Birth weight has been reported to be associated with the risk of incident cardiovascular disease (CVD); however, the relationship remains inconclusive. Here, we aimed to prospectively assess the associations between birth weight and CVD risk using the data from UK Biobank, a large-scale, prospective cohort study.We included 270,297 participants who were free of CVD at baseline and reported their birth weight for analyses. The primary outcome was incident CVD. Hazard ratios (HRs) and 95% confidence intervals (CIs) for outcomes were calculated using Cox proportional hazards models adjusted for potential confounding variables.p = 0.001). No association between high birth weight and risk of cardiovascular outcomes was found.During a median follow-up of 8.07 years (IQR: 7.4–8.7 years), 10,719 incident CVD events were recorded. The HRs for low birth weight vs. normal birth weight (2.5–4.0 kg) were 1.23 (95% CI: 1.09–1.38) for risk of incident CVD, 1.52 (95% CI: 1.18–1.95) for stroke, 1.33 (95% CI: 1.07–1.64) for myocardial infarction, and 1.15 (95% CI: 1.01–1.32) for CHD. For the ones with low birth weight, the risk of CVD is reduced by 11% for every kilogram of birth weight gain. The association of low birth weight with CVD was stronger among those younger than 55 years (Low birth weight was associated with an increased risk of cardiovascular events. These findings highlight the longstanding consequence of low birth weight on cardiovascular system. The “fe infancy . This ma infancy , 6 or duBirth weight is one of the most important surrogate measures of intrauterine growth and the prenatal environment, normally ranging from 2.5 to 4.0 kg . In 1989In this prospective cohort study, we examined the association between birth weight and risk of cardiovascular outcomes based on data from a large population-based cohort study. This study could provide information on clarification of the relationship between birth weight and cardiovascular outcomes.The UK Biobank is a large-scale, population-based prospective cohort study designed to improve the prevention, diagnosis and treatment of a wide range of diseases and to promote health throughout society . BetweenThe primary outcome was a composite of incident CVD events including myocardial infarction, CHD and stroke. Secondary outcomes were stroke (ischemic and hemorrhagic stroke), myocardial infarction, CHD, CVD mortality, and all-cause mortality. Outcome adjudication involved hospital admission data or death records after birth. The hospital admission data were identified by linking to the Scottish Morbidity Records for participants from Scotland and health episode statistics for participants from England and Wales . AdditioAt baseline participants were asked to enter their own birth-weight. High birth weight implied growth beyond an absolute birth weight of 4.0 kg, while low birth weight was generally defined as any birth below 2.5 kg .1A baseline touch screen questionnaire was used to assess several potential confounders as follow. Sociodemographic characteristics involved age, sex, ethnicity, Townsend Deprivation Index (TDI) and education. Lifestyle behaviors included body mass index (BMI), physical activity (MET minutes per week for moderate activity), smoking status, alcohol consumption, vegetable and fruit consumptions. Maternal smoking, breastfed as a baby, and part of multiple birth were included in early life exposures. Drug use and dietary supplementations included aspirin use, non-aspirin NSAIDs use, vitamin , mineral and other dietary supplementations . Health conditions consisted of hypertension, diabetes and dyslipidemia. Dyslipidemia was defined as elevated total cholesterol [≥240 mg/dL (6.20 mmol/L)], LDL cholesterol [>160 mg/dL (4.13 mmol/L)], or triglyceride [>200 mg/dL (2.25 mmol/L)]; or reduced HDL cholesterol [<40 mg/dL (1.03 mmol/L)] ; or currBaseline characteristics are presented as means (SD) for continuous variables and as number (%) for categorical variables. We used ANOVA or chi-square tests to examine participant characteristics according to whether participants were normal, low or high birth weight at baseline. Cox proportional hazards models were conducted to estimate hazard ratios (HR) and 95% confidence intervals (95% CI) for associations of birth weight with risk of cardiovascular outcomes. We ran two models including the basic model (model 1) that was adjusted for age and sex, and the fully adjusted model (model 2) that was further adjusted for ethnicity (white or others), TDI, education (degree or no degree), BMI, physical activity (<500 or ≥500 MET-min/week), smoking status , alcohol consumption , vegetable and fruit consumptions , maternal smoking (yes or no), and following variables which were reported as present or absent, including breastfed as a baby, part of multiple birth, aspirin use, non-aspirin NSAID use, vitamin supplements use, mineral and other dietary supplements use, hypertension, diabetes, dyslipidemia. These factors were included because were well-known to be associated with the risk of developing CVD , 14, or We conducted predefined stratified analyses to assess potential effect modifications by the following factors: sex , age (<55 vs. ≥55 years), ethnicity (white vs. others), obesity (yes vs. no), physical activity (<500 vs. ≥500 MET-min/week), and following factors which were reported as present or absent, including hypertension, diabetes, dyslipidemia, aspirin use, non-aspirin NSAID use. We tested potential effect modifications by modeling the cross-product term of the stratifying variable with birth weight in the fully adjusted model.We also conducted several sensitivity analyses to explore the robustness of our main results by (1) excluding the participants with family history of CVD, (2) performing a competing risk analysis that took all-cause death as a competing event for CVD, and (3) performing a model adjusted for the propensity score, where the propensity score was calculated by a logistic regression that included the aforementioned baseline covariates for birth weight. Besides, considering that chronic diseases  may be a link in the chain of CVD etiology –20, we fP-value less than 0.05 (two-sided) to be statistically significant.We used the SAS , IBM SPSS software  and R  for analyses in this study. We considered a in utero. They also had a higher prevalence of hypertension, diabetes mellitus and dyslipidemia. When compared with participants with normal birth weight, those with high birth weight were older, more likely to be males, current smokers, former drinkers and breastfed as a baby. Baseline characteristics of participants  excluded from our analysis are summarized in We included 280,252 participants who had data on the birth weight, and excluded 9,276 with CVD at baseline and 679 who subsequently withdrew from the study. Finally, our analysis included a total of 270,297 participants . Baselinp-values < 0.001.A total of 10,719 cardiovascular events were observed during a median follow-up of 8.07 years (IQR: 7.4–8.7 years). p-values > 0.05; In the multivariable analyses, low birth weight remained significantly related with increased risk of CVD , stroke , hemorrhagic stroke , myocardial infarction , and CHD . No association between macrosomia and risk of cardiovascular outcomes was found . NeitherBesides, as shown in In the sensitivity analyses, the associations between low birth weight and CVD outcomes did not change appreciably when we excluded the participants with family history of CVD; when we performed a competing risk analysis that took all-cause death as a competing event for CVD; when we performed a model adjusted for the propensity score, and when we excluded the covariates about health condition in the fully adjusted model .In this large cohort study involving 270,297 individuals, we found that low birth weight was significantly associated with a 23% higher risk of overall cardiovascular events and a 15–59% higher risk of individual cardiovascular events . For the ones with low birth weight, the risk of CVD is reduced by 11% for every kilogram of birth weight gain. The associations were independent of sociodemographic characteristics, lifestyle behaviors, early life exposures, medication use and dietary supplementations, and health conditions. The findings remained robust on multiple sensitivity analyses. Additionally, the associations between low birth weight and CVD were significantly modified by age. No association between high birth weight and cardiovascular outcomes was observed.The association between fetal growth and cardiovascular outcomes has been extensively studied in many prospective cohort studies. Birth weight has been considered as a proxy measure of fetal growth, and a combined measure of at least two important components including fetal growth rate and gestational age at birth . The majIn our study, low birth weight was found to relate with a higher risk of CVD  in more than a quarter of a million UK participants born during 1934–1970. Besides, we observed a U-shaped effect of continuous birth weight on CVD incidence, with the lowest CVD risk falling in normal birth weight and the highest risk in low birth weight. This further supports the relationship between low birth weight and elevated risk of CVD as reported in our main analyses. However, we did not observe a significant association between birth weight and CVD mortality or death from individual cardiovascular outcomes, similar to other publications . This miin utero has also been shown to associate with an increased risk of hypertension, non-insulin dependent diabetes and higher concentrations of low-density lipoprotein cholesterol in adult life , though absolute levels of accuracy may be poor . This isWhen compared with normal birth weight, low birth weight, but not macrosomia, was associated with an increased risk of cardiovascular events. These findings may inform CVD screening and primary prevention strategies for those with low birth weight.www.ukbiobank.ac.uk/).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: UK Biobank (The studies involving human participants were reviewed and approved by the National Health Service’s National Research Ethics Service. The patients/participants provided their written informed consent to participate in this study.XH, GWL, and QM conceived and designed the study. XH and GWL conducted the data analysis. XH drafted the manuscript. JL, LQ, JA, JW, ZL, QM, GWL, and GYHL participated in the interpretation of the results and critically revised the manuscript for important intellectual content. GWL was guarantor, had full access to all of the data in the study, took responsibility for the integrity of the data, and the accuracy of the data analysis. QM and GWL attested that all the listed authors meet authorship criteria and that no others meeting the criteria have been omitted. All authors contributed to data analysis, drafting or revising the article, gave final approval of the version to be published, and agreed to be accountable for all aspects of the work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


