Grain Number 1a (Gn1a) and Wealthy Farmer’s Panicle (WFP) were introgressed and stacked in selected NERICA cultivars by marker-assisted backcross breeding (MABB). The DNA markers RM3360, RM3452, and RM5493 were used for foreground selection. At BC3F4 and BC3F5 generation, a combination of marker-assisted selection and phenotypic evaluation were carried out to select lines with target alleles and traits. Further, genotyping-by-sequencing (GBS) was conducted to validate the introgression and determine the recurrent parent genome recovery (RPGR) of the selected lines. The Gn1a and/or WFP introgression lines showed significantly higher numbers of spikelets per panicle and primary branching compared to the recurrent parents. In addition, lines with Gn1a and/or WFP alleles were comparatively similar to the recurrent parents (RP) in most yield-related traits. This study demonstrates the success of utilizing yield QTLs and marker-assisted selection to develop and improve rice cultivars.The era of the green revolution has significantly improved rice yield productivity. However, with the growing population and decreasing arable land, rice scientists must find new ways to improve rice productivity. Although hundreds of rice yield-related QTLs were already mapped and some of them were cloned, only a few were utilized for actual systematic introgression breeding programs. In this study, the major yield QTLs  Oryza sativa L.) is the main staple for almost half of the world’s population. Although mostly consumed in Asia, it is also considered the fastest-growing staple in Africa and Latin America. In sub-Saharan Africa (SSA), rice consumption has significantly increased over the years. This is due to factors such as economic growth, population growth, and changing consumer preference. In SSA countries such as Kenya, Nigeria, and Tanzania, people are changing their food preferences from tubers to rice as their income rises [Rice , panicle length (PL), number of primary branches per panicle (PBPP), and number of secondary branches (SBPP) . To undeHd1, Ghd7, and Ehd1 [GS3, GW2, and GW5 [Gn1a and DEP1 [WFP/IPA1 [grain number 1a (Gn1a) was first identified in the high-yielding rice variety Habataki. It encodes cytokinin oxidase/dehydrogenase (OsCKX2), an enzyme that degrades bioactive cytokinin. When the expression of OsCKX2 is reduced, cytokinin accumulates, resulting in an increase in the branching of inflorescence [Wealthy Farmer’s Panicle (WFP) was first isolated from the rice line ST12. This gene encodes SQUAMOSA promoter binding protein-like 14 (OsSPL14), which is then regulated by microRNA OsmiR156. An increase in OsSPL14 expression during the vegetative stage suppresses tillering and enhances panicle branching. In rice line ST12, an abundance of OsSPL14 transcripts is regulated by a heritable epigenetic mechanism [Gn1a and WFP were previously used in some breeding programs for improvement of the indica and japonica rice cultivars [The advent of genome sequencing has significantly influenced rice breeding studies. The completion of rice genome sequencing has led to the identification, isolation, and characterization of agronomically important traits and their application in varietal improvement ,6. To daand Ehd1 ,9), grai and GW5 ,11,12), and DEP1 ,14), andWFP/IPA1 ). The marescence . On the ultivars ,17,18. HThe concept of backcrossing is to transfer the specific allele to the target locus. However, traditional backcrossing is laborious and requires more generations to obtain lines with high RPGR. The inclusion of DNA markers in the backcrossing program is called MABB. As compared to traditional backcrossing, MABB makes use of DNA markers that are tightly linked or flank the target locus . This shOver the years, the use of DNA markers in breeding programs has transitioned from the traditional simple sequence repeat (SSR) to single nucleotide polymorphic (SNP) markers. The shift in marker preference is due to the advances in the field of sequencing technology ,25. GBS Gn1a, and WFP, (ii) identify promising lines with improved grain number and primary branching, and (iii) evaluate the effect on the yield and yield-related components of rice. The data obtained in this study is part of the Wonder Rice Initiative for Food Security and Health (WISH) project [Introducing beneficial genes alone cannot efficiently improve the target traits due to limiting factors such as genetic and environmental effects . To over project .Gn1a and/or WFP from the donor lines ST12 [Gn1a, and RM3452 and RM5493, which flank the WFP locus [1F1 generation were shipped to the International Rice Research Institute (IRRI) for further backcrossing and generation advancement. The selected BC1F1 plants were advanced to BC3F1 and subsequently followed through selfing generations to identify plants carrying homozygous alleles at the target locus. The first two lanes on the gel images  , with a Gn1a-ST12, WFP-ST12, and a combination of both alleles had an RPGR ranging from 85.4–98%. The WISH line 1:1-3-4-3-3 (NERICA1 + Gn1a-ST12-1) carrying the Gn1a-ST12 allele had the highest RPGR at 98%. In lines carrying the WFP-ST12, WISH 1:4-15-1-7-1 (NERICA1+WFP-ST12-2) had the highest RPGR at 97.3%, and for lines carrying both alleles, WISH 1:6-12-8-12-4 (NERICA1 + Gn1a + WFP-ST12-1) had the highest RPGR at 92.2%. In the genetic background of NERICA6, WISH 14:23-7-6-4-7 (NERICA6 + Gn1a-ST12-1) had an RPGR of 94.8%. The lines in the genetic background of WAB56-104 with a single QTL introgression and stacked QTLs had an RPGR ranging from 77–96.9%. In lines carrying the Gn1a-ST12 allele, WISH 22:1-4-1-6-7 (WAB56-50 + Gn1a-ST12-3) had the highest RPGR at 96.4%, while in lines carrying the WFP-ST12 allele, WISH 22:5-3-3-1-2 (WAB56-104 + WFP-ST12-4) had the highest RPGR at 96.9%. On the other hand, in lines carrying both alleles, WISH 22:5-6-1-3-7 (WAB56-104 + Gn1a + WFP-ST12-2) showed the highest RPGR at 95.1%. In the genetic background of WAB56-50, lines were identified with an RPGR ranging from 88.3–96.7%. The WISH line 23:1-7-4-16-1-4 (WAB56-50 + Gn1a-ST12-2), carrying the Gn1a-ST12 allele, showed the highest RPGR at 96.7%. In lines carrying the WFP-ST12 allele, WISH 23:1-4-4-16-2-3 had the highest RPGR at 96%. Lastly, WISH 23:1-12-10-20-1-5 (WAB56-50 + Gn1a + WFP-ST12-1) showed the highest RPGR at 94.4%In the genetic background of NERICA1, lines carrying the RPGR was also evaluated in lines carrying an introgression from the donor parent ST6. The lines in the genetic background of NERICA4 x ST6 had an RPGR ranging from 84.5–87.7% with WISH 13:1-3-1-8-1-6 (NERICA4+Gn1a-ST6-1) having the highest RPGR at 87.7%. In the genetic background of WAB56-104, WISH 21:7-13-5-1-5 (WAB56-50 + Gn1a-ST6-4) and WISH 21:7-13-6-1-7 (WAB56-104 + Gn1a-ST6-3) showed the highest RPGR at 93%. The WISH lines 24:3-14-4-1-3 (WAB56-50 + Gn1a-ST6-1) and 24:3-20-10-1-4 (WAB56-50 + Gn1a-ST6-2) showed the highest RPGR at 95%. In the same genetic background, WISH 24:5-8-4-1-1 (WAB56-50 + WFP-ST6-2) showed an RPGR of 94%. In lines carrying both alleles, the WISH line 24:13-1-10-11-3 (WAB56-50 + Gn1a + WFP-ST6-1) showed the highest RPGR at 92.1%. The graphical genotypes of lines with the highest RPGR in each genetic background are presented in Gn1a on yield and yield-related components were evaluated during the dry season (DS) and wet season (WS) of 2017. In the genetic background of NERICA1, two out of the three lines were observed to have a significant improvement in NSPP during the WS of 2017. The WISH line 1:1-1-3-4-3-3 (NERICA1 + Gn1a-ST12-1) significantly improved by 72.3%, while the WISH line 1:6-16-10-4-1 (NERICA1 + Gn1a-ST12-3) significantly improved by 56.74% as compared to that of NERICA1  was observed to have a significantly improved NSPP by 28.10% during DS, and 45.13% during the WS of 2017. On the other hand, the NSPP of the WISH line 13:4-5-6-1-7 (NERICA4 + Gn1a-ST6-3) significantly improved by 4.9% during DS  was observed to have a 33.78% increase in PBPP during DS and 118% during WS, while the WISH line 1:2-1-7-5-1 (NERICA1 + WFP-ST12-1) was observed to have a significant improvement in PBPP by 55.51% during WS  and WISH 24:5-8-4-1-1 (WAB56-50 + WFP-ST6-2) during WS  and 1:6-12-8-12-7 (NERICA1 + Gn1a + WFP-ST12-2) showed a significant improvement in PBPP by 62.57% and 49.51%, respectively , plant height (PH), tiller number per plant (TNPP), PL, SBPP, percent fertility (PF), and thousand-grain weight (TGW).Gn1a-ST12 in the genetic background