u0026nbsp;twins, the\u0026nbsp;Bray-Curtis\u0026nbsp;distances were higher in the FT twins than in the PT twins (\u003cem\u003ep\u003c/em\u003e = 0.002). A similar trend of the\u0026nbsp;Bray-Curtis\u0026nbsp;distances was found between DCFT versus MCFT (DCFT \u0026gt; MCFT, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and between DCPT versus MCPT (DCPT \u0026lt; MCPT, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Finally, subgroup analysis was conducted to confirm the difference according to gestational age at birth and chorionicity. The\u0026nbsp;Bray-Curtis\u0026nbsp;distances between samples in the PT group were significantly higher than those in the FT group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Supplemental Figure 2A). In addition, the\u0026nbsp;Bray-Curtis\u0026nbsp;distances between samples in the dichorionic-diamniotic group were higher than those in the monochorionic-diamniotic group (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Supplemental Figure 2B). These results indicated that preterm birth and dichorionic-diamniotic twins might have higher heterogeneity between individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePrincipal coordinate analysis (PCoA)\u0026nbsp;was used to further investigate the clustering effect of microbial communities among the four groups. The samples were similar within each of the four groups and separated between different groups (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Adonis test), with the distribution of\u0026nbsp;DCFT and DCPT samples relatively more dispersed than that of MCFT and MCPT samples (Figure 1D). Subgroup analysis found that the bacterial signatures in PT twins were significantly distinct from that in the FT twins (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Supplemental Figure 2C), while no significant difference was found between dichorionic-diamniotic and monochorionic-diamniotic groups (\u003cem\u003ep\u003c/em\u003e =0.2) (Supplemental Figure 2D). These results indicated that preterm birth might lead to the distinctions among individuals outweighing genetic effects.\u003c/p\u003e\n\u003cp\u003eBy conducting a clustering analysis of microbial classification and abundance in each group, we identified 16 genera in four groups with different relative abundances. Log transformed means and standard deviation are shown in Figure 1E. The cumulative frequency of the 16 discriminatory genera in the four groups was displayed in Figure 1F. Figure 1F shows that the genus \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eKosakonia, Parabacteroides\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Anaerotruncus\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Acinetobacter\u003c/em\u003e were among the microbiota with highest cumulative frequency of log transformed RA, and the cumulative frequency of the DCFT groups was significantly larger than that in the MCFT groups. These results further confirmed that chorionicity significantly shapes the gut microbiota in 12 months old twins. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSpecific taxa correspond to the environment or genetic factors\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to determine how the environment and genetic factors impact the 16 specific genera, apart from the total sample size, 119 twin pairs were further separated into full-term and preterm twins (Figure 2). We observed that the effect of environmental and genetic factors differed among the 16 specific taxa in total sample size and between twins with full-term and preterm birth. From the total sample size, in most genera, the effect of unique environment effect (E) and common/shared environment effect (C) was larger than that of additive genetic effect (A), apart from genera \u003cem\u003eHydrogenophaga\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFinegoldia\u003c/em\u003e. Compared to the effects of specific taxa between preterm and full-term twins, the effect of A was larger in full-term twins for genera \u003cem\u003eHydrogenophaga\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e, \u003cem\u003eNegativicoccus,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eAnaerostipes\u003c/em\u003e, and the effect of C versus E were specifically different for most taxa. For example, for genera \u003cem\u003eSerratia\u003c/em\u003e, the effect of C and E in the full-term twins was 6.3% and 81.1%, while in the preterm twins, it was 77.67% and 22.32%, respectively (Supplemental Table 1). These results demonstrated that the effect of genetic and environmental factors on the microbiota was influenced by preterm birth.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAltered fecal metabolites co-occurred with distinguished gut microbiota in preterm births\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUntargeted metabolomics analysis was simultaneously applied to compare the metabolic signatures among the four groups. Based on generalized linear models (GLMs), we compared the metabolic differences among the four groups for all the 2721 metabolites (1765 positive and 956 negative metabolites) with confounding factors controlled. The concentration of 285 metabolites significantly differed among the four groups (Supplemental Table 2).