Our objective is to determine how apo-CIII glycosylation affects lipid traits and type 2 diabetes prevalence, and to investigate the genetic basis of these relations with a genome-wide association study (GWAS) on apo-CIII glycosylation. We conducted GWAS on the four apo-CIII proteoforms in the DiaGene study in people with and without type 2 diabetes (n = 2318). We investigated the relations of the identified genetic loci and apo-CIII glycosylation with lipids and type 2 diabetes. The associations of the genetic variants with lipids were replicated in the Diabetes Care System (n = 5409). Rs4846913-A, in the GALNT2-gene, was associated with decreased apo-CIII0a. This variant was associated with increased high-density lipoprotein cholesterol and decreased triglycerides, while high apo-CIII0a was associated with raised high-density lipoprotein-cholesterol and triglycerides. Rs67086575-G, located in the IFT172-gene, was associated with decreased apo-CIII2 and with hypertriglyceridemia. In line, apo-CIII2 was associated with low triglycerides. On a genome-wide scale, we confirmed that the GALNT2-gene plays a major role i O-glycosylation of apolipoprotein-CIII, with subsequent associations with lipid parameters. We newly identified the IFT172/NRBP1 region, in the literature previously associated with hypertriglyceridemia, as involved in apolipoprotein-CIII sialylation and hypertriglyceridemia. These results link genomics, glycosylation, and lipid metabolism, and represent a key step towards unravelling the importance of O-glycosylation in health and disease. Apolipoprotein-CIII (apo-CIII) is involved in triglyceride-rich lipoprotein metabolism and linked to beta-cell damage, insulin resistance, and cardiovascular disease. Apo-CIII exists in four main proteoforms: non-glycosylated (apo-CIII Type 2 diabetes and cardiovascular disease are two of the leading causes of disease burden worldwide . TriglycAPOC3 gene contribute to dyslipidaemia, artery wall inflammation, atherogenesis, and cardiovascular disease (CVD) risk [APOC3 promotor variants increase type 2 diabetes risk and the need for insulin treatment, particularly among lean patients [Apo-CIII inhibits lipoprotein lipase  and hepaVD) risk ,10,11. MVD) risk ,13. APOCpatients .GALNT2 gene cause rare congenital glycosylation disorders affecting apo-CIII glycosylation [0a, and the proteoforms containing a mucin-type core-1 O-glycosylation with zero (apo-CIII0c), one (apo-CIII1), and two (apo-CIII2) sialic acids [0a, apo-CIII0c, and apo-CIII1 have been associated with lower small dense LDL [Polypeptide N-acetylgalactosaminyltransferase 2 , encoded by the GALNT2 gene, initiates mucin-type O-glycosylation of apo-CIII in the Golgi apparatus . Mutatiosylation  and affesylation . Withoutsylation ,19. The ic acids ,19,20  of apo-CIII O-glycosylation in the DiaGene Study and explored the associations between genetic variants, apoCIII proteoforms, lipid levels, and type 2 diabetes. The genetic associations with lipid levels were subsequently replicated in the Hoorn Diabetes Care System cohort.2, 68% had type 2 diabetes, and 48% used lipid-lowering therapy. In the Hoorn DCS, genotypes of 5409 participants with type 2 diabetes were available; the mean age was 61.1 (SD 11.0) years, 44.6% was female, the median BMI was 29.4 (IQR 26.6 to 33.1) kg/m2 and 42.1% used lipid-lowering therapy  years, 51% was female, the median body mass index (BMI) was 28.0 (IQR 25.2 to 31.6) kg/m therapy .0a had a significant, negative association with the prevalence of type 2 diabetes  (p = 0.065) and low triglycerides (p = 0.006) in the meta-analysis  and with higher prevalence of type 2 diabetes (p = 0.006) in the relatively small DiaGene study for such analyses (We conducted GWASs on the four main proteoforms of apo-CIII and the apo-CIII× 10−36) . This SNanalysis , in line× 10−146  and 2.2846 [−235 , respectparately . The varanalyses .PPFIBP1 locus, was associated with decreased apo-CIII1  and low triglycerides (p = 0.039) with nominal significance in meta-analysis ; and was significant for higher triglycerides (p = 1.07 × 10−5) in the meta-analysis, and for higher LDL-c, total cholesterol, and non-HDL-c  in the DiaGene study (p = 4.84 × 10−287) [p = 2.90 × 10−51) [p = 4.76 × 10−26) [Two variants reached a suggestive significance level for associations with one or more apo-CIII proteoforms without reaching formal genome-wide significance but were also associated with one or more lipid traits in the meta-analyses. The rs10842926-C in the analysis  but was ne study , Table 4 10−287) , LDL-c  to proteins, which is the essential first step in the addition of mucine-type O-linked glycans [GALNT2 variants rs4846913-A and rs35498929-T were associated with a low proportion of non-glycosylated apo-CIII0a, reflecting increased O-glycosylation of apo-CIII. The rs4846913 variant is located within a regulatory element that drives the expression of the GALNT2-gene in human hepatocytes [GALNT2 [We confirmed on a genome-wide significant scale that the  glycans . Apo-CII glycans . In our atocytes , which i [GALNT2 , while t [GALNT2 .GALNT2 missense variants resulted in attenuated glycosylation of apo-CIII and better triglyceride clearance [GALNT2 variant. They speculated that this was due to a smaller amount of glycosylated apo-CIII available for sialylation, increasing the number of disialylated species. In our study, sialylation within O-glycosylated species  did not associate with the rs4846913 and rs35498929 GALNT2 variants. Our genetic loci may not share the mechanism described by Holleboom et al. In our data, apo-CIII0a had a small positive association with triglyceride levels, which is in line with findings by Yassine et al. [0a was associated with higher HDL-c. Unfortunately, there is barely any literature about apo-CIII0a and HDL-c. Koska et al. found opposite directions of effect in their two study cohorts for the association of apo-CIII0a with HDL-c [GALNT2-gene, also glycosylates angiopoietin-like protein 3 (ANGPTL3), lecithin-cholesterol acyltransferase (LCAT), and phospholipid transfer protein (PLTP), which all play important roles in lipid metabolism, and especially triglyceride and HDL metabolism. Cholesteryl ester transfer protein (CETP) is another glycoprotein whose activity was directly associated with serum triglycerides and inversely with HDL-c [GALNT2 can affect lipid profiles through these or other proteins [Rs4846913-A also had a negative association with triglyceride levels, in line with findings in European ,27 and Alearance . Hollebolearance  found ale et al. . Apo-CIIth HDL-c . As demoth HDL-c . We cann2 and the apo-CIII1/apo-CIII2 ratio had strong negative and positive associations with triglyceride levels, respectively. Furthermore, rs67086575-G was associated with low apo-CIII2, high apo-CIII1/apo-CIII2 ratio, and with high triglycerides in the meta-analyses. The variant also had a positive association with LDL-c, non-HDL-c and total cholesterol but this was confined to the DiaGene study. The association of this variant with triglycerides, LDL-c and total cholesterol is in agreement with findings in public GWAS data [IFT172-gene. This gene encodes a peripheral subunit of the intraflagellar transport subcomplex-B (IFT-B) [NRBP1-gene [NRBP1-gene is suggested to play a role in subcellular trafficking between the endoplasmic reticulum and Golgi apparatus [O-glycans takes place at the Golgi-membrane [NRBP1-gene might explain the association of this SNP with apo-CIII sialylation.Regarding sialylation, apo-CIIIWAS data ,27. Rs67 (IFT-B) . Mutatio (IFT-B) . However (IFT-B) . In blooBP1-gene . This NRpparatus . Since smembrane , the lin2 is associated with higher HDL-cholesterol, lower LDL-cholesterol, and lower triglyceride levels, while apo-CIII0c and apo-CIII1 showed opposite effects. Our findings are in accordance with findings in type 2 diabetes and prediabetes by Koska et al. [2 or increasing apo-CIII1 and apo-CIII0a might cause a shift towards a more atherogenic lipid profile. These glycoforms might be an interesting treatment target for the prevention of cardiovascular disease in the general population and in type 2 diabetes. Future large-scale research is needed to confirm the effects of apo-CIII proteoforms on lipid metabolism and cardiovascular disease risk, and to investigate the effect of medication on apo-CIII proteoform distributions, which is relevant for the development and effectivity of lipid-lowering therapy.Apo-CIIIa et al. . Moreovea et al. ,39. FromHepatic receptors differentially clear sialylated apo-CIII glycoforms , therefoO-glycosylation, and therefore provides new insights into the genetic background of O-glycosylation. Current knowledge regarding the genetics of apo-CIII O-glycosylation is mainly based on studies addressing congenital disorders of glycosylation, where rare genetic variants have been shown to affect apo-CIII O-glycosylation [O-glycosylation on a large scale, using a high-throughput method to measure apo-CIII proteoforms. To date, the relations between lipids and apo-CIII glycoforms are only investigated in small cohorts. Our findings are in line with those studies, although non-glycosylated apo-CIII0a, has received little attention in the current literature.This is the first GWAS of apo-CIII sylation . FindingO-glycosylation. Therefore, direct replication of our GWAS is not possible; this limits our power to detect small effect sizes. Nevertheless, we could only detect associations with substantial effect sizes, which are most relevant as a starting point. Notably, we did replicate the identified lead SNPs from the GWAS with plasma lipids in the independent Hoorn DCS cohort to improve power for effects on the clinical endpoints and decrease the risk of false-positive results. The Hoorn DCS cohort included patients with type 2 diabetes treated by their general practitioner, whereas the DiaGene study included patients with type 2 diabetes from all lines of care and people without diabetes. This could explain some of the differences between these cohorts. A few associations had opposing directions of effect in the DiaGene and Hoorn DCS cohorts. However, the confidence intervals were mostly overlapping, which does not allow definite conclusion on the directions of the effect. Unfortunately, we do not have total apo-CIII plasma concentration data. The distribution of glycoforms in plasma is stable in variable apo-CIII concentrations in young, healthy men [There are no previous large-scale studies with data on apo-CIII lthy men . By addiGALNT2-gene affect apo-CIII O-glycosylation, lipid metabolism, and the risk of type 2 diabetes. The GALNT2-gene encodes the GalNAc-T2 enzyme that covalently links N-acetylgalactosamine  to proteins. This suggests that the order of causality