of NERICA1 and WAB56-104 showed a significant improvement in SBPP by 32.2–105% during DS and 72.7–117.2% during WS  was significantly shorter as compared to its RP, but only during WS. The WISH 23:1-12-6-3-2-5 (WAB56-50 + WFP-ST12-2) was observed to be heavier, as compared to the RP, but only during WS . The samGn1a and WFP. In this study, NERICA lines and their WAB progenitors were used as the recipient parents of yield QTLs Gn1a and WFP. The NERICA plant materials have big and heavy panicles but with a fewer number of tillers [Marker-assisted breeding has served as a hallmark of an efficient way to improve several traits across different crops. To improve the existing rice cultivars in Africa, the WISH project made use of an MABB scheme to transfer and stack the yield QTLs  tillers . These l tillers .Gn1a alleles in this study coincide with the findings of Sakamoto et al. (2008) [Gn1a had the same number of PBPP but developed a higher number of SBPP on both primary branches and at the panicle base. The developed WISH lines in this study with Gn1a-ST12 and Gn1a-ST6 introgression showed a significant increase in SBPP by as much as 13–117% in the genetic backgrounds of NERICA1, NERICA4, NERICA6, and WAB56-104. The improvement in SBPP in the WISH lines significantly increased the NSPP by as much as 4.9–72.3% in the same genetic backgrounds. In a previous study of Ashikari et al. (2005) [Gn1a + Gn1b from Habataki were able to increase grain productivity by as much as ~45% in the Koshihikari background. In 2017, Feng et al. [Gn1a allele from the donor parent GKBR improved the yield per plant of a japonica rice cultivar Kongyu 131 by 8.3% and 11.9% in two locations in Heilongjian, China. The effects of the Gn1a allele on NERICA and WAB56-104 introgression lines that we developed are the same as the results of other Gn1a introgression studies. In a previous study of Kim et al. (2018) [Gn1a alleles from Habataki, ST12, and ST6 were found to be ineffective in some indica rice cultivars as they have the same type of Gn1a allele with the donor parents. The same may hold true in the WISH lines in the genetic background of WAB56-50 × ST12.The effects of the introgression of . (2008) . Their fg et al.  showed t. (2018) , introgrWFP allele showed a significant improvement in PBPP in the genetic backgrounds of NERICA1, WAB56-104, and WAB56-50 by 33.77–118%. This significant increase in PBPP resulted in an increase in NSPP by 33.77–82.25%. However, the lines carrying the WFP-ST12 and WFP-ST6 alleles showed a significant reduction in TNPP  [OsSPL14 from the rice line Shaoniejing, a japonica cultivar, significantly increased the PBPP and NSPP of Taichung 1, an indica rice variety. However, TNPP was greatly reduced. Miura et al. (2010) [OsSPL14 allele of the rice line ST12 and Aikawa. Their result showed that lines carrying the WFP-ST12 allele significantly improved PBPP and NSPP without a decrease in TNPP. However, the lines carrying the WFP-Aikawa allele showed significant improvement in PBPP and NSPP, but with significant reduction in TNPP [WFP-ST12 and WFP-Aikawa were introgressed to an indica rice cultivar. Their result showed that WFP-ST12 significantly improved PBPP and NSPP without reducing the TNPP. In the study of Yamada et al. (2020) [WFP-ST12 allele in the genetic background of IRBB60 significantly improved the PBPP. The developed WISH lines in our study deviate from the findings of Miura et al. (2010) [WFP-ST12 and WFP-ST6 were significantly reduced. The reduction in TNPP was previously associated with DEP1 by Lu et al. (2013) [IPA1/WFP/OsSPL14 and DEP1. The IPA1 functions as a positive regulator to DEP1, increasing PH and PL. In addition, IPA1/OsSPL14 can also interact and directly bind to several genes that regulate plant architecture such as OsTB1, PIN1b, SLR, LOG, and LAX [The introgression of the  in TNPP . The res. (2010) , OsSPL14. (2010) , on the  in TNPP . In the  in TNPP , WFP-ST1. (2020) , introgr. (2010)  and Kim . (2010) , wherein. (2013) . It was  and LAX ,38.WFP allele.Gene stacking has been applied mostly for biotic and abiotic stresses. However, only a few breeding programs apply gene stacking for yield traits. In this study, stacking of the yield QTLs from the donor parents ST12 and ST6 showed a significant improvement in PBPP, SBPP, and NSPP. However, the improvement observed in PBPP, SBPP, and NSPP was on par with those in lines with single QTL introgression. This result coincides with the findings of Kim et al. (2018) , whereintropical japonica genetic background. In terms of RPGR analysis, variation between and within the genetic backgrounds were observed. Theoretically, the RPGR of BC3 generation should be at 93.75%. The best performing lines in our study showed an RPGR that is within and higher than the theoretical RPGR. However, we observed some lines that had an RPGR lower than the theoretical. For example, NERICA4 × ST6 lines were observed to have the lowest maximum RPGR at 87.7%. The low RPGR observed in these lines could have been avoided if the analysis was carried out as early as BC1 generation [The background genome recovery in this study was determined by genotyping-by-sequencing. As compared to SSR markers, GBS has a greater number of markers per sample and a greater number of alleles per marker . In addineration ,41. NowaGn1a, WFP, and combination of both alleles showed a significant improvement in SBPP, PF, and TGW  cultivars  and progenitor lines (WAB56-50 and WAB56-104) were used as the recurrent parents in this study . The donindica , and ST6japonica , were us1F1 generation were exported to IRRI for further backcrossing and generation advancement. The lines were backcrossed for three consecutive seasons , generating BC3F1 lines. MABB was carried out at BC2F1 and BC3F1 to assure that the donor alleles of Gn1a and WFP were present in each generation using the DNA markers RM3360 for Gn1a, and RM3452 and RM5493 for WFP [3F2 lines were screened to obtain lines that were homozygous for the donor alleles in the target loci. Selected lines were advanced to BC3F4 and BC3F5. At BC3F4 and BC3F5, lines were evaluated phenotypically and genotyped by GBS  with an initial denaturation of 95 °C for 5 min; 35 cycles at 95 °C for 30 s; annealing at 55 °C for 30 s; and extension at 72 °C for 30 s with a final extension at 72 °C for 7 min. The PCR product was separated on 3% agarose gel at 200 v for 40 min. Gel results were visualized by using a UVIdoc-HD2/20M  Gel Documentation System.The RM markers used to identify lines carrying KpnI–MspI  and ligated with unique barcoded adapters. Individual barcoded samples were pooled into a single tube. The sequencing of the library was carried using Illumina HiSeq X Ten .To determine the recurrent genome recovery, the population was subjected to GBS, as described by Poland et al. . In summO. sativa Nipponbare reference genome using BWA [TASSEL-GBS 5.0  was usedsing BWA ,47. The sing BWA  and awk The RPGR of the lines was computed using Graphical Genotyper (GGT 2.0) software, and graphical genotypes were created using Microsoft Excel ,50. OnlyField experiments were conducted at the hybridization block (HB) (14°10′18.4″ N 121°15′33.3″ E) of IRRI. The WISH lines were planted with twelve plants per line and 20 cm × 20 cm plot spacing. The field management was based on the standard practice at the research institute. To summarize, fertilizer was applied thrice; basal dressing at final harrowing; first topdressing 21–25 days after seeding; second topdressing 30–35 days after seeding. Pests and diseases were controlled using chemicals to avoid yield loss in plants.At maturity, five plants that were homozygous at the target loci were selected in each line and evaluated for their agronomic traits. The HD was determined from seeding day until 50% of plants per line were flowering. PH was measured from the base of the main stem (above ground) to the tip of the primary panicle. TNPP was recorded as productive tillers in a single plant. PL was measured from the base of the peduncle to the tip of the primary panicle. The PBPP is the total number of branches directly from the peduncle. The SBPP is the number of branches from the PBPP. The NSPP in this study was measured as the total number of spikelets present in each panicle. The PF was measured as the total number of filled grains/total number of grains per panicle × 100. The TGW was measured as the total weight of 1000 grains.p < 0.05) were used.The recorded agronomic data were analyzed using Statistical Tool for Agricultural Research software (STAR) v2.01 . To dete