\u0026nbsp;Among the 285 metabolites, multiple metabolites displayed enrichment or depletion among the four groups, as shown in supplemental table 2. These altered metabolites were mainly involved in histidine metabolism, the Phosphotransferase system, isoflavonoid biosynthesis, valine, leucine, and isoleucine degradation in up-regulated metabolites, and the degradation of aromatic compounds, flavonoid biosynthesis, and aminobenzoate degradation in down-regulated metabolites (Figure 3).\u003c/p\u003e\n\u003cp\u003eWe explored the potential correlations of abundances of the 16 differential bacterial species and 285 fecal metabolites. Overall, the co-occurrence analysis revealed that the genera \u003cem\u003eAnaerotruncus\u003c/em\u003e and \u003cem\u003eEscherichia Shigella\u003c/em\u003e were correlated with the greatest number of metabolites, with 53 and 41 metabolites having correlation values greater than 0.2. Furthermore, the top two metabolites correlated with most microbiota genera were Benzophenone (Com_12390_pos) and 5-Methyl-2\u0026apos;-deoxycytidine (Com_2803_pos) (Supplementary table 3).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGut microbiota and metabolites correlated with twins\u0026apos; physical and neurocognitive development in preterm births\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the correlation between gut microbiota, metabolites, and twins\u0026apos; physical and neurocognitive development, we first performed Analysis of Variance (ANOVA) and chi-square tests to explore the differences between\u0026nbsp;WFA, LFA, and BMI_Z, and differences of the five neurocognitive development domains,\u0026nbsp;communication, gross motor, fine motor, problem-solving and personal social, among the four groups. No significant difference was found in WFA among the four groups (Figure 4A). We found that twins in the\u0026nbsp;DCPT group have the smallest LFA compared with the other three groups (Figure 4B), and the BMI_Z in the\u0026nbsp;DCPT twins was larger than that in the DCFT twins\u0026nbsp;(Figure 4B). Results from chi-square tests showed that twins in the\u0026nbsp;MCFT group have the highest risks for communication problems; no significant difference was found in the other four domains among the four groups (Table 3). Next, we used GLMs to evaluate the association between the 16 specific gut microbiota genera and WFA, LFA, and BMI_Z with twins\u0026apos; sex, birthweight, delivery mode, and antibiotic used within the first 12 months of life adjusted for. Finally, only seven microbiota genera, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e, \u003cem\u003eKosakonia\u003c/em\u003e, \u003cem\u003eNegativicoccus\u003c/em\u003e, \u003cem\u003eAcinetobacter,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eFinegoldia\u003c/em\u003e, were found to be associated with WFA or BMI_Z (Table 4). We adopted multiple logistic regression models to evaluate the association between\u0026nbsp;the 16 specific gut microbiota genera and the six neurocognitive development variables. We found \u003cem\u003eParabacteroides\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eFinegoldia\u003c/em\u003e were significantly associated with communication in 12 months old twins. \u003cem\u003eAcinetobacter\u003c/em\u003e was associated with gross motor, \u003cem\u003eSubdoligranulum\u003c/em\u003e was associated with problem-solving, and \u003cem\u003eNegativicoccus\u003c/em\u003e was associated with personal social and neurocognitive problems (Supplementary table 4). Thus, the seven microbiota genera as the candidate microbiota shaped by the gestational age and chorionicity might influence twins\u0026apos; physical and neurocognitive development at 12 months of age. Then, we used similar statistical strategies to screen the candidate 285 metabolites significantly different between the four groups and associated with the seven microbiota genera and the physical and neurocognitive development variables. Finally, 19 metabolites were filtered out, as shown in Figure 4D. Finally, based on the KEGG database, we searched the functional pathway of the 19 metabolites, and only three were found in three pathways. Among them, N-Oleoyl dopamine functioned in the neuroactive ligand-receptor interaction pathway (Supplemental figure 3). Ecgonine functioned in the tropane, piperidine, and pyridine alkaloid biosynthesis pathway (Supplemental figure 4). Methyl jasmonate functioned in the alpha-Linolenic acid metabolism and biosynthesis of secondary metabolites (Supplemental figure 5).