has the O-glycosylation at its basis, subsequently affecting apo-CIII function and then altering the lipid profile and the risk of type 2 diabetes, and not vice versa. Moreover, we propose the NRBP1-gene as a possible player in the sialylation of apolipoprotein-CIII and hypertriglyceridemia. Our results confirm a link between genomics, glycosylation, and lipid metabolism. This is a key step towards unravelling the regulation of serum and plasma glycoprotein O-glycosylation in health and disease. Glycophenotype characterisation alongside genetic variant identification should serve as relevant prognostic and predictive tools and should be considered for target identification of new pharmacological agents.In conclusion, variants of the O-glycosylation for 1572 persons with type 2 diabetes and 746 persons without diabetes, of which, for 1872 participants, genetic data were also available to perform the GWAS analysis.We used cross-sectional data from the DiaGene Study. This study has been described in more detail elsewhere . BrieflyFor replication of the genetic associations with lipid levels, we used data from the Hoorn Diabetes Care System (Hoorn DCS) cohort, described in detail elsewhere . PatientInformed consent was obtained from all subjects involved in the study. Both studies were approved by the Medical Ethics Committees of the involved hospitals in compliance with the Declaration of Helsinki principles .O-glycosylation in the DiaGene Study was performed using a high-throughput method based on solid-phase extraction, matrix-assisted laser desorption/ionization (MALDI) and ultrahigh-resolution Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS), described by Demus et al. [0a represents non-glycosylated apo-CIII; the non-, mono- and di-sialylated glycoforms are described as apo-CIII0c, apo-CIII1 and apo-CIII2, respectively. Apo-CIII0a was normalized to the sum of all four proteoforms: apo-CIII0a, apo-CIII0c, apo-CIII1, and apo-CIII2 to reflect the proportion of non-glycosylated apo-CIII. The sum of apo-CIII0c, apo-CIII1, and apo-CIII2 was set to 1.0 to obtain the proportion of these three glycoforms within all glycosylated species of apo-CIII. Additionally, the ratio of apo-CIII1 and apo-CIII2 was calculated. Individuals with missing glycosylation data, missing age, sex, or genetic principal components were excluded from the analyses. Glycan measurements were natural log-transformed prior to GWAS analysis, because of the skewness of their distributions.The analysis of apo-CIII s et al. . The metp-Value ≤ 1 × 10−4 were excluded. Using zCall [1/apo-CIII2 ratio with RVtest [p-Values lower than or equal to 5 × 10−8 were considered genome-wide significant. To differentiate diabetes-specific and general genetic effects, the associations of the genetic variants with the apo-CIII proteoforms were investigated separately for people with or without type 2 diabetes, using linear regression models in SPSS.In the DiaGene and Hoorn DCS cohorts, quality control was performed in PLINK , and genng zCall  we aimedng zCall . In the h RVtest  and qualh RVtest . We assep-Value for significance was applied as p < 0.0125 (0.05/4).The associations of apo-CIII glycosylation with lipid markers were analysed in the DiaGene Study using linear regression, separately for each apo-CIII proteoform. The proteoforms were considered the dependent variables. First, the analysis was performed with adjustments for age and sex; subsequently, a sensitivity analysis was performed for the use of lipid-lowering therapy . Next, this analysis was repeated for people with and without type 2 diabetes separately to investigate potential differences in medication effects. Since four separate proteoforms were analysed, the Bonferroni corrected p-Value < 1 × 10−6 in the GWASs were selected for further statistical analyses with lipid markers and prevalence of type 2 diabetes. We performed multiple linear regression analyses of the association of the genetic variants with lipid markers HDL-c, non-HDL-c, LDL-c, total cholesterol, and triglycerides, adjusted for age and sex. The triglyceride concentrations were natural log-transformed for the statistical analyses; p-Values are shown for log-transformed triglyceride levels, while effect sizes are shown for non-transformed triglyceride concentrations. Next, we performed logistic regression analyses for the associations of the genetic variants with the prevalence of type 2 diabetes, adjusted for age and sex. The Bonferroni corrected threshold for significance was calculated as p < 0.0045 (0.05/11), as we analysed 11 single nucleotide polymorphisms (SNPs). Variants showing associations with a p-Value < 1 × 10−6 in the GWASs were investigated for associations with lipid markers in the Hoorn DCS Study. The results of DiaGene and Hoorn DCS of these variants with clinical outcomes were meta-analysed using beta, standard error, and number of participants of each cohort. Subsequently, SNPs were looked up in public GWAS databases Type 2 Diabetes Knowledge Portal [The genetic variants with a e Portal  and IEU e Portal  and in ee Portal  on 25 MaIBM SPSS 25.0 was used for all statistical analyses within the DiaGene study, R version 4.0.5 was used for statistical analysis in the Hoorn DCS and meta-analyses.


P < .05; OR: 3.391; 95% CI: 1.85–28.78; P < .05). The findings may demonstrate that higher NLR and PLR are independently and significantly associated with the development of BPD.To determine whether neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are correlated with bronchopulmonary dysplasia (BPD) on the first day of prematurity and to help with early warning, identification, and intervention in the development of BPD. From January 2017 to June 2022, newborns who were diagnosed with BPD conducted a retrospective cohort study. Complete blood cells were measured within the first 24 hours of life in preterm neonates of 32 gestational weeks with BPD as the observation group and non-BPD infants as the control group. In all groups, the NLR and PLR levels were measured. Both univariate and multivariate logistic regression analyses were used to evaluate the data. In this research 76 cases of non-BPD and 48 cases of BPD were used as controls. Compared with the non-BPD group, the NLR and PLR levels were considerably higher in the BPD group. Logistic regression analysis suggested that NLR and PLR were independent risk factors for BPD (OR [odds ratio]: 3.786; 95% CI [confidence interval]: 1.75–8.16;  In recent years, due to advances in perinatal medicine, the use of pulmonary surfactants and ventilators, the birth and survival rates of very low birth weight infants have significantly increased. Meanwhile, the incidence of BPD has been increasing year by year, seriously impacting the survival and prognosis of preterm infants. However, there is still a lack of effective measures for preventing and treating BPD.,4 Therefore, identifying markers to predict the risk of BPD and exploring the pathogenesis of BPD can help in the clinical prevention, diagnosis, and treatment of BPD at an earlier stage, therefore improving the prognosis and quality of survival of preterm infants. The blood count is widely used in clinical practice and its measurement is a routine examination for all patients at hospital admission. It has received growing attention in recent years, because of its advantages of being easily accessible and requiring no additional trauma to the child. Among them, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are new evaluation indicators of systemic inflammation, which have been proven to have diagnostic and prognostic value in the respiratory system and a variety of inflammatory diseases.,6 However, studies on the correlation between NLR, PLR, and BPD in preterm infants after birth remains unclear. Therefore, this study was performed to analyze the relationship between PLR, NLR, and BPD after birth in preterm infants admitted to our hospital at gestational age <32 weeks, to provide early help in the diagnosis and treatment of BPD and thus improve the prognosis.Bronchopulmonary dysplasia (BPD) is the most common chronic lung disease in preterm infants. Children <32 weeks of gestational age were assessed at 36 weeks of corrected gestational age. This study was approved by the medical ethics committee of the Linping Branch of the Second Affiliated Hospital of Zhejiang University. Parents receive written information about the research and give their written agreement for their children to participate.Premature infants with a gestational age <32 weeks who were admitted to our hospital neonatal medicine unit from January 2017 to June 2022 were selected for retrospective analysis. The exclusion criteria were: admission at more than 1 day of age, death or abandonment of treatment within 4 weeks of birth, infants who were born with hematologic diseases, congenital anomalies or genetic metabolic abnormalities, and neonatal sepsis and severe asphyxia. The diagnostic criterion for BPD, namely the need for oxygenation for ≥28 days, was based on the diagnostic and grading criteria for BPD developed by the National Institute of Child Health and Human Development in 2000.Maternal age, gestational diabetes mellitus (GDM), small for gestational age, delivery methods, gestational hypertension, Apgar score, sex, neonatal respiratory distress syndrome, birth weight, intraventricular hemorrhage, surfactant treatment, patent ductus arteriosus (PDA), and necrotizing enterocolitis were all gathered from the hospital electronic medical record system. Whole blood testing was collected within the first 24 hours of life. NLR stands for neutrophils divided by lymphocyte count, and PLR stands for platelet count divided by lymphocyte count. Blood tests were performed within 24 hours of birth using samples taken from the newborns’ umbilical veins.2 test or Fisher exact test was used for comparison between groups. Normally distributed measures are expressed as mean ± standard deviation, and the t test for 2 independent samples was used for comparison between groups. Non-normally distributed measures are expressed as median (interquartile range), and the Kruskal–Wallis rank sum test was used for comparison between groups. In the multifactor logistic regression model, the risk factors with statistical significance in the univariate analysis were selected as covariates, and BPD and non-BPD subgroups were used as dependent variables to analyze the independent risk factors for BPD. A receiver operating characteristic curve (ROC) was used to analyze the predictive value of the NLR and PLR on the first day of life for BPD, to determine the best cutoff point, and to calculate the sensitivity and specificity of NLR and PLR for predicting BPD. Differences were considered