Pregnancy and parturition involve extensive changes in the maternal immune system. In our randomized, multi-site, double-blind superiority trial using a Bayesian adaptive design, we demonstrated that 1000 mg/day of docosahexaenoic acid (DHA) was superior to 200 mg/day in preventing both early preterm birth (less than 34 weeks’ gestation) and preterm birth (less than 37 weeks’ gestation). The goal of this secondary study is to compare the effects of 1000 mg/day versus 200 mg/day on maternal inflammation, a possible mechanism by which DHA may prevent preterm birth. Maternal blood samples were collected at enrollment (12–20 weeks’ gestation) and at delivery. Red blood cell DHA levels were measured by gas chromatography, and plasma concentrations of sRAGE, IL-6, IL-1β, TNFα, and INFγ were measured by ELISA. Data were analyzed for associations with the DHA dose, gestational age at birth, and preterm birth (<37 weeks). Higher baseline and lower delivery levels of maternal sRAGE were associated with a greater probability of longer gestation and delivery at term gestation. Higher-dose DHA supplementation increased the probability of a smaller decrease in delivery sRAGE levels. Higher IL-6 concentrations at delivery were associated with the probability of delivering after 37 weeks, and higher-dose DHA supplementation increased the probability of greater increases in IL-6 concentrations between enrollment and delivery. These data provide a proposed mechanistic explanation of how a higher dose of DHA during pregnancy provides immunomodulatory regulation in the initiation of parturition by influencing sRAGE and IL-6 levels, which may explain its ability to reduce the risk of preterm birth. Parturition is associated with leukocyte infiltration and the release of cytokines to initiate labor ,2. The pDHA is a metabolically active fatty acid that has been extensively studied in the context of nutrition, neurodevelopment, and immunology ,7,8,9,10Preterm birth is associated with both low maternal blood levels of DHA and low DHA dietary intake ,17. MateGiven the important role of inflammation in the preterm parturition cascade, this study tested a secondary hypothesis in this Phase 3 clinical trial: that supplementation with 1000 mg per day of DHA would be superior to 200 mg per day in regulating inflammatory responses and would provide a mechanism by which DHA prevents preterm birth. Several markers have been identified as indicative of inflammation in pregnancy ,22 and sThe study design and enrollment details of the parent study have been previously published . Women wBriefly, pregnant women were randomly assigned to take 2 capsules of algal oil daily  or soybean and corn oil (0 mg of DHA) beginning between 12 and 20 weeks of gestation. Both groups received a commercially available prenatal supplement containing 200 mg of DHA. Therefore, the experimental group received 1000 mg of DHA per day and the control group received 200 mg of DHA per day. The final enrollment for the parent study included 1100 randomized participants; 492 women in the 200 mg group and 540 in the 1000 mg group   . sRAGE wNormal Bayesian models were used to assess the effect of the sRAGE and cytokine levels of the treatment group at enrollment or delivery on maternal serum DHA sRAGE and cytokine concentrations. Normal Bayesian models also quantified the effect of the sRAGE and cytokine levels at enrollment and delivery on the outcome of gestational age at birth in weeks while accounting for the confounding variables ). Similarly, binomial Bayesian models were fit for the dichotomous outcome of preterm birth . Data are reported as posterior means and Bayesian credible intervals with posterior probabilities. The findings can be interpreted as the probability that DHA supplementation and the marker of interest are associated.http:openbugs.net/w/OpenBUGS_3_2_3?Action=AttachFile&do=get&target=OpenBUGS-3.2.3.tar.gz). All analyses were fitted using 10,000 burn-in draws of Markov chain Monte Carlo, followed by 40,000 draws for inference. All prior distributions were non-informative.We utilized OpenBUGS version 3.2.3 rev 1012 for all Bayesian analyses  were missing baseline blood samples while 11.3% (117/1032) were missing delivery blood samples. A total of 11.7% (121/1032) were unable to have their sRAGE and cytokine data analyzed due to missing either a baseline or delivery blood draw. There were also 0.9% (9/1032) with samples unusable for processing and analysis, detailed in 2). Twelve participants (5 at 200 mg/day and 7 at 1000 mg/day) were removed from the final models because data on baseline BMI were missing. The primary outcomes were gestational age at birth in weeks and preterm birth prior to 37 weeks of gestation.Differences in the probability of a higher dose of DHA (1000 mg per day) to prevent preterm birth compared to a lower dose (200 mg per day) were noted in the parent trial between women with high  baseline DHA levels versus low baseline DHA  levels. As previously reported, 54/492 (11%) births occurred at <37 weeks in the 200 mg/day group compared to 44/540 (8.2%) in the 1000 mg/day group, with a 0.95 posterior probability that 1000 mg was better than 200 mg . BecauseAs found in the parent study, treatment with 1000 mg per day demonstrated a significant posterior probability (pp = 0.99) of having a greater gestational age at birth compared to treatment with 200 mg/day . sRAGE cAdditional analyses using preterm birth  as a binary outcome revealed similar findings . As baseThe effects of the cytokines IL-6, IL-1β, TNFα, and IFNγ on gestational age at birth and preterm birth were analyzed individually, while adjusting for the covariates mentioned earlier . Higher The relative changes (delivery concentrations minus enrollment concentrations) in sRAGE, IL-6, IL-1β, TNFα, or IFNγ were analyzed with the treatment group alone as a predictor . A smallModulation of the maternal immune system occurs during pregnancy to protect the fetus . InflammLeukocyte infiltration, cytokine release, and the resulting inflammation contribute to the initiation of parturition, and the maternal response to this inflammation provides protective effects for both mother and infant ,2. RAGE Several studies identified gradual increases in maternal serum sRAGE levels through the second trimester and then significant decreases approaching birth ,34,35,36Mothers who give birth prematurely <37 weeks) have lower levels of sRAGE than mothers who deliver at term ,32,36,37 weeks haOur data are in line with previous reports that a higher baseline level of sRAGE offers protection against preterm birth, allowing for a buffer against pregnancy-related inflammatory responses. We observed that higher levels of sRAGE at enrollment were associated with a high probability of longer gestation and a greater probability of birth at term (≥37 weeks) . HoweverInflammatory responses due to paracrine interactions within the pregnant uterus are essential components in the initiation of labor. Elevations in IL-6, IL-1β, and TNFα levels occur within the process of normal delivery at term, but levels are often more elevated in the context of pregnancy complications and preterm birth . IL-6 isIL-1β is a significant regulator of inflammation and labor-inducing genes. It is a stimulator of COX-2 and thus prostaglandin synthesis and is an upstream inducer of IL-6 production ,22,40. IIn summary, our data indicate that higher baseline and lower delivery levels of maternal sRAGE are associated with a greater probability for longer gestation and delivery at term gestation. Furthermore, we found that higher-dose DHA supplementation increased the probability of a smaller decrease between sRAGE levels at enrollment and delivery. Higher IL-6 concentrations at delivery were also associated with the probability of delivering after 37 weeks and higher-dose DHA supplementation increased the probability of greater increases in IL-6 concentrations between enrollment and delivery. These data obtained from a prospective clinical trial provide a potential mechanistic explanation of how a higher-dose DHA supplement during pregnancy might provide immunomodulatory regulation of the initiation of parturition through its influence on sRAGE and IL-6 levels, which may explain its ability to reduce the risk of preterm birth. A prospective study to evaluate these relationships is needed.