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study exploring the relationship between gestational age and chorionicity and 12 months old gut microbiota, metabolic, and physical and neurobehavioral development in twins. We found a significant difference in the relative abundance of 16 microbiota genera and the concentration of 285 metabolites among the gestational age and chorionicity specified four group twins. The top two active gut microbiota and metabolites were\u0026nbsp;\u003cem\u003eAnaerotruncus and Escherichia Shigella in\u0026nbsp;\u003c/em\u003egenera and\u003cem\u003e\u0026nbsp;\u003c/em\u003eBenzophenone and 5-Methyl-2\u0026apos;-deoxycytidine, respectively. In addition\u003cem\u003e, Parabacteroides\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u003c/em\u003e, \u003cem\u003eNegativicoccus\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e, \u003cem\u003eAcinetobacter, Alistipes\u003c/em\u003e, and \u003cem\u003eKosakonia\u003c/em\u003e were associated with WFA or BMI_Z. Furthermore, \u003cem\u003eParabacteroides\u003c/em\u003e,\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eFinegoldia, Acinetobacter,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Subdoligranulum\u0026nbsp;\u003c/em\u003ewere associated with infant neurocognitive development in some aspects. For metabolism, 19 metabolites were filtered to be significantly associated with infants\u0026apos; physical and neurocognitive development. They were mainly involved in the\u0026nbsp;neuroactive ligand-receptor interaction pathway, tropane, piperidine, and pyridine alkaloid biosynthesis pathway, alpha-Linolenic acid metabolism, and biosynthesis of secondary metabolites.\u003c/p\u003e\n\u003cp\u003eGestational age at birth and chorionicity are two main signature features of twins. Their combination may lead to different maternal and neonatal birth outcomes and influence infants\u0026apos; bacterial colonization\u0026nbsp;(18). For instance, studies on preterm birth reported that the preterm microbiota displays delayed maturity with prolonged membership of facultative anaerobic bacteria compared to that of the predominantly strict anaerobic community of term infants\u0026nbsp;(30, 31). For another, the difference in chorionicity may lead to the difference in fetus gut microbiota establishment, thus influencing microbiota assembly after birth\u0026nbsp;(29). This study found a significantly higher richness of relative abundance in the\u0026nbsp;MCPT group than in the MCFT and DCFT groups. In addition, higher Bray-Curtis distances were found in preterm birth and dichorionic-diamniotic twins. Results from PCoA also displayed similar results. All these results reflected that preterm birth might influence gut microbiota structure at 12 months of age after controlling for the effect of genetic influence (adjusting for chorionicity)\u0026nbsp;(12, 32). We also filtered 16 specific gut microbiota genera for the four groups by comparison analyses. Among the 16 genera, the cumulative frequency in the relative abundance of the genus \u003cem\u003eAnaerostipes\u003c/em\u003e, \u003cem\u003eKosakonia, Parabacteroides\u003c/em\u003e, \u003cem\u003eAlistipes\u003c/em\u003e,\u003cem\u003e\u0026nbsp;Anaerotruncus\u003c/em\u003e, \u003cem\u003eDialister\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Acinetobacter\u0026nbsp;\u003c/em\u003ewas significantly higher in the\u0026nbsp;DCFT groups\u0026nbsp;than that in the\u0026nbsp;MCFT groups. These results indicated that chorionicity plays a significant role in shaping the gut microbiota in 12 months old twins, consistent with the results of Yang et al. for twin neonates who suffered from fetal growth restrictions\u0026nbsp;(29). To further confirm the effects of genes and environment on gut microbiota, we assessed the influence of genetic and environmental factors on the 16 distinct microbiota. The effect of E and C was larger than that of A for most genera. At the same time, there was a trend that the effect rationale of E and C was inconsistent between full-term and preterm twins for each genus. These results reflected that environmental factors, including preterm birth, may impact gut microbiota more at 12 months of age\u0026nbsp;(33, 34).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study also detected fecal metabolites as a functional readout of gut microbiota. We identified\u0026nbsp;285 metabolites that significantly differed in concentration among the four groups.\u0026nbsp;With KEGG analyses, the 285\u0026nbsp;metabolites\u0026nbsp;were categorized into 9 main metabolism pathways. Four of the 9 main metabolism pathways were essential amino acids, such as histidine metabolism,\u0026nbsp;valine, leucine, and isoleucine degradation. This finding was consistent with Nilsson et al.\u0026apos;s association between serum metabolomics and highly premature infants\u0026nbsp;(35). Apart from the essential amino acid pathways, we also detected\u0026nbsp;up-regulated\u0026nbsp;phosphotransferase systems\u0026nbsp;in preterm births\u0026apos; fecal samples at 12 months of age. Furthermore, the phosphotransferase system participated in coupled transport and phosphorylation of sugars\u0026nbsp;(36),\u0026nbsp;demonstrating a gestational age and chorionicity dependence\u0026nbsp;in sugar transport and phosphorylation. Flavonoids and their secondary metabolite, isoflavonoids, are plant-derived natural products that have been shown to improve human health\u0026nbsp;(37, 38). In this study, the isoflavonoid biosynthesis was up-regulated, and flavonoid biosynthesis was down-regulated in twins\u0026apos; fecal samples, suggesting the gestational age and chorionicity difference in fecal metabolisms. Apart from the reported pathways, we also found decreased metabolic levels in aromatic compounds and aminobenzoate degradation. More research is needed to understand the mechanisms underlying these correlations. In addition to the metabolic analysis for twins in the four groups, we found correlations between the specified gut microbiota genera and the distinct metabolites. Our results indicated that as one of the\u0026nbsp;top four distinct community states of preterm birth microbiota genera,\u0026nbsp;\u003cem\u003eEscherichia Shigella\u003c/em\u003e was responsible for the metabolism for preterm birth as it correlated with most of the distinct metabolites\u0026nbsp;(31, 39). Besides, we also detected \u003cem\u003eAnaerotruncus\u0026nbsp;\u003c/em\u003eas another top active genus in our study, and the top two active metabolites are Benzophenone and 5-Methyl-2\u0026apos;-deoxycytidine, while there is a lack of studies to report the possible mechanisms among them. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe topics of preterm birth, gut microbiota and metabolism, and child growth are not uncommon, but studies linking all of these topics together while accounting for genetic effects are lacking. Twins are a unique population in research on environmental and genetic effects. Jing Yang et al. conducted the only twins study on this topic. They focused on fetal growth restriction (FGR) twins and discussed the twin neonates\u0026apos; gut microbiota and metabolism and their effects on physical and neurobehavioral developments.\u0026nbsp;They found that twins with\u0026nbsp;FGR have dysbiotic gut microbiota and metabolic alterations, and the altered fecal cysteine level is positively correlated with the physical and neurocognitive developments of twins at 2 or 3 years of age\u0026nbsp;(29). We used this study design to investigate the relationship between preterm birth and chorionicity distinct gut microbiota and metabolites and twins\u0026apos; physical and neurobehavioral growth at 12 months of age. We found 7 gut microbiota genera were associated with physical growth, and four also had a relationship with neurocognitive development. Among the 7 gut microbiota, \u003cem\u003eParabacteroides\u003c/em\u003e\u003cem\u003e(40, 41)\u003c/em\u003e, \u003cem\u003eSubdoligranulum\u0026nbsp;\u003c/em\u003e(42, 43), and \u003cem\u003eAlistipes\u0026nbsp;\u003c/em\u003e(44, 45)\u003cem\u003e\u0026nbsp;\u003c/em\u003ewere reported to be functioned in obesity and mental health, while the other four genera \u003cem\u003eNegativicoccus\u003c/em\u003e, \u003cem\u003eFinegoldia\u003c/em\u003e, \u003cem\u003eAcinetobacter,\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;Kosakonia\u0026nbsp;\u003c/em\u003ewere not found to be discussed on health and disease. We also identified 19 metabolites significantly associated with twins\u0026apos; physical and neurocognitive development. Our results revealed that\u0026nbsp;N-Oleoyl dopamine might influence vas interplay\u0026apos;s physical and neurobehavioral development in the neuroactive ligand-receptor interaction pathway, as reported in a mice model\u0026nbsp;(46). Ecgonine was reportedly involved in energy metabolism in an animal experiment\u0026nbsp;(47). Our study discovered that it was related to tropane, piperidine, and pyridine alkaloid biosynthesis. Recently, Methyl jasmonate was found to influence the neuroprotective active and stress-induced behavioral in animal models\u0026nbsp;(48, 49).\u0026nbsp;We demonstrated that Methyl jasmonate might play a role in twins\u0026apos; growth by functioning in the alpha-Linolenic acid metabolism and biosynthesis of secondary metabolites.\u003c/p\u003e\n\u003cp\u003eApart from the advantages we have, there are several limitations in our research. Firstly, as it showed in our study, genetic effects functioned on infants\u0026rsquo; gut microbiota. However, we did not evaluate the exact gene or locus that may influence gut microbiota. Further studies are needed in this area. Secondly, the number of infants in MCFT and MCPT groups are relatively small, which may reduce the power to identify some more gut microbiota and metabolites that are correlated with environmental effects. Thirdly, almost all twins were delivered by Cesarean section, which may lack generalizability to twins born vaginally. Fourthly, we did not collect data about antibiotics revived at birth, which may influence the initial gut microbiota. Lastly, although we found the correlations between group specified gut microbiota and metabolites, we did not conduct animal experiments to confirm and explain the correlations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China grant No. 81903332, the Applied Frontier Project of Wuhan Municipal Science and Technology Bureau grant 2019020701011488 and 2020020601012307, and the Medical Young Talents (2019) of Hubei Province.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to all the families who took part in this study. We also thank the Wuhan Twin Birth Cohort study team members, which included interviewers, nurses, computer and laboratory technicians, volunteers, managers and receptionists. We also thank Home for Researchers editorial team (www.home-for-researchers.com) for language editing service.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHM, FYX, HZ, RZL, and ANP coordinated the Wuhan Twin Cohort Study and collected the data. HM, JDZ, AFZ, and HX designed the study and obtained funding; HM, XNC, and GLH analyzed the data and wrote the manuscript; LQH and MY finalized the tables and figures in the manuscript. All authors critically reviewed the manuscript and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics \u0026amp; Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation/Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA007379) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang K, Waken RJ, Luke AA, Carter EB, Lindley KJ, Joynt Maddox KE. 