Predicting preterm birth using machine learning techniques in oral microbiome

doi:10.21203/rs.3.rs-3118055/v1

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Predicting preterm birth using machine learning techniques in oral microbiome

https://doi.org/10.21203/rs.3.rs-3118055/v1

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Background

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.

Methods

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 hours 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.

Results

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. Thirty-two differentially abundant taxa were identified, including 26 full-term birth-enriched taxa and six preterm birth-enriched taxa. The random forest classifier achieved high balanced accuracies (0.778 ± 0.061) using the 20 most important taxa.

Conclusion

Our study identified 32 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.

Machine learning

Mouthwash

Oral microbiome

Pregnancy outcomes

Preterm birth

Preterm birth (PTB), defined as delivery of newborns before 37 weeks of gestation, is a leading cause of morbidity and mortality in newborns [1]. Established risk factors for PTB include genitourinary tract infections, short cervical length, and multiple pregnancies [2]. 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 outcomes [3].

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 strategies [4]. Numerous attempts have been made to predict PTB using machine learning techniques combined with data from health records, inflammatory markers, and vaginal microbiome [5]. 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 specialist [7].

Approximately 70% of PTBs result from spontaneous onset of preterm labor and preterm pre-labor rupture of membranes due to intrauterine infection and inflammation [8]. However, the mechanism of PTB cannot be fully explained by inflammatory and infectious pathways as anti-inflammatory and antibiotic treatments could not reduce PTB rates [9]. With advancements in molecular genetic technology, studies on maternal microbiomes using 16S ribosomal RNA (rRNA) sequencing have emerged to explore unknown pathways of PTB [10].

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 route [11]. Recent research has identified vaginal microbial signatures in women who later experience PTB and attempted to predict PTB using cervicovaginal fluid [12]. Although existing reports have verified a potential relationship of vaginal microbiome with PTB, they can only explain an ascending route.

Decades of epidemiological research studies have suggested that periodontitis is an independent risk factor for various adverse birth outcomes, including PTB [13]. Based on these precedents, it is expected that the oral microbiome can explain another hematogenous route. However, prenatal oral microbiome is not well understood [14].

Thus, 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.

Study design and participants

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 collection and grouping

Data on current and historical pregnancy outcomes were collected from questionnaires and electronic medical records. This information encompassed demographic factors (gestational age, birth weight, sex) and maternal risk factors (maternal age at delivery, cesarean section, preterm pre-labor rupture of membranes, previous preterm delivery history, gestational or overt diabetes mellitus, pregnancy-induced or chronic hypertension, and pre-pregnancy overweight or gestational weight gain). 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 sample collection

Oral microbiome samples were collected using mouthwash within 24 hours 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 minutes before sampling. Saliva samples were obtained by rinsing the mouth with 12 mL of a gargle solution (E-zen Gargle, JN Pharm, Pyeongtaek, Korea) for 30 seconds. 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 (GeneAll Biotechnology, Seoul, Korea) according to the manufacturer’s instructions and stored at -20°C.

16S rRNA gene sequencing and taxonomy assignment

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 (Illumina, San Diego, CA, USA) commissioned by Macrogen (Macrogen, Seoul, Korea). 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.

Bioinformatics and statistical analysis

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 \(p<0.05\).

16S rRNA sequences from study subjects were imported with Quantitative Insights Into Microbial Ecology (version 2022.2) for further processing [14]. Sequences were filtered with DADA2 [15]. Amplicon sequence variants were assigned taxonomy with the Human Oral Microbiome Database (version 15.22) [16].

To 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.

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 tutorial [17]. Taxa with \(\left|{\text{log}}_{2}\text{FoldChange}\right|>1\) and \(\text{adjusted }\text{p}<0.05\) were considered as significantly different.

Machine learning prediction model development

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 robustness [18]. Random forest classifier was implemented to predict PTB based on oral microbiome compositions. To assure consistence and reliable classification results, we performed stratified \(k\)-fold cross-validation (\(k=5\)). Moreover, to decide the best features that could maximize classification evaluations, we performed random forest classification evaluations only with some DAT selected by their importances. Evaluations for classification included accuracy, balanced accuracy, precision, sensitivity, and specificity.

Study participant demographics

In this study, a total of 60 volunteer mothers were initially recruited. However, one participant was excluded due to incomplete data. 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 1. There was no significant difference in maternal clinical characteristics between the PTB group and the FTB group.

Table 1

Baseline clinical characteristics of study subjects
	PTB (n = 30)	FTB (n = 29)	P
Maternal age	31.8 ± 5.2	33.7 ± 4.5	0.687
Cesarean section, n (%)	20 (66.7)	24 (82.7)	0.233
Previous preterm birth history, n (%)	4 (13.3)	1 (3.4)	0.353
Prelabor rupture of membrane, n (%)	12 (40)	1 (3.4)	0.001
Pre-pregnant overweight, n (%)	8 (26.7)	7 (24.1)	1.000
Gestational weight gain, g	9.0 ± 5.9	11.5 ± 4.6	0.262
Gestational or overt diabetes, n (%)	2 (6.7)	2 (6.9)	1.000
Pregnancy-induced or chronic hypertension, n (%)	11 (36.7)	4 (13.8)	0.072
Gestational age, wks	32.5 ± 3.4	38.3 ± 1.1	< 0.001
Birth weight, g	1973.4 ± 686.6	3283.4 ± 402.7	< 0.001
Male, n (%)	14 (46.7)	13 (44.8)	1.000
Mean ± standard deviation
continuous variable for Independent t-test
categorical variable for Pearson’s Chi-square test

Comparison of oral microbiomes

The oral microbiome comprised 13,953,804 sequences from 59 oral microbiome samples, with an averaging 101,511 reads per samples (range: 73,830 − 157,657). 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 (Supplementary Fig. 1).