statistically significant at P < .05Statistical analyses were performed using SPSS for Windows Version 26. Statistical data are expressed as n (%), and the χP < .05) (Table P < .05), and birth weight  (Table P > .05).In total,124 premature infants with gestational age <32 weeks were enrolled during the study period. Of those infants, 48 were diagnosed with BPD, whereas 76 did not have BPD . The platelet count was significantly lower in infants with than without BPD .The comparison of laboratory examinations at birth between infants with and without BPD was displayed in Table P < .05), PLR , gestational age and birth weight were independent risk factors for BPD, (Table The logistic regression analysis showed that the NLR (OR [odds ratio]: 3.786; 95% CI [confidence interval]: 1.75–8.16;  but the use of NLR and PLR to predict the risk of BPD is less commonly reported. In this study, we analyzed the relationship between NLR, PLR, and BPD in preterm infants on the first day of life and found that NLR and PLR on the first day of life were independent risk factors for BPD and that NLR and PLR after birth were valuable in predicting the development of BPD.In recent years, the incidence of BPD has increased secondary to the increased morbidity and survival of very early preterm infants. Previous studies have reported that gestational age, birth weight, and PDA are all risk factors for BPD,,10 Premature infants who are exposed to hyperoxia, intrauterine infection, require mechanical ventilation, and develop BPD, have visible infiltration of inflammatory cells and inflammatory factors in their lungs. Within such infiltrations, neutrophils and macrophages play a key role in systemic inflammation in the lung and extra-pulmonary tissues. Compared to non-BPD patients, children with BPD have a higher concentration and greater persistence of inflammatory cells in the bronchoalveolar lavage fluid. These findings demonstrate a link between inflammatory responses and the pathogenesis of BPD.In recent years, it is known that the elevated level of inflammatory cytokines is associated with BPD. allergic rhinitis, chronic obstructive pulmonary disease, and acute pulmonary embolism. Neutrophils are important cells in the immune defense system and regulate the functions of mast cells, epithelial cells, and macro cells. The NLR is a marker of inflammation and is used in conjunction with other inflammatory markers to determine the severity of the body condition in some inflammatory diseases.,19NLR was significantly higher in patients with severe pneumonia and acute respiratory distress syndrome compared to controls. In our research, we found that the NLR in the BPD group was higher than that in the non-BPD group at birth. We confirmed that the NLR has the capacity to predict BPD.Generally, Inflammation is usually accompanied by relative changes in the absolute values of circulating peripheral blood leukocyte subpopulations such as neutrophils and lymphocytes. In recent years, leukocyte and their subpopulation counts have been used as markers of the degree of inflammation in several diseases, such as acute appendicitis,,22 Recent research has demonstrated the critical role platelets play in angiogenesis, fibrin formation and deposition, platelet parameters, and alterations in premature birth-related diseases, such as sepsis and RDS.,24 According to this study, PLR is demonstrated to be associated with the development of BPD, which may be that Platelets and their activation products can participate in the inflammatory response of the body by promoting the aggregation and infiltration of inflammatory cells such as leukocytes and neutrophils.Another predictor of systemic inflammation that has been proven to work in some diseases is PLR.The limited patient population is the primary drawback of this study. Furthermore, because this was a retrospective observational study, it was challenging to ensure consistency of the clinical data because patients were not treated by the same physicians. Finally, this study only included Chinese people, which limits the data available to assess the impact of ethnic diversity. If the level of evidence for the relationship between NLR, PLR, and BPD is to be improved, future studies will need to conduct multi-center, large-sample studies to further validate its association with BPD.In conclusion, as new inflammatory markers, the NLR and PLR have the advantages of being simple, economical, and rapid, have a certain sensitivity and specificity for predicting the occurrence of BPD, and have important clinical application value.All authors read and approved the final version of the manuscript.Conceptualization: Junsheng Jiang.Data curation: Junsheng Jiang.Formal analysis: Junsheng Jiang, Yueyan Mao.Funding acquisition: Junsheng Jiang, Qian Zhou.Investigation: Qian Zhou.Project administration: Qian Zhou, Jiabo Wu.Resources: Qian Zhou, Jiabo Wu.Software: Yueyan Mao, Jiabo Wu.Supervision: Jiabo Wu.Validation: Yueyan Mao.


To assess the association between maternal characteristics, adverse birth outcomes  and/or preterm) and neonatal mortality in rural Nepal.This is a secondary observational analysis to identify risk factors for neonatal mortality, using data from a randomised trial to assess the impact of newborn massage with different oils on neonatal mortality in Sarlahi district, Nepal.Rural Sarlahi district, Nepal.40 119 pregnant women enrolled from 9 September 2010 to 16 January 2017.The outcome variable is neonatal death. Cox regression was used to estimate adjusted Hazard Ratios (aHRs) to assess the association between adverse birth outcomes and neonatal mortality.There were 32 004 live births and 998 neonatal deaths. SGA and/or preterm birth was strongly associated with increased neonatal mortality: SGA and preterm ), SGA and term/post-term ), appropriate-for-gestational-age/large-for-gestational-age and preterm ). Neonatal mortality was increased with a history of prior child deaths ), being a twin or triplet ), births at health posts/clinics or in hospital ) and on the way to facilities or outdoors ). Risk was lower with increasing maternal height from <145 cm to 145–150 cm ) to ≥150 cm ), four or more antenatal care (ANC) visits ) and education >5 years ).SGA and/or preterm birth are strongly associated with increased neonatal mortality. To reduce neonatal mortality, interventions that prevent SGA and preterm births by promoting ANC and facility delivery, and care of high-risk infants after birth should be tested.NCT01177111. A large number of pregnancies followed with frequent visits in the neonatal period ensure a statistically powered analysis.The availability of a large amount of data for each participant enables a wide range of risk factor to be investigated.The date of last menstrual period was obtained early in pregnancy, limiting recall bias, and stillbirths were identified, but both could lead to some misclassification of preterm birth and neonatal death.Missing weights due to early newborn deaths or weighing of the baby 72 hours or more after birth were estimated by using multiple imputation, which increased the variability but reduced bias of risk estimates.Globally, 2.3 million children died in the first 28 days of life in 2021, with the neonatal mortality rate of 18 deaths per 1000 live births.5Although Nepal has continuously reformed its primary healthcare system and expanded the number of health facilities,17To identify relatively important ones and explore more evidence for interventions to reduce neonatal mortality in specific settings, we used data from the Nepal Oil Massage Study (NOMS), a community-based, cluster-randomised controlled trial in rural southern Nepal to conduct an observational analysis to assess the association between maternal characteristics, adverse birth outcomes (SGA and/or preterm) and other maternal and infant characteristics and neonatal mortality.This study was conducted in Sarlahi district of Southern Nepal, a poor rural area of subsistence farming in the low-lying plains of Nepal bordering Bihar, India. In Sarlahi district, neonatal mortality ranges from 30.0 to 41.9 deaths per 1000 live births, even higher than the average in Nepal19The NOMS was a cluster-randomised, community-based trial ). The geographic area was divided into sectors that were randomised to receive promotion of full-body massage with mustard oil, which is the standard practice, or the intervention group that received promotion of full-body massage with sunflower seed oil. All women of childbearing age in the 34 participating Village Development Committees were eligible for the trial. Women who consented to pregnancy surveillance were visited every 5 weeks and asked whether they had their period since the last visit. If not, they were offered a pregnancy test. If positive, they were enrolled in the trial. Women were followed with monthly visits until delivery. A birth assessment was conducted on the first visit to the newborn baby (the day of birth or as soon after birth as possible), and neonates were followed up on 1, 3, 7, 10, 14, 21 and 28 days of age. Vital status was collected on mothers and infants at each of these time points. A verbal autopsy was conducted on the causes of neonatal death and the date of death was also recorded. For the analysis of risk factors for neonatal mortality, only live born infants and their mothers were included.Data for the trial were collected from November 2010 through January 2017. All data collectors were employed and trained by the study. This included local village women who collected data from the pregnant women in their communities. Data such as anthropometry were collected by study employees trained and standardised to collect these measurements. For more complex survey data and supervisory staff, local people with prior experience collecting these types of data were employed. At the time women were identified as pregnant and enrolled, date of last menstrual period, maternal age, height, education and reproductive history were recorded. Information about household assets, caste and ethnicity of the family was also collected at the time of enrolment. During pregnancy, baseline and monthly visits in pregnancy recorded receipt of tetanus vaccination, tobacco and alcohol use and maternal morbidity. In the first visit within 72 hours after birth, late pregnancy morbidity, labour and delivery characteristics and immediate newborn care practices were collected. Neonatal characteristics included sex, weight and the time since birth at which weight was measured. The aim was to measure weight as soon after birth as possible.Our study was an institutional review board-approved secondary analysis of a pre-existing dataset generated from the NOMS trial. Therefore, it