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Gastroenterology. 2021;160(6):2029-42.e16.\u003c/li\u003e\n\u003cli\u003eParker BJ, Wearsch PA, Veloo ACM, Rodriguez-Palacios A. The Genus Alistipes: Gut Bacteria With Emerging Implications to Inflammation, Cancer, and Mental Health. Frontiers in immunology. 2020;11:906.\u003c/li\u003e\n\u003cli\u003eKentish SJ, Frisby CL, Kritas S, Li H, Hatzinikolas G, O\u0026apos;Donnell TA, et al. TRPV1 Channels and Gastric Vagal Afferent Signalling in Lean and High Fat Diet Induced Obese Mice. PLoS One. 2015;10(8):e0135892.\u003c/li\u003e\n\u003cli\u003eBurczynski FJ, Boni RL, Erickson J, Vitti TG. Effect of Erythroxylum coca, cocaine and ecgonine methyl ester as dietary supplements on energy metabolism in the rat. Journal of ethnopharmacology. 1986;16(2-3):153-66.\u003c/li\u003e\n\u003cli\u003eAlabi AO, Ajayi AM, Ben-Azu B, Omorobge O, Umukoro S. Methyl jasmonate ameliorates rotenone-induced motor deficits in rats through its neuroprotective activity and increased expression of tyrosine hydroxylase immunopositive cells. Metabolic brain disease. 2019;34(6):1723-36.\u003c/li\u003e\n\u003cli\u003eAdebesin A, Ajayi AM, Olonode EO, Omorogbe O, Umukoro S. Methyl Jasmonate Ameliorates Unpredictable Chronic Mild Stress-Induced Behavioral and Biochemical Alterations in Mouse Brain. Drug development research. 2017;78(8):381-9.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Sample information at 12 months old\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"709\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.348377997179124%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003eDCFT\u003c/p\u003e\n \u003cp\u003egroup (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003eDCPT\u003c/p\u003e\n \u003cp\u003eGroup (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003eMCFT\u003c/p\u003e\n \u003cp\u003eGroup (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003eMCPT\u003c/p\u003e\n \u003cp\u003eGroup (n)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.258110014104373%\" valign=\"top\"\u003e\n \u003cp\u003eTwin pairs\u003c/p\u003e\n \u003cp\u003e（n）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\" valign=\"top\"\u003e\n \u003cp\u003eTotal sample size\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.348377997179124%\"\u003e\n \u003cp\u003eGut microbiome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.258110014104373%\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.348377997179124%\"\u003e\n \u003cp\u003eGut metabolome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.258110014104373%\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.348377997179124%\"\u003e\n \u003cp\u003eAnthropometric measurements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.258110014104373%\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.348377997179124%\"\u003e\n \u003cp\u003eNeurobehavior\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.399153737658674%\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.988716502115656%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.258110014104373%\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.61777150916784%\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: n refers to number of twins; DCFT refers to dichorionic-diamniotic full-term twins; DCPT refers to dichorionic-diamniotic preterm term twins; MCFT refers to monochorionic-diamniotic full-term twins; and MCPT refers to monochorionic-diamniotic preterm term twins; preterm term was defined as delivery with a gestational age less than 37 weeks.\u003c/p\u003e\n\u003cp\u003eTable 2 Basic characteristics of participants in the four groups\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003eDCFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003eDCPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003eMCFT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003eMCPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eMaternal pre-pregnant BMI (kg/m\u003csup\u003e2\u003c/sup\u003e);\u0026nbsp;mean (SD)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e21.01 (2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e22.11 (2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e21.50 (2.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e23.57 (4.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003ePaternal BMI (kg/m\u003csup\u003e2\u003c/sup\u003e);\u0026nbsp;mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e24.02 (4.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e24.34 (4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e24.87 (3.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e22.56 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"58.3739837398374%\" colspan=\"3\"\u003e\n \u003cp\u003eMaternal educational level;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.585365853658537%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.284552845528456%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Middle school or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e64 (53.