Evolution of oral microbiota during adolescence – a longitudinal study from Finland

Evolution of oral microbiota during adolescence – a longitudinal study from Finland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Evolution of oral microbiota during adolescence – a longitudinal study from Finland Nitin Agrawal, Rebecka Ventin-Holmberg, Binu Mathew, Heli Viljakainen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8755490/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background: Obesity is increasing globally among children and adolescents. Saliva microbiota is being studied as an alternative to gut microbiota due to its links to diseases, including obesity, though findings are inconsistent. We conducted a follow-up study to examine changes in saliva microbiota composition among adolescents with stable weight compared to those with increased weight. Methods: We analyzed saliva samples from 440 adolescents in the Finnish Health in Teens cohort at ages 11.7 and 14.2, using 16S rRNA gene sequencing. Adolescents were grouped by BMI based on International Obesity Task Force criteria. We examined differences in alpha diversity, beta diversity, and composition between children who stayed in the same BMI category and those who increased their BMI. Results: The core microbiota composition varied between two timepoints, with significant alterations in adolescents with persistent overweight or obesity. Specifically, the genera Haemophilus and Neisseria increased in these individuals. The microbiota remained more stable in adolescents who were previously normal weight, even if they gained weight. Conclusions : Our findings suggest that saliva microbiota evolves during adolescence. The changes in saliva microbiome with time were more pronounced in those with persistent overweight/obesity, pointing towards chronic, low-grade inflammation and metabolic dysregulation. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Biological sciences/Microbiology salivary microbiota composition change in weight status persistent overweight/ obesity Figures Figure 1 Figure 2 Introduction The oral cavity hosts one of the richest microbial ecosystems in the human body, which is acquired during childhood and influenced by oral diseases, oral hygiene habits, and various lifestyle choices [1]. However, the development and changes over time within this ecosystem are not well understood, despite their potential to provide insights into human health and well-being [2]. Current evidence is based mainly on cross-sectional studies comparing the oral microbiome in children of different ages suggesting that the microbiome matures with age [3,4]. During the puberty, oral microbiome composition is distinct from that of adults, and recent reviews suggest that the primary succession of the oral microbiome continues until young adulthood [5]. This age dependency likely results from changes in dentition and the development of both immune and endocrine systems. Some studies indicate that the oral microbiome remains relatively stable over time, with core microbiota preserved despite events such as tooth eruption, travel, and illness [3,4]. A longitudinal study of only five individuals showed changes in microbial profiles over time [6], with intra-individual variation at the strain level generally being smaller than differences between individuals. Another study found that while alpha diversity varied between months, beta diversity remained stable in 10 individuals throughout a one-year period [7]. The only exception was bacterial load, indicated by 16S rRNA gene copy numbers, peaked at the end of the winter months [7]. However, weight changes or metabolic alterations among participants were not monitored in previous studies, even though these could interact the oral microbiome. Understanding short-term changes in the oral microbiome can provide valuable insights into pathways leading to microbiome-related conditions like obesity [8]. Our study aimed to describe longitudinal changes in saliva microbiota composition between two timepoints in a population-based sample of over 400 Finnish adolescents. We investigated whether the bacterial microbiota from the first timepoint could predict short-term weight changes; and explored intra-individual alterations of salivary microbiota in adolescents with a stable weight status, and in adolescents whose weight category changed during the follow-up time. Results Background characteristics The mean (SD) age for the participants at baseline was 11.6 (0.3), and at follow-up was 14.2 (0.3) years. The mean follow-up time was between 2.3 (0.8) years. Majority of participants had a normal weight, while 19–20% lived with overweight/obesity at both time point. The frequency of central obesity was 10%. All children with central obesity also belonged to the overweight/obesity BMI category at both timepoints. The background characteristics of the participants are shown in Table 1 . Table 1 Background characteristics of the participants. Sex, n (%) Baseline (n = 440) Follow-up (n = 473) Girls 244 (55.5) 259 (54.8) Boys 196 (44.5) 214 (45.2) Age, years 1 11.6 (0.3) 14.2 (0.3) Follow up time, years 2 1.69–2.96 BMI categories (%) Underweight (UW) 57 (12.9) 38 (8) Normal-weight (NW) 300 (68.1) 340 (71.8) Overweight/Obesity (OW) 83 (18.8) 95 (20) Central Obesity (%) Without central obesity (nonCO) 391 (88.8) 423 (89.4) With central obesity (CO) 45 (10.2) 50 (10.5) 1 mean (SD) 2 range Alpha-diversity Alpha diversity – Shannon index and Inverse Simpson index – did not differ between genders (Wilcoxon test; Shannon index p = 0.97; Inverse Simpson p = 0.73) suggesting that the genders could be analyzed together. Overview of baseline and follow-up microbiota abundance The baseline core saliva microbiota at the phylum level consisted of Firmicutes (51.7%), Bacteroidetes (18%), Proteobacteria (15.5%), and Actinobacteria (8.1%), as previously reported [ 9 ]. Corresponding microbiota composition at the follow-up was Bacteroidetes (28.7%), Firmicutes (23.9%), Proteobacteria (20.7%), and Actinobacteria (18.1%). Figure 2 illustrates the distribution of microbiota at the genus level with relative abundance across different BMI categories (UW, NW, OW) and central obesity status (CO vs nonCO) at both baseline and follow-up. Compositional differences in follow-up microbiota by obesity measures The baseline differences in microbiota composition based on BMI are previously reported [ 9 ]. Here, we observed no differences in microbiota composition between central obesity categories at baseline ( p corr > 0.9). We investigated compositional differences between BMI/central obesity categories at the follow-up timepoint. Composition analysis suggested the relative abundance of Prevotella 7 , Rothia , and Veillonella differed between the BMI categories (Fig. 2 ), with Veillonella showing the largest difference between the categories (1.9% at UW, 3.9% at NW, 16.5% at OW). Post hoc, group-wise comparisons confirmed the Veillonella composition to be higher in OW category than in UW category ( p corr = 0.056, fold change = 0.36). Additionally, the relative abundance of Actinobacteriota at the phylum and class levels was lower in OW category ( p corr = 0.077, fold change = -2.54) compared with UW ( Table 2 ). Table 2 Differences in microbiota between UW (n = 38) vs OW (n = 95) groups at follow-up. Taxon Fold Change P P corr (FDR) Bacteria_Firmicutes_Negativicutes_Veillonellales.Selenomonadales_Veillonellaceae_Veillonella 0.36 0.005 0.056 Bacteria_Firmicutes_Negativicutes_Veillonellales.Selenomonadales_Veillonellaceae 0.36 0.006 0.056 Bacteria_Firmicutes_Negativicutes 0.40 0.008 0.056 Bacteria_Firmicutes_Negativicutes_Veillonellales.Selenomonadales 0.40 0.008 0.056 Bacteria_Actinobacteriota -2.54 0.01 0.077 Bacteria_Actinobacteriota_Actinobacteria -2.55 0.01 0.077 When comparing the differences between central obesity groups, the abundance of Veillonella was noted to be twice as high in CO compared to non-CO (11.8% vs. 5.6%). In group-wise comparisons the relative abundance of Rothia ( p = 0.04; fold change = − 1.88) was lower in the CO category compared to nonCO category at all taxonomic levels. However, these differences were not retained after FDR correction ( p corr > 0.1). Alterations in the composition of the microbiota during the follow-up period according to changes in obesity metrics We investigated whether the microbiota composition changed within the same individuals over time. This analysis was segmented by