was not appropriate to involve patients or the public in the design, conduct, reporting, or dissemination plans for this research.Neonatal death was defined as the death of a live-born baby in the first 28 days of life and the neonatal mortality rate was defined as the number of neonatal deaths per 1000 live births.28The data described above were managed and analysed using Stata V.16.0.Maternal characteristics included demographics, socioeconomic status, lifestyle and reproductive history. Maternal age in pregnancy was categorised as ≤18 years, 18 years to 35 years and >35 years. Maternal height was categorised as <145 cm, 145 cm to <150 cm and ≥150 cm. Socioeconomic information included years of education , and wealth quintiles based on a standardised score of the total number of household assets including land, animal, transport and mobile phone ownershipFor healthcare in pregnancy and characteristics of delivery, tetanus vaccination receipt, place of delivery  and the number of ANC visits  were examined.Neonatal characteristics included birth weight (taken within 72 hours of birth), gestational age, sex, size for gestational age and singleton/twin/triplet in our analysis. Because weights were taken at varying times after birth, and birth weight was missing for some infants who died very soon after birth or was measured more than 72 hours after birth, we multiply imputed birth weights (and hence SGA percentiles) for those with missing weights  and also imputed weight at time zero for all those with weights and times of weight measurement. This was done using an empirical Bayes regression model of early neonatal weight change by estimating, then recalibrating from the conditional distribution of each child’s birth weight given a single measurement at a known later time or imputing given missing weight based on longitudinal daily weights from day of birth through 10 days in a population of infants from the same study area as this trial.31We did not include the intervention as a covariate in the regression model because the results of the randomised trial are being written up for publication but are not yet citable. However, there was no significant difference in mortality between the intervention and control groups. Hence, not including this variable in the regression should not impact the results presented here.For estimating HRs, we used Cox regression. The outcome was defined as time from birth to death or time the baby was last seen alive. This analysis takes account of loss to follow-up and variable ages at death.In the simple Cox regression models, we calculated the crude HR (cHR) and their 95% CI for each covariate. In multivariable regression, we did not include some variables because of collinearity. For example, we did not include both parity and gravidity. We did not include prior miscarriage or stillbirth because we included prior child deaths. We developed four different models to examine the adjusted HRs (aHRs) and their 95% CIs for risk factors. In model 1, we included maternal age, height, years of education, wealth quintile, caste, parity, prior child deaths, tetanus vaccination, place of delivery, number of ANC visits, sex of the infant, preterm, adverse birth outcomes  and singleton/twin/triplet. In model 2, we included all the variables in model 1, except place of delivery and number of ANC visits. We ran the models with and without these two variables because about 10% of the population was missing these variables and we wanted to see whether excluding these variables produced similar regression results for the predictors of interest to when they were included. For models 3 and 4 , we included gestational age and size for gestation separately, to compare with models 1 and 2, respectively. We ran each of these four models twice, one time where we used weights and associated SGA/preterm taken within 72 hours of birth, and again using the multiply imputed weights and imputed SGA. This allowed us to compare how the imputation impacted the regression coefficients. Since some women had more than one pregnancy included in the analysis, we accounted for correlation within women using robust variance estimation.32For multivariable Cox regression, we have assessed the fitness of each of the four models using Stata and found that none indicated a significant deviation from the proportional hazard assumption.A total of 32 004 live-born babies were included in this analysis. Based on the cHRs, maternal height, education, wealth quintile, caste, parity, gravidity, prior child deaths and prior adverse pregnancy outcomes, place of delivery, number of ANC visits, multiple births, birth weight, SGA and preterm were all significantly associated with neonatal mortality. Birth weight imputation altered the cHRs for high birth weight (>4000 g) from being a risk factor to being protective and LGA (>90th percentile) from no association to being a significant risk factor. While being SGA and preterm had a large and significant HR in the analysis without imputed weight values, this HR increased significantly when birth weights were imputed.Variables with high proportions of missing data (around 10%) were ANC visits (10.7%), place of delivery (10.6%), birth weight (12.4%) and weights taken after 72 hours (13.5%). Neonatal mortality was higher among women who delivered at a facility or on their way to a facility compared with home births. Women who attended four or more ANC visits had lower neonatal mortality risk. Both before and after imputation, VLBW and LBW significantly increased neonatal mortality. Preterm or post-term resulted in higher neonatal mortality. SGA and LGA babies were at higher risk compared with AGA ones. Alcohol and tobacco use was not included in multivariate models, given the low prevalence of their use in pregnancy.Multivariable regression results are shown in The total number of live births in the analysis was 26 626 with original data (weights taken within 72 hours), and 27 819 with imputed birth weight and SGA data in model 1 . Place oIn model 2 , the numAfter imputation and adjustment, variables such as maternal age, wealth quintile and caste of the family were not significantly associated with neonatal mortality. Increasing maternal height from <145 cm to 145–150 cm ) to ≥150 cm ) and education to >5 years ) remained protective factors. Parity was not significantly associated with neonatal mortality. Prior live births and child death ) were associated with higher risk. Tetanus vaccination did not have a significant impact on neonatal mortality. Being a twin or triplet ) was also significantly associated with neonatal mortality. The findings were similar in models 1 and 2.Any SGA and/or preterm was strongly associated with an increased likelihood of neonatal mortality : SGA andWe also created two other multivariate regression models, models S1 and S2 as shown in 10.1136/bmjopen-2022-066931.supp1Supplementary dataNepal was one of the few countries to meet the Millennium Development Goal 4 for child mortality but neonatal mortality rates are still high and need to be reduced by twofold to meet the Sustainable Development Goals of 12 per 1000 live births.Compared with previous existing studies, maternal age,A previous systematic review indicated that neonatal mortality is reduced by vaccination of women of childbearing age with tetanus toxoid in LMICs,Strengths of this study included a large cohort of pregnancies followed through 28 days postpartum with up to eight visits in the neonatal period. The frequent visits reduced loss to follow-up and improved estimates of age at death. While gestational age was not measured using gold standard ultrasound, the date of last menstrual period was obtained early in the pregnancy, limiting recall bias. However, the reliance on date of last menstrual period could have led to some misclassification of preterm births. Another strength of this study was that stillbirths were identified using maternal recall of whether the baby moved, cried or breathed after birth. If the infant did none of these, they were classified as a stillbirth. However, there could be some misclassification of stillbirths as live births and vice versa. Another limitation is that we did not include maternal morbidity in pregnancy in this analysis. Maternal morbidity may have impacted neonatal mortality but would likely have worked through adverse pregnancy outcomes such as preterm birth and SGA, which were the primary focus of risk factors for mortality in this analysis. Some other limitations include many missing weights due to early newborn death or weighing of the baby 72 hours or more after birth. Most of the missing values for birth weight were estimated by using multiple imputation, which increased the uncertainty of this analysis. The imputation of birth weights also had a qualitative impact on the regression coefficients, in particular for the AGA/LGA and preterm category, which went from an aHR of 3.13 to one of 5.46. This may be due to the fact that preterm infants who died soon after birth were likely to be missing weights. The neonatal mortality rate among preterm births was 33.02 per 1000 before imputation and 83.19 per 1000 after imputation. Similarly, the neonatal mortality rate among SGA before imputation was 19.10 per 1000 and 32.92 per 1000 after imputation.In addition to missing birth weights, about 10% of values were missing for place of delivery and number of ANC visits. We also conducted sensitivity analyses including and excluding these covariates and found that the other regression coefficients were not altered significantly by their being included.Our study found that maternal age, wealth quintile, caste of the family, parity and tetanus vaccination were not significantly associated with neonatal mortality after adjustment and imputation. Although these findings conflict with some of the existing literature, it is possible that the inclusion of more proximate risk factors such as gestational age and size for gestational age lessened the importance of these other more distal risk factors.Maternal height, maternal education, prior child deaths, SGA, preterm, twins or triplets, place of delivery and number of ANC visits were significantly associated with neonatal mortality risk. Low birthweight babies who are preterm and SGA, preterm and LGA, or SGA and term have different mortality risks and these adverse outcomes likely have different aetiologies. These findings could help refine interventions to reduce these adverse birth outcomes. Interventions that focus on preventing SGA and preterm births, improving education for women, promoting quality antenatal healthcare and facility delivery, and care of vulnerable infants after birth should continue to be emphasised and prioritised.