Four patterns of dietary consumption were identified for each trimester of the pregnancy evaluated. Adherence to the “Meat, Eggs, Fried Snacks and Processed foods” dietary pattern (pattern 1) and the “Sugars and Sweets” dietary pattern (pattern 4) in the third trimester directly reduced birth weight, by 98.42 g  95%: 24.26, 172.59) and 92.03 g , respectively. It was also observed that insufficient dietary consumption in the third trimester increases maternal complications during pregnancy, indirectly reducing birth weight by 145 g . Inadequate dietary intake in the third trimester appears to have negative results on birth weight, directly and indirectly, but more studies are needed to clarify these causal paths, especially investigations of the influence of the maternal dietary pattern on the infant gut microbiota and the impacts on perinatal outcomes. The mother’s diet during pregnancy is associated with maternal and child health. However, there are few studies with moderation analysis on maternal dietary patterns and infant birth weight. We aim to analyse the association between dietary patterns during pregnancy and birth weight. A prospective cohort study was performed with pregnant women registered with the prenatal service . A food frequency questionnaire was used to evaluate dietary intake. Birth weight was measured by a prenatal service team. Statistical analyses were performed using factor analysis with a principal component extraction technique and structural equation modelling. The mean age of the pregnant women was 27 years old (SD: 5.5) and the mean birth weight was 3341.18 g. It was observed that alcohol consumption ( Birth weight is a significant factor in children’s health: the evidence indicates that both high or low birth weight are risk factors for the occurrence of infectious and respiratory diseases and can adversely affect growth and development in infancy. The inadequate nutritional status of children in the intrauterine environment also increases the possibility of the emergence of chronic diseases in later life, in particular obesity, excess weight, diabetes mellitus, metabolic syndrome, and other chronic diseases ,2, thus The current dietary pattern of pregnant women is characterized by insufficient intake of fruit and vegetables, sources of fibre, and micronutrients, and a greater intake of processed and ultra-processed sources of saturated fats, trans fats, and added sugar ,9,10,11.Epidemiological studies reveal that women who adhered to a dietary pattern considered to be healthy during pregnancy  presented lower chances of having low-weight  and smalAlthough these studies have examined the determinants of birth weight, factors associated with moderation analysis on maternal dietary patterns in the first and third gestational trimesters and their relationship with birth weight remain unelucidated among socioeconomically vulnerable populations living in low- and middle-income countries, such as Brazil. It is thus postulated that adequate food consumption can reduce unfavourable results and risks during pregnancy and childbirth for both the mother and foetus, thereby offering protection to the intestinal health and nutritional state of the children, particularly with respect to birth weight. Thus, this study aims to identify the association between dietary patterns during pregnancy and birth weight.This is a longitudinal NISAMI  study involving pregnant women residing in the urban area of the municipality of Santo Antônio de Jesus, in Bahia, Brazil, between April of 2012 and June of 2013.Sample size calculation was based on an average weight at birth of 3196 g and a standard deviation of 456 g , assuminThe sample included pregnant women aged 19 or above who were resident and domiciled in the urban area of the municipality and registered with the prenatal service of the Family Health Units (FHUs), and who were in their first gestational trimester. The following women were considered ineligible for the study: pregnant women monitored at the prenatal service but who resided outside the municipality studied; adolescents; those with multiple pregnancies, stillbirths, and anembryonic pregnancies; those lacking monitoring in any of the rounds or pregnant women who were not found at their homes .The pregnant women were recruited at the prenatal services of the FHUs of the municipality of Santo Antônio de Jesus, according to the inclusion criteria for entry into the research. The study was conducted in four rounds, the first three of which were carried out during pregnancy and the last of which occurred in the immediate postpartum period. The stages of monitoring the cohort were considered as rounds. The baseline (first round) occurred in the first trimester at the FHU and involved semi-structured interviews to meet the pregnant women and understand their socioeconomic conditions, demographics, reproductive and/or obstetric history, and lifestyle, with their answers being recorded in a questionnaire. Information about biochemical parameters, such as glycemia, hemogram, VDRL, HTLV, cytomegalovirus, rubella, and faeces parasitology, was obtained from the prenatal service patient records; the anthropometric assessment (height and weight) was carried out at the time of the interviews by suitably trained researchers and the dietary intake evaluation was conducted using the food frequency questionnaire (FFQ) . The secIn the remaining rounds, this information was collected using a validated semi-quantitative food frequency questionnaire (FFQ). It consists of 74 food items that represent the local dietary culture and includes over 11 food groups .The frequency of consumption was represented by the following categories: daily, weekly, monthly, never, or almost never . The valThe details of the cohort procedures are described in previous studies . In the We used the pre-pregnancy body mass index (PP-BMI) and classified it according to the Institute of Medicine parameters . Materna® portable digital scale with a capacity of 150 kg was used to record weight. Height was measured using a Welmy® brand stadiometer with a 2000 mm maximum height. Anthropometric measurements were taken in duplicate. Maximum variations of 0.5 cm, for measuring height, and 100 g, for measuring weight, were accepted [We used gestational BMI as a proxy for gestational anthropometric status, and this was classified according to the Atalah curve . Weight accepted .®), with a capacity of 15 kg and a 10 g interval. The anthropometric measurement was performed in duplicate, and a maximum variation of 10 g was acceptable. When different values were obtained, a third measurement was performed. The final measurement was calculated as the mean of the values of the closest measurements [In the follow-up of the study (April 2012 and June 2013), the pregnant women were monitored until the birth of their child ,20. The urements .Information on birth weight was collected from the municipality’s Epidemiological Survey (VIEP) registration system. When necessary, home visits were performed at the end of the study for those pregnant women whose data on gestational outcomes were not found in the VIEP ,26. t test for equal variances.Prevalence was determined to describe the categorical variables. Mean and standard deviation (SD) were used for the continuous variables. The mean of birth weight according to the exposure variables was compared using Student’s To identify the dietary patterns in the sample, factor analysis by principal component was used. Varimax rotation of the factors was employed, with graphic Scree plot used to define the extracted (retained) factors. A factorial charge ≥0.4 was adopted as the selection criterion for the dietary groups to be included in the pattern. Internal consistency of each factor was evaluated by Cronbach’s alpha. Detailed information on the identification of dietary patterns during pregnancy is reported elsewhere .p < 0.05. The variables  were included continuously into the SEM. Patterns of dietary consumption (constructed) were entered into the model as a factorial score in a continuous way.To evaluate the association between dietary patterns during pregnancy and birth weight, structural equation modelling (SEM) was used. In this sense, birth weight represented the response variable (endogenous variable); pattern of dietary consumption (constructed) represented the Exposure variables (exogenous variable); and complications during pregnancy, PP BMI, and weight gain (observable variables), intermediate variables. Associations were accepted with a We used the likelihood-ratio Chi-square statistical test to analyse the appropriateness of the model. The results of the analysis for the group of mediating variables were evaluated by means of standardized coefficients (SC) and interpreted according to the results of Kline .Excel software programs were used for processing the dietary consumption data. The Statistical Package for Social Sciences program, version 17, was used for data entry and for descriptive and factor analyses. Stata software, version 12.0, was used for SEM analysis.The characteristics of the mothers and their newborns are presented in n = 14). The most frequent reasons were those related to not finding the address provided by the pregnant woman to the family clinic , absence in one of the rounds of the study , and refusal to participate in the study   .The prevalence of adequate weight at birth was 96.2%  (data not presented in the tables). The highest mean birth weight was found for infants born to mothers with family income of more than one minimum salary (3371.67 SD: 508.31) and for those whose mothers had more than seven medical appointments in the prenatal period , had no complications during pregnancy , and with more than 10 kg weight gain during pregnancy .In the first and third trimesters, four dietary consumption patterns were identified . The intThe first pattern of the first trimester  represented 19.42% of the consumption data; the second  accounted for 11.23%; the third , 10.68%; and the fourth (fruit), 9.11% of the consumption data .In the third trimester, we identified four patterns. The first  represented 14.08% of the consumption data; the second , 13.28%; the third (coffee and butter/margarine), 11.03%; and the fourth (sugars and sweets), 10.62% of the consumption data . p = 0.6); Pattern 2: 0.023 (p = 0.7); Pattern 3: 0.085 (p = 0.2); Pattern 4: 0.051 (p = 0.4).Pearson correlation between the dietary patterns of the first and third trimester were: Pattern 1: 0.035 (p > 0.05). The first  and fourth patterns (sugars and sweets) in the third trimester of gestation had a direct and significant negative impact, where the birth weight was reduced by 98.42 g (p = 0.009) and 92.03 g (p = 0.03), respectively (p = 0.04) and morbid complications during pregnancy   had a direct and significant impact on birth weight. Gestational weight gain had a direct and positive impact on birth weight . It was observed that for every 1 kg gained during pregnancy, there was a reported increase of 160 g in newborn birth weight. With regard to morbid complications in pregnancy, these directly and negatively impacted the weight of the child, reducing total weight at birth by 145 g (The SEM presented satisfactory fit (ectively . There wby 145 g .n = 14). The most frequent reasons were those related to not finding the address provided by the pregnant woman to the family clinic , absence in one of the rounds of the study , and refusal to participate in the study   .This is one of the few studies conducted in Brazil that aims to identify patterns of dietary consumption during pregnancy and their relation to birth weight. It uses robust methodological modelling of follow-up and a convenient framework of multivariate data analysis, permitting the estimation of a series of relationships between variables. This study provides a convenient framework of multivariate data analysis and allows the estimation of a series of relationships between variables. In this sense, it was found that consumption of meat, eggs, fried/savoury foods, and processed food products (pattern 1) and consumption of sugar and sweets (pattern 4) in the third trimester had a direct and negative impact on birth weight, reducing it by 98.42 g and 92.03 g, respectively. Although some of these foods, such as meat and eggs, included in pattern 1, contain protein of high biological value that has a recognized physiological function in pregnancy, these foods are mainly fried because of the cultural influence in the population of the local municipality. Savoury, fried, and processed food products, contain high levels of saturated fat, sodium and cholesterol in their composition, which negatively affect the health of the individual and can also adversely affect newborn birth weight . FurtherThe third dietary pattern had an indirect association with anaemia. Observational studies show that coffee consumption is associated with anaemia . HoweverThus, the third dietary pattern may favour weight gain during pregnancy, increasing the likelihood of complications during pregnancy  as well as reducing the availability of essential nutrients for the proper development of the foetus. The patterns of dietary consumption identified among the women in this study are similar to those found in the Brazilian population as a whole, which is explained by changes taking place in the population’s diet, and reflected in its epidemiological and nutritional profile at the different stages of life.In Brazil, as in other countries, these changes are related to changes in lifestyle, which are expressed in the reduction in daily caloric expenditure; increased consumption of saturated fats, simple sugars, and processed foods; and reduced consumption of fruits and vegetables. This dietary pattern is a matter of concern at all stages of life, and during pregnancy it can be associated with poor maternal health conditions and compromise foetal growth and development. The lack or excess of nutrients during pregnancy can lead to morbid complications for both the mother and foetus, impacting the child’s health at later stages of life ,8,29,30.The four different dietary patterns identified in the first and third trimesters of pregnancy represented 50.45% and 49.03%, respectively, of the total consumption variance, indicating that the patterns extracted represent the eating habits of the population being studied.The dietary intake profile of the pregnant women in this study predominantly consisted of foods rich in saturated fats, trans fats, cholesterol, sugars, and sodium, and poor in vegetables and fruit, which are good sources of fibre and micronutrients. This dietary profile, in a critical period of human development such as pregnancy, has consequences for the mother and foetus ,32.The causal paths of this relationship are still being studied, but the scientific evidence that reveals the influence of the maternal diet on the maternal–foetal gut microbiome stands out as promising ,33. It hIn the trimesters evaluated, we reported the consumption of fruits but at an extremely low frequency on a daily basis, accounting for only 9.11% in the first and 13.28% in the third trimester. This low consumption particularly restricts the availability of vitamins, minerals, and nutrients that directly impact appropriate weight and size at birth. Observational studies found that fruit consumption during pregnancy is conducive to adequate weight and size at birth and associated with a low incidence of preterm births ,34.One similar study involving pregnant women reported two patterns of food consumption: healthy  and unhealthy  . The heaThis study highlights the epidemiological importance of assessing the dietary intake of pregnant women during pregnancy; we identified the different consumption patterns during each trimester and associated these patterns to the different nutritional needs in these periods.The need to avoid imbalanced food consumption must be emphasized, both in relation to excess and deficit. The adoption of healthy living habits, with a varied dietary intake, qualitatively balanced in nutrients and energy to meet each trimester’s nutritional needs, allows satisfactory physiological adaptations of the maternal organism and adequate foetal growth and development. Women who intend to become pregnant should maintain a healthy lifestyle, which includes maintaining a healthy weight, practicing physical activity, stopping tobacco and alcohol use, and adopting a balanced diet . In addiDespite the need to follow the nutritional recommendations in this stage of life, evidence from a meta-analysis on micronutrient intake during pregnancy shows that women in this period ingest an insufficient quantity of micronutrients as opposed to the recommended values .It is known that when pregnant women begin their pregnancy with an age-appropriate weight, their nutritional reserves (energy and nutrients) supply the foetus with its nutritional needs in the first trimester , which mHowever, some methodological issues of this study should be considered, especially regarding techniques of collection and analysis of dietary intake; sufficient measures were adopted to minimize the measurement biases that could be caused by recall, such as the use of a photo album containing pictures of food portions during the investigation of dietary consumption.In this study, factor analysis with the principal component technique was adopted to identify dietary consumption patterns. Still, this method is not without criticism, mainly due to the subjectivity in determining the number of factors to be extracted. This restriction has been minimized with the adoption of methodological assumptions in the statistical model .It is understood that losses in monitoring may contribute to selection bias in the investigated sample. However, on statistical analysis of the losses, it was noted that these did not differ in the sample throughout the monitoring period for some exposure variables, for example, pre-gestational anthropometric status, maternal age, income, education, smoking and alcohol consumption (data not shown in table), which indicates that the losses occurred at random. Of note, the losses were negligible (12%), so it was unlikely that they resulted in biased results.Thus, longitudinal studies with a broad methodology for assessing consumption, suitable to the complexity of the Brazilian population’s diet, generate consistent information that can support the definition and implementation of health measures for ensuring adequate nutrition during pregnancy. The objective is to reduce maternal morbidity and mortality rates and guarantee ideal health and nutritional conditions for foetal growth and development.It warrants mentioning that more studies are needed to clarify the causal paths involved in the relationship studied, especially investigations of the influence of the maternal dietary pattern on the infant gut microbiota and the impacts on perinatal outcomes. The results of this study indicate that unhealthy dietary consumption in the third trimester has negative effects on birth weight in two ways. Patterns one  and four (sugars and sweets) directly impact birth weight by reducing it. The third pattern (coffee and butter/margarine), which also indicates inadequate intake, has the same impact on birth weight, however it acts indirectly, mediating the occurrence of increased morbidity.These findings are relevant to the field of nutritional health for mothers and their infants, since food consumption is considered a modifiable factor; dietary and nutritional interventions during gestation, which are important determinants of birth weight, could contribute positively to the health of the mother and foetus.


Preterm birth and low birth weight (LBW) affect one in ten and one in seven livebirths, respectively, primarily in low-income and middle-income countries (LMIC) and are major predictors of poor child health outcomes. However, both have been recalcitrant to public health intervention. The maternal intestinal microbiome may undergo substantial changes during pregnancy and may influence fetal and neonatal health in LMIC populations.Within a subgroup of 207 mothers and infants enrolled in the SHINE trial in rural Zimbabwe, we performed shotgun metagenomics on 351 fecal specimens provided during pregnancy and at 1-month post-partum to investigate the relationship between the pregnancy gut microbiome and infant gestational age, birth weight, 1-month length-, and weight-for-age z-scores using extreme gradient boosting machines.Blastoscystis sp, Brachyspira sp and Treponeme carriage were high compared to Western populations. Resistant starch-degraders were important predictors of birth outcomes. Microbiome capacity for environmental sensing, vitamin B metabolism, and signalling predicted increased infant birth weight and neonatal growth; while functions involved in biofilm formation in response to nutrient starvation predicted reduced birth weight and growth.Pregnancy gut microbiome taxa and metabolic functions predicted birth weight and WAZ at 1 month more accurately than gestational age and LAZ. The pregnancy gut microbiome in rural Zimbabwe is characterized by resistant starch-degraders and may be an important metabolic target to improve birth weight.Bill and Melinda Gates Foundation, UK Department for International Development, Wellcome Trust, Swiss Agency for Development and