changes in BMI/central obesity categories. Firstly, we compared the microbiota of the participants who remained in the NW category from baseline until follow-up. They presented no changes in microbiota composition between the two timepoints. Initially, we observed an increase in Firmicutes, at the phylum and class level, where bacterial class of Lactobacillales appeared increased, but not after applying FDR correction ( p corr > 0.19). Participants who experienced weight gain from normal weight to overweight/obesity showed no change in microbiota between the two timepoints either ( p corr > 0.7). Next, the microbiota of the participants with stable OW were compared at two timepoints. These participants experienced an increase in the abundance of Haemophilus ( p corr = 0.052; fold change = 1.75) at all taxonomic levels. Correspondingly, Neisseria increased during the follow-up, but only at the family level ( p corr = 0.1, fold change = 1.62) ( Table 3 ) . Table 3 Alterations in microbiota with time in participants with stable overweight/obesity BL OW vs FU OW (n = 59) Taxon Fold Change P P corr (FDR) Bacteria_Proteobacteria_Gammaproteobacteria_Enterobacterales_Pasteurellaceae_Haemophilus 1.75 0.004 0.052 Bacteria_Proteobacteria_Gammaproteobacteria_Enterobacterales_Pasteurellaceae 1.78 0.007 0.052 Bacteria_Proteobacteria_Gammaproteobacteria_Enterobacterales 1.78 0.007 0.052 Bacteria_Proteobacteria_Gammaproteobacteria 1.78 0.01 0.052 Bacteria_Proteobacteria 1.78 0.01 0.052 Bacteria_Proteobacteria_Gammaproteobacteria_Burkholderiales_Neisseriaceae 1.62 0.02 0.10 The category with persistent central obesity (CO) showed a decrease in Neisseriaceae at family level ( p corr = 0.04, fold change = -1.84) between the two timepoints ( Table 4 ) . Interestingly, participants who recovered from central obesity during follow-up time also showed an increase in Actinobacteriota at phylum ( p corr = 0.05, fold change = 1.57) and class level ( p corr = 0.005, fold change = 4.80) ( Table 4 ) . Table 4 Alterations in microbiota with time in participants with a) stable central obesity and b) decreased central obesity. a) BL CO vs FU CO (n = 21) Taxon Fold Change P P corr (FDR) Bacteria_Proteobacteria_Gammaproteobacteria_Burkholderiales_Neisseriaceae -1.84 0.003 0.048 Bacteria_Proteobacteria_Gammaproteobacteria_Burkholderiales -1.81 0.004 0.048 b) BL CO vs FU nonCO (n = 24) Taxon Fold Change P P corr (FDR) Bacteria_Actinobacteriota_Actinobacteria 4.80 0.0002 0.005 Bacteria_Actinobacteriota 1.57 0.006 0.05 Baseline microbiota composition was not related to short-term weight changes ( Supplementary material ). Discussion The key findings of our longitudinal study in 440 adolescents, conducted approximately two years apart, revealed significant compositional alterations saliva microbiota in individuals with persistent overweight status. Specifically, the abundance of Haemophilus and Neisseria increased during the follow-up time. In contrast, the microbiota composition remained stable in adolescents who were previously normal-weight, even if they experienced weight gain. As changes in microbiota were observed also in participants with persistent central obesity, we assume that prolonged inflammation may drive these alterations. Initially, among the 11-year-old participants, the core microbiota was predominated by six genera (in descending order): Veillonella, Prevotella 7, Streptococcus, Neisseria, Rothia , and Haemophilus that together accounted for 80% of the total abundance. After two years, the order of these genera was altered: Prevotella 7 , Rothia , Haemophilus , Neisseria , Veillonella , and Streptococcus , and together they accounted for 60% of the overall abundance, implying that some evolution concurred with time in the group level. This is a unique finding, as there are only a few longitudinal studies describing temporal changes in saliva microbiota composition in adults [ 6 , 7 ], but none concerning this age group. However, the evolution of saliva microbiota is assumed to continue until the permanent dentation [ 5 ] which is implied by cross-sectional studies as well [ 3 , 4 ]. Surprisingly, none of the genera were consistently linked with overweight or obesity at either timepoint, but the genus Veillonella caught our attention. Previously, Veillonella was reported to be less abundant in 11-year-old participants with obesity compared to those with normal-weight [ 9 ]. However, in the same individuals at 14 years, a higher abundance of Veillonella was noted in the OW category compared to the UW category. Others have demonstrated Veillonella spp. (especially V. parvula ) to associate with caries among children [ 27 ]. Further evidence shows that V. denticariosi occurs only in diseased sites, whereas V. rogosae is found only in healthy plaque [ 28 , 29 ]. Here, we did not include data on oral health, but in our previous study, we have reported an association between caries experience and development of overweight [ 30 ] suggesting that caries experiences occur prior to weight development, which could explain the higher Veillonella abundance observed here. Additionally, Actinobacteriota showed a lower abundance at both the phylum and class levels in the OW category compared to the UW category in adolescence. Of the phylum Actinobacteriota, genera Actinomyces, Rothia , and Bifidobacterium are commonly observed in the oral cavity, and their lower abundance has been associated with obesity in previous studies in children [ 31 – 33 ]. In fact, in a study by Coker et al., A. odontilyculus was inversely associated with various obesity outcomes i.e., higher fat mass, BMI z-score and presence of overweight. Similarly, the abundance of Rothia is reported to be lower in individuals living with obesity [ 34 ]. Moreover, R. mucilaginosa and R. dentocariosa were inversely associated with several obesity outcomes in children [ 33 ]. Previous studies have identified several unique associations between oral microbiota and weight outcomes in children [ 33 , 35 , 36 ]. However, these results have been rarely replicated in other studies, likely due to variations in geographical location, participant age, microbiota analysis methods, obesity measures, among other things. We were unable to replicate the previously reported differences in microbiota composition between weight categories, as reported [ 9 ] among the same individuals after two years. Additionally, we could not predict short-term weight changes with initial microbiota composition ( Supplementary material ). Therefore, we believe the microbiota signals related to excess weight are not robust as suggested before [ 37 ]. On the other hand, multiple obesity phenotypes with varying pathophysiology [ 38 ] could explain the varying microbiota signals. A follow-up study involving the same individuals enables the tracking of intra-individual patterns over time. Since changes occurred only among those with persistent overweight, this suggests that metabolic abnormalities affect the oral microbiota evolution. An increase in the abundance of Haemophilus and Neisseria was observed among adolescents with persistent overweight in a longitudinal setting. While Haemophilus is typically regarded as commensal, meaning it coexists with the host without causing harm, certain Haemophilus species can also serve as opportunistic pathogens, promoting infections beyond the oral cavity. In our pilot study of 50 adolecents using shotgun metagenomic sequencing, we found Haemophilus genus, particularly H. parainfluenzae , was the second most common species in the saliva of adolecents, and positively associated with sugar intake [ 39 ]. Correspondingly, the genus Neisseria is a commensal inhabitant in the mouth, where certain species are thought to play a role in maintaining oral health, potentially by preventing the colonization of harmful bacteria [ 40 – 42 ]. As we do not have species level information, we are not able to evaporate the clinical relevance of our findings. Interestingly, a reduction in the abundance of the Neisseria family was observed among participants with persistent central obesity, which may contrast with our findings based on BMI categories. The groups with persistent BMI-based overweight and persistent central obesity overlapped, and the number of participants was halved for the central obesity group. These groups, representing distinct obesity phenotypes as previously discussed, might account for the observed decrease in family-level abundance while permitting an