Aerobic exercise training (AET) prescribed as lipid management treatment positively affects the standard lipid profile and reduces cardiovascular disease (CVD) risk. Apolipoproteins, lipid and apolipoprotein ratios, and lipoprotein sub-fractions may more effectively predict CVD risk than the standard lipid profile but an AET response in these biomarkers has not been established.We conducted a quantitative systematic review of randomised controlled trials (RCTs) to (1) determine the effects of AET on lipoprotein sub-fractions, apolipoproteins and relevant ratios; and (2) identify study or intervention covariates associated with change in these biomarkers.We searched PubMed, EMBASE, all Web of Science and EBSCO health and medical online databases from inception to 31 December 2021. We included published RCTs of adult humans with ≥ 10 per group of participants; an AET intervention duration ≥ 12 weeks of at least moderate intensity (> 40% maximum oxygen consumption); and reporting pre/post measurements. Non-sedentary subjects, or those with chronic disease other than Metabolic Syndrome factors, or pregnant/lactating, as well as trials testing diet/medications, or resistance/isometric/unconventional training interventions, were excluded.P = .01); lowered atherogenic apoliproteins and lipoprotein sub-fractions , P = .05); and improved atherogenic lipid ratios , P < .0001). Multivariate meta-regression showed intervention variables contributed to change in lipid, sub-fraction, and apoliprotein ratios.Fifty-seven RCTs totalling 3194 participants were analysed. Multivariate meta-analysis showed AET significantly raised antiatherogenic apolipoproteins and lipoprotein sub-fractions (mmol/L mean difference (MD) 0.047  0.011, 0.082), Aerobic exercise training positively impacts atherogenic lipid and apolipoprotein ratios, alipoproteins, and lipoprotein sub-fractions; and antiatherogenic apolipoproteins and lipoprotein sub-fractions. Cardiovascular disease risk predicted by these biomarkers may be lowered when AET is prescribed as treatment or prevention.CRD42020151925.The online version contains supplementary material available at 10.1007/s40279-023-01817-0. The standard lipid profile (SLP) biomarkers used to evaluate cardiovascular (CVD) risk comprise total cholesterol (TC), triglycerides (TRG), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) . DyslipiApolipoproteins (Apo) A1 and A2 are the largest protein constituent of HDL . The ApoLack of aerobic physical activity increases CVD risk . Aerobic2MAX)), against no exercise on apolipoproteins, lipoprotein sub-fractions, associated ratios and lipid ratios in adults of like health status. Further, using multivariate meta-regression, we wanted to investigate whether a priori covariates were associated with change in outcome measures.We thus aimed to conduct a multivariate meta-analysis of RCTs comparing the effects of AET achieving a minimum aerobic intensity  , CRD4202Potential studies were identified by systematic online searches of PubMed, EMBASE, all Web of Science and EBSCO health and medical databases from inception to 31 December 2021, for RCTs published in English language journals. Searches included a mix of Medical Subject Headings (MeSH) and free text terms  Table S1 for search terms, exclusions and example search strategy). Other systematic reviews and reference lists of papers were hand searched for additional RCTs.Studies were eligible for inclusion if the study design was an RCT comparing an AET intervention against a non-exercising control group.GW, ET, AP and VN conducted online database searches and reviewed search results on the basis of title and abstract independently, using Microsoft Excel (Version 16.31 2019). GW, ET, AP and VN assessed and reviewed the full PDF texts of potentially eligible RCTs independently. NS was consulted to resolve discussion regarding the final list of RCTs for inclusion.2; hypertensive blood pressure > 130/85 mm Hg; TRG ≥ 10.7 mmol/L; HDL-C < 1 mmol/L (men) or HDL-C < 1.3 mmol/L (women); fasting blood sugar > 5.5 mmol/L, diabetes mellitus type 1 or 2, or medication prescribed to manage these factors) , assuming a correlation coefficient r = 0.5, considered a conservative estimate [Statistical analyses were performed using Comprehensive Meta-Analysis (CMA) 3.0 . To allow for multiple missing and/or correlated outcomes , 27, we estimate . GW and Comprehensive Meta-Analysis permits joining non-independent or correlated outcomes and calculating a pooled mean or selecting the largest mean amongst pooled studies with various missing outcomes. Although the former may under-estimate effect and significance, it aids in avoiding type 1 errors and increases the potential accuracy of estimated effect sizes and CIs, and hence was selected as the appropriate method. A continuous random effects multivariate meta-analysis was conducted in CMA as follows: outcomes were joined, using the mean of multiple per-study non-independent and potentially correlated outcomes, to assess the impact of AET. In each continuous random effects multivariate meta-analysis of the outcomes, RCTs were sorted chronologically according to year of publication and hence analysed cumulatively (i.e. over time). For outcomes unable to be joined (either because of effect size direction or unit of measurement), RCTs were sorted chronologically according to year of publication and analysed cumulatively using a univariate, rather than multivariate, continuous random effects meta-analysis.K − 1, where K = total number of pooled RCTs, and each RCT is excluded once) sensitivity analysis was also performed to evaluate the influence of each RCT on the effect size of pooled data [Sub-analyses were conducted in CMA for study quality using TESTEX scores (RCTs with a score ≥ 10) and within-study bias analysis (low to medium). Data were entered by GW and reviewed by NS for accuracy. A leave-one-out  [I2 ranges from 0% (complete homogeneity) to 100% (complete heterogeneity) [Heterogeneity was quantified using the , and I2 . The Q sd SD (τ) . The relgeneity) . MW reviP values using a random effects restricted maximum likelihood model with a Hartung–Knapp adjustment to detect whether any a priori study or intervention covariates might explain a change in statistically significant outcomes. A priori AET intervention covariates  that have been shown to influence lipid outcomes were analysed [Meta-regression was conducted in CMA without adjustment for analysed , 45, 46.The search and inclusion process is presented in Fig. Combined searches resulted in a total of 1704 potential papers. After removal of duplicates and exclusion of articles based on abstract and title, 109 full-text articles remained for screening against inclusion and exclusion criteria. Screening resulted in the inclusion of 57 RCTs for data extraction, pooling and analysis, published between 1979 and 2020. We contacted three lead authors of whomh one lead author responded and provided data as requested. Two papers presented data graphically which was converted using WebPlotDigitzer .2MAX. Intervention duration ranged from 12–52 weeks. Sessions per week ranged from 1.8 to 5.2, and minutes per session ranged from 15 to 210.Details are provided in Table Intervention protocols included weight-bearing activities such as running or walking on treadmills or outdoors, circuit training with no or minimal resistance components, and non-weight-bearing activities such as swimming, cycling and ergocycle. Trials included supervised and unsupervised training sessions. Effort was either unchanged or progressive in response to training adaptations. Measures of effort were clinically or self monitored and reported via training logs or electronic devices. These protocol attributes are further detailed in the Study Quality TESTEX and within-study risk-of-bias assessements.The ratio outcomes extracted from included RCTs were TC/HDL-C, LDL-C/HDL-C, HDL-C/TC, HDL-C/LDL-C, Apo B100/A1, and Apo A1/Apo B100. Sub-fractions extracted (mmol/L and mg/dL) were VLDL, HDL2 and HDL3. Apolipoproteins extracted (mmol/L and mg/dL) were Apo A1, Apo A2 and Apo B100.Outcomes were joined according to antiatherogenicity, atherogenicity, effect size direction and reporting measurement. The TC/HDL-C, LDL-C/HDL-C and Apo B100/A1 ratios were joined (negative effect size direction) and analysed. The Apo A1/Apo B100, HDL-C/TC and HDL-C/LDL-C ratios were joined (positive effect size direction) and analysed. Apolipoprotein A1 and A2 mmol/L were joined with HDL2 and HDL3 mmol/L (antiatherogenic) and analysed. Apolipoprotein B100 mmol/L was joined with VLDL mmol/L (atherogenic) and analysed. Apolipoprotein A1 and A2 reported as mg/dL were joined (antiatherogenic) and analysed. Apolipoprotein B100 reported as mg/dL (atherogenic) was analysed separately. Summary statistics of the models are shown in Table P = 0.01) and Fig. P < 0.02) below. The joined TC/HDL-C + LDL-C/HDL-C + Apo B100/Apo A1 ratio was reduced with AET by a statistically significant amount, as shown in Fig. P < 0.001). Sub-analyses using K − 1 sensitivity analysis for statistically significant outcomes did not change the results; see Figs. S1–S3 (OSM).Antiatherogenic Apo A1 and A2, with or without the inclusion of HDL2 and HDL3, and independent of unit of measurement, were statistically significantly raised by AET, as shown in Fig. P = 0.05), and the TC/HDL-C + LDL-C/HDL-C + Apo B100/Apo A1 ratio remained statistically significant; see Table S7 (OSM). Better quality studies increased the effect size for Apo B100 reported in mg/dL but did not attain statistical significance .The median study quality TESTEX score was 10 , see Table S5 (OSM). Within-study risk of bias was mainly low or medium, see Table S6 (OSM). No RCT attained a TESTEX score ≥ 10 with a high within-study risk of bias score. Sub-analyses using TESTEX scores ≥ 10 resulted in statistical significance for atherogenic Apo B100 combined with VLDL, as shown in Fig. The included RCTs extracted blood from individuals in fasted states and in seated or supine positions and thus no RCT was excluded for phlebotomy reasons (data not shown). Lipoprotein and apolipoprotein analysis, although under-reported in detail, included density gradient ultracentrifugation; immunoturbidimetric methods (Roche) in serum; sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE), immunoprecipitation enhanced by polyethylene glycol, and nuclear magnet resonance spectroscopy; however, sensitivity analysis according to analytical method, when possible to perform, did not change the results (data not shown).N, Orwin’s fail-safe N, Duval and Tweedie’s trim-and-fill, Egger’s regression test, and Begg and Mazumdar’s rank correlation test, nor following visual inspection of precision and standard error funnel plots. Given the minimal evidence, the impact of the potential small study effects is trivial, which suggests validation of the results of the corresponding multivariate meta-analyses; see Tables S8–S11, Figs. S4a–S7b (OSM).Included studies exceeded the minimum number of effect sizes to be pooled . There wτ2) nor the relative heterogeneity (I2) for each analysed outcome indicated that trials should not be pooled, or that significance testing should not be undertaken; see Table S12 (OSM).Neither the degree of absolute between-study heterogeneity ; see Table S13a (OSM). The study covariate publication year was minimally associated with improvement in the joined TC/HDL-C + LDL-C/HDL-C + Apo B100/Apo A1 ratio ; see Table S13b (OSM). Our maximum likehood modelling of intervention and study covariates, whether singly or combined, alluded to overfitting of the data for antiatherogenic outcome Apo A1 + Apo A2 + HDL2 + HDL3 mmol/L; see Tables S14a and b (OSM). Variations of the model  suggested no fit or overfitting, respectively (data not shown).Exploratory multivariate meta-regression modelling of significant results suggested an association between AET volume  and improvement in the joined TC/HDL-C + LDL-C/HDL-C + Apo B100/Apo A1 ratio . A one SD increment of Apo A1 is associated with a reduced hazard ratio of major cardiovascular events , 104. Wh2MAX or > 1200 kcal/week has a beneficial influence on lipids [2MAX [2MAX) should be targeted.Our exploratory meta-regression analysis indicated that AET intervention volume  contribute to positive change in these CVD risk biomarkers. Other studies have found that an AET protocol of at least 180 min per week at > 40% VOn lipids , 109. A ds [2MAX  to positds [2MAX  to the tGiven the paucity of apolipoprotein, sub-fraction and ratio data reported in exercise trials, we propose that future research should compare AET protocols of appropriate volume against non-exercising interventions and report apolipoproteins, lipoprotein sub-fractions and relevant ratios, including the atherogenic index of plasma (log ratio of TRG/HDL-C). Since TRG better predicts CVD risk in women , we recoOur study quality TESTEX and within-study risk of bias analyses indicated that included RCTs failed to specify one or more of the following: method of randomisation and allocation concealment; medication