Cooperation, US National Institutes of Health, and UNICEF. Preterm birth and low birth weight are a persistent public health challenge in low-income and middle-income countries (LMIC) that predict poor child health. There is need to identify more effective targets for intervention. A limited number of small studies, predominantly from high-income western populations, suggest that the maternal gut microbiome undergoes important metabolic changes during pregnancy that may play an underappreciated role in preterm, low birth weight and subsequent neonatal health. However, studies of the pregnancy gut microbiome in LMICs, where preterm and low birth weight are most prevalent, are extremely sparse; and metagenomic studies to date severely underrepresent populations outside of North America and Europe.We used shotgun metagenomics to investigate the relationship of pregnancy gut microbiome taxa and metabolic functions with infant gestational age, birth weight, and neonatal growth in a subsample of women and their infants enrolled in the Sanitation, Hygiene, Infant Nutrition Efficacy trial in rural Zimbabwe. In this population, resistant-starch degrading bacteria were the predominant gut taxa and were important predictors of birth weight and neonatal growth. Microbiome functions involved in signaling, starch, vitamin B and energy metabolism were also important predictors. Gut microbiome predictors of birth weight and neonatal growth largely reflected the low-diversity, resistant-starch dominated diet of the study population. This is the first study to identify gut microbiome markers of birth weight and early infant growth that likely reflect the dietary patterns of the mother, in a sub-Saharan African population where there is a very high prevalence of preterm birth and low birth weight.The pregnancy gut microbiome of rural Zimbabwean mothers, primarily the abundance of resistant-starch degraders, is an important contributor to birth weight and neonatal growth. Microbiome functions that predicted these outcomes suggested that bacterial capacity to respond to nutrient availability in the maternal diet may be important to promoting improved birth weight and early infant growth in populations with a monotonous starch-rich diet.Alt-text: Unlabelled boxChildren weighing <2,500 grams (g) at birth (low birth weight [LBW]), due to intrauterine growth retardation or preterm birth (<37 weeks completed gestation), are at high risk of morbidity and mortality during infancy and childhood, and of adverse effects over the life course Recent evidence suggests that the composition and function of the maternal gut microbiome before and during pregnancy may affect birth weight and duration of gestation The gut microbiome can directly suppress ,15 or prBifidobacteria sp was positively associated with plasma folic acid Nutrition during pregnancy can also influence fetal growth and development ,26. DietBacteroides, Clostridium, Staphylococcus and Enterobacteriaceae (Escherichia coli) abundance [Indirectly, the gut microbiome may affect pregnancy outcomes through an impact on maternal nutritional status and gestational weight gain during pregnancy, which influence fetal growth. In adults, gut bacteria-derived SCFAs regulate food intake, body weight, energy expenditure, and satiety ,39, and bundance ,41.Furthermore, the maternal gut is a predominant source of bacteria to colonize the infant gastrointestinal tract at birth ,43. ChilHowever, the evidence for a relationship between the pregnancy gut microbiome and adverse birth outcomes comes predominantly from high-income settings, and the results of these small studies have been inconsistent We performed whole metagenome shotgun sequencing of maternal fecal specimens collected during pregnancy and at 1mo post-partum to identify maternal microbes and metabolic functions that might influence infant gestational age, size at birth, or growth during the neonatal period. We also investigated the impact of the SHINE WASH intervention, baseline sanitation and hygiene-related factors, and maternal characteristics on the maternal fecal microbiome.The study design and methods for the SHINE trial and for microbiome analyses have been reported previously ,52. In bIn a pre-specified substudy of SHINE, 1,656 mother-child pairs were selected for biological specimen collection from the mother during pregnancy then at 1mo post-partum, and intensive collection from the infant at 1, 3, 6, 12 and 18 months of follow-up. This substudy sample was enriched for HIV-positive mothers because we were also interested in investigating outcomes associated with fetal HIV-exposure. Of these substudy pairs, two hundred seven mother-child pairs with nearly complete longitudinal fecal sample collection were investigated as part of the fecal microbiome study; these are the focus of our analyses in this manuscript.Mothers collected fecal specimens prior to a home visit by a research nurse during pregnancy and at the 1mo post-partum study visit. Fecal specimens were immediately placed in a cold box and transported to the regional laboratory, where they were stored at -80°C until transfer to the central laboratory in Harare for long-term archiving at -80°C. Fecal specimens were transferred via private courier on dry ice from Harare, Zimbabwe to Vancouver, British Columbia. The Qiagen DNeasy PowerSoil Kit was used to extract total DNA from 200mg of feces, according to manufacturer's instructions.Paired-end libraries were constructed using the Illumina TruSeq kit and using New England Biosystem TruSeq compatible library preparation reagents. Libraries were sequenced at the British Columbia Genome Sciences Centre using the Illumina HiSeq 2500 platform. Forty-eight libraries were pooled and included per sequencing lane. Sixteen negative process controls, consisting of sterile water subjected to DNA extraction, library preparation, and sequencing, were included to capture microbial contamination of laboratory reagents.Sequenced reads were trimmed of adapters and filtered to remove low-quality, short (<60 base-pairs), and duplicate reads, as well as those of human, other animal or plant origin using KneadData with default settings −7% for EC and pathway features, at ≥5% prevalence, in all downstream analyses.Functional gene and metabolic pathway composition was determined using HUMAnN3 with default settings against the UniRef90 database All analyses were conducted in R version 3.3.2 veganadonis function vegan.Maternal fecal microbiome composition was investigated using descriptive approaches (e.g. boxplots) and α- and β-diversity metrics. α-diversity was assessed using the Shannon, Simpson, inverse Simpson, evenness and richness metrics; while β-diversity was evaluated using the Bray–Curtis dissimilarity index and visualized using principal coordinate analyses (PCoA) performed in the package gamlssThe bivariate relationship between epidemiologic variables and maternal fecal taxa was examined using zero-inflated beta regression fitted by generalized additive models for location, scale and shape (GAMLSS), with a log-link, in the R package Independent associations between the maternal pregnancy microbiome and gestational age, birth weight, WAZ or LAZ at 1mo was evaluated using extreme gradient boosting machines (XGBoost). Microbiome data are very complex, consisting of hundreds of species or pathways, and thousands of enzyme commissions (EC), and comprise of highly right-skewed distributions with many zeros. Analytic methods that can address these challenges and select the most important subset of features for a given outcome are limited. XGBoost builds an optimized predictive model by creating an ensemble from a series of weakly predictive models. In aggregate, each additional model that is fitted is parameterized to improve the overall prediction accuracy of the ensemble when the new model is combined with previous models. Only features that improve model accuracy are retained in the process. XGBoost is also non-parametric, can capture non-linear relationships, and can accommodate high-dimensional data BayesianOptimization function of the rBayesianOptimization package was used with 10-fold cross-validation to select model hyperparameters (Supplementary Table 1) by minimizing the mean squared error (MSE). Models with the lowest MSE (in the 5th percentile) were retained, and from these models the variables that contributed to the top 95% of variable importance by proportion were retained. In stage two, all epidemiologic variables were included with the microbiome variables obtained in stage one. BayesianOptimization was run as described in stage one but using leave-one-out cross-validation. Microbiome variables that contributed to the top 95% of variable importance by proportion were retained. In stage three, all epidemiologic variables, microbiome features, and hyperparameters selected in stage two were used to fit our final models. Final models were fit using leave-one-out cross-validation to minimize the MSE. This process was implemented separately for each outcome , and separately for microbiome taxa, pathways, and enzymes. Finally, all models were re-run excluding epidemiological variables to assess the contribution of the maternal pregnancy microbiome alone to the accuracy of our final models. XGBoost models were fitted using the H20.ai engine and h2o R package interface with the XGBoost package.The XGBoost models were developed using microbiome relative abundances, diversity measures, percent human DNA reads, percent unknown reads, and selected epidemiologic variables. Epidemiologic variables were selected for inclusion in the XGBoost model based on: (i) their association with birth weight, LAZ or WAZ at 1mo in multiple linear regression models ; (ii) their association with microbiome diversity indices in simple linear regression models (p<0.05); and (ii) their association with microbiome taxon abundance in GAMLSS models (q<0.05). XGBoost model selection was performed in 3 stages. In stage one, the 2) between the final XGBoost-predicted outcome value and the observed outcome value, where pseudo-R2=1·0 is perfect prediction; (ii) the mean absolute error (MAE), which is defined as the average absolute difference between observed and predicted outcome values estimated from cross-validation and summarizes model performance in the actual units of the outcome variable ; and (iii) the root mean squared error, which is defined as the average squared difference between observed and predicted outcome values estimated from cross-validation. We used the scaled relative importance for each variable in a model to identify the twenty most informative variables for further interpretation, where the most important variable