We estimated the global burden of low birth weight (LBW) and preterm birth (PTB) and impacts on reduced birth weight and gestational age (GA), attributable to ambient and household PM2.5 pollution in 2019.Particulate matter <2.5 micrometer  of articles published through April 4, 2021, and resulting uncertainty intervals (UIs) accounted for unexplained between-study heterogeneity. Separate nonlinear relationships relating exposure to risk were generated for each outcome and applied in the burden estimation.We searched PubMed, Embase, and Web of Science for peer-reviewed articles in English. Study quality was assessed using 2 tools: (1) Agency for Healthcare Research and Quality checklist; and (2) National Institute of Environmental Health Sciences (NIEHS) risk of bias questions. We conducted a meta-regression (MR) to quantify the risk of PM3 increment in ambient PM2.5. We estimated a global population–weighted mean lowering of 89 grams  of birth weight and 3.4 weeks  of GA in 2019, attributable to total PM2.5. Globally, an estimated 15.6%  of all LBW and 35.7%  of all PTB infants were attributable to total PM2.5, equivalent to 2,761,720  and 5,870,103  infants in 2019, respectively. About one-third of the total PM2.5 burden for LBW and PTB could be attributable to ambient exposure, with household air pollution (HAP) dominating in low-income countries. The findings should be viewed in light of some limitations such as heterogeneity between studies including size, exposure levels, exposure assessment method, and adjustment for confounding. Furthermore, studies did not separate the direct effect of PM2.5 on birth weight from that mediated through GA. As a consequence, the pooled risk estimates in the MR and likewise the global burden may have been underestimated.The MR included 44, 40, and 40 birth weight, LBW, and PTB studies, respectively. Majority of the studies were of retrospective cohort design and primarily from North America, Europe, and Australia. A few recent studies were from China, India, sub-Saharan Africa, and South America. Pooled estimates indicated 22 grams  lower birth weight, 11% greater risk of LBW , and 12% greater risk of PTB , per 10 μg/m2.5 were associated with reduced birth weight and GA, which are, in turn, associated with neonatal and infant mortality, particularly in low- and middle-income countries.Ambient and household PM Rakesh Ghosh and co-workers report on associations between particulate matter air pollution and adverse perinatal health outcomes. 2.5) during pregnancy.Air pollution is a leading risk factor for global disease burden, yet its impacts on perinatal outcomes have not previously been included, despite evidence from more than 150 original research articles showing evidence on the adverse effects of exposure to particulate matter <2.5 micrometer , a major source of PM2.5 to estimate the complete burden from total PM2.5 exposure.To our knowledge, this is the first assessment of global burden that includes 4 perinatal health indicators  and includes both ambient and household PM2.5.We used a conservative nonlinear risk relationship generated using both ambient and HAP studies and with uncertainty intervals (UIs) accounting for between-study heterogeneity in risk estimates. The novel inclusion of HAP provides a more complete representation of the global burden attributable to PM2.5 and describes uncertainty in the estimated attributable burden. It also includes an updated review of largest number of published studies to date, from all inhabited continents including high-pollution countries.This study provides separate burden estimates for ambient and household PM2.5 air pollution on these perinatal health outcomes contributes substantially to the overall global disease burden attributable to air pollution.LBW and PTB are key risk factors for early life mortality and lifetime morbidity. The impact of PMThis attributable burden assessment extends the impact of air pollution from diseases primarily impacting older adults.2.5 exposure may lead to large reductions in the global incidence of LBW and PTB infants and the associated disease burden. Mitigation measures even in low-exposure regions will likely manifest in significant improvement in these outcomes because the increase in risk is steeper at lower than in higher exposures, as shown by our risk curves.Implementation of air quality management and other approaches to reduce PM The World Health Organization (WHO) estimated that 20 million infants were born low birth weight  and 15 million were preterm births (PTBs: gestation less than 37 completed weeks) in 2014 to 2015 . For stlleagues . Briefly2.5 was estimated from multiple satellite retrievals of AOD, a chemical transport model to relate column measurements of AOD to surface PM2.5 concentrations, and calibrated to available ground monitor measurements of PM2.5. These inputs were combined in a spatiotemporal Bayesian hierarchical model as described in detail previously ) for LBW and PTB. We used country-specific total live birth counts, LBW and PTB proportions, and annual PMGBD 2019 . The bur2.5 exposures for each country.Step 1: A total of 1,000 simulated draws of outcome-specific RRs from the MRBRT risk curve for an exposure were matched with the annual average PM3) were merged with the 1,000 simulated draws of PM2.5 exposure for each country, so that draw 1 of exposure from step 1 corresponded with draw 1 of TMREL for a country. The loop was repeated for all 204 countries and territories.Step 2: A total of 1,000 draws of outcome-specific RRs corresponding with 1,000 TMREL values  using the Step 5: The 1,000 RRi) for the year 2019, as shown in Step 6: The global PAF was generated by weighting the country-specific oap and PAFhap using Eqs Step 7: The total PAF was apportioned in to PAFi or PTBi, and the livebirths (Livebirthi), all at the country level, for 2019 as shown in Step 8: The PANs were estimated using the 2.5. Figs A and B in The MR included 44 studies on birth weight, 40 studies on LBW, and 40 studies on PTB that investigated association with ambient PM3 increase in the entire pregnancy average PM2.5 exposure (I2) was >99%. The funnel plot shows that studies were relatively evenly distributed on both sides of the null value  was nonsignificant.The summary linear estimate from the 44 studies shows −22 grams  lower birth weight per 10 μg/mexposure . The estexposure . However3 increase in entire pregnancy average PM2.5 exposure  was 95%, the funnel plot shows evidence of asymmetry  was significant.The summary linear estimate for LBW was 11% greater risk  per 10 μg/m3 increase in entire pregnancy average PM2.5 exposure  was >99%. The funnel plot shows evidence of asymmetry; smaller studies tended to show positive effect, while relatively larger studies were evenly distributed on both sides of the null value  was nonsignificant.The summary linear estimate for PTB was 12% greater risk  per 10 μg/m2.5 were 20.8 (11.7 to 33.7) and 38.4 (5.2 to 208.6) μg/m3, respectively, for 2019 (2.5 levels by the 7 GBD super regions was the lowest in North America and Western Europe (9.8 μg/m3) and the highest in South Asia (55.7 μg/m3). Likewise, median HAP PM2.5 levels by the 7 super regions was the lowest in North America and Western Europe (2.3 μg/m3) and highest in sub-Saharan Africa (326.3 μg/m3).The global medians and the IQRs for ambient and HAP PMfor 2019 . The med2.5 exposure was 89 grams  , with trends across GBD regions and countries similar to those for birth weight  . In otheh weight . Of the 2.5, i.e., 2,761,720 LBW infants , for the year 2019  PTB infants  of all LBW infants globally could be attributed to exposure to total PMear 2019 . The bur infants . The hig2.5 exposure during the entire pregnancy was reduced to the TMREL. South Asia and sub-Saharan Africa combined could have decreased the 2019 LBW and PTB incidence by about 78%. LBW and PTB are leading contributors toward neonatal mortality, and these two regions register high incidence of the two outcomes. Globally, modest decreases in birth weight and relatively large decreases in GA at birth were attributable to long-term PM2.5 exposure during pregnancy. South Asia and sub-Saharan Africa registered the highest decreases in birth weight and GA and, in these regions, population level mean birth weight and GA are lower than in high-income countries. The results indicate that PM2.5 induced a modest shift in the mean of the distributions of birth weight and GA, which, in turn, could be responsible for a considerable part of the burden for LBW and PTB.To our knowledge, this is the first assessment of global burden of adverse perinatal outcomes that includes both ambient and HAP. Our findings suggest that about 2.8 million LBW and 5.9 million PTB infants, globally, could have been averted in 2019 if the mean PM2.5 in 2010, compared to our estimate of 11,646  from ambient PM2.5 in 2019  are generally consistent with prior meta-analyses because all of these were quantified on a linear scale. The assumption of linearity is likely an oversimplification of the actual form of the relationships. The first meta-analysis (MA) of 20 studies published by Sapkota and colleagues in 2010 indicated summary estimates (odds ratio [OR]) of 1.09 for LBW and 1.15 for PTB, per 10 μg/m3 increment [3 increment [3 increment [2.5 exposure [Our SR–MR results [an estimated 22 grams  of lower birth weight and 11%  and 12%  greater estimated risks of LBW and PTB, respectively, per 10 μg/mncrement ,35,36. Texposure ,38. The exposure .2.5 exposure and reduced birth weight or LBW, and if there are unmeasured confounders, adjustment for GA likely biases the direct effect [2.5 on birthweight, the majority of the studies in the SR–MR either adjusted for GA or restricted the study population to term births, and, sometimes, both, inadvertently biasing the estimate. If we restrict the SR–MR to studies that did not account for GA, in one way or the other, the vast majority of the studies would be excluded. We have shown that the summary effects were higher for birth weight and LBW when using only those studies that included all births , compared to the estimate obtained from all eligible studies. Thus, adjustment for GA attenuated the risk, empirically confirming the