Associations of Central Obesity and Habitual Food Consumption with Saliva Microbiota and Its Enzymatic Profiles - a Pilot Study in Finnish Children

Overview

Journal Front Microbiol

Specialty Microbiology

Date 2024 Jan 23

PMID 38260892

Authors

Nitin Agrawal

Federico Fontana

Chiara Tarracchini

Sohvi Lommi

Marco Ventura

Christian Milani

Heli Viljakainen

Affiliations

Soon will be listed here.

Abstract

Background: Variation in diversity and composition of saliva microbiota has been linked to weight status, but findings have been inconsistent. Focusing on clinically relevant conditions such as central obesity and using advanced sequencing techniques might fill in the gaps of knowledge.

Aims: We investigated saliva microbiota with shallow metagenome sequencing in children with ( = 14) and without ( = 36) central obesity. Additionally, we examined the role of habitual food consumption on microbial enzymatic repertoire.

Methods: Data comprised 50 children (50% male) with a mean age of 14.2 (SD 0.3) years, selected from the Finnish Health in Teens (Fin-HIT) cohort. Dietary scores for consumption frequency of sweet treats (STI), dairy products (DCI) and plants (PCI) were derived based on a self-administered food frequency questionnaire. Central obesity was defined based on waist-height ratio using the cut-off 0.5. Saliva samples were subjected to whole-metagenome shotgun sequencing, and taxonomic and functional profiling was achieved with METAnnotatorX2 bioinformatics platform.

Results: Groups had an average 20 (95% CI 14-27) cm difference in waist circumference. We identified the lack of and and as putative biomarkers associated with central obesity and observed a total of 16 enzymatic reactions differing between the groups. DCI was associated with the highest number of enzyme profiles (122), followed by STI (60) and DCI (25) (Pearson correlation < 0.05). Intriguingly, STI showed a high positive/negative correlation ratio (5.09), while DCI and PCI showed low ratios (0.54 and 0.33, respectively). Thus, the main driver of enzymatic reactions was STI, and the related pathways involved nitrate metabolism induced by and among others.

Conclusion: Clinically relevant differences in central obesity were only modestly reflected in the composition of saliva microbiota. Habitual consumption of sweet treats was a strong determinant of enzymatic reactions of saliva microbiota in children with and without central obesity. The clinical relevance of these findings warrants further studies.

References

Rosier B, Moya-Gonzalvez E, Corell-Escuin P, Mira A . Isolation and Characterization of Nitrate-Reducing Bacteria as Potential Probiotics for Oral and Systemic Health. Front Microbiol. 2020; 11:555465. PMC: 7522554. DOI: 10.3389/fmicb.2020.555465. View

Deo P, Deshmukh R . Oral microbiome: Unveiling the fundamentals. J Oral Maxillofac Pathol. 2019; 23(1):122-128. PMC: 6503789. DOI: 10.4103/jomfp.JOMFP_304_18. View

Lommi S, Augusta de Oliveira Figueiredo R, Tuorila H, Viljakainen H . Frequent use of selected sugary products associates with thinness, but not overweight during preadolescence: a cross-sectional study. Br J Nutr. 2020; 124(6):631-640. PMC: 7525105. DOI: 10.1017/S0007114520001361. View

Ross R, Neeland I, Yamashita S, Shai I, Seidell J, Magni P . Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat Rev Endocrinol. 2020; 16(3):177-189. PMC: 7027970. DOI: 10.1038/s41574-019-0310-7. View

Vereecken C, Rossi S, Giacchi M, Maes L . Comparison of a short food-frequency questionnaire and derived indices with a seven-day diet record in Belgian and Italian children. Int J Public Health. 2008; 53(6):297-305. DOI: 10.1007/s00038-008-7101-6. View

McCarthy H, Jarrett K, Emmett P, Rogers I . Trends in waist circumferences in young British children: a comparative study. Int J Obes (Lond). 2004; 29(2):157-62. DOI: 10.1038/sj.ijo.0802849. View