is ranked first, and the importance of subsequent variables are relative to the first variable.XGBoost model performance was evaluated using three estimates: (i) pseudo-R-squared , Johns Hopkins Bloomberg School of Public Health (JHU IRB # 4205.), and the University of British Columbia (H15-03074) approved the study protocol, including the microbiome analyses.http://ClinEpiDB.org in mid-2021. Prior to that time, the data are housed on the ClinEpiDB platform at the Zvitambo Institute for Maternal and Child Health Research and available upon request from Ms. Virginia Sauramba (vsauramba@zvitambo.co.zw).All relevant data are within the paper and its Supporting Information files except for the raw data which the trial team will begin loading as individual participant data with an accompanying data dictionary at The funders had no role in study design, data collection, data analyses, interpretation, or writing of the manuscript.3 . The majority (93%) were receiving antiretroviral therapy (ART) and 46% were receiving cotrimoxazole prophylaxis at baseline  and at 1mo post-partum (between 12 and 90 days after birth); an additional 43 provided only a pregnancy sample and 20 only a postpartum sample. There were median 1 and 2 datasets per randomization cluster at each visit, respectively. Since there was little opportunity for expression of intra-cluster correlation, it was not adjusted for in any of our analyses. There was very little variability in sequencing depth across specimens . Negative controls revealed negligible levels of microbial contamination of reagents  relative to the sequencing depth achieved across specimens. The median proportion of human reads detected was 0·01% but ranged widely from 0·0004% to 19·3% , indicating high human DNA enrichment in some fecal specimens.After applying our relative abundance and prevalence threshold criteria, 144 unique taxa at the species level or above, 424 microbiome pathways, and 1,956 enzyme commission features were included in downstream analyses from the pregnancy and 1mo post-partum metagenome datasets. Of these, 39 taxa (28%) were defined by MetaPhlAn3 as co-abundance gene groups (CAGs) In PCoA analyses of microbiome β-diversity by epidemiologic variables (using Bray-Curtis distances), maternal microbiome composition differed modestly by HIV-status  and randomized WASH assignment  . However, microbiome composition did not vary by trimester of pregnancy  .Treponema berlinense  was observed in HIV-negative mothers at the post-partum visit; while higher Brachyspira sp CAG 700  and lower Akkermansia muciniphila  were observed in mothers assigned to the WASH intervention (Supplementary Table 3) at the post-partum visit. There was no evidence that any of the α-diversity metrics varied by these variables.In bivariate analyses of differences in relative abundance of individual taxa by epidemiologic variables using GAMLSS models, only one taxon differed by maternal HIV-status and two taxa differed by WASH intervention, after FDR adjustment, at the post-partum visit only. Higher Brachyspira pilosicoli, a spirochete associated with human zoonotic spirochaetosis, was carried by 20 mothers, while 22 mothers carried Brachyspira sp CAG 700. Interestingly, lower wealth quintile, lower primary education and greater maternal height were associated with higher Brachyspira sp abundance (Supplementary Table 3). Higher percentage of human DNA was associated with higher relative abundance of Blastocystis and Alistipes shahii at 1mo postpartum . Other non-pathogenic spirochetes were identified in many SHINE mothers, including Tremponema succinifaciens (n=68) and Treponema berlinense (n=17) . Spirochetes have been shown to be over-represented in many non-Western populations Blastocystis sp subtype 1 during at least one visit. Giardia intestinalis or Cryptosporidium spp were detected but below the 0·1% relative abundance threshold. Shigella was not detected and Salmonella enterica, Campylobacter jejuni and C. coli were also present at <0·1% relative abundance. Escherichia coli was common, but MetaPhlAn3 does not separate by E. coli pathotype; therefore, presence of enteroaggregative or enteropathogenic E. coli could not be assessed.Overall, based on MetaPhlAn3 results, carriage of pathogens was uncommon. At a minimum relative abundance of 0·1%, In multivariable linear regression models, maternal height, MUAC, and gestational age, were associated with infant birth weight, as expected (Supplementary Table 4). While maternal MUAC and gestational age were associated with WAZ and LAZ at 1mo respectively.p-value = 0·09).To ensure temporality between maternal microbiome characteristics and pregnancy outcomes, all analyses of the association between maternal microbiome, gestational age, birth weight, and neonatal growth utilized the pregnancy visit metagenome datasets only. In simple linear regression models, microbiome Shannon diversity was not associated with infant birth weight or LAZ at 1mo  but was associated with higher WAZ at 1mo . Higher pregnancy microbiome taxon evenness was associated with higher infant WAZ at 1mo, and there was evidence for a positive association with the Shannon diversity index . Performance measures for final models of microbiome species, ECs, or pathways, including or excluding epidemiologic variables, are presented in Supplementary Table 5. For all outcomes, the performance of models that included only microbiome features performed as well as the models that also included epidemiologic variables, indicating that model performance was predominantly driven by microbiome composition. Overall, model performance was best for birth weight, followed by neonatal growth, then gestational age. In terms of microbiome features, pathway relative abundance was more predictive than species abundance, while EC abundance was as good or better than pathway abundance at predicting outcomes, although this may be driven by the higher overall number of ECs.ALE plots are shown in Ruminococcaceae, Lachnospiraceae, and Eubacteriaceae families [E. eligens predicted a cumulative decrease of 250g from the lowest abundance up to approximately the 40th abundance percentile, while R. torques predicted a cumulative decrease of 150g above the 25th abundance percentile. In contrast, R. intestinalis and Butyrivibrio sp CAG 318 predicted a cumulative increase of 200g and 100g, respectively, above the 80th abundance percentile , exhibiting greater importance in the model than gestational age and maternal height . Glycogen synthase is involved in starch metabolism. The remaining top 20 enzymes are involved in metabolic pathways that engage in signalling, energy, and vitamin B metabolism . For example, histidine kinase  predicted a 300g increase in birth weight in the lowest abundance percentile. While thiazole biosynthesis pathway (vitamin B1 metabolism) predicted a 200g increase in the lowest abundance percentile and N10-formyl-tetrahydrofolate biosynthesis (vitamin B9 metabolism) predicted a 75g increased .th abundance percentile . The signalling ppGpp biosynthesis pathway predicted an 85g decrease; while, in contrast, the signalling pathway purine ribonuceosides degradation predicted a ~150g increase .Similarly, top ranking metabolic pathways are engaged in starch degradation, energy metabolism and signalling processes . For example, sucrose degradation II pathway and the superpathway of anaerobic sucrose degradation predicted a 200g increase above the 80Ruminococcaceae, Lachnospiraceae, and Eubacteriaceae (Catenibacterium mitsuokai), that is also associated with fiber fermentation Treponema berlinense predicted a 0.6 sd decrease above the 97th abundance percentile. The Simpson diversity index and evenness were also important predictors, indicating a 0.2 sd and 0.7 sd increase through the range of observed diversity values, respectively. However, species richness predicted a 1 sd cumulative decrease in WAZ. As with birth weight, gestational age and maternal anthropometry were also important predictors of 1mo WAZ . While enzymes involved in signalling predicted 0.2 sd increase (e.g cytidine deaminase) or >0.2 sd decrease (e.g. GMP reductase) .Important microbiome pathways and ECs to 1mo WAZ are also involved in vitamin B, starch metabolism, and signalling. For example, flavin biosynthesis pathway III (vitamin B2 synthesis) predicted an increase of 0.5 sd . Cellulase abundance predicted ~0.5 sd increase above the 87Anaerotruncus sp CAG 528 exhibited a higher relative importance score than maternal height and MUAC . Resistant-starch degraders also figured prominently in the set of taxa important to LAZ, as well as the dietary fiber fermenting Prevotella copri [th abundance percentile .Gestational age, maternal height and MUAC were also among the most important predictors of LAZ at 1mo; however, la copri ,74 . Species richness also predicted ~0.5 week shorter gestation in enzyme and pathway models .The most important predictors of gestational age were all microbiome taxa or microbiome diversity metrics, except for trimester of specimen collection and maternal MUAC . As with other outcomes, several important taxa are associated with resistant-starch degradation, including members of the families ,70 and dla copri ,74 which2=0·23), 1mo WAZ (pseudo-R2=0·21) and LAZ (pseudo-R2=0·11) as accurately as models that combined microbiome features with sociodemographic and epidemiologic variables, pointing to the importance of the pregnancy microbiome. Gestational age was predicted less accurately (pseudo-R2=0·05). Prediction accuracy for all outcomes was improved when functional enzyme relative abundances were used (pseudo-R2: 0·25-0·37), demonstrating the potential value of whole metagenome shotgun sequencing for investigating the human microbiome and health. There were only modest global differences between mothers allocated to the WASH versus the non-WASH arms of the SHINE trial, as measured by β-diversity, and only few differences in taxon abundances after FDR p-value adjustment. In prior analyses, the WASH intervention also had only a modest impact on pathogen carriage and diarrhea in SHINE infants In this study of 207 mothers and their infants in rural Zimbabwe where there is a high prevalence of preterm birth and LBW, we investigated the relationship between pregnancy microbiome characteristics and gestational age, birth weight or neonatal growth. Taxonomic features of the maternal microbiome alone predicted birth weight  and pathways for sucrose and fiber degradation (e.g. D-fructuronate degradation) predicted greater birth weight and neonatal growth. These provide further evidence that a microbiome with an increased capacity to degrade the maize-rich, plant-based Zimbabwean