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e16 (39.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e5 (13.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e10 (40.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; High school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e46 (38.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e13 (31.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e24 (64.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e12 (48.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; College or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e10 (8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e12 (29.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e8 (21.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e3 (12.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eMaternal age at delivery (year) ;\u0026nbsp;mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e31.34 (3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e31.26 (3.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e29.63 (5.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e28.90 (3.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eART;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e55 (45.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e22 (53.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e2 (5.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e2 (8.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eC-section delivery rate;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e118 (98.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e39 (95.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e37 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e23 (92.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eChild sex;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Boy\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e58 (48.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e22 (53.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e20 (54.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e13 (52.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Girl\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e62 (51.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e19 (46.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e17 (45.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e12 (48.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eGestational age (week) ;\u0026nbsp;mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e37.50 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e35.60 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e37.41 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e35.56 (1.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eBirth weight (g) ;\u0026nbsp;mean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e2636.25 (307.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e2394.15 (435.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e2657.84 (365.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e2226.80 (479.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.2439024390244%\" colspan=\"5\"\u003e\n \u003cp\u003eFeeding pattern at 1\u003csup\u003est\u003c/sup\u003e month old;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Exclusive breastfeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e10 (8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e3 (7.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e3 (8.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e5 (21.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Mixed feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e95 (80.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e31 (79.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e28 (80.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e16 (69.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Formula feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e13 (11.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e5 (12.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e4 (11.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e2 (8.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"90.2439024390244%\" colspan=\"5\"\u003e\n \u003cp\u003eFeeding pattern at 6\u003csup\u003eth\u003c/sup\u003e months old;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.75609756097561%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Exclusive breastfeeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e2 (1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e2 (5.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Mixed feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e60 (54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e18 (46.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e23 (67.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e14 (60.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003e\u0026nbsp; Formula feeding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e48 (43.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e19 (48.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e11 (32.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e9 (39.