increase in certain genera of Neisseria . Finally, we marked a higher abundance of Actinobacteriota at the phylum and class level in participants who recovered from the central obesity during the follow-up. In the gut, the genus Actinobacteria has been associated with leanness in children [ 43 ], and may have a similar association in the mouth as well. The finding implies that the microbiota alterations may be more pronounced when based on waist circumference than BMI. Waist-to-height is considered a superior indicator of obesity-related health risks than BMI [ 44 – 46 ], which may partly explain the different outcomes observed here. Still, this finding supports the role of low-grade inflammation driving microbiota changes, as we can assume that the inflammatory status was alleviated when the waist circumference normalized. More in-depth sequencing and additional biomarkers could reveal the precise mechanism involved in microbiota modification. A key strength of our study is the longitudinal study design with large sample size, using the same sequencing protocol. However, the study is limited by the ability to measure microbiota only at the genus level. More comprehensive sequencing, such as shotgun sequencing, would have enabled species-level identification and functional annotation of the microbiota. We addressed technical variation in our sequencing by incorporating it as a confounding factor in our analysis, along with age, which can be seen as a strength. We relied on self-reported anthropometric data at follow-up; while self-reports are recognized as a valid method in epidemiological studies [ 47 ], some may view this as a limitation. Factors such as puberty, lifestyle choices like physical activity, and diet were not addressed, as we focused to reveal among who and how oral microbiota evolved during the follow-up period. The relative nature of our microbiota data poses another limitation, particularly in a longitudinal context. Conclusion We analyzed the saliva microbiota composition in over 400 Finnish adolescents at the ages of 11 and 14. Overall, minor alterations were detected in the microbiota composition with time. The most notable changes were observed in adolescents with persistent overweight or central obesity, suggesting that inflammation might be influencing alterations in microbiota composition. However, these changes were not fully consistent, indicating variability in obesity-related signals within the saliva microbiota. Further research employing advanced sequencing techniques and additional biomarkers is needed to comprehend the underlying mechanisms. Methods Data source This study utilized material from the Finnish Health in Teens (Fin-HIT) study, a prospective cohort initially consisting of over 11,000 Finnish children. The cohort is described in detail elsewhere [10,11]. The baseline data collection, which included self-reported health behaviors, saliva sampling, and anthropometric measurements, was conducted in 2011–2014 in schools under supervision. In 2015–2016, 53% of the participants took part in the follow-up at their homes. Saliva samples Children provided unstimulated saliva samples using the Oragene ® DNA Self-Collection Kit (DNA Genotek Inc., Ottawa, ON, Canada) [12]. The saliva samples were mixed with a stabilizing reagent within the collection tube and stored at room temperature until the analysis [13]. Anthropometry Anthropometric measures, including height, weight, and waist circumference, were obtained as previously described [10]. For this study, we used two obesity measures: BMI (kg/m 2 ) and waist-to-height ratio. Initially, children were categorized into four BMI categories: underweight, normal-weight, overweight, and obesity based on the International Obesity Task Force cut-offs [14]. To ensure adequate sample size, the overweight and obesity categories were combined, leaving us with three categories: underweight (UW), normal-weight (NW), and overweight/obesity (OW). Waist-to-height ratio (WHtR) was used to define central obesity. The participants were divided into two categories: with central obesity (WHtR > = 0.5; CO), and without central obesity (WHtR < 0.5; nonCO). We monitored the changes in BMI categories between the baseline and the follow-up. Consequently, participants were classified as having increased, decreased, or stable weight status between the two time points ( Figure 1a-c ). A similar classification was applied for central obesity as well ( Figure 1d-e ). At baseline, no participants moved from UW to OW category at follow-up or vice-versa. DNA Extraction and Library Preparation The baseline saliva samples were sequenced in 2015, and the follow-up saliva samples in 2018. The procedure for 16S rRNA gene sequencing was the same both times and is detailed elsewhere [9]. In brief, intensive lysis and mechanical disruption protocol of microbial cells were performed, and genomic DNA was extracted using a CMG-1035 saliva kit and Chemagic MSM1 nucleic acid extraction robot (PerkinElmer) [9]. The V3–V4 variable regions of the 16S rRNA gene were amplified with primers [S-D-Bact-0341-b-S-17 (5′ CCTACGGGNGGCWGCAG 3′) and S-D-Bact-0785-a-A-21 (5′ GACTACHVGGGTATCTAATCC 3′)] [15]. The Truseq (TS)-tailed 1-step amplification protocol was used to amplify the 16S rRNA gene [16]. The 2 × 270 bp paired-end sequencing of the PCR amplicons was carried out on the Illumina HiSeq1500 platform (Illumina Inc., San Diego, CA, United States). Analysis of Sequencing Data We had repeated saliva measures from both baseline and follow-up from 486 participants, which were processed together (total n = 972) using the CLC Genomics Workbench (Version 20.0.4) (https://digitalinsights.qiagen.com). To ensure high-quality data for analysis, reads containing ambiguous bases, more than one mismatch in the primer sequence, fewer than 40 base pair assembly overlaps, and over 5 unaligned mismatched ends under the default parameters in CLC were removed. Assembled reads shorter than 100 bp and over 470 bp in length were excluded from the analysis. The high-quality assembled reads were aligned to the SILVA 16S rRNA database (Version v138.1) [17], clustered into Operational Taxonomic Units (OTUs) and assigned taxonomy with a cut-off value of > 99% similarity levels among sequences. Due to low sequencing-depth (< 10,000 reads) 59 samples were omitted during the analysis, leaving the total count of 913 samples (440 baseline samples and 473 follow-up samples). The sequencing generated ~49 million reads from 913 samples. The reads were categorized based on sequence similarities into 4790 Operational Taxonomic Units (OTUs), divided into 26 phyla, 41 classes, 85 orders, 124 families, and 241 genera. Statistical Analysis To illustrate the differences within the sample, the saliva microbiota alpha-diversity indices (Shannon and inverse Simpson) were tested with Wilcoxon test. The overall microbiota composition was calculated up to genus level with R packages vegan [18] and microbiome [19]. Phyloseq objects were created from both the baseline and follow-up data together for processing [20]. Generalized linear models with negative binomial distribution (glm.nb) from the MASS package [21] and Generalized Least Squares (gls) from the nlme package [22] within the mare package [23] were used to analyze differences in the microbiota composition between the weight categories. The p -values for taxa-specific differences were corrected using false discovery rate (FDR; Benjamini–Hochberg method) [24]. In our study, FDR p-value ≤ 0.1 was considered significant [25,26]. Sequencing batch (technical variation) and age were considered as confounders in all our analyses. Declarations Ethics declarations The Fin-HIT study protocol was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (decision number 169/13/03/00/10). The study protocol was conducted according to the Declaration of Helsinki. Children and their guardians have given written informed consent to combine the national health register data as part of the research material. Funding This work is supported by grants from the Paavo Nurmi Foundation, the Finnish Foundation for Cardiovascular Research, the Päivikki ja Sakari Sohlberg Foundation and the Minerva Foundation received by Heli Viljakainen. Data availability The datasets generated and analysed during the current study are available in the EGA repository, https://ega-archive.org/studies/EGAS00001003039. Author contributions NA, RVH, and HV have contributed to the conception of the study. All authors have assisted with data acquisition, analysis, and interpretation of data. All authors have drafted the work, critically revised and approve the current presentation of it. Acknowledgements We are grateful to the Fin-HIT study participants, as well as personnel at schools and researchers that contributed to the data collection. We thank Dr Sajan Raju and Dr Trine B Rounge for the conception of the study and data managers, Kris Elomaa and Dylan Pashley for the integrity of the data. Additional Information The authors declare no competing interests. References Deo, P. N. & Deshmukh, R. Oral microbiome: Unveiling the fundamentals. J Oral Maxillofac Pathol 23, 122 (2019). Baker, J. L., Mark Welch, J. L., Kauffman, K. M., McLean, J. S. & He, X. The oral microbiome: diversity, biogeography and human health. Nature Reviews Microbiology 2023 22:2 22, 89–104 (2023). Crielaard, W. et al. Exploring the oral microbiota of children at various developmental stages of their dentition in the relation to their oral health. BMC Med Genomics 4, 1–13 (2011). Mason, M. R., Chambers, S., Dabdoub, S. M., Thikkurissy, S. & Kumar, P. S. 