diet, is beneficial for healthier growth. Also, greater abundance of histidine kinase predicted larger birth weight. Histidine kinases are important in bacterial sensing, signal transduction and energy utilization Slackia isoflavoniconvertens. When present, S. isoflavoniconvertens converts dietary isoflavones, largely found in soy Prevotella copri predicted reduced gestation. P. copri has been associated with increased gut inflammation, damage An important taxonomic predictor of longer gestation was Brachyspira which is associated with intestinal spirochetosis Treponema berlinense predicted lower 1mo WAZ. Treponemes have been found in the microbiota of non-Western populations Helicobacter pylori, another spirochete, which typically resides in the stomach. The contribution of non-pathogenic Treponema sp to intestinal microbiome function is unknown. In a macaque model that tested the impact of a high- versus low-fat diet on maternal and offspring microbiota, the largest change was a significant reduction in non-pathogenic Treponema spp , in dams administered a high-fat diet, regardless of obese or lean status. The authors suggested that these Treponema spp benefited from a low fat, plant-rich diet (corn and soy bean) Overall, pathogen carriage was low, aside from E. coli was common and predicted decreasing birth weight, but specific E. coli pathotypes could not be distinguished by MetaPhlAn3, limiting our ability to assess the impact of these pathogens. Differences in microbiome characteristics could also be influenced by dietary deprivation. However, wealth quintile was not an important predictor in any models.There are notable limitations to our analyses. A large fraction of the sequenced reads was un-assignable. The fact that there was a large proportion of unknown or unidentifiable DNA in these samples is not surprising, as microbiome data from LMICs are under-represented in reference databases. However, the sheer scale of un-assignable DNA reads was an important observation and is rarely reported in microbiome studies. In conclusion, our analyses illustrate that the pregnancy fecal microbiome, primarily the abundance of resistant-starch degraders, is an important contributor to birth weight and neonatal growth, and to a lesser extent gestational age, in infants of rural Zimbabwean mothers who consume a diet high in maize. The functional capacity for starch metabolism and environmental sensing may be important microbiome mechanisms during pregnancy; while a nutrient-poor diet dominated by maize may promote microbiome biofilm formation in response to nutrient starvation in the gut, with potentially detrimental consequences for the infant. Future work is warranted to confirm these mechanisms and explore whether interventions to facilitate starch degradation and improve dietary diversity can improve birth weight and neonatal growth in populations with a monotonous starch-rich diet.ARM, LES, RJS, LHM, JHH and AJP conceptualized and designed the study. KM, RN, BC, FDM, NVT, JT, and BM collected data and biospecimens. HMG, IB, SKG, RCR, FF and LC processed biospecimens. TJE performed bioinformatics. ARM, TJE and EKG analyzed and interpreted data. ARM and TJE drafted the manuscript. EKG substantially revised the manuscript. ARM, AJP and JHH verified the underlying data. All authors read and approved the final version of the manuscript.RCR declares monetary support from Nestle Nutrition Institute for conference attendance (April 2019), outside the submitted work. TJE was paid a scientific consulting fee in relation to the analysis of the data presented here by Zvitambo Instititute for Maternal and Child Health Research. All other authors have no interests to declare.None.


This study employs machine learning and population data for testing the associations of preterm birth with inflammatory bowel disease (IBD), salivary gland disease, socioeconomic status and medication history, including proton pump inhibitors. The source of population-based retrospective cohort data was the Korea National Health Insurance Service claims data for all women aged 25–40 years and who experience their first childbirths as singleton pregnancy during 2015 to 2017 . These participants were divided into the Ulcerative Colitis (UC) Group (1782 women), the Crohn Group (1954 women) and the Non-IBD Group . For each group, the dependent variable was preterm birth during 2015–2017, and 51 independent variables were included. Random forest variable importance was employed for investigating the main factors of preterm birth and testing its associations with salivary gland disease, socioeconomic status and medication history for each group. The proportion of preterm birth was higher for the UC Group and the Non-IBD Group than for the Crohn Group: 7.86%, 7.17% vs. 6.76%. Based on random forest variable importance, salivary gland disease was a top 10 determinant for the prediction of preterm birth for the UC Group, but this was not the case for the Crohn Group or the Non-IBD Group. The top 5 variables of preterm birth for the UC Group during 2015–2017 were socioeconomic status (8.58), age (8.00), proton pump inhibitors (2.35), progesterone (2.13) and salivary gland disease in 2014 (1.72). In conclusion, preterm birth has strong associations with ulcerative colitis, salivary gland disease, socioeconomic status and medication history including proton pump inhibitors. Preterm birth and inflammatory bowel disease (IBD) are major parts of the disease burden on the globe ,6,7,8,9.To our best knowledge, however, no study with population-based data has been available on the associations of preterm birth with IBD and salivary gland disease. This study presents a comprehensive machine-learning analysis on this topic, using a population-based cohort of 402,092 participants and a rich collection of 51 predictors. This study brings new results on the associations of preterm birth with UC, salivary gland disease, socioeconomic status and medication history, including proton pump inhibitors. Proton pump inhibitors medication, which is common for gastrointestinal disease, is expected to cause gastric acidity and compositional changes in the gut microbiota . It is aThe source of retrospective cohort data for this study was the Korea National Health Insurance Service claims data for all women in Korea who aged 25–40 years and gave first childbirths as singleton pregnancy during 2015 to 2017 . These inclusion criteria were adopted given that data with a wider range and a bigger size went beyond the maximum capacity of the Korea National Health Insurance Service data analysis center located in Seoul, Korea.https://www.mayoclinic.org/diseases-conditions/ulcerative-colitis/symptoms-causes/syc-20353326) (accessed on 1 December 2021); (Crohn’s disease) “an inflammatory bowel disease causing the inflammations of the digestive tract, which can lead to abdominal pain, severe diarrhea, fatigue, weight loss and malnutrition” (https://www.mayoclinic.org/diseases-conditions/crohns-disease/symptoms-causes/syc-20353304) accessed on (1 December 2021);  “if the salivary glands are damaged or aren’t producing enough saliva, it can affect taste, make chewing and swallowing more difficult, and increase the risk for cavities, tooth loss, and infections in the mouth”  accessed on (1 December 2021).The 402,092 participants were divided into the UC Group (1782 women), the Crohn Group (or Crohn’s Disease Group) (1954 women) and the Non-IBD Group . For each group, the dependent variable was preterm birth during 2015–2017 (birth before 37 weeks of gestation). Four categories of preterm birth were introduced according to the ICD-10 Code: (1) PTB 1—preterm birth with premature rupture of membranes (PROM) only; (2) PTB 2—preterm labor and birth without PROM; (3) PTB 3—PTB 1 or PTB 2; (4) PTB 4—PTB 3 or other indicated preterm birth . Each ofLogistic regression, the random forest and the artificial neural network were employed for the prediction of preterm birth ,11,12. TThis retrospective study was approved by the Institutional Review Board (IRB) of Korea University Anam Hospital, Seoul, Korea, on 5 November 2018 (2018AN0365). Informed consent was waived by the IRB.The results of this study are summarized in In terms of accuracy, the random forest was slightly better than logistic regression and the artificial neural network . The aveThe results of logistic regression  present In general, the proportion of preterm birth was higher for the UC Group and the Non-IBD Group than for the Crohn Group. Based on random forest variable importance, salivary gland disease was a top 10 determinant for the prediction of preterm birth for the UC Group, but it was not the case for the Crohn Group or the Non-IBD Group. The top 5 variables of preterm birth for the UC Group during 2015–2017 were socioeconomic status, age, proton pump inhibitors, progesterone and salivary gland disease in 2014.Existing literature focuses on microbial infection as a possible mechanism between IBD and preterm birth or salivary gland disease ,16,17. IFirstly, this study did not examine possible mediating effects among variables. Secondly, this study adopted the binary category of preterm birth as no vs. yes. However, preterm birth can have multiple categories, and it will be a good topic for future study to compare different predictors for various categories of preterm birth, e.g., extremely preterm (less than 28 weeks), very preterm (28 to 32 weeks) and moderate to late preterm (32 to 37 weeks). Thirdly, this study considered the most comprehensive collection of diseases and medications regarding preterm birth available in the Korea National Health Insurance Service data, but it was not the scope of this study to explore and evaluate various pathways among diseases, complications, medications and preterm birth. Little research has been conducted, and more investigation is needed on this topic. Fourthly, according to a recent review, optimal machine learning methods are expected to vary with different types of data for predicting preterm birth: the random forest, logistic regression and/or the artificial neural networsk in the case of numeric data; the support vector machine in the case of electrohysterogram data; the convolutional neural network in the case of image data; and the recurrent neural network in the case of text data . UnitingPreterm birth has strong associations with ulcerative colitis, salivary gland disease, socioeconomic status and medication history including proton pump inhibitors. For preventing preterm birth, appropriate medication would be needed alongside preventive measures for ulcerative colitis and salivary gland disease and the promotion of socioeconomic status for pregnant women.