use, drop-out reasons, or adverse events; monitoring of the non-exercising group or adherence to either the exercising or non-exercising protocol; a minimum compliance level; the use of objective measuring devices; and reporting of post-intervention exercise volume . Phlebotomy specifics of post-intervention blood extraction methodology and analyses were not always recorded. Participant data for pre–post body weight, body fat or lean mass, waist circumference or BMI, systolic and diastolic blood pressure, and fasting blood glucose, were also missing. Researchers investigating the effects of AET can better report their findings by including quantitative data for these participant characteristics.Our work has a number of strengths. To the best of our knowledge, this quantitative review is the first to have compared the effects of a minimum prescribed AET dose against no exercise on lipid sub-fractions, ratios and apolipoproteins in a cohort characterised as free of chronic disease except for the possible presence of the CVD risk factors comprising MetS, using a multivariate meta-analytic approach.Previous systematic reviews did not use TESTEX  to measuN < 10, and the number of RCTs included with longer durations were few, which may have reduced estimated effect sizes. We included AET protocols with an intensity (> 40% VO2MAX) at the lower end of the moderate-intensity range; this low-intensity may elicit very small changes in lipids [2MAX, maximum heart rate, METs, Borg scale). Bias in the measurement of data reported in the included RCTs may have thus occurred. Some RCTs reported scant data on AET protocol or energy expenditure, and we therefore estimated intensity as a percent of VO2MAX. A very small number of RCTs noted that control groups increased physical activity levels during the duration of the study; this may have reduced our estimated effect size. Since our meta-regression covariates were not randomised at study level, our meta-regression findings are best viewed as exploratory. With respect to data pooling, where the standard deviation of the mean difference, or exact P values within groups, or 95% CIs were not available, statistical analyses depended on extrapolated data. Our imputation of these statistics was conservative and this approach may have weakened results.A potential limitation of our work is the use of aggregated RCT data and not individual subject data , 114, win lipids , and theThis multivariate meta-analysis with meta-regression of pooled data indicated that AET programs of moderate intensity with a minimum 12-week duration significantly reduced the joint TC/HDL-C, LDL-C/HDL-C and Apo B100/Apo A1 ratios, as well as Apo B100 and VLDL values, while significantly raising Apo A1 and A2 and the sub-fractions HDL2 and HDL3, in sedentary adults free of chronic disease other than possible MetS factors. Our results mimic the results of previous reviews examining standard lipid CVD risk biomarkers and our exploratory meta-regression suggested AET volume is associated with change in lipid ratios and antiatherogenic apolipoproteins and lipoprotein sub-fractions. Few studies reported the Apo B100/Apo A1 ratio, considered an equivalent if not more accurate lipid CVD risk biomarker in comparison to standard lipid CVD risk biomarkers. Aerobic exercise training has been shown to affect lipoprotein sub-fractions, ratios and apolipoproteins, and positive changes in these biomarkers are associated with reduction in CVD risk. Clinicians can confidently prescribe aerobic exercise training programs as part of overall CVD risk management and monitor the effect of these programs using these novel lipid biomarkers.Supplementary file1 (DOCX 1514 KB)Below is the link to the electronic supplementary material.


It has examined the evidence on many of the commonest intrapartum practices and has made recommendations about those interventions that should be utilized to support the processes of normal birth. A seminal report was the WHO definition in 1996 of ‘normal birth’ as the birth that is of spontaneous onset, with gestational age of 37 to 42 weeks of pregnancy, and the baby being born in a vertex position. What is most important is that the WHO definition continued stating that the aim of a normal birth is a healthy mother and child, with the least number of interventions, and that there should be valid reasons for interfering with the natural process of birthing. Since this report in 1996, childbirth in both developed and developing countries has moved away from the concept of normality and the least possible level of interventions and has become significantly medicalized3.The World Health Organization (WHO) has published over the years several reports addressing the issues of care in normal birth, irrespective of the setting, the level of care or country4. We need to further question the existing evidence-base behind our intrapartum interventions, some of which have become routine, such as the regular vaginal examinations we perform in labor, that they are not even considered as an intervention5.If we were to support normality at childbirth and to reduce the number of medical interventions, we probably need to revisit our intrapartum care practices and philosophy of care6. Among the most common intrapartum interventions that pregnant women are very likely to receive is regular vaginal examinations, the intravenous use of oxytocin for labor augmentation, the use of an epidural analgesia for labor pain relief, restricted or no mobility during labor, and giving birth in a lithotomy position. Nevertheless, there is now abundant evidence in the literature demonstrating that for every intrapartum medical intervention there is a clinically equivalent natural approach leading to the same desired effect, provided that there is no urgent indication to expedite delivery.In most high-income countries most women give birth in maternity hospitals, even if they have a low-risk pregnancy7. Because vaginal examinations may be painful and may cause emotional distress and embarrassment, and in order to promote normality at birth while at the same time evaluating the progress of labor in an objective way, the WHO and the National Institute for Health and Care Excellence (NICE) recommend that a vaginal examination should be performed every four hours during the first stage of labour8.It has been reported that women receive, on average, three vaginal examinations during labor, with the maximum number of examinations as high as seven in some cases9. Nevertheless, there are high rates of misuse of oxytocin identified in the literature for the purpose of shortening the duration of labor, with the result of many babies suffering asphyxia at birth10. The nonmedical and natural alternatives to intravenous oxytocin can easily be elucidated when considering the neurobiological basis and the hormonal blueprint of an undisturbed labor and birth12. It is known that psychological stress and pain can have a negative effect on the progress of labor, as they inhibit the release and effective action of endogenous oxytocin11. Moreover, when a pregnant woman mobilizes and assumes upright positions during labor, gravity assists the fetal head descent deeper in the pelvis and supports the proper and even application of the fetal head on the cervix, which in turn activates the Ferguson reflex and leads to additional release of endogenous oxytocin13. If we were to replace intravenous oxytocin with a natural alternative, we would have to mobilize women during labor and have them assume upright positions and use natural methods, such as the use of water immersion in labor, to relieve stress and pain. There is evidence that women who were mobilized and assumed upright positions, as opposed to lying down on the bed, had a first stage of labor that was shorter by about one hour and 22 minutes13. Other reports have found that water immersion may shorten the duration of the first stage of labor by 42 minutes (95% CI: 3.4–89.9), on average14.Oxytocin is the most frequently used medication intrapartum to increase uterine activity and is indicated in cases of prolonged labor or dystocia16. It is considered the gold standard against which all the other non-pharmacological methods of pain relief are compared. Despite its high effectiveness in managing labor pain, it is an intrusive method nevertheless and it has been associated with a prolonged second stage of labor, with a higher occurrence of malpresentations and higher percentage of assisted vaginal births16.It has been reported that epidural analgesia is the most effective form of pain relief in labor, with approximately 30% of laboring women in the UK and 60% in the USA receiving epidural analgesia18. Moreover, the use of hypnosis during labor was found to reduce, by 70%, the need for epidural analgesia 19. The need for an epidural was reduced by about 19%  when women were ambulating and in an upright position during labour1.Natural approaches that may act as an alternative to an epidural analgesia with a high magnitude of effect size involve water birthing and the use of hypnosis in labor. Water birthing has been found to significantly reduce, by 83%, the need for epidural analgesia  and thus is often referred to as ‘aquadural’ or ‘wet epidural’13. Studies have shown that if women were allowed to assume any position they wished during labor, then they would opt to mobilize and change positions with an average of 7 to 8 positions20.Ambulation and maintaining an upright position such as kneeling, squatting or standing during the first stage of labor, are critical as they are a natural method of pain relief and also act as a stimulant of endogenous oxytocin release22.Furthermore, giving birth in an alternative birthing position, and not in a lithotomy position, has been shown to ease delivery and to facilitate the birthing process as it allows greater coccyx movement and widens the diameter of the bony pelvis2. Practices that should be eliminated involve the routine pubic shaving and use of enema, the routine manual exploration of the uterus after delivery, and the application of manual fundal pressure (known as the Kristeller maneuver) to facilitate childbirth during the second stage of labor. In addition, it is not recommended to restrict oral fluids and food intake in low-risk women, as there is no evidence to support this2.There are several other clinical practices still in use today during labor and birth that have been refuted by the WHO in its 2018 recommendations for intrapartum care and it has been strongly suggested that they should be withdrawn24. Childbirth should be one of the most transformative and rewarding events in a woman’s life. For this reason, she should be provided with both medical and non-medical  options during the birthing process to ensure her safety and that of her baby, clinical effectiveness, and high quality of care.As previously described, there are several non-medical approaches to some of the most common medical interventions that pregnant women endure during labor and birth. These alternative natural approaches that we have described need to be embraced and integrated in a respective philosophy of care. The birth accoucheurs need to understand that labor and birth are profound experiences that carry a significant meaning for women, and therefore intrapartum clinical practices need to be woman-centered, respectful, evidence-based, and genuine