concept alluded to by Wilcox and colleagues [Although our results are consistent with previous MAs and natural experiments, the summary relative effects were small, and the possibility for alternative explanations remains. We examined the extent of confounder adjustment in the individual studies and categorized them as sufficient or insufficient, as described in the Methods. Accounting for the extent of confounder adjustment increased the summary estimates for all three outcomes, suggesting that residual confounding is a potential source of bias and that risks in the original studies were likely to have been underestimated. GA is also considered to be on the causal pathway between PMt effect . Consistlleagues . Since tlleagues .Studies of the effects of air pollution on fetal and maternal physiology provide additional evidence consistent with the mechanisms thought to contribute to adverse perinatal outcomes. Air pollution affects both the anatomy and the physiology of the placenta and the umbilical cord. Particles induce antiangiogenic profiles, leading to thinner and less voluminous umbilical cord affecting oxygen diffusion in murine models ,41 and r2.5 and/or other factors , the real burden could be much higher, especially in low- and middle-income countries. As highlighted in the methods, we focused on PM2.5 to be consistent with the GBD analyses and because it is one of the most potent and extensively investigated pollutant measures in relation to these outcomes. As evidence accrues, additional pollutants including speciated or source-specific PM2.5 could be considered in the global burden estimation. Another limitation is the uncertainty in the assessment of ambient and HAP exposure. For example, we modeled HAP exposure based on a global database of short-term measurement studies, which are surrogates for exposures throughout the period of pregnancy. Further, the burden estimation incorporates the assumption that the mean population-level entire pregnancy average exposure was approximated by the country-level annual population-weighted average. We also assumed that the exposure and risk were constant over the duration of pregnancy. In other words, we did not have time-varying exposure over the duration of pregnancy to quantify varying risks over the course of the pregnancy. Our uncertainty distribution  likely accounted for some degree of the time-varying exposure that each individual may encounter. Finally, the burden estimates for LBW and PTB should not be interpreted as mutually exclusive because some of the LBW infants are also likely to be PTB.The SR–MR was restricted to articles in English, as it was beyond the scope to include articles in other languages. We quantified the risk of exposure for the entire duration of pregnancy because the largest number of studies investigated this exposure window. However, other short- and long-term exposure windows may also be critical. We assumed no interaction between ambient and HAP exposure, consistent with the design of the epidemiologic analyses that contribute to the SR–MR. To our knowledge, we have not seen evidence to the contrary for any of the four outcomes investigated. If there is synergism between the two sources of PM2.5. This differs from the mediation of the burden of disease attributable to PM2.5 via short gestation and reduced birth weight that was first introduced in GBD 2019 using the methodology described here to estimate shifts in the distributions of birth weight and GA [2.5 through shifts in birth weight and GA via a mediation analysis where LBW and PTB are risk factors for neonatal causes including mortality  and years lived with disability attributable to PTB. Birth weight and GA were estimated with joint distributions with RR estimated for birth weight and GA categories. To do this, the MRBRT curves for birth weight and GA described here were used to shift the estimated birth weight and GA exposure distribution for a given location and year. This shifted distribution represented the expected distribution if PM2.5 was at the TMREL. By comparing the estimated to the expected (assuming exposure at the TMREL) distribution of birth weight and GA, we calculated PAFs for the stated outcomes to estimate mortality, years lived with disability, years of life lost, and disability-adjusted life years attributable to PM2.5 mediated through birth weight and GA. In the GBD 2019, 135,000 and 237,000 deaths from neonatal disorders were attributable to ambient and household PM2.5, respectively [2.5, respectively, a portion of which were mediated by reduced birth weight and short gestation.In this paper, we have evaluated birth weight, GA, LBW, and PTB as outcomes and provide direct estimates of the burden of these outcomes that is attributable to PM2.5 exposure during pregnancy to be a risk factor for adverse outcomes across a wide range of exposures. We estimated that 2.8 million LBW and over 5.9 million PTB infants could be attributable to PM2.5 air pollution exposure during pregnancy in 2019. As these perinatal health indicators are key drivers of early life mortality, particularly in middle- and low-income countries, reducing air pollution will likely have substantial benefits for neonatal and infant health.This study investigated relevant indicators of perinatal health and provide strong evidence for PMS1 PRISMA Checklist(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Early neonatal death caused by preterm birth contributes the most for perinatal death. The prevalence of preterm birth continues to rise and is a significant public health problem. The exact cause of preterm birth is yet unanswered, as mostly preterm birth happens spontaneously. Predictors of preterm birth in developing countries like Ethiopia were not well investigated, and no study was conducted before this in the study area.To identify predictors of preterm birth in Western Ethiopia, 2017/2018.Health facility-based unmatched case-control study was conducted from October 20/2017-march 20/2018 in 4 Hospitals. A total sample size of 358 women was recruited. From this 72 were cases and 286 were controls. Cases were mothers who gave Preterm birth, and controls were mothers who gave birth at term. Ethical clearance was obtained from Wollega University ethical review committee. A pre tested, structured questionnaire was used to collect data. Data entry and analysis was done using Epi Data 3.1 and SPSS version 21, respectively. Logistic regression was done to identify predictors of preterm birth.Three hundred fifty-eight women participated in this study of which 72 were cases and 286 were controls; making the overall response rate of 100%. Lack of antenatal care visit ),,history of previous preterm),Short Interpregnancy Interval [AOR = 4.41.95% CI 2.05–9.47],Having Reproductive tract infections  and having Obstetric complications  were found to be predictors of preterm birth.Risk factors of preterm delivery are multifactorial and depend on geographical and demographic features of the population studied. Hence results of studies from one area might not be applicable to another area. Antenatal care visits are unique opportunities for early diagnosis and treatment of problems. Therefore, antenatal care should be strengthened, and appropriate counseling should be given at each antenatal care follow up. Maintainning optimum birth interval through family planning, and early identification and treatment of reproductive tract infections are mandatory. World Health Organization (WHO) defines preterm birth as all birth less than 37 completed weeks of gestational age or fewer than 259 days since the first day of last normal menstrual period. Fifteen million preterms happen each year globally and remain the major contributor of neonatal morbidity and mortality. –3. TherePreterm birth is the single most common causes of perinatal mortality in developing and developed countries; its adverse health outcomes even worse in developing countries –8. MoreoCase—control study conducted in Yemen indicated that women who gave birth of premature infants were less educated (60% vs 53%), more socioeconomically deprived (67% vs 50%), and rural residence (65% vs 60%) irrespective of birth spacing. Maternal age <20 years was observed in high proportion (43.2%) among cases with IPI <12 months compared to 16.6% in controls –15. AcroEarly neonatal death caused by preterm birth contributes the most for perinatal death. Even if they were delivered preterm, infants could survive. However, they often face lifelong adverse health effects such as respiratory difficulties, cerebral palsy, vision and hearing loss, feeding and digestive problems, and intellectual disabilities –19. The Therefore, more investigation is needed to identify preterm birth predictors for effective intervention strategies . Risk faThis study was conducted in East Wollega, West Wollega, Horo Guduru Wollega and Kellem Wollega zones of Oromia, Western Ethiopia. The study was conducted from October 20/2017-march 20/2018.Hospital-based unmatched case control study was conducted.All mothers who gave birth after 28 weeks of gestational ages in selected hospitals.Mothers who gave birth after 28 weeks of gestational age in the selected hospitals’ during data collection period.Mothers with singleton birth were included in the study.Those who had an abortion in the preceding pregnancy .A sample size of 72 cases and 286 controls was determined using the methods of "difference between population proportions" with 80% power, 95% confidence level. The ratio of cases to controls being one to four… The proportion of short interpregnancy interval among controls considering 46%  and takicases) and who have not (controls). Then, they interviewed both groups to determine their exposure status. Thus, four mothers who had given term babies were interviewed for every mother who gave birth to a preterm baby. The supervisor was assigned to supervise the data collection process and perform quality checks.Data was collected by face to face interview using a structured and pretested questionnaire first prepared in english for this study and translated to Afan Oromo  before the start of the fieldwork. Data was collected by 12 B.Sc midwives who are fluent in the official language of the region from October 20/2017-March 20/2018. First, data collectors identified mothers who had given birth to a preterm baby A birth which occurs within less than 37 completed weeksMothers who gave birth to preterm babies during data collection period in the selected hospitals.Mothers who gave birth to term babies during data collection period in the same selected hospitals.Interval between delivery of a live birth of