Of 465 genera and species analyzed, 32 DAT between the PTB group and the FTB group were selected by DESeq2, including 26 FTB-enriched DAT and six PTB-enriched DAT (Fig. 1). Figure 2 displays DAT volcano plot in the oral microbiome sorted by differences between FTB-enriched DAT and PTB-enriched DAT proportions, indicating a decrease in gestational age for FTB-enriched DAT proportions. Pearson correlation analysis revealed a strong negative correlation (\(r=-0.542\)) between gestational age and difference between PTB-enriched DAT and FTB-enriched DAT proportions.

Random forest classification

Random forest classifiers were established to classify PTB based on DAT. The best balanced accuracy (0.778 ± 0.061) was achieved using 20 most important taxa (Fig. 3a). The Random forest model calculated the importance of each DAT (Fig. 3b). Overall accuracy, precision, sensitivity, and specificity were 0.764 ± 0.050, 0.767 ± 0.170, 0.762 ± 0.047, and 0.767 ± 0.170, respectively.

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 pregnancy [11]. 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 miscarriages [19]. Nevertheless, these results have been inconsistent due to methodologies employed in studies that only target known pathogens.

Fusobacterium 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 microbiome [20, 21]. Intra-amniotic Fusobacterium nucleatum infection leading to PTB has been reported in human and animal studies [22]. 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 prematurely [23, 24]. In the present study, although Bergeyella spp. was enriched in the FTB group, it was one of the DAT between the PTB and FTB group. It was also one of the 20 most important taxa consisting of Random forest classifiers. Furthermore, Campylobacter gracilis was one of the FTB-enriched DAT that can aid colonization by periodontal pathogens including Porpyhromonas gingivalis in subgingival microbiome [25]. Lactobacillus gasseri was also one of the FTB-enriched DAT. It is known that Lactobacillus gasseri in vaginal microbiome can decrease early PTB risk [21, 26, 27].

We found that decisive species differentiating between two groups were mainly abundant in the FTB group, with DAT consisting of 26 FTB-enriched DAT and six 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 bacteremia [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 unit [29–30]. 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.

It 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.

Due to limited power resulting from a small number of participants, 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.

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.

PTB

Preterm birth

rRNA

ribosomal RNA

FTB

Full-term birth

DAT

Differentially abundant taxa

Ethics approval and consent to participate

The 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.

Consent for publication

The authors have read and approved the final manuscript.

Availability of data and material

Sequences for all 59 samples are deposited in the Sequence Read Archives under the project ID PRJNA985119 (https://dataview.ncbi.nlm.nih.gov/object/PRJNA985119?reviewer=6fdj2e9c8gp9vtf52n330e2h8j).

All codes used during this study are available on GitHub at https://github.com/CompbioLabUnist/Helixco_Premature.

Competing interests

The authors declare that they have no competing interests.

Funding

This paper was supported by Fund of Biomedical Research Institute, Jeonbuk National University Hospital and This paper was supported by research funds of Jeonbuk National University in 2022.

Authors’ contributions

YMH, JL, DHC, JHJ, JK, MGK, SL and JKK participated in the study design. YMH, JL, DHC, JHJ, JK, MGK, SL and JKK performed the experiment. YMH, JL, DHC, JK and JKK were involved in data collection and data interpretation. YMH, JL, DHC, JHJ, JK, MGK, SL and JKK participated in the statistical analyses. YMH, JL, DHC, JHJ, JK, MGK, SL and JKK wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgments

None.

Authors’ information

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No competing interests reported.

Supplementaryfigure1.Diversity.jpg
Supplementary Figure 1. Diversity indices. a Alpha diversity index (Faith PD). Mann-Whitney-Wilcoxon (M.W.W.) test did not find statistically significant difference between the PTB group and the FTB group. Mean values are marked with star-point. b t-SNE plot with beta diversity index (Hamming distance). PERMANOVA multivariate test did not find statistically significant difference between the PTB group and the FTB group. PTB, Preterm birth; FTB: Full-term birth

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Version 1

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You are reading this latest preprint version

Predicting preterm birth using machine learning techniques in oral microbiome

Status:

Version 1

Abstract

Figures

Background

Methods

Study design and participants

Data collection and grouping

Oral microbiome sample collection

16S rRNA gene sequencing and taxonomy assignment

Bioinformatics and statistical analysis

Machine learning prediction model development

Results

Study participant demographics

Comparison of oral microbiomes

Random forest classification

Discussion

Abbreviations

Declarations

Ethics approval and consent to participate

Consent for publication

Availability of data and material

Competing interests

Funding

Authors’ contributions

Acknowledgments

Authors’ information

References

Additional Declarations

Supplementary Files

Status:

Version 1