Preterm birth prediction is essential for improving neonatal outcomes. While many machine learning techniques have been applied to predict preterm birth using health records, inflammatory markers, and vaginal microbiome data, the role of prenatal oral microbiome remains unclear. This study aimed to compare oral microbiome compositions between a preterm and a full-term birth group, identify oral microbiome associated with preterm birth, and develop a preterm birth prediction model using machine learning of oral microbiome compositions. Participants included singleton pregnant women admitted to Jeonbuk National University Hospital between 2019 and 2021. Subjects were divided into a preterm and a full-term birth group based on pregnancy outcomes. Oral microbiome samples were collected using mouthwash within 24 h before delivery and 16S ribosomal RNA sequencing was performed to analyze taxonomy. Differentially abundant taxa were identified using DESeq2. A random forest classifier was applied to predict preterm birth based on the oral microbiome. A total of 59 women participated in this study, with 30 in the preterm birth group and 29 in the full-term birth group. There was no significant difference in maternal clinical characteristics between the preterm and the full-birth group. Twenty-five differentially abundant taxa were identified, including 22 full-term birth-enriched taxa and 3 preterm birth-enriched taxa. The random forest classifier achieved high balanced accuracies (0.765 ± 0.071) using the 9 most important taxa. Our study identified 25 differentially abundant taxa that could differentiate preterm and full-term birth groups. A preterm birth prediction model was developed using machine learning of oral microbiome compositions in mouthwash samples. Findings of this study suggest the potential of using oral microbiome for predicting preterm birth. Further multi-center and larger studies are required to validate our results before clinical applications. Established risk factors for PTB include genitourinary tract infections, short cervical length, and multiple pregnancies2. However, there is still disagreement regarding the magnitude of these factors’ effects on birth outcomes. Early identification of pregnant women at high risk of PTB can facilitate the implementation of strategies to prolong gestation and improve birth outcomes3.Preterm birth (PTB), defined as delivery of newborns before 37 weeks of gestation, is a leading cause of morbidity and mortality in newborns4. Numerous attempts have been made to predict PTB using machine learning techniques combined with data from health records, inflammatory markers, and vaginal microbiome5. Fetal fibronectin is widely used clinically due to its affordability and simplicity. However, it has a low prediction rate with a sensitivity of only 56%6. Cervical length measurement also has limitations due to the hassle and inaccuracy of the procedure and the need for a skilled specialist7.Despite increased understanding of risk factors contributing to PTB, there remains a considerable lack of sensitivity in predictive models that can serve as a framework for intervention strategies8. However, the mechanism of PTB cannot be fully explained by inflammatory and infectious pathways as anti-inflammatory and antibiotic treatments could not reduce PTB rates9. With advancements in molecular genetic technology, studies on maternal microbiomes using 16S ribosomal RNA (rRNA) sequencing have emerged to explore unknown pathways of PTB10.Approximately 70% of PTBs result from spontaneous onset of preterm labor and preterm pre-labor rupture of membranes due to intrauterine infection and inflammation11. Recent research has identified vaginal microbial signatures in women who later experience PTB and attempted to predict PTB using cervicovaginal fluid12. Although existing reports have verified a potential relationship of vaginal microbiome with PTB, they can only explain an ascending route.Microorganisms associated with PTB have been postulated to originate from one of two places: the reproductive or genitourinary tract ascending through the cervix or a hematogenous route13. Based on these precedents, it is expected that the oral microbiome can explain another hematogenous route. However, prenatal oral microbiome is not well understood14.Decades of epidemiological research studies have suggested that periodontitis is an independent risk factor for various adverse birth outcomes, including PTBThus, this study aimed to compare oral microbiome compositions between a PTB group and a full-term birth group, to identify oral microbiome associated with PTB, and to develop a machine learning prediction model of PTB based on oral microbiome compositions.This study was conducted on singleton pregnant women admitted for delivery at Jeonbuk National University Hospital between 2019 and 2021. This study received approval from the Ethical Research Committee (IRB file No. 2019-01-024). All participants provided written informed consent. Eligible participants included women admitted for induction delivery, elective cesarean section, and those who were hospitalized due to symptoms of preterm labor or preterm pre-labor rupture of membranes.Data on current and historical pregnancy outcomes were collected from questionnaires and electronic medical records. This information encompassed demographic factors  and maternal risk factors . All subjects were divided into a PTB group or a full-term birth (FTB) group, with PTB defined as delivery before 37 weeks of gestation.Oral microbiome samples were collected using mouthwash within 24 h before delivery. Standard sterile techniques were employed. Medical staff supervised all sample collection procedures. Participants were instructed to avoid brushing their teeth, eating, or drinking 30 min before sampling. Saliva samples were obtained by rinsing the mouth with 12 mL of a gargle solution  for 30 s. Sample were labeled with the subject’s anonymous ID and stored at 4 °C until further processing. The resuspended sample was transferred to a microcentrifuge tube. Genomic DNA was extracted using an Exgene™ Clinic SV kit  according to the manufacturer’s instructions and stored at − 20 °C.Specimens were sent to Department of Biomedical Engineering, Ulsan National Institute of Science and Technology for taxonomy assignment. Then 16S rRNA sequencing was performed using an Illumina MiSeq Reagent Kit v3  commissioned by Macrogen . Library protocols for amplifying V3 and V4 regions were used. Pooled library was sequenced using a v3 600 cycle chemistry after diluting the sample to a final concentration of 6 pM with 20% PhiX control to generate 300 bp paired end reads.Independent t-test and chi-square test were used to compare differences between the PTB group and the FTB group. Statistical Package for Social Sciences version 20.0 was used for all data analyses. Statistical significance was considered at 14. Sequences were filtered with DADA215. Amplicon sequence variants were assigned taxonomy with the Human Oral Microbiome Database (version 15.22)16.16S rRNA sequences from study subjects were imported with Quantitative Insights Into Microbial Ecology (version 2022.2) for further processingTo measure richness of microbiomes, two diversity indices were calculated. Alpha diversity, a measure of species within a particular community, was calculated using the Faith’s phylogenetic diversity index within the Quantitative Insights Into Microbial Ecology platform. Communities numerically dominated by a few species will exhibit a low Faith’s phylogenetic diversity index, whereas communities in which abundance is distributed equally among species will exhibit a high evenness. Mann–Whitney–Wilcoxon test was used to find statistically significant differences in Faith’s phylogenetic diversity index. Beta diversity measures dissimilarity between pairs of communities. It was calculated using the Hamming diversity index. PERMANOVA multivariate test was used to calculate statistically significant differences in the Hamming diversity index.17. Taxa with To identify differentially abundant taxa (DAT) with distinct abundance differences between the PTB group and the FTB group, DESeq2 was implemented using “DESeqDataSetFromMatrix” method as described in the package tutorial18. Random forest classifier was implemented to predict PTB based on oral microbiome compositions. To assure consistence and reliable classification results, we performed stratified Following qualitative and quantitative analyses of associations between the PTB group and each bacterium, a random forest classifier was used to find the criteria to predict PTB with oral microbiome data. Random forest classification is an ensemble machine learning algorithm that summarizes many decision trees to improve classification evaluations and robustnessThe research protocol was approved by the Institutional Review Board of Jeonbuk National University Hospital (No. 2019-01-024) and was performed according to the Declaration of Helsinki.In this study, a total of 69 volunteer mothers were initially recruited. However, one participant with incomplete data and nine individuals with twin pregnancies were excluded from the study cohort. As a result, 59 women (30 in the PTB group and 29 in the FTB group) were included in the final analysis. Demographic and clinical characteristics of subjects in the PTB group and the FTB group are summarized in Table The oral microbiome comprised 13,953,804 sequences from 59 oral microbiome samples, with 102,305.95 ± 19,095.60 and 64,823.41 ± 15,841.65 reads per sample before and after filtering low-quality reads and trimming extra-long tails, respectively. After filtering low-quality reads and trimming extra-long tails, remaining representative reads were clustered in amplicon sequence variants with their exact sequence match. There were no significant differences in measures of alpha diversity (Faith’s phylogenetic diversity index) and beta diversity (Hamming distance) indices for samples between the PTB group and the FTB group  was achieved using 9 most important taxa Fig. a. The Ra11. However, prenatal oral microbiome is not well understood yet. Some research has shown that oral microbial dysbiosis combined with gingival inflammation can lead to adverse pregnancy outcomes, including low birth weight, PTB, preeclampsia, and miscarriages19. Nevertheless, these results have been inconsistent due to methodologies employed in studies that only target known pathogens.In this study, we developed a method for predicting PTB based on random forest classifier using oral microbiome compositions. Recently, several sporadic reports have suggested a bidirectional relationship between oral microbiome and pregnancyFusobacterium nucleatum is the most prevalent oral microbiome studied. Fusobacterium nucleatum is a Gram-negative, anaerobic, filamentous oral microbiome. It is considered one of the most abundant species in the oral microbiome. It can also be isolated from vaginal microbiome21. Intra-amniotic Fusobacterium nucleatum infection leading to PTB has been reported in human and animal studies22. Other studies have shown that other oral pathogens including Porphyromonas gingivalis and intrauterine Bergeyella spp. can be isolated from the placenta of women who deliver prematurely24. In the present study, although Bergeyella spp. was overrepresented in the PTB group with PROM, it was excluded in the finally selected 25 DATs. Furthermore, Campylobacter gracilis was one of the FTB-enriched DAT that can aid colonization by periodontal pathogens including Porpyhromonas gingivalis in subgingival microbiome25. Lactobacillus gasseri was also one of the FTB-enriched DAT. It is known that Lactobacillus gasseri in vaginal microbiome can decrease early PTB risk27.28. The second mechanisms proposed that systemic dissemination of endotoxins and/or inflammatory mediators derived from periodontal plaque and secreted by the subgingival inflammatory site could be carried to the fetoplacental unit30. Although certain microbiota has the same species, their subgroups can have both positive and negative influences on pregnancy outcomes. Following this line of thought, we believe that composition or dysbiosis of the oral microbiome is more important than the presence of specific microbiota.We found that decisive species differentiating between two groups were mainly abundant in the FTB group, with DAT consisting of 22 FTB-enriched DAT and 3 PTB-enriched DAT. We hypothesize that deficiency of species having a protective impact might have triggered the pathophysiology of PTB. Two different mechanisms have been proposed to explain the relationship between unhealthy microbiota composition and adverse pregnancy outcomes. The first mechanism proposed that periodontal bacteria originating in the gingival biofilm could translocate from the unhealthy oral cavity and cross the placenta, reach the intra-amniotic fluid and fetal circulation and directly affect the fetoplacental unit, resulting in bacteremiaIt is worth nothing that microbial changes occurring during pregnancy might be nature consequences of a healthy pregnancy. Three reasons can explain the susceptibility to oral diseases such as periodontitis during pregnancy. These diseases are common in pregnant women due to hormonally driven hyper-reactivity of the gingiva to bacteria in the subgingival biofilm. Other factors that increase the risk of poor oral health during pregnancy include changes in dietary habits (frequent snacking or increased consumption of carbohydrate-rich foods), stomach acids from nausea and vomiting that contribute to the breakdown of tooth enamel, and a decreased likelihood of seeking dental care during pregnancy. We plan to implement pathway analyses to investigate direct link between the microbiome and PTB.Even though there was limited power resulting from a small number of participants and restricted validation sample size, our study verified that oral microbiota might provide potential biomarkers for predicting pregnancy complications using machine learning methods including random forest classification. Additionally, the fact that the entire microbiome was not analyzed was a limitation of this study because our analysis only used relative values measured by 16S rRNA sequencing, not 16S metagenome sequencing. We did not investigate other factors could impact the oral microbiome, such as participant’s diet and socioeconomic status.Despite these limitations, this prospective study demonstrated the potential of a PTB prediction model using oral microbiome in mouthwash. Further multi-center and larger-scale studies are needed to confirm out results before applying techniques developed in this study in the clinical field.Supplementary Figure 1.Supplementary Figure 2.Supplementary Figure 3.