previous pregnancy and the birth of the current child Which is calculated as the interval between the birth of the most recent previous child and the birth of the current child minus the gestational age of the current child.The data gathered through the structured questionnaire was, compiled, Cleaned, coded and entered in to EpiData version 3.1 and exported to SPSS version 20 for analysis. Frequency was used to clean data. All the variables were entered in to bivariate analysis and those explanatory variables with a p value < 0.2 in crude analysis were considered as a candidate variable for multivariable analysis and those variables with a p value < 0.05 in multivariable analysis were considered as significant association. Strength of association was presented using odds ratios with 95% confidence intervals.Ethical clearance was obtained from Wollega University College of Health Science Institutional Research Ethics Review Committee (IRERC). Written informed consent was obtained from each participant after fully explaining the study’s purposes before the interview. Formal letter of cooperation was written for Each respected zonal Health Office and respective Hospitals. Participants are informed about the study’s objective and their rights that it contributed necessary information for policy makers and other concerned bodies. Any involvement in the study was after their full consent is obtained. They were also informed that all data obtained from them would be kept confidential by using codes instead of any personal identifiers and is meant only for the purpose of the study. The interviews were conducted in separate private places.Three hundred fifty-eight women participated in this study of which 72 were cases, and 286 were controls; making the overall response rate of 100% In the bivariate analysis, number of ANC Visit, history of abortion, sex of the newborn,ANC follow up, Iron supplementation, outcome of the previous delivery, interpreganancy interval, reproductive tract infection, obstetric complication and anxiety during pregnancy were found to be predictors of preterm birth. multiple logistic regressions showed that the number of ANC visit, the outcome of the previous delivery, interpregnancy interval, reproductive tract infections, and obstetric complications were predictors of preterm birth, revealing a significant association with preterm birth in this study . CompareSimilarly, Compared to those with no history of previous preterm birth; those who had a history of previous preterm birth were 5.19 times the high risk of preterm birth . Likewise, those Women giving preterm birth were 4.41 more likely to have an inter-pregnancy interval of less than 18 months than those giving birth at term [AOR = 4.41.95% CI 2.05 9.47]. Similarly, those Women giving preterm birth were 2.54 more likely to have reproductive tract infection than those giving birth at term . Correspondingly, those women who gave preterm birth were 2.48 more likely to have pregnancy complication than those giving birth at term  .This study identified the number of ANC visits, the outcome of previous delivery (previous history of preterm birth), short interpregnancy interval, having reproductive tract infection, and having pregnancy complications as a predictor of preterm birth. Compared to those with 3–4 ANC visits, women with 1–2 ANC visits and those with no ANC visit had 2.7  and 3.2  times higher risk to have a preterm birth, respectively.This is similar to the previous study conducted at Qom Hospitals, Yemen, and a cross-sectional study conducted at Debremarkos Ethiopia , 15, 23.Similarly, those women giving preterm birth were 5.19 more likely to have a history of previous preterm birth than those giving birth at term . This finding is consistent with a previous cross-sectional study in Ethiopia at Felegehiwot referral hospital, Debremarkos town, and Western China , 23, 24.Women giving preterm birth were 4.41 more likely to have an inter-pregnancy interval of fewer than 18 months than those giving birth at term [AOR = 4.41.95% CI 2.05–9.47]. This is in line with a Case-control study conducted in Yemen, Qom Hospitals, Scotland, and Brazil , 15, 16,Women who gave preterm birth were 2.48 more likely to have pregnancy complications than those giving birth at term . This is in line with the cross-sectional study conducted in the Debre markos town and the case-control study conducted in Southern India , 25. TheAs this is a case-control study, it is not as powerful as other studies confirming a causal relationship. Case-control studies are often used to provide early clues and inform further research using more rigorous scientific methods. Since the survey deals with sensitive issues, there is a possibility of falsified reporting among mothers, especially given the face-to-face interview modality of data collection. Another limitation of the study is being institution based study; the number of institutional delivery is very low in Ethiopia and this study may not represent the general population.The etiology of preterm birth is multifactorial and depends on the population studied geographical and demographic features; hence, the results of studies from one area might not apply to another site. This study identified that number of ANC visit, previous history of preterm birth, short interpregnancy interval, having reproductive tract infection and having pregnancy complication as a predictor of preterm birth.ANC visits are unique opportunities for early diagnosis and treatment of problems and play an important role in the women’s reproductive health, mainly through caring for pregnant women and early identification and treatment of obstetric complications. Therefore, ANC visits should be strengthened, and appropriate counseling should be provided at each visit of ANC to avoid missed opportunities and address reproductive health needs, especially family planning to promote optimum birth spacing. Even though there is no Strict optimal IPI has not been agreed upon universally, <18 months’ interval was a period found to be associated with a high risk of preterm birth. The women with a previous history of preterm birth were also in a high-risk group whose health professionals should pay more attention during antenatal care. Similarly, early identification and treatment of RTI are also crucial.S1 Questionnaires(DOCX)Click here for additional data file.S2 Questionnaires(DOCX)Click here for additional data file.S1 Data(SAV)Click here for additional data file.


The APR is prospective exposure-registration cohort study, monitoring for early warning signals of major teratogenic effects of antiretrovirals (ARV) used during pregnancy. This analysis aimed to assess maternal demographics, pregnancy and neonatal outcomes including birth defects among infants with periconception and prenatal exposure to DTG using APR data.Descriptive analysis with frequency tabulation of pregnancy and neonatal outcomes is reported. Periconception is defined as any exposure within two weeks prior to or through 28 days after conception. st trimester, 260 during 2nd trimester and 119 during 3rd trimester. Maternal median age at conception was 30 years and 77.0% of pregnancies were reported from the United States. At the time of reporting, 46.6% had CD4 count ≥500 cells/µL, 31.8% had 200-499 cells/µL, 12.5% had < 200 cells/µL and 9.1% unknown. There were 1010 prospectively reported pregnancies with exposure to DTG through 31January2021, with 526 periconception exposures, 105 exposed later during 1st trimester exposures, overall defect prevalence was 3.3%  and for 2nd/3rd trimester exposures defect prevalence was 5.3% . One neural tube defect (NTD) case of anencephaly with periconception DTG exposure was reported. The 1010 DTG exposed pregnancies resulted in 1036 outcomes: 956 (92.3%) live births (26 twin pairs), 12 (1.2%) stillbirths, 28 (2.7%) induced abortions, and 38 (3.7%) spontaneous abortions. Among live births, 39 (4.1%) reported birth defects. For 1Among the 873 singleton, live births without birth defects, 92 (10.5%) were preterm (< 37 weeks of gestation); 103 (11.8%) had low birth weight (lbw) < 2500 grams including 22 (2.5%) < 1500 (very lbw) grams.APR data do not demonstrate an increased risk of overall birth defects with DTG use above the population expected rate of defects . The number of periconception exposure outcomes is not yet sufficient to evaluate potential association of DTG with NTD. The Registry continues to closely monitor birth defects, including NTDs in pregnancies exposed to DTG and other integrase inhibitors.Vani Vannappagari, MBBS, MPH, PhD, ViiV Healthcare Limited (Employee) Jessica Albano, PhD, MPH, Syneos Health  Leigh Ragone, MS, GlaxoSmithKline (Shareholder)ViiV Healthcare (Employee) Taylor Cook, BS, Syneos Health (Employee) Angela Scheuerle, MD, ViiV (Independent Contractor) William R. Short, MD, Gilead Sciences  Involved: Self): Consultant; ViiV  Involved: Self): Consultant Claire Thorne, MSc, PhD, MSD (Grant/Research Support)ViiV Healthcare


Background: The open birth interval -- the time since the woman’s latest birth -- is closely correlated to the usual fertility measures, but it adds important information from the age of the woman’s youngest child, with its implications for her freedom from domestic roles.  Studies of the open interval by age and parity can elucidate the transitions in reproductive behavior that women experience over time.Methods:  249 surveys of married women in 75 countries in the DHS series provide information on the open  interval by age and parity, and by the  fertility measures of the total fertility rate (TFR), the general fertility rate (GFR), and children ever born (CEB), with time trends.  