Hansen T, Kern T, Bak E, Kashani A, Allin K, Nielsen T . Impact of a vegan diet on the human salivary microbiota. Sci Rep. 2018; 8(1):5847. PMC: 5895596. DOI: 10.1038/s41598-018-24207-3. View

Nishida A, Inoue R, Inatomi O, Bamba S, Naito Y, Andoh A . Gut microbiota in the pathogenesis of inflammatory bowel disease. Clin J Gastroenterol. 2017; 11(1):1-10. DOI: 10.1007/s12328-017-0813-5. View

Milani C, Lugli G, Fontana F, Mancabelli L, Alessandri G, Longhi G . METAnnotatorX2: a Comprehensive Tool for Deep and Shallow Metagenomic Data Set Analyses. mSystems. 2021; :e0058321. PMC: 8269244. DOI: 10.1128/mSystems.00583-21. View

10.

Raju S, Lagstrom S, Ellonen P, de Vos W, Eriksson J, Weiderpass E . Gender-Specific Associations Between Saliva Microbiota and Body Size. Front Microbiol. 2019; 10:767. PMC: 6467948. DOI: 10.3389/fmicb.2019.00767. View

11.

Wade W . Resilience of the oral microbiome. Periodontol 2000. 2021; 86(1):113-122. DOI: 10.1111/prd.12365. View

12.

Roslund M, Puhakka R, Nurminen N, Oikarinen S, Siter N, Gronroos M . Long-term biodiversity intervention shapes health-associated commensal microbiota among urban day-care children. Environ Int. 2021; 157:106811. DOI: 10.1016/j.envint.2021.106811. View

13.

Coker M, Lebeaux R, Hoen A, Moroishi Y, Gilbert-Diamond D, Dade E . Metagenomic analysis reveals associations between salivary microbiota and body composition in early childhood. Sci Rep. 2022; 12(1):13075. PMC: 9338228. DOI: 10.1038/s41598-022-14668-y. View

14.

Chrzanowska M, Suder A . Changes in central fatness and abdominal obesity in children and adolescents from Cracow, Poland 1983-2000. Ann Hum Biol. 2009; 37(2):242-52. DOI: 10.3109/03014460903193237. View

15.

Raisanen L, Lommi S, Engberg E, Kolho K, Viljakainen H . Central obesity in school-aged children increases the likelihood of developing paediatric autoimmune diseases. Pediatr Obes. 2021; 17(3):e12857. PMC: 9285017. DOI: 10.1111/ijpo.12857. View

16.

Lommi S, Manzoor M, Engberg E, Agrawal N, Lakka T, Leinonen J . The Composition and Functional Capacities of Saliva Microbiota Differ Between Children With Low and High Sweet Treat Consumption. Front Nutr. 2022; 9:864687. PMC: 9085455. DOI: 10.3389/fnut.2022.864687. View

17.

Caspi R, Altman T, Billington R, Dreher K, Foerster H, Fulcher C . The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 2013; 42(Database issue):D459-71. PMC: 3964957. DOI: 10.1093/nar/gkt1103. View

18.

Vereecken C, Maes L . A Belgian study on the reliability and relative validity of the Health Behaviour in School-Aged Children food-frequency questionnaire. Public Health Nutr. 2003; 6(6):581-8. DOI: 10.1079/phn2003466. View

19.

Parikh S, Pollock N, Bhagatwala J, Guo D, Gutin B, Zhu H . Adolescent fiber consumption is associated with visceral fat and inflammatory markers. J Clin Endocrinol Metab. 2012; 97(8):E1451-7. PMC: 3410273. DOI: 10.1210/jc.2012-1784. View

20.

Gantner B, LaFond K, Bonini M . Nitric oxide in cellular adaptation and disease. Redox Biol. 2020; 34:101550. PMC: 7235643. DOI: 10.1016/j.redox.2020.101550. View