Ureaplasma Urealyticum (UU) infection and chest radiograph images. Methods: A retrospective study was performed. Moderate to severe BPD or death was set as the main outcome. The association between putative variables and the main outcome were assessed by bivariate analyses and logistic regression. Results: A total of 134 infants were enrolled. Bivariate analyses showed the gestational age, birth weight, appearances of diffuse opacities or grid shadows/interstitial opacities or mass opacities or cystic lucencies on chest radiographic images, a ductal diameter ≥1.5 mm and whether UU infection was associated with BPD. After adjustment by logistic regression, the risk of BPD with gestational age, sex and specific chest-radiographic manifestations remained significant. Conclusions: Chest radiograph images  could provide a quick prediction of developing BPD in clinical practice, in addition to gestational age and sex. UU infection was not an independent risk factor for BPD.Introduction: It is urgent to make a rapid screening of infants at the highest risk for bronchopulmonary dysplasia (BPD) via some succinct postnatal biomarkers, such as  Bronchopulmonary dysplasia (BPD) is a common chronic lung disease in preterm infants that is associated with long-term lung dysfunction, cardiovascular involvement, extrauterine growth retardation and neurodevelopmental disorders . In esseSeveral factors have been suggested to predict the risk for BPD. It is generally accepted that susceptibility to BPD is partially determined by nonmodified factors  and intrauterine exposure, such as maternal smoking and hypertension, fetal growth restriction (FGR), and chorioamnionitis . FurtherA chest radiograph is the most convenient tool for evaluating respiratory problems in seriously ill infants. In 1967, Northway et al. first reported four stages of chest-radiographic findings that characterized classical BPD . In addiUreaplasma urealyticum (UU) is an opportunistic pathogen that colonizes the urogenital tract of women [UU on their respiratory tract and were defined as being at risk of developing BPD [UU screening and BPD [of women . It had of women . Infantsping BPD . However and BPD . The conUU infection and the development of BPD in a cohort of very preterm infants; and (2) the value of chest-radiographic images in predicting BPD.Based on previous studies, we designed this study with the aim of investigating: (1) the association between This was a retrospective study. Preterm infants born at less than 32 weeks gestational age and surviving to 36 weeks’ PMA admitted to the neonatal intensive care unit (NICU) of Zhangzhou Affiliated Hospital of Fujian Medical University between January 2022 and December 2022 were included in this study. Infants with major congenital malformations and those who were forced to withdraw treatment by parents were excluded. Clinical data were collected from the hospital’s electronic medical record system. This study was approved by the Institutional Review Board (IRB) of Zhangzhou Affiliated Hospital of Fujian Medical University, with a waiver of informed consent (2022KYZ297).2 < 30%) at 36 weeks PMA. Severe BPD was defined as oxygen supplementation for at least 28 days and persistent need for oxygen (FiO2 ≥ 30%) and/or ventilatory support  at 36 weeks postmenstrual age.Moderate or severe BPD or death was set as the main outcome because this degree of severity is highly associated with long-term adverse outcomes of preterm infants. Diagnosis of BPD was based on the NICHD Workshop summary . PretermIn our NICU, infants had a chest radiograph at their bedside within the first hours of life, and subsequent chest radiographs were performed according to the condition. Chest radiographs obtained within 14 days of life were reviewed for analysis. Four types of images were supposed to be meaningful: (1) diffuse opacities (blurred cardiac and diaphragmatic edge or white lung) on two consecutive chest radiographs within 7 days of life; (2) grid shadows/interstitial opacities; (3) mass opacities; (4) cystic lucencies Shown in . Any oneColor Doppler echocardiography was first performed around the third day of life in every preterm infant and rechecked if necessary. PDA was confirmed if ductal flow was visualized, irrespective of hemodynamic significance, and the calibers were measured. A ductal diameter ≥ 1.5 mm was considered positive.UU examination was performed in every preterm infant via testing nasopharyngeal secretion or tracheal aspirates by PCR at admission. UU colonization was defined as a PCR test positive for UU. UU infection was diagnosed if both of the following were satisfied: (1) UU colonization; (2) two consecutive elevations of total white blood counts (both ≥ 20 × 109/L) within 7 days of life, which suggested an inflammatory response caused by UU [ed by UU .Serum CRP was also included in analysis because it was a frequently used monitor of infection in NICU. Generally, the first CRP was acquired after 6 h of birth and monitored during 3 consecutive days. Two consecutive elevations of CRP (both ≥ 10 mg/dl) within 7 days of life were considered to be an intrauterine infection.p-value of ≤0.2 was used in univariate analysis for inclusion of putative risk factors into the multivariate (adjusted) model. A p-value of <0.05 was considered to be statistically significant.Analysis was performed via SPSS version 26 software. Descriptive data were presented as means ± SD or percentages. To compare differences between two groups, chi-square tests for categorical variables and two-sample t-tests or Mann–Whitney U tests for continuous variables were applied. Analyses of the main outcome and risk factors were performed via logistic regression, with 95% CIs calculated for ORs adopting the forward entry method. A +5~31+6 weeks), and the mean birth weight was 1335 ± 313 g (range 600~2100 g). The male-to-female ratio was 1.6:1 . Seventeen infants were diagnosed as moderate to severe BPD, and four infants died. The dead infants were all born extremely preterm , with a birth weight under 1000 g. The incidence of moderate to severe BPD or death in the studied subjects was 15.7% (21/134). The UU colonization rate was 22.4% (30/134), and the incidence of UU infection was 8.2% (11/134).During the study period, 149 infants were born before 32 gestational weeks and admitted to our NICU. Among them, 15 infants were excluded: 13 infants who died before 28 days of life and 2 infants who had a tracheoesophageal fistula malformation. In total, 134 infants were included in the analyses. The mean gestational age was 29.5 ± 1.7 weeks  itself and an “indirect effect” more greatly caused by a low gestational age [UU infection contributed indirectly to BPD through gestational age. Although the UU colonization rate was higher in BPD infants, after adjustment for gestational age and sex, UU infection was not significantly associated with BPD. The problem of identifying infants with PDA or infected by UU with the highest risk for developing BPD still needs to be solved. As was shown in our study, the remaining significant factors for BPD were a lower gestational age, male sex and appearances of diffuse opacities or grid shadows/interstitial opacities or mass opacities or cystic lucencies on a chest radiograph. In summary, the most valuable predictors for BPD were gestational age and a chest radiograph.Bivariate analyses suggested that PDA and onal age . The intMaturity at birth is the strongest predictor of not only survival, but also complications in preterm infants . The oveAfter adjustment for gestational age, male sex still appeared to increase the odds for BPD, and this finding is consistent with other studies . The sexThe chest-radiographic images serving as a postnatal predictive factor in our study were easy to identify and achieved a good value. Cystic lucencies, to some extent representative of alveolar growth abnormalities, were recognized as the typical radiographic pattern of BPD. However, this typical appearance was less frequent within the first days of life. As supplementation, we incorporated radiographic signs of diffuse opacities (blurred cardiac and diaphragmatic edge or white lung), grid shadows/interstitial opacities and mass opacities, which could be present ahead of cystic lucencies. With the prevalence of antenatal steroids clinical practice, the incidence and severity of respiratory distress syndrome (RDS) has decreased . An appeThere were several limitations to our study. First, it was from a single center that could not avoid selection bias. Second, the retrospective nature of the study does not allow direct relational associations to be extracted, which should be more clearly expressed. Third, the small sample size did not allow further analysis at each gestational age, respectively.The strength of our study was that we provided a quick and simple way for clinicians to identify infants with the highest risk of BPD via specific features . The images we suggested as being positive  on the chest radiograph were easy to identify, which may avoid ambiguity and inconformity between observers.UU infection was not an independent risk factor for BPD.Chest radiograph images  could provide a quick prediction of developing BPD in clinical practice, in addition to gestational age and sex.