Stata 15 and the “R” software were used, and a two-parameter equation was employed to model the distribution.Results:  The distribution of women by the open interval follows a downward curve from birth to 20 years; it varies across countries and over time only by its starting level and the steepness of the curve. Declines in the shortest intervals soon after birth reflect recent fertility declines. Variations are large by both age and parity, but in quite different patterns. Past modeling analyses demonstrate the effects of female and spouse mortality, declining fecundability, contraceptive use, and reduced sexual exposure. Both period and cohort effects can impact the curve. The open interval distribution is modelled in an equation with two parameters and calculated for the latest surveys in the 75 countries.Conclusions: The time since a woman’s birth is easily captured with a single question in successive surveys. Changes in the open interval distribution serve as sensitive indicators of recent fertility changes, and the dynamics of reproductive behavior across women’s life stages are captured in new ways, as gauged by age and parity trends in the distributions. The open birth interval – the time since a woman’s most recent birth – has been of interest since the early 1960s for its relation to closed intervals, for its relationship to fertility rates, and for its use as a way to trace changes in contraceptive use and the effects of family planning programs. In addition, the changing roles of women for activities outside the home, as the open interval grows and the youngest child ages, have been studied. Modeling exercises have investigated some of these features over the years, including the effects of age structure upon the open intervals. However, the modeling work has been seriously hampered by the lack of empirical data on large sets of data. Here we explore the nature of the open birth interval distribution based upon a set of 249 surveys in 75 countries.We proceed with two major parts of the article, first to look back at past analyses of the open birth interval, with its extensive modeling work. Much of that work has been forgotten and deserves a fresh review, and it helps shape the focus of the new work here. Second, against that background, we examine information on the empirical patterns that emerge from a large set of national surveys containing extensive data not available in the past, with particular attention to the interactions of the open birth interval with age and parity.Efforts to model the open birth interval go back more than 50 years to the pioneering work ofAssuming a marital duration of ten years,The effects of variations in fecundability and sterility according to parity were explored byLater,M andm estimates for 723 municipalities in Minas Gerais State and demonstrated the superiority of using date of last birth over births in the last year for producing the key parameter estimates in the Coale-Trussell model. The 1991 Minas Gerais data were chosen as a test case because of earlier work byThenThe open interval distribution was shown to parallel the age distribution in a general population byA general review of much of the literature to 2015 is given byThe modeling literature gives little attention to contraceptive use or to policy uses of open birth interval information, including any effects of family planning programs. However, in probably the most sophisticated study so far,There has been nearly no experimental work to relate program effects on interval lengths. In one exception, open intervals shorter than 30 months proved to be the best predictor of contraceptive adoption over a two period in a Korean study, better than a variety of other personal and demographic determinants . At abouIn review, some models have been concerned with finding a few parameters to capture the distribution of the open interval, some of them parity specific. Others have focused on the open interval as an index of fertility levels and change. Parity progression ratios have been part of a few models, as well as allowance for distributions by age and marital duration or both. Some have allowed for the effects of postpartum infecundability or heterogeneity of conception risk. Rather few publications have tested the models against actual data. One model has been fitted to the open birth interval distributions ; it is cSpecifically, modeling has been used: to explore the relation of open intervals to closed ones, and ways to estimate each from the other to explore the utility of open intervals as indices of fertility and fertility change, by using technical modeling work, with attention to specific age, marital duration, or parity subgroups to explore how they can reflect a diminution in the risk of conception through declines in fecundability, mortality of spouse, or decreased sexual exposure to investigate how they might measure changes in contraceptive use and the effects of family planning programs1 from 75 countries, with repeat surveys in many, permitting time trend assessments. The DHS series is implanted through collaboration with a large number of countries, primarily those in the developing world. It is now comprised of over 250 household surveys taken from the late 1960s to the present, in over 80 countries, with sample sizes varying from about 5,000 to 30,000. Participants are selected by scientific sampling methods from sample frames in each country, in cooperation with local experts, for example in census bureaus. Surveys are typically conducted about every five years to allow comparisons over time. Data are presented both nationally and by sub-national reporting area. The DHS series is available free, with open access and is available online. Full documentation is available athttps://dhsprogram.com/What-We-Do/Survey-Types/DHS-Methodology.cfm.We draw on national surveys in the Demographic and Health Survey (DHS) seriesAll available DHS surveys were included in our analysis, excluding only those lacking information on the time since the woman’s latest birth. As a way to include more countries with the required data, only married women were studied. The 75 countries in the analysis and survey dates appear inThe open interval distributions were generated both overall and specific to women’s age and parity. Currently pregnant women were included in the first-year interval, being close to birth and having very similar characteristics to the first-year group.Data availability section  of women, but by the end they fall to a mere 7%. The levels for parities 4 and 5 stay within smaller ranges: parity 4 starts at 11%, rises to a steady 14% to 17%, and ends at 13%. Parity 5 begins at 8%, levels off at 11% to 12%, and ends at 9%.Meanwhile parities 1 and 2 together hold the largest shares in the first five intervals. Then parity 1 declines while parity 2 stays level at about 20%. Both gain somewhat in the final intervals, as parity 3 also does, though until then parity 3 is nearly constant at 18%.These changes reflect conflicting tendencies. A timing limit helps explain the middle hump for parities 6+. Having six or more children uses up much of the childbearing time, so few women will still have a large gap left over since the latest birth. That constrains the proportion who are found in the final intervals. A counter tendency is that high fertility women tend to have short birth intervals and a recent birth, which would drive up their share in the early intervals, but there they face competition from the early childbearing of younger women at short marital durations. Such cross currents produce the patterns shown inA remarkable finding inParity by age: how does the parity pattern change as women age, and how does the pattern change when fertility declines? Parts of the modeling literature focused on this, especially as noted above byOver time, the average parity of women declined, but less so at ages 15–19 where about a third were still at parity zero with another half at parity 1 , and 0.85 with children ever born . The relThe cross-sectional association can be augmented by examining the slope and degree of the association in each of the 59 countries with at least two surveys. Comparing the change in the TFR to the change in the mean open interval, we found a negative slope in 53 of the 59 countries, the other six showing either no TFR change or a positive slope. The median slope was -1.04 and the mean -1.18, for approximately a one-point decline in the TFR for each year of increase in the mean interval.The closeness of the relationship is based heavily upon the common response by both measures to recent births: for the TFR the births often come from the previous three years; for the open interval they come from many births in those years as well as some in prior periods. Any downward or upward change in fertility as measured by the TFR or GFR must on average affect entries into the first interval, with its sizeable effect upon the mean.Actual distributions for the open interval respond to numerous influences, but we can consider a growing population with a stable age distribution (involving fixed age-specific birth and death rates), with a steady flow of births annually and a stable open interval distribution. Period effects are then absent, and the shape of the open interval curve depends upon the fertility rate. With a low rate, new entries (births) would be few in relation to the entire group of women, just as the population age structure is more vertical under a low fertility rate. With low fertility the curve would start lower and remain flatter. A counter example is approximated by the Niger curve inThe remarkable similarity of the shape of the open birth interval curve across countries invites efforts to find a few summarizing parameters. Limiting this to two parameters at most, we tested four options for best fit: power, exponential, logarithmic, and polynomial. Illustrative results appear inCalculations for the power equation start with the empirical distribution of the open birth interval in a country and follow the relation:That is, the percent of women in an interval equals “a” times the interval length in years taken to the “b” power.The “a” parameter specifies the starting level of the curve, which is higher where more women fall into the first interval by being pregnant or in the first year after birth. It is highest in countries with high fertility, as in many sub-Saharan African countries. The “b” parameter captures the downward slope of the curve and is always negative; it too is largest where the curve starts high and descends rapidly across the first few intervals. It then levels off since women in all intervals add up to 100%. Therefore , the twoThe open birth interval provides information not otherwise available on reproductive behavior. Past work to model the distribution of women according to the interval since the latest birth and its relation to fertility has been constrained for lack of empirical data. Information on the open interval distribution has now been generated across surveys covering most women in most developing countries. These show a systematic increase of age with interval length, and a systematic but much different pattern in change of parity. The data also show an intimate correlation between interval length and the three fertility measures of the TFR, GFR, and CEB. Moreover, the level and shape of the distribution of women by interval is captured with little error by an equation with two parameters.Variation in the distribution is very large across countries, and in many it has changed considerably over time. The differences