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Lancet Diabetes Endocrinol 13, 221–262 (2025). Sarkkola, C. et al. Validity of home-measured height, weight and waist circumference among adolescents. Eur J Public Health 26, 975–977 (2016). Additional Declarations No competing interests reported. Supplementary Files 20260108Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 11 Mar, 2026 Reviewers invited by journal 11 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Editor invited by journal 06 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 04 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8755490","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605184066,"identity":"79cc96df-e61d-44ad-9eb5-1df522ce2200","order_by":0,"name":"Nitin Agrawal","email":"","orcid":"","institution":"Folkhälsans Forskningscentrum","correspondingAuthor":false,"prefix":"","firstName":"Nitin","middleName":"","lastName":"Agrawal","suffix":""},{"id":605184067,"identity":"68bbf6b8-c5f5-41f6-b14b-6898050efcae","order_by":1,"name":"Rebecka Ventin-Holmberg","email":"","orcid":"","institution":"University of Helsinki","correspondingAuthor":false,"prefix":"","firstName":"Rebecka","middleName":"","lastName":"Ventin-Holmberg","suffix":""},{"id":605184068,"identity":"a33b4e0c-f01a-4166-ac71-75c33b9fe785","order_by":2,"name":"Binu Mathew","email":"","orcid":"","institution":"University of Eastern Finland","correspondingAuthor":false,"prefix":"","firstName":"Binu","middleName":"","lastName":"Mathew","suffix":""},{"id":605184069,"identity":"75e0e36a-5c74-43d5-9a61-07b65e584e89","order_by":3,"name":"Heli Viljakainen","email":"data:image/png;base64,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","orcid":"","institution":"University of Helsinki","correspondingAuthor":true,"prefix":"","firstName":"Heli","middleName":"","lastName":"Viljakainen","suffix":""}],"badges":[],"createdAt":"2026-02-01 10:38:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8755490/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8755490/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104689868,"identity":"76182da9-40ae-409e-ab73-2f6204b82feb","added_by":"auto","created_at":"2026-03-16 06:03:23","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185115,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of participants between BMI categories (a-c) and central obesity groups (d-e) from baseline (BL) to follow-up (FU). With the red font we have indicated the groups whose microbiota composition was focused in this study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8755490/v1/af2ac8d9a68aaf40f745f658.png"},{"id":104689869,"identity":"77a6e631-a04b-4cc8-bbd4-82c3c4f9323e","added_by":"auto","created_at":"2026-03-16 06:03:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":188053,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative abundance distribution of microbiota between a) BMI categories (UW, NW, OW) and b) central obesity categories (CO vs nonCO) at baseline and follow-up. The error bars represent SD.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8755490/v1/b2072c1e0d2dd0d845792abb.png"},{"id":104783006,"identity":"75f823b0-0892-4852-ab38-5c97f8280b4a","added_by":"auto","created_at":"2026-03-17 07:58:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1076975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8755490/v1/3bcb5ba0-a516-4b7f-8ec4-873d4ef1e614.pdf"},{"id":104689870,"identity":"3ed308f0-1527-4908-8a07-51c9dd00e1c3","added_by":"auto","created_at":"2026-03-16 06:03:23","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":15426,"visible":true,"origin":"","legend":"","description":"","filename":"20260108Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8755490/v1/833dfe3c783776740640e145.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evolution of oral microbiota during adolescence – a longitudinal study from Finland","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe oral cavity hosts one of the richest microbial ecosystems in the human body, which is acquired during childhood and influenced by oral diseases, oral hygiene habits, and various lifestyle choices [1]. However, the development and changes over time within this ecosystem are not well understood, despite their potential to provide insights into human health and well-being [2]. Current evidence is based mainly on cross-sectional studies comparing the oral microbiome in children of different ages suggesting that the microbiome matures with age [3,4]. During the puberty, oral microbiome composition is distinct from that of adults, and recent reviews suggest that the primary succession of the oral microbiome continues until young adulthood [5]. This age dependency likely results from changes in dentition and the development of both immune and endocrine systems.\u003c/p\u003e\n\u003cp\u003eSome studies indicate that the oral microbiome remains relatively stable over time, with core microbiota preserved despite events such as tooth eruption, travel, and illness [3,4]. A longitudinal study of only five individuals showed changes in microbial profiles over time [6], with intra-individual variation at the strain level generally being smaller than differences between individuals. Another study found that while alpha diversity varied between months, beta diversity remained stable in 10 individuals throughout a one-year period [7]. The only exception was bacterial load, indicated by 16S rRNA gene copy numbers, peaked at the end of the winter months [7]. However, weight changes or metabolic alterations among participants were not monitored in previous studies, even though these could interact the oral microbiome.\u003c/p\u003e\n\u003cp\u003eUnderstanding short-term changes in the oral microbiome can provide valuable insights into pathways leading to microbiome-related conditions like obesity [8]. Our study aimed to describe longitudinal changes in saliva microbiota composition between two timepoints in a population-based sample of over 400 Finnish adolescents. We investigated whether the bacterial microbiota from the first timepoint could predict short-term weight changes; and explored intra-individual alterations of salivary microbiota in adolescents with a stable weight status, and in adolescents whose weight category changed during the follow-up time.\u003c/p\u003e"},{"header":"Results","content":"\n\u003ch3\u003eBackground characteristics\u003c/h3\u003e\n\u003cp\u003eThe mean (SD) age for the participants at baseline was 11.6 (0.3), and at follow-up was 14.2 (0.3) years. The mean follow-up time was between 2.3 (0.8) years. Majority of participants had a normal weight, while 19\u0026ndash;20% lived with overweight/obesity at both time point. The frequency of central obesity was 10%. All children with central obesity also belonged to the overweight/obesity BMI category at both timepoints. The background characteristics of the participants are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBackground characteristics of the participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;440)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;473)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e244 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259 (54.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e196 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214 (45.