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eAntibiotic used at 6\u003csup\u003eth\u003c/sup\u003e months old;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e61 (50.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e20 (48.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e16 (43.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e15 (60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.246753246753247%\"\u003e\n \u003cp\u003eAntibiotic used at 12\u003csup\u003eth\u003c/sup\u003e months old;\u0026nbsp;n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e63 (52.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.935064935064934%\"\u003e\n \u003cp\u003e23 (56.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.558441558441558%\"\u003e\n \u003cp\u003e18 (48.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.25974025974026%\"\u003e\n \u003cp\u003e15 (60.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.74025974025974%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: DCFT refers to dichorionic-diamniotic full-term twins; DCPT refers to dichorionic-diamniotic preterm term twins; MCFT refers to monochorionic-diamniotic full-term twins; and MCPT refers to monochorionic-diamniotic preterm term twins; preterm term was defined as delivery with a gestational age less than 37 weeks; BMI: body mass index; ART: assisted reproductive technology.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3. Neurocognitive development for twins with different gestational age and chorionicity (number of ND/DD)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.807692307692307%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576923076923077%\"\u003e\n \u003cp\u003eDCFT group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003eDCPT group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003eMCFT group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003eMCPT group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.807692307692307%\" valign=\"top\"\u003e\n \u003cp\u003eCommunication\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576923076923077%\"\u003e\n \u003cp\u003e97/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e32/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e22/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e15/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.807692307692307%\" valign=\"top\"\u003e\n \u003cp\u003eGross motor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576923076923077%\"\u003e\n \u003cp\u003e95/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e32/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e24/7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e14/1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.807692307692307%\" valign=\"top\"\u003e\n \u003cp\u003eFine motor\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576923076923077%\"\u003e\n \u003cp\u003e96/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e32/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e29/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e15/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.807692307692307%\" valign=\"top\"\u003e\n \u003cp\u003eProblem solving\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576923076923077%\"\u003e\n \u003cp\u003e93/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e29/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e26/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e15/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.807692307692307%\" valign=\"top\"\u003e\n \u003cp\u003ePersonal social\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576923076923077%\"\u003e\n \u003cp\u003e90/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e33/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e24/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e14/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"29.807692307692307%\" valign=\"top\"\u003e\n \u003cp\u003eNeurocognitive problem\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.576923076923077%\"\u003e\n \u003cp\u003e99/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e33/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.615384615384615%\"\u003e\n \u003cp\u003e31/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e15/0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.692307692307692%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: ND/DD refers to normal development/development delay; DCFT refers to dichorionic-diamniotic full-term twins; DCPT refers to dichorionic-diamniotic preterm term twins; MCFT refers to monochorionic-diamniotic full-term twins; and MCPT refers to monochorionic-diamniotic preterm term twins; preterm term was defined as delivery with a gestational age less than 37 weeks; false discovery rate was used for \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 4. Associations between the gestational age and chorionicity specific gut microbiota genera and physical development\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"663\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.602409638554217%\" colspan=\"2\"\u003e\n \u003cp\u003eWFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.246987951807228%\" colspan=\"2\"\u003e\n \u003cp\u003eLFA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.903614457831324%\" colspan=\"2\"\u003e\n \u003cp\u003eBMI_Z\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003eCo-efficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003eCo-efficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003eCo-efficient\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eAnaerostipes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e-0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eKosakonia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eAcinetobacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eBilophila\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eFinegoldia\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eNegativicoccus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eEscherichia_Shigella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eOdoribacter\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eHydrogenophaga\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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width=\"11.44578313253012%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eAlistipes\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.02\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.246987951807228%\"\u003e\n \u003cp\u003e\u003cem\u003eAnaerotruncus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.30722891566265%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.44578313253012%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.156626506024097%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.939759036144578%\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.36144578313253%\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.542168674698795%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: WFA -weight for age z score, LFA - length for age z score, and BMI_Z - BMI z score. GLM models were used with twins\u0026apos;\u0026nbsp;sex, birthweight, delivery mode and antibiotic used within the first 12 months of life adjusted; false discovery rate was used for \u003cem\u003ep\u0026nbsp;\u003c/em\u003evalues.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4381172/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4381172/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePerinatal factors can influence gut microbiota, adversely impacting infant health outcomes. However, little is known about the combined effect of preterm birth and chorionicity on gut microbiota, metabolism, physical and neurobehavioral development for twin infants. In this study, we profiled and compared the gut microbial colonization of 350 twins aged 12 months. Twins were divided into four groups based on their gestational age at birth and chorionicity as dichorionic-diamniotic full-term birth group, dichorionic-diamniotic preterm-birth group, monochorionic-diamniotic full-term birth group, and monochorionic-diamniotic preterm birth group. Gut microbiota diversity and fecal metabolic alterations at 12 months old were determined by 16S rDNA sequencing and untargeted metabolomics, respectively. Wilcoxon's rank-sum tests were used to compare alpha diversity between the four groups. The general linear models were applied to identify microbiota species that were differentially abundant among the four groups and the health effects of gut microbiota on physical and neurobehavioral development conducted at 12 months of age. In addition, the twin-based ACE model was used to evaluate the contribution of genetic and environmental effects on the composition and function of the gut microbiota. We found that preterm birth and chorionicity dominated genetics in altering the composition of gut microbiota and concentration of metabolites over 12 months of age. The influence of genetic factors differed between preterm and full-term births. There were 16 gestational age and chorionicity specified gut microbiota genera and 285 group-specified metabolites. Association analysis filtered 7 microbiota genera and 19 metabolites associated with twins' physical and neurobehavioral development. Three metabolites, N-Oleoyl dopamine, Ecgonine, and Methyl jasmonate participated in the neuroactive ligand-receptor interaction pathway, tropane, piperidine, and pyridine alkaloid biosynthesis pathway, and alpha-Linolenic acid metabolism and biosynthesis of secondary metabolites, respectively. We concluded that preterm birth is associated with dysbiotic microbiota profiles and significant metabolic alterations, which may eventually influence physical and neurobehavioral development.\u003c/p\u003e","manuscriptTitle":"Preterm birth affects the gut microbiota, metabolome and health outcomes of twins at 12 months of age: a case control study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-23 18:28:51","doi":"10.21203/rs.3.rs-4381172/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"848d8d01-4ba3-4b18-87b5-d387c967c26b","owner":[],"postedDate":"May 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-04T08:23:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-23 18:28:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4381172","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4381172","identity":"rs-4381172","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}