are most sensitive to the percentage of women in the first interval: women either pregnant or in the first year after birth. That percentage ranges from 10% to 47% of women, spanning low-fertility to high-fertility countries.Determinants of the downward slope of the curve toward subsequent intervals include first the diminution of women in the general population with age, and then the various factors that extend birth-free time, including reduced fecundability and sexual frequency, mortality or other absence of the spouse, abortion, and contraceptive use.The shape of the distribution is affected by both period and cohort effects, the former affecting all intervals at once, and the latter modifying the paths that successive groups of women follow across the intervals as they give birth in varying numbers and behavioral characteristics.Repeated surveys that, with a single question, trace changes in key patterns of childbearing, offer an efficient way to gain information for policies and programs directed to helping women defer or avoid unplanned births. They also apply to social policies related to women’s status; as the age of the youngest child increases, women’s freedom for roles and economic activities outside of the home is enlarged. The open birth interval can be a valuable resource for planning, and it deserves greater attention than it currently receives.https://www.dhsprogram.com/Data/. A guide for how to apply for dataset access is available at:https://dhsprogram.com/data/Access-Instructions.cfm. The specific data files accessed were taken from each country listed inThe DHS Program prepares and makes available DHS survey data in the form of standard recode files in a range of file formats for use with several statistical software packages. All data used in this study are publicly available and free of charge upon registration athttps://github.com/kristinbietsch/OpenBirthIntervalSource code available from:https://doi.org/10.5281/zenodo.4015278 , and from this information, the length of the open birth interval is easily calculated. This manuscript analyzes data from 249 surveys in 75 countries conducted under the Demographic and Health Surveys program – a treasure trove of information! In fact, another 150+ surveys are available if one augments DHS with surveys conducted under other international survey programs, most notably the UNICEF MICS program. This body of data on the length of the open interval are overdue for systematic analysis such as provided here.   What I find most interesting and most valuable in this manuscript, is the analysis in Figures 4 - 6 and Tables 3 and 4. This analysis: (i) assesses the ability of open birth interval distributions to predict standard measures of the level of fertility ; (ii) examines how to represent the open interval distribution parsimoniously, e.g. no more than two parameters. This is an analysis of fundamental interest and value. In light of this – admittedly my own preferences – I am disappointed that the manuscript does not present the mean open birth interval . The distribution among the 249 surveys and 75 countries, summarized via some means , would be of considerable interest, in my view. Or, rather, it’s a striking absence.I also wonder whether the authors have examined whether predicting the fertility measures  might be even more successful if the two parameters from exercise (ii) were used?     Other points are as follows: I find little benefit from the two-page review of past approaches to open birth interval distributions provided in the Introduction. The capsule summaries of a diverse set of approaches, and heated methodological debates, I doubt will be comprehensible to most readers. (I speak for myself.)Instead, what I think is key is to provide a good exposition of the pieces by Schmertmann in the late 1990s. This will require a bit of discussion, and possibly some formulae. In the Introduction, or possibly later, there should be some explanation of how the analysis in this manuscript connects to Schmertmann’s work.Isn’t a crucial assumption when employing the open birth interval to compare across populations  that the historical trajectory of births in the years preceding the survey is the same? I don’t mean the level, rather the shape of the historical trend. This point is, finally, noted toward the end of the manuscript (pp. 11-12), and even then rather obscurely. Concern about the validity of this assumption is a main reason, I believe, for misgivings over the decades about analyzing open birth intervals alone .Data: DHS began in the late 1980s, not the late 1960s. Number of women: this exceeds 30K in a number of surveys, most notably the most recent survey in India  but other surveys as well (e.g. the most recent three surveys in Colombia). There are many DHS surveys with less than 5000 respondents, but some of these may not be included in this analysis. Note that, contrary to the text, Table 4 does NOT provide a complete listing of surveys.st birth interval, 2nd birth interval, 3rd birth interval, etc. Therefore it is confusing that this manuscript often uses “interval” to refer to various durations of the open interval . For this reader, this was disorienting. For clarity, I urge the authors to adopt different terminology.It is common to refer to “birth interval” and to say “by interval” when referring to 1Figure 1: I do not find this helpful, except for overall visual impression. Individual surveys are indistinguishable.Figure 2: I do not find these two sub-figures helpful. Beyond six or seven years, few women remain, as the text concedes. Yet these figures give essentially equal weighting to each duration. Moreover, I think percentaging in a different direction would be more to the point: by age-group, distribution of intervals by 8-10 duration categories maximum ; and, by parity, distribution of intervals by 8-10 duration categories.Table 1: This is a detailed, space-consuming table that strikes me as tangential to the main purpose of this manuscript, which is to examine distributions of the open birth interval.The paragraph on China on page 7 strikes me as a sidetrack.Figure 3: Like Figure 1, the inability to distinguish clearly the various lines detracts from the value of this figure. Tabular presentation of the survey-by-survey distributions, by country, could be far more illuminating.per se.) This is a major omission.Pp. 9-10, discussion of direct determinants of open birth interval distribution. Post-partum behaviors – breastfeeding, abstinence – are not mentioned. .Pg. 13 “Repeated surveys that, with a single question, trace changes in key patterns of childbearing, offer an efficient way to gain information for policies and programs directed to helping women defer or avoid unplanned births.” Doesn’t drawing inferences about unplanned births require classification of births as planned or unplanned? I don’t see how analysis of open birth interval distributions alone can do the trick.  Is the work clearly and accurately presented and does it cite the current literature?PartlyIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?PartlyAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Demography - fertility, reproductive health, family planningI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.


Preterm birth is one of the leading causes of perinatal morbidity and mortality. Gut microbiome dysbiosis is closely related to adverse pregnancy outcomes. However, the role of the gut microbiome in the pathogenesis of preterm birth remains poorly studied.We collected fecal samples from 41 women  were recruited for the study. We performed 16S rRNA amplicon sequencing to compare the composition of the gut microbiome in threatened preterm labor cases and controls and among women who delivered preterm and at term. By annotating taxonomic biomarkers with the Human Oral Microbiome Database, we observed an increased abundance of potential oral-to-gut bacteria in preterm patients.Porphyromonas, Streptococcus, Fusobacterium, and Veillonella, were enriched, whereas Coprococcus and Gemmiger were markedly depleted in the preterm group. Most of the enriched bacteria were annotated oral bacteria using the Human Oral Microbiome Database. These potential oral-to-gut bacteria were correlated with clinical parameters that reflected maternal and fetal status.Patients with preterm birth showed a distinct gut microbiome dysbiosis compared with those who delivered at term. Opportunistic pathogens, particularly This study suggests that patients who deliver preterm demonstrate altered gut microbiome that may contain higher common oral bacteria. Preterm birth, defined as birth before 37 weeks, is one of the leading causes of global neonatal morbidity. An estimated 15 million infants are born preterm every year . PretermIn the recent decade, growing evidence has shown that the human microbiome plays a critical role in the development of many diseases. While the changes in the maternal vaginal microbiome and the development of the infant microbiome across different body sites have been well studied, the relationship between the maternal gut microbiome and preterm birth is rarely reported . The dysIn the present study, we recruited patients experiencing threatened preterm labor and healthy pregnant women not in labor to provide fecal samples at similar gestational age to investigate the association of intestinal microbes with the outcome of preterm birth. We performed 16S rRNA gene amplicon sequencing to identify gut bacteria exhibiting altered abundance in women who experienced early/late preterm birth.Healthy pregnant women and those with threatened preterm labor were recruited in the Department of Obstetrics, The First Affiliated Hospital of Nanchang University, Jiangxi Province, China. Ethical approval of the study was granted by the Ethics Committee of the First Affiliated Hospital of Nanchang University (2019-061). All participants have been informed of the purpose, background, process, risks, and benefits of the study, and signed the informed consent to participate in this study. The Department of Gynecology and Obstetrics, the First affiliated Hospital of Nanchang University was responsible for the study.The inclusion criteria of patients: (1) The patients delivered between 28 weeks and 37 weeks of gestation, which were classified into preterm group. (2) The patients at 28 weeks and less than 37 weeks gestation suffered regular or irregular contractions accompanied by progressive dilation or shortening of the cervical canal, which were classified into Sym.preterm group in this study.The exclusion criteria of patients: (1) Administration of any antibiotic or probiotic