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.6 (0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.2 (0.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow up time, years\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.69\u0026ndash;2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI categories (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight (UW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal-weight (NW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e300 (68.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e340 (71.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight/Obesity (OW)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95 (20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral Obesity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout central obesity (nonCO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391 (88.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e423 (89.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith central obesity (CO)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45 (10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e1\u003c/sup\u003e mean (SD)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003csup\u003e2\u003c/sup\u003e range\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAlpha-diversity\u003c/h3\u003e\n\u003cp\u003eAlpha diversity \u0026ndash; Shannon index and Inverse Simpson index \u0026ndash; did not differ between genders (Wilcoxon test; Shannon index \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.97; Inverse Simpson \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73) suggesting that the genders could be analyzed together.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview of baseline and follow-up microbiota abundance\u003c/h2\u003e \u003cp\u003eThe baseline core saliva microbiota at the phylum level consisted of Firmicutes (51.7%), Bacteroidetes (18%), Proteobacteria (15.5%), and Actinobacteria (8.1%), as previously reported [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Corresponding microbiota composition at the follow-up was Bacteroidetes (28.7%), Firmicutes (23.9%), Proteobacteria (20.7%), and Actinobacteria (18.1%).\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the distribution of microbiota at the genus level with relative abundance across different BMI categories (UW, NW, OW) and central obesity status (CO vs nonCO) at both baseline and follow-up.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCompositional differences in follow-up microbiota by obesity measures\u003c/h3\u003e\n\u003cp\u003eThe baseline differences in microbiota composition based on BMI are previously reported [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Here, we observed no differences in microbiota composition between central obesity categories at baseline (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.9). We investigated compositional differences between BMI/central obesity categories at the follow-up timepoint.\u003c/p\u003e \u003cp\u003eComposition analysis suggested the relative abundance of \u003cem\u003ePrevotella 7\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, and \u003cem\u003eVeillonella\u003c/em\u003e differed between the BMI categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with \u003cem\u003eVeillonella\u003c/em\u003e showing the largest difference between the categories (1.9% at UW, 3.9% at NW, 16.5% at OW).\u003c/p\u003e \u003cp\u003ePost hoc, group-wise comparisons confirmed the \u003cem\u003eVeillonella\u003c/em\u003e composition to be higher in OW category than in UW category (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e = 0.056, fold change\u0026thinsp;=\u0026thinsp;0.36). Additionally, the relative abundance of Actinobacteriota at the phylum and class levels was lower in OW category (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e = 0.077, fold change = -2.54) compared with UW \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferences in microbiota between UW (n\u0026thinsp;=\u0026thinsp;38) vs OW (n\u0026thinsp;=\u0026thinsp;95) groups at follow-up.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003csub\u003ecorr\u003c/sub\u003e (FDR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBacteria_Firmicutes_Negativicutes_Veillonellales.Selenomonadales_Veillonellaceae_Veillonella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBacteria_Firmicutes_Negativicutes_Veillonellales.Selenomonadales_Veillonellaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBacteria_Firmicutes_Negativicutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBacteria_Firmicutes_Negativicutes_Veillonellales.Selenomonadales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBacteria_Actinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBacteria_Actinobacteriota_Actinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen comparing the differences between central obesity groups, the abundance of \u003cem\u003eVeillonella\u003c/em\u003e was noted to be twice as high in CO compared to non-CO (11.8% vs. 5.6%). In group-wise comparisons the relative abundance of \u003cem\u003eRothia\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04; fold change = \u0026minus;\u0026thinsp;1.88) was lower in the CO category compared to nonCO category at all taxonomic levels. However, these differences were not retained after FDR correction (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.1).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAlterations in the composition of the microbiota during the follow-up period according to changes in obesity metrics\u003c/span\u003e \u003c/p\u003e \u003cp\u003eWe investigated whether the microbiota composition changed within the same individuals over time. This analysis was segmented by changes in BMI/central obesity categories.\u003c/p\u003e \u003cp\u003e Firstly, we compared the microbiota of the participants who remained in the NW category from baseline until follow-up. They presented no changes in microbiota composition between the two timepoints. Initially, we observed an increase in Firmicutes, at the phylum and class level, where bacterial class of \u003cem\u003eLactobacillales\u003c/em\u003e appeared increased, but not after applying FDR correction (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.19). Participants who experienced weight gain from normal weight to overweight/obesity showed no change in microbiota between the two timepoints either (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e \u0026gt; 0.7).\u003c/p\u003e \u003cp\u003eNext, the microbiota of the participants with stable OW were compared at two timepoints. These participants experienced an increase in the abundance of \u003cem\u003eHaemophilus\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e = 0.052; fold change\u0026thinsp;\u003cem\u003e=\u003c/em\u003e\u0026thinsp;1.75) at all taxonomic levels. Correspondingly, \u003cem\u003eNeisseria\u003c/em\u003e increased during the follow-up, but only at the family level (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e = 0.1, fold change\u0026thinsp;=\u0026thinsp;1.62) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlterations in microbiota with time in participants with stable overweight/obesity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBL OW vs FU OW (n\u0026thinsp;=\u0026thinsp;59)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecorr\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(FDR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria_Gammaproteobacteria_Enterobacterales_Pasteurellaceae_Haemophilus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria_Gammaproteobacteria_Enterobacterales_Pasteurellaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria_Gammaproteobacteria_Enterobacterales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria_Gammaproteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria_Gammaproteobacteria_Burkholderiales_Neisseriaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe category with persistent central obesity (CO) showed a decrease in \u003cem\u003eNeisseriaceae\u003c/em\u003e at family level (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e = 0.04, fold change = -1.84) between the two timepoints \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Interestingly, participants who recovered from central obesity during follow-up time also showed an increase in Actinobacteriota at phylum (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e = 0.05, fold change\u0026thinsp;=\u0026thinsp;1.57) and class level (\u003cem\u003ep\u003c/em\u003e\u003csub\u003e\u003cem\u003ecorr\u003c/em\u003e\u003c/sub\u003e = 0.005, fold change\u0026thinsp;=\u0026thinsp;4.80) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAlterations in microbiota with time in participants with a) stable central obesity and b) decreased central obesity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ea) BL CO vs FU CO (n\u0026thinsp;=\u0026thinsp;21)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaxon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFold Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003csub\u003ecorr\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e(FDR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria_Gammaproteobacteria_Burkholderiales_Neisseriaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Proteobacteria_Gammaproteobacteria_Burkholderiales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eb) \u003cb\u003eBL CO vs FU nonCO (n\u0026thinsp;=\u0026thinsp;24)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTaxon\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFold Change\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003csub\u003e\u003cb\u003ecorr\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(FDR)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Actinobacteriota_Actinobacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBacteria_Actinobacteriota\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBaseline microbiota composition was not related to short-term weight changes (\u003cb\u003eSupplementary material\u003c/b\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe key findings of our longitudinal study in 440 adolescents, conducted approximately two years apart, revealed significant compositional alterations saliva microbiota in individuals with persistent overweight status. Specifically, the abundance of \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003eNeisseria\u003c/em\u003e increased during the follow-up time. In contrast, the microbiota composition remained stable in adolescents who were previously normal-weight, even if they experienced weight gain. As changes in microbiota were observed also in participants with persistent central obesity, we assume that prolonged inflammation may drive these alterations.\u003c/p\u003e \u003cp\u003eInitially, among the 11-year-old participants, the core microbiota was predominated by six genera (in descending order): \u003cem\u003eVeillonella, Prevotella 7, Streptococcus, Neisseria, Rothia\u003c/em\u003e, and \u003cem\u003eHaemophilus\u003c/em\u003e that together accounted for 80% of the total abundance. After two years, the order of these genera was altered: \u003cem\u003ePrevotella 7\u003c/em\u003e, \u003cem\u003eRothia\u003c/em\u003e, \u003cem\u003eHaemophilus\u003c/em\u003e, \u003cem\u003eNeisseria\u003c/em\u003e, \u003cem\u003eVeillonella\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e, and together they accounted for 60% of the overall abundance, implying that some evolution concurred with time in the group level. This is a unique finding, as there are only a few longitudinal studies describing temporal changes in saliva microbiota composition in adults [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], but none concerning this age group. However, the evolution of saliva microbiota is assumed to continue until the permanent dentation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] which is implied by cross-sectional studies as well [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSurprisingly, none of the genera were consistently linked with overweight or obesity at either timepoint, but the genus \u003cem\u003eVeillonella\u003c/em\u003e caught our attention. Previously, \u003cem\u003eVeillonella\u003c/em\u003e was reported to be less abundant in 11-year-old participants with obesity compared to those with normal-weight [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, in the same individuals at 14 years, a higher abundance of \u003cem\u003eVeillonella\u003c/em\u003e was noted in the OW category compared to the UW category. Others have demonstrated \u003cem\u003eVeillonella\u003c/em\u003e spp. (especially \u003cem\u003eV. parvula\u003c/em\u003e) to associate with caries among children [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Further evidence shows that \u003cem\u003eV. denticariosi\u003c/em\u003e occurs only in diseased sites, whereas \u003cem\u003eV. rogosae\u003c/em\u003e is found only in healthy plaque [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Here, we did not include data on oral health, but in our previous study, we have reported an association between caries experience and development of overweight [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] suggesting that caries experiences occur prior to weight development, which could explain the higher \u003cem\u003eVeillonella\u003c/em\u003e abundance observed here.\u003c/p\u003e \u003cp\u003eAdditionally, Actinobacteriota showed a lower abundance at both the phylum and class levels in the OW category compared to the UW category in adolescence. Of the phylum Actinobacteriota, genera \u003cem\u003eActinomyces, Rothia\u003c/em\u003e, and \u003cem\u003eBifidobacterium\u003c/em\u003e are commonly observed in the oral cavity, and their lower abundance has been associated with obesity in previous studies in children [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In fact, in a study by Coker et al., \u003cem\u003eA. odontilyculus\u003c/em\u003e was inversely associated with various obesity outcomes i.e., higher fat mass, BMI z-score and presence of overweight. Similarly, the abundance of \u003cem\u003eRothia\u003c/em\u003e is reported to be lower in individuals living with obesity [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, \u003cem\u003eR. mucilaginosa\u003c/em\u003e and \u003cem\u003eR. dentocariosa\u003c/em\u003e were inversely associated with several obesity outcomes in children [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious studies have identified several unique associations between oral microbiota and weight outcomes in children [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, these results have been rarely replicated in other studies, likely due to variations in geographical location, participant age, microbiota analysis methods, obesity measures, among other things. We were unable to replicate the previously reported differences in microbiota composition between weight categories, as reported [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] among the same individuals after two years. Additionally, we could not predict short-term weight changes with initial microbiota composition (\u003cb\u003eSupplementary material\u003c/b\u003e). Therefore, we believe the microbiota signals related to excess weight are not robust as suggested before [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. On the other hand, multiple obesity phenotypes with varying pathophysiology [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] could explain the varying microbiota signals. A follow-up study involving the same individuals enables the tracking of intra-individual patterns over time. Since changes occurred only among those with persistent overweight, this suggests that metabolic abnormalities affect the oral microbiota evolution.\u003c/p\u003e \u003cp\u003eAn increase in the abundance of \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003eNeisseria\u003c/em\u003e was observed among adolescents with persistent overweight in a longitudinal setting. While \u003cem\u003eHaemophilus\u003c/em\u003e is typically regarded as commensal, meaning it coexists with the host without causing harm, certain \u003cem\u003eHaemophilus\u003c/em\u003e species can also serve as opportunistic pathogens, promoting infections beyond the oral cavity. In our pilot study of 50 adolecents using shotgun metagenomic sequencing, we found \u003cem\u003eHaemophilus\u003c/em\u003e genus, particularly \u003cem\u003eH. parainfluenzae\u003c/em\u003e, was the second most common species in the saliva of adolecents, and positively associated with sugar intake [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Correspondingly, the genus \u003cem\u003eNeisseria\u003c/em\u003e is a commensal inhabitant in the mouth, where certain species are thought to play a role in maintaining oral health, potentially by preventing the colonization of harmful bacteria [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. As we do not have species level information, we are not able to evaporate the clinical relevance of our findings.\u003c/p\u003e \u003cp\u003eInterestingly, a reduction in the abundance of the \u003cem\u003eNeisseria\u003c/em\u003e family was observed among participants with persistent central obesity, which may contrast with our findings based on BMI categories. The groups with persistent BMI-based overweight and persistent central obesity overlapped, and the number of participants was halved for the central obesity group. These groups, representing distinct obesity phenotypes as previously discussed, might account for the observed decrease in family-level abundance while permitting an increase in certain genera of \u003cem\u003eNeisseria\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFinally, we marked a higher abundance of Actinobacteriota at the phylum and class level in participants who recovered from the central obesity during the follow-up. In the gut, the genus \u003cem\u003eActinobacteria\u003c/em\u003e has been associated with leanness in children [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and may have a similar association in the mouth as well. The finding implies that the microbiota alterations may be more pronounced when based on waist circumference than BMI. Waist-to-height is considered a superior indicator of obesity-related health risks than BMI [\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which may partly explain the different outcomes observed here. Still, this finding supports the role of low-grade inflammation driving microbiota changes, as we can assume that the inflammatory status was alleviated when the waist circumference normalized. More in-depth sequencing and additional biomarkers could reveal the precise mechanism involved in microbiota modification.\u003c/p\u003e \u003cp\u003eA key strength of our study is the longitudinal study design with large sample size, using the same sequencing protocol. However, the study is limited by the ability to measure microbiota only at the genus level. More comprehensive sequencing, such as shotgun sequencing, would have enabled species-level identification and functional annotation of the microbiota. We addressed technical variation in our sequencing by incorporating it as a confounding factor in our analysis, along with age, which can be seen as a strength. We relied on self-reported anthropometric data at follow-up; while self-reports are recognized as a valid method in epidemiological studies [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], some may view this as a limitation. Factors such as puberty, lifestyle choices like physical activity, and diet were not addressed, as we focused to reveal among who and how oral microbiota evolved during the follow-up period. The relative nature of our microbiota data poses another limitation, particularly in a longitudinal context.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe analyzed the saliva microbiota composition in over 400 Finnish adolescents at the ages of 11 and 14. Overall, minor alterations were detected in the microbiota composition with time. The most notable changes were observed in adolescents with persistent overweight or central obesity, suggesting that inflammation might be influencing alterations in microbiota composition. However, these changes were not fully consistent, indicating variability in obesity-related signals within the saliva microbiota. Further research employing advanced sequencing techniques and additional biomarkers is needed to comprehend the underlying mechanisms.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cu\u003eData source\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThis study utilized material from the Finnish Health in Teens (Fin-HIT) study, a prospective cohort initially consisting of over 11,000 Finnish children. The cohort is described in detail elsewhere\u0026nbsp;[10,11]. The baseline data collection, which included self-reported health behaviors, saliva sampling, and anthropometric measurements, was conducted in 2011\u0026ndash;2014 in schools under supervision. In 2015\u0026ndash;2016, 53% of the participants took part in the follow-up at their homes.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSaliva samples\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eChildren provided unstimulated saliva samples using the Oragene\u003csup\u003e\u0026reg;\u003c/sup\u003e DNA Self-Collection Kit (DNA Genotek Inc., Ottawa, ON, Canada)\u0026nbsp;[12]. The saliva samples were mixed with a stabilizing reagent within the collection tube and stored at room temperature until the analysis\u0026nbsp;[13].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAnthropometry\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAnthropometric measures, including height, weight, and waist circumference, were obtained as previously described\u0026nbsp;[10]. For this study, we used two obesity measures: BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) and waist-to-height ratio. Initially, children were categorized into four BMI categories: underweight, normal-weight, overweight, and obesity based on the International Obesity Task Force cut-offs\u0026nbsp;[14].\u0026nbsp;To ensure adequate sample size, the overweight and obesity categories were combined, leaving us with three categories: underweight (UW), normal-weight (NW), and overweight/obesity (OW).\u003c/p\u003e\n\u003cp\u003eWaist-to-height ratio (WHtR) was used to define central obesity. The participants were divided into two categories: with central obesity (WHtR \u0026gt; = 0.5; CO), and without central obesity (WHtR \u0026lt; 0.5; nonCO).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe monitored the changes in BMI categories between the baseline and the follow-up. Consequently, participants were classified as having increased, decreased, or stable weight status between the two time points (\u003cstrong\u003eFigure 1a-c\u003c/strong\u003e). A similar classification was applied for central obesity as well (\u003cstrong\u003eFigure 1d-e\u003c/strong\u003e). At baseline, no participants moved from UW to OW category at follow-up or vice-versa. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDNA Extraction and Library Preparation\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe baseline saliva samples were sequenced in 2015, and the follow-up saliva samples in 2018. The procedure for 16S rRNA gene sequencing was the same both times and is detailed elsewhere\u0026nbsp;[9]. In brief, intensive lysis and mechanical disruption protocol of microbial cells were performed, and genomic DNA was extracted using a CMG-1035 saliva kit and Chemagic MSM1 nucleic acid extraction robot (PerkinElmer)\u0026nbsp;[9]. The V3\u0026ndash;V4 variable regions of the 16S rRNA gene were amplified with primers [S-D-Bact-0341-b-S-17 (5\u0026prime; CCTACGGGNGGCWGCAG 3\u0026prime;) and S-D-Bact-0785-a-A-21 (5\u0026prime; GACTACHVGGGTATCTAATCC 3\u0026prime;)]\u0026nbsp;[15]. The Truseq (TS)-tailed 1-step amplification protocol was used to amplify the 16S rRNA gene\u0026nbsp;[16]. The 2 \u0026times; 270 bp paired-end sequencing of the PCR amplicons was carried out on the Illumina HiSeq1500 platform (Illumina Inc., San Diego, CA, United States).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAnalysis of Sequencing Data\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eWe had repeated saliva measures from both baseline and follow-up from 486 participants, which \u0026nbsp; were processed together (total n = 972) using the CLC Genomics Workbench (Version 20.0.4) (https://digitalinsights.qiagen.com). To ensure high-quality data for analysis, reads containing ambiguous bases, more than one mismatch in the primer sequence, fewer than 40 base pair assembly overlaps, and over 5 unaligned mismatched ends under the default parameters in CLC were removed. Assembled reads shorter than 100 bp and over 470\u0026thinsp;bp in length were excluded from the analysis. The high-quality assembled reads were aligned to the SILVA 16S rRNA database (Version v138.1) [17], clustered into Operational Taxonomic Units (OTUs) and assigned taxonomy with a cut-off value of \u0026gt;\u0026thinsp;99% similarity levels among sequences. Due to low sequencing-depth (\u0026lt; 10,000 reads) 59 samples were omitted during the analysis, leaving the total count of 913 samples (440 baseline samples and 473 follow-up samples).