treatment one month before sample collection. (2) Diseases that may affect microbiome composition such as thyroid disorders, asthma, lipid metabolic disorders, inflammatory bowel disease, irritable bowel syndrome, and celiac disease. (3) Other obstetric conditions complicating pregnancy, such as gestational hypertension, gestational diabetes, twin or multiple pregnancies, placenta previa. (4) Other chronic diseases, such as chronic hypertension, chronic kidney disease. (5) Termination of pregnancy due to fetal or maternal factors. Fecal samples were collected from all enrolled subjects at the hospital, while samples from patients with threatened preterm labor were taken at symptom initiation. The fecal samples were collected at admission and the average sampling time of preterm group was 25.9 gestational weeks and 26.5 gestational weeks for the control group. All fecal samples were stored at −80°C until further processing.Bacterial genomic DNA was extracted using a MinkaGene Stool DNA kit  according to the manufacturer’s instructions. After extraction, the 16S rRNA V4 region was amplified by quantitative real-time PCR with the following barcoded primers (shown from 5′ to 3′): V4F, GTGYCAGCMGCCGCGGTAA and V4R, GGACTACNVGGGTWTCTAAT. Sun and colleagues  showed twww.homd.org)  . SequencThe significance of differences between the two groups was determined by the Wilcoxon rank-sum test. The Kruskal–Wallis test was used for multi-groups. P values less than or equal to 0.05 were considered significant. The Benjamini and Hochberg method was used to adjust the P-value for multiple hypotheses . CorrelaAfter rarefied to 3,000 sequences, a total of 19 women with threatened preterm labor and 22 healthy pregnant women were included in the present study. In the threatened preterm group, eight patients with preterm symptoms delivered (Sym.PTB) at term, and 11 patients delivered preterm. The preterm-delivered group was further divided into an early week preterm group  and a late-week preterm group , depending on whether the condition was detected before 34 weeks of gestation. The clinical parameters are summarized in The fecal samples of these participants were collected for DNA extraction and 16S rRNA sequencing. By employing QIIME2 bioinformatic software, we showed that alpha-diversity indices, including observed ASVs, Shannon Index, and PD Whole Tree Index, were increased in the preterm group, indicating a higher richness and evenness in preterm patients than in healthy controls. We did not observe significant differences in alpha diversity within the preterm subgroups , neonatal weight, and Apgar scores   to show the relationship of continuous parameters across samples by using R package vegan based on the Bray–Curtis distance metric. The projection of parameters demonstrated that gestational age (in weeks) and Apgar scores extended along axis-1, which distinguished between preterm patients and healthy controls , as in the three subgroups , neonatal weight and Apgar scores Figure 4vs. late preterm birth. The profile of the gut microbiome in preterm patients revealed that a group of common resident oral bacteria was enriched and associated with clinical parameters that reflected gestational and infantile status. These potential oral-to-gut bacteria, evaluated by the random forest model, would provide us another aspect to understand the relationship of gut bacteria and preterm birth.In the last few decades, a lack of effective methods for the prediction and prevention of preterm birth has placed a burden on the medical system and millions of families . The prePorphyromonas, Fusobacterium, Veillonella, Streptococcus, Bilophila, and Haemophilus in preterm patients. Similar to the gut bacterial profile of pregnancy adverse outcome patients, Fusobacterium, Streptococcus, and Veillonella are associated with chronic inflammatory conditions, gut barrier damage, upregulate inflammation and led to adverse pregnancy outcomes , accession number PRJEB39133.The datasets presented in this study can be found in online repositories. The data presented in the study are deposited in the EBI repository . The patients/participants provided their written informed consent to participate in this study.CY, JC and Y-EH designed the ideas and methods of this study; XW, JC and QH collected the data and processed the samples; JC, YL and YC analyzed the data; SL guided, supervised and supported the study. CY, JC and SL drafted and revised the manuscript. All authors contributed to the article and approved the submitted version.The study was financially supported by Science and technology research project of Jiangxi Education (No. GJJ170077 and No. GJJ181326) and Science and technology project of Health Commission of Jiangxi Province (No. 20201023).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Are fetal and infant weight growth associated with cerebral and cerebellar gray and white matter volumes at age 10 years?In this population-based cohort study of 3098 children, increased weight gain during the fetal period and the first 2 years of life was associated with larger brain volumes at age 10 years. Compared with children with normal fetal and infant growth, those who experienced fetal weight deceleration followed by infant catch-up growth had similar brain volumes later in childhood.The findings of this study suggest that early-life growth patterns may be associated with cerebral and cerebellar brain volumes in children at school age. This cohort study examines the association of fetal and infant grown patters with brain morphology at school age among children in the Netherlands. Preterm birth and low birth weight are associated with brain developmental and neurocognitive outcomes in childhood; however, not much is known about the specific critical periods in fetal life and infancy for these outcomes.To examine the associations of fetal and infant growth patterns with brain morphology in children at school age.This population-based, prospective cohort study was conducted from February 1 to April 16, 2021, as a part of the Generation R Study in Rotterdam, the Netherlands. The study included 3098 singleton children born between April 1, 2002, and January 31, 2006.Fetal weight was estimated in the second and third trimesters of pregnancy by ultrasonography. Infant weight was measured at birth and at 6, 12, and 24 months. Fetal and infant weight acceleration or deceleration were defined as a change in SD scores greater than 0.67 between time points. Infant measurements also included peak weight velocity, and age and body mass index reached at adiposity peak.Brain structure, including global and regional brain volumes, was quantified by magnetic resonance imaging at age 10 years.3; 95% CI, 1.2-10.2 cm3; third trimester: 15.3 cm3; 95% CI, 11.0-19.6 cm3; birth: 20.8 cm3; 95% CI, 16.4-25.1 cm3; 6 months: 15.6 cm3; 95% CI, 11.2-19.9 cm3; 12 months: 11.3 cm3; 95% CI, 7.0-15.6 cm3; and 24 months: 11.1 cm3; 95% CI, 6.8-15.4 cm3). Compared with children with normal fetal and infant growth, those with fetal and infant growth deceleration had the smallest total brain volume . Children with fetal weight deceleration followed by infant catch-up growth had similar brain volumes as children with normal growth. Higher peak weight velocity and body mass index reached at adiposity peak were associated with larger brain volumes. Similar results were observed for cerebral and cerebellar gray and white matter volumes.The study evaluated 3098 children . One SD score–higher weight gain until the second and third trimesters, birth, and 6, 12, and 24 months was associated with larger total brain volume independently of growth during any other age windows (second trimester: 5.7 cmThis cohort study’s findings suggest that both fetal and infant weight growth might be critical for cerebral and cerebellar brain volumes during childhood. Whether these associations link to neurocognitive outcomes should be further studied. The growth rates of the cortical gray matter and cerebellum peak during this period.4 Cortical gray matter volume also increases up to 150% in the first year of postnatal life and up to 20% in the second year.6 Adverse experiences occurring during these periods may permanently influence brain structure and function.9 Currently, evidence to support this hypothesis is mainly based on studies showing that children born preterm or with low birth weight are at risk for suboptimal neurodevelopmental outcomes.19 However, gestational age and weight at birth are merely the end point of fetal development and the starting point for infancy. In infancy, children born small for gestational age (SGA), appropriate for gestational age (AGA), and large for gestational age (LGA) have different growth patterns.20 Children born SGA tend to have catch-up growth, whereas those born LGA tend to have infant growth deceleration.21 Observational studies suggest that rapid weight gain in infancy is associated with benefits to later neurocognitive functioning, especially among those born preterm or SGA; however, little is known about its association with brain morphology.24 Population-based studies on the associations of fetal and infant growth patterns with brain structure enable identification of windows of vulnerability for brain development.Fetal life and infancy are critical periods for human brain development.We hypothesized that early-life growth patterns might be associated with childhood brain morphology. In a large, population-based, prospective cohort study among 3098 children, we examined the associations of fetal and infant growth with global and regional brain volumes, measured by magnetic resonance imaging (MRI) at age 10 years.25 Written informed consent was obtained for all participants. The medical ethics committee of Erasmus Medical Center approved the study. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline. We had information on fetal or infant growth in 9494 singleton births. Of these, 5704 children visited the research center at age 10 years. All children visiting the center were invited to undergo brain MRI. In total, 3882 children underwent neuroimaging assessment. Analyses were conducted for 3098 children for whom we had good-quality neuroimaging , term (37-42 weeks), or postterm (>42 weeks).13 Birth weight was obtained from medical records and was categorized into low (<2500 g), normal (2500-4500 g), or high (>4500 g).29 We created sex- and gestational age–adjusted SD scores for weight at birth within our study population by using the Growth Analyzer 3.5 (Dutch Growth Research Foundation), based on North European reference charts.30 Children born SGA and LGA were defined as gestational age– and sex-adjusted SD scores for birth weight below the 10th percentile and above the 90th percentile, respectively.31Fetal ultrasonography examinations were performed in each trimester of pregnancy.32Infant weight was measured in community health centers using a mechanical personal scale at approximately 6, 12, and 24 months. Age- and sex-adjusted SD scores were obtained using Dutch reference growth charts.31 Growth acceleration or deceleration were defined as a change in SD scores greater than 0.67 between time points. This clinically significant change reflects the difference between 2 percentile lines on the growth charts.33We categorized fetal and infant weight change into 3 groups  and created combined variables that reflect 9 different growth patterns. Fetal weight change was defined as growth between the second trimester and birth. Infant weight change was defined as growth from birth to 24 months in 69.4% of the full group. To achieve adequate sample size on subgroup analyses, if weight at 24 months was missing, we calculated the weight change by using weight at 12 or 6 months .35 To obtain age and body mass index  reached at adiposity peak, we analyzed repeated infant BMI measurements using a cubic mixed-effects model fitted on a log(BMI) scale from age 14 days to 1.5 years, adjusted for sex, which showed the best fit to the data.36 The growth measures are presented in detail in eTable 1 in the Peak weight velocity, reflecting the greatest weight growth in infancy, was derived using the Reed1 model by sex on all weight measurements taken from birth to 3 years of age, including birth weight.25 Brain imaging acquisition has been described previously.37 Briefly, all images were acquired using the same scan protocol on a single 3-Tesla (T) scanner  using an 8-channel head coil. A high-resolution 3-dimensional T1-weighted structural scan was obtained with an inversion recovery fast-spoiled gradient recalled sequence. Parameters were as follows: repetition time = 8.77 ms; echo time = 3.4 ms; inversion time = 600 ms; flip angle = 10°; field of view = 220 × 220 mm; acquisition matrix = 220 × 220; slice thickness = 1 mm; and number of slices = 230. At the scanner, T1 image quality was rated using a 6-point Likert scale: unusable, poor, fair, good, very good, and excellent. If the initial T1-weighted scan was rated as unusable or poor, the T1 sequence was repeated.37 Images were processed using FreeSurfer, version 6.0 (Florida State University Research Computing Center). Global and regional volumes, including total brain and cerebral and cerebellar gray and white matter volumes, were extracted. FreeSurfer procedures had good test-retest reliability across scanners.38 The quality of FreeSurfer output was visually inspected, and all scans rated as unusable (mostly due to motion) were excluded.39 Scans were reviewed by radiologists for the presence of incidental findings.40During the study visit at 10 years, head circumference was measured according to standardized procedures.41Information on maternal characteristics, including age, ethnicity, educational level, family income, prepregnancy BMI, smoking, alcohol consumption, and folic acid supplement use, were obtained from questionnaires during pregnancy. Child sex and age at neuroimaging assessment, available from medical records, were included to account for the association of these variables with brain measurements. Intracranial volume was obtained from MRI scans. We measured BMI at 10 years, using height and weight measured without shoes and heavy clothing. Age- and sex-adjusted SD scores for BMI were obtained with Dutch reference growth charts .43 We regressed weight at each time point on weight at all previous time points and saved the residual scores, creating noncorrelated growth variables that reflect growth during a certain time period independently of growth during all previous time periods.44 Fourth, we used similar regression models to assess the associations of fetal and infant growth patterns, peak weight velocity, and BMI and age at adiposity peak with brain volumes at 10 years.First, participants were compared to nonparticipants to assess possible bias due to loss to follow-up. Second, we assessed the associations between the birth outcomes of gestational age and weight and size for gestational age and brain volumes at 10 years using multiple linear regression models. Third, we performed conditional linear regression analysis to identify independent critical periods for early-life growth associated with brain outcomes. Conditional regression analyses take into account correlations between early life growth measures at different ages.45 Because results were similar when we excluded children born preterm and with low birth weight, we did not restrict or stratify on birth outcomes. Missing data in covariates were multiple-imputed using a Markov chain Monte Carlo approach.46 In total, 30 imputed data sets were created and analyzed, presenting pooled effect estimates with 95% CIs. No correction for multiple testing was performed, as all exposures and outcomes were highly correlated among each other, and thus a small number of independent tests was expected.47 Participants were compared with nonparticipnts by using Pearson χ2 tests, independent-sample t tests, and Mann-Whitney tests. Statistical significance was defined as P < .05 (2-sided). Statistical analyses were performed using the Statistical Package of Social Sciences, version 25.0 for Windows (IBM Inc).We used a hierarchical approach to examine the associations of fetal and infant growth with brain outcomes. If an association with a subcortical volume was observed, we performed post hoc analyses of the specific subcortical structures adjusted for intracranial volume. Nonlinearity of the association was assessed using generalized additive models and ruled out. For all analyses, we present a basic model including child sex and age at outcome, a confounder model, and a BMI model. Potential confounders were represented in a directed acyclic graph  . Nonresp3; 95% CI, –43.9 to –12.8 cm3). Compared with children with a normal weight at birth, those with low birth weight had smaller total brain volume . Compared with children born AGA, those born SGA had smaller total brain volume . Similar results were observed for subcortical gray, cerebral gray and white, and cerebellar gray and white matter volumes. After additional adjustment for childhood BMI, results remained significant  and third  trimesters, birth , 6 months , 12 months , and 24 months  was, independent of weights at the other age windows, associated with larger total brain volume. Similarly, higher weight gain during all of the examined time periods was associated with larger subcortical gray, cerebral gray and white, and cerebellar gray and white matter volumes. After additional adjustment for childhood BMI, results remained significant , whereas those with fetal weight deceleration followed by infant weight acceleration had similar brain volumes. The largest brain volumes were observed for children who had both fetal and infant growth acceleration , whereas age at adiposity peak was not. Similar results were observed for subcortical gray, cerebral gray and white, and cerebellar gray and white matter volumes. Results remained significant after additional adjustment for childhood BMI  with those born AGA (n = 19) suggested that being born SGA, even after postnatal catch-up growth, was associated with reduced cerebral and cerebellar white matter volumes at the age of 4 to 7 years.52 Such stress-related factors may persist after pregnancy and alter infant brain growth. The current study suggests that suboptimal fetal or infant growth may be associated with fetal programming differences in brain outcomes in the longer term.53 Another potential mechanism is genetic predisposition.54 Previous studies have found that the genetic background of birth weight is also associated with head circumference at birth and in later life.56Fetal programming mechanisms might partly explain these associations. Maternal age, substance use, poor diet, and psychological distress are known to influence fetal growth. An adverse intrauterine environment may lead to adaptive fetal responses, eg, circulatory redistribution, and negatively affect brain development.Altogether, the findings of the current study suggest that fetal and infant weight growth are associated with brain morphology in school-age children. Even though the observed associations are relatively small, they are important from a developmental and preventive perspective. Early postnatal life seems to be a window of opportunity to improve childhood growth and evaluate the healthy consequences for brain development. Future research should investigate potential causal pathways and underlying mechanisms of the observed associations and explore whether these associations link to neurocognitive outcomes.57 Further, for the infant weight change analysis, we considered the period from birth to 24 months. When weight was missing at 24 months, we used weight at 12 or 6 months. We used all data available to optimize statistical power but might have introduced bias. Brain morphology has been associated with behavioral and cognitive functions.58 Future studies should investigate repeated assessments of brain morphology as well as multimodal neuroimaging in combination with behavioral and cognitive functioning in large population-based cohorts. Finally, owing to the observational design of the study, unmeasured residual confounding might still be present.This study has some limitations. Because mothers of children with and without good-quality brain MRI data available were different regarding socioeconomic and lifestyle characteristics, we cannot exclude the possibility of selection bias. We had a relatively healthy population. Children born preterm or SGA do not reflect the full neonatal group, which mainly comprised children born late preterm after 35 weeks or children with mild SGA. Also, fetal weight estimated by ultrasound might be prone to measurement error, mainly among extremes , which might have influenced our findings.The results of this cohort study suggest that early-life growth is associated with brain morphology later in childhood. Both fetal life and infancy seem to be important critical periods of childhood brain development. Also, faster infant growth in the first 2 years of life may be associated with improved brain development later in life. Further studies are needed to assess whether a causal relationship exists and focus on potential long-term consequences of early growth patterns on brain structure and function.