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe sequencing generated ~49 million reads from 913 samples. The reads were categorized based on sequence similarities into 4790 Operational Taxonomic Units (OTUs), divided into 26 phyla, 41 classes, 85 orders, 124 families, and 241 genera.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStatistical Analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eTo illustrate the differences within the sample, the saliva microbiota alpha-diversity indices (Shannon and inverse Simpson) were tested with Wilcoxon test. The overall microbiota composition was calculated up to genus level with R packages vegan [18] and microbiome [19].\u0026nbsp;Phyloseq objects were created from both the baseline and follow-up data together for processing [20]. Generalized linear models with negative binomial distribution (glm.nb) from the MASS package [21] and Generalized Least Squares (gls) from the nlme package [22] within the mare package\u0026nbsp;[23] were used to analyze differences in the microbiota composition between the weight categories. The \u003cem\u003ep\u003c/em\u003e-values for taxa-specific differences were corrected using false discovery rate (FDR; Benjamini\u0026ndash;Hochberg method) [24]. In our study, FDR \u003cem\u003ep-value\u003c/em\u003e \u0026le; 0.1 was considered significant [25,26]. Sequencing batch (technical variation) and age were considered as confounders in all our analyses.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cu\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe Fin-HIT study protocol was approved by the Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (decision number 169/13/03/00/10). The study protocol was conducted according to the Declaration of Helsinki. Children and their guardians have given written informed consent to combine the national health register data as part of the research material.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by grants from the Paavo Nurmi Foundation, the Finnish Foundation for Cardiovascular Research, the P\u0026auml;ivikki ja Sakari Sohlberg Foundation and the Minerva Foundation received by Heli Viljakainen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are available in the EGA repository, \u0026nbsp;https://ega-archive.org/studies/EGAS00001003039.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA, RVH, and HV have contributed to the conception of the study. All authors have assisted with data acquisition, analysis, and interpretation of data. All authors have drafted the work, critically revised and approve the current presentation of it.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the Fin-HIT study participants, as well as personnel at schools and researchers that contributed to the data collection. We thank Dr Sajan Raju and Dr Trine B Rounge for the conception of the study and data managers, Kris Elomaa and Dylan Pashley for the integrity of the data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eDeo, P. N. \u0026amp; Deshmukh, R. 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Overweight and obesity status in pregnant women are related to intestinal microbiota and serum metabolic and inflammatory profiles. \u003cem\u003eClin Nutr\u003c/em\u003e 37, 1955\u0026ndash;1966 (2018).\u003c/li\u003e\n \u003cli\u003eFrid, P. \u003cem\u003eet al.\u003c/em\u003e Salivary Oral Microbiome of Children With Juvenile Idiopathic Arthritis: A Norwegian Cross-Sectional Study. \u003cem\u003eFront Cell Infect Microbiol\u003c/em\u003e 10, (2020).\u003c/li\u003e\n \u003cli\u003eKanasi, E. \u003cem\u003eet al.\u003c/em\u003e Clonal analysis of the microbiota of severe early childhood caries. \u003cem\u003eCaries Res\u003c/em\u003e 44, 485\u0026ndash;497 (2010).\u003c/li\u003e\n \u003cli\u003eZhou, P., Manoil, D., Belibasakis, G. N. \u0026amp; Kotsakis, G. A. Veillonellae: Beyond Bridging Species in Oral Biofilm Ecology. \u003cem\u003eFrontiers in Oral Health\u003c/em\u003e 2, 774115 (2021).\u003c/li\u003e\n \u003cli\u003eArif, N., Sheehy, E. C., Do, T. \u0026amp; Beighton, D. 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Looking for a Signal in the Noise: Revisiting Obesity and the Microbiome. \u003cem\u003emBio\u003c/em\u003e 7, (2016).\u003c/li\u003e\n \u003cli\u003eAcosta, A. \u003cem\u003eet al.\u003c/em\u003e Selection of Antiobesity Medications Based on Phenotypes Enhances Weight Loss: A Pragmatic Trial in an Obesity Clinic. \u003cem\u003eObesity (Silver Spring)\u003c/em\u003e 29, 662\u0026ndash;671 (2021).\u003c/li\u003e\n \u003cli\u003eAgrawal, N. \u003cem\u003eet al.\u003c/em\u003e Associations of central obesity and habitual food consumption with saliva microbiota and its enzymatic profiles - a pilot study in Finnish children. \u003cem\u003eFront Microbiol\u003c/em\u003e 14, (2024).\u003c/li\u003e\n \u003cli\u003eFukuda, S. \u003cem\u003eet al.\u003c/em\u003e Commensal Neisseria Inhibit Porphyromonas Gingivalis Invasion of Gingival Epithelial Cells. \u003cem\u003eOral Health Prev Dent\u003c/em\u003e 22, 609\u0026ndash;616 (2024).\u003c/li\u003e\n \u003cli\u003eDemirci, M. 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Saliva microbiota is being studied as an alternative to gut microbiota due to its links to diseases, including obesity, though findings are inconsistent. We conducted a follow-up study to examine changes in saliva microbiota composition among adolescents with stable weight compared to those with increased weight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe analyzed saliva samples from 440 adolescents in the Finnish Health in Teens cohort at ages 11.7 and 14.2, using 16S rRNA gene sequencing. Adolescents were grouped by BMI based on International Obesity Task Force criteria. We examined differences in alpha diversity, beta diversity, and composition between children who stayed in the same BMI category and those who increased their BMI.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe core microbiota composition varied between two timepoints, with significant alterations in adolescents with persistent overweight or obesity. Specifically, the genera \u003cem\u003eHaemophilus\u003c/em\u003e and \u003cem\u003eNeisseria\u003c/em\u003eincreased in these individuals. The microbiota remained more stable in adolescents who were previously normal weight, even if they gained weight.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Our findings suggest that saliva microbiota evolves during adolescence. The changes in saliva microbiome with time were more pronounced in those with persistent overweight/obesity, pointing towards chronic, low-grade inflammation and metabolic dysregulation.\u003c/p\u003e","manuscriptTitle":"Evolution of oral microbiota during adolescence – a longitudinal study from Finland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 06:03:18","doi":"10.21203/rs.3.rs-8755490/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"90856517212599719148127786938639569713","date":"2026-05-06T13:30:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T19:06:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"3427915891286566585220913801160053375","date":"2026-03-16T16:38:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112957598363495811370278648552592470137","date":"2026-03-11T20:11:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-11T11:52:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-11T11:40:12+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-06T08:30:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T19:58:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-04T12:38:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d9a056ba-c55a-4a75-948c-38fd740c7e9e","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"90856517212599719148127786938639569713","date":"2026-05-06T13:30:12+00:00","index":45,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64406310,"name":"Health sciences/Diseases"},{"id":64406311,"name":"Health sciences/Health care"},{"id":64406312,"name":"Health sciences/Medical research"},{"id":64406313,"name":"Biological sciences/Microbiology"}],"tags":[],"updatedAt":"2026-03-16T06:03:18+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 06:03:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8755490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8755490","identity":"rs-8755490","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}