- Research
- Open access
- Published:
Genome-wide association meta-analysis identifies two novel loci associated with dental caries
BMC Oral Health volume 24, Article number: 1003 (2024)
Abstract
Background
Tooth loss significantly impacts oral function and overall health deterioration. Dental caries and periodontal disease are major contributors to tooth loss, emphasizing the critical need to prevent these conditions. Genetic studies have played a crucial role in deepening our understanding of the underlying mechanisms of these diseases. While large-scale genome-wide association studies (GWAS) on dental caries and periodontal disease have been conducted extensively, research focusing on Asian populations remains limited. Given substantial genetic and lifestyle variations across ethnicities, conducting studies across diverse populations is imperative. This study aimed to uncover new insights into the genetic mechanisms of these diseases, contributing to broader knowledge and potential targeted interventions.
Methods
We conducted a GWAS using genome data from 45,525 Japanese individuals, assessing their self-reported history of dental caries and periodontal disease. Additionally, we performed a meta-analysis by integrating our results with those from a previous large-scale GWAS predominantly involving European populations.
Results
While no new loci associated with periodontal disease were identified, we discovered two novel loci associated with dental caries. The lead variants of these loci were intron variant rs10974056 in GLIS3 and intron variant rs4801882 in SIGLEC5.
Conclusion
Our study findings are anticipated to advance understanding of the underlying mechanisms of dental caries and periodontal disease. Thes insights may inform better management strategies for patients affected by these conditions.
Background
The oral cavity plays important roles in verbal and nonverbal communication, diet intake, conversation, and physical appearance. Oral functions include chewing, formation and transfer of food masses, swallowing, articulation, taste, tactile sensation, and saliva secretion, all of which are basic functions necessary for a healthy life. Undoubtedly, teeth play a crucial role in oral functions. However, a decrease in the number of remaining teeth can influence food selection behavior, often leading to the avoidance of foods that are difficult to chew. This raises the risk of developing lifestyle-related diseases and malnutrition [1, 2]. Hence, tooth loss may lead not only to a decrease in oral function but also to deterioration in general health [3, 4]. Caries and periodontal diseases are major factors responsible for tooth loss, and oral bacteria have been reported to cause these diseases (e.g., periodontal disease, Porphyromonas gingivalis; caries, Streptococcus mutans) [5, 6]. The management of periodontal disease via attempted removal of bacteria is only partially effective for periodontitis and fails in high-risk individuals [7]. Thus, it is crucial to consider the interactions among the host response, genetic and environmental factors, and microbiology. A large-scale genome-wide association study (GWAS) has been conducted on the onset of these diseases [8]. However, such studies in Asian populations are limited. Therefore, we conducted a GWAS using data from Japanese populations to obtain new insights into the mechanisms of tooth decay and periodontal disease.
Methods
Study participants
Data were sourced from Japanese direct-to-consumer (DTC) genetic testing services “Genequest ALL,” “Euglena MyHealth,” and “HealthData Lab,” which are provided by Genequest Inc. (Tokyo, Japan), Euglena Co., Ltd. (Tokyo, Japan) and Yahoo! Japan Corporation (Tokyo, Japan), respectively. We included individuals aged ≥ 18 years who agreed to participate in the study. The participants completed online questionnaires about sociodemographic factors, lifestyle habits, and medical history.
DNA sampling, genotyping, quality control, and genotype imputation
Collection and stabilization of saliva samples were conducted utilizing either the Oragene DNA Collection Kit (DNA Genotek Inc., Ottawa, Ontario, Canada) or GeneFix Saliva DNA Collection Kit (Cell Projects Ltd., Harrietsham, Kent, UK). Genotyping was executed employing various Illumina Infinium BeadChips: Global Screening Array v1 + Custom BeadChip (Illumina, San Diego, CA, USA), which contains 704,589 markers; Global Screening Array-24 v3.0 + Custom BeadChip, which contains 655,471 markers; HumanCore-12 + Custom BeadChip, which contains 302,073 markers; HumanCore-24 + Custom BeadChip, which contains 309,725 markers; and InfiniumCore-24 + Custom BeadChip, which contains 308,500 markers. Participants were divided into two groups based on the chip used because of the differences in the marker sets across these genotyping chips. Population A used the Global Screening Array v1 + and Global Screening Array-24 v3.0 + Custom BeadChips (595,105 common markers), while population B utilized the HumanCore-12+, HumanCore-24 + and InfiniumCore-24 + Custom BeadChips (289,930 common markers). Quality control and association analysis were conducted separately for each cohort.
The quality control criteria for variant filtering included: call rate per variant < 0.95, Hardy–Weinberg equilibrium exact test p-value < 1 × 10− 6, minor allele frequency < 0.01, and exclusion of variants not located on autosomes. For participant filtering, criteria included: inconsistent sex information between the genotype and the questionnaire, call rate per subject < 0.95, closely related pairs identified via the identity-by-descent method (PI_HAT > 0.1875), and estimated non-Japanese ancestry. Quality control analyses were performed using PLINK [9, 10] (version 1.90b3.42) and Eigensoft [11] (version 6.1.3) software. To predict non-Japanese ancestry, a principal component analysis (PCA) was conducted using genomic data of African (YRI), European (CEU), Chinese (CHB), and Japanese (JPT) populations from the International HapMap Project [12]. The data were then visualized through PCA plots, and only data belonging to the Japanese cluster were used for further analyses.
For genome-wide genotype imputation, a pre-phasing/imputation stepwise approach was applied using EAGLE2 [13] (version 2.4) and Minimac3 [14] (version 2.0.1). The imputation reference panel was 1000 Genomes Phase 3 [15] (version 5). Variants demonstrating low imputation quality (R2 < 0.3) and minor allele frequency (< 0.01) were excluded from further analyses. Finally, we used dosage data for 8,306,085 variants for the following GWAS.
Phenotype measurement
Phenotype data on sociodemographic factors, lifestyle habits, and medical history were collected from study participants via online questionnaires. These questionnaires included a list of medical conditions and diseases, asking participants to indicate any they currently have or have had in the past. The individuals who checked for “dental caries” were treated as cases, whereas those who did not check for “dental caries” were treated as controls. Similarly, those who checked for “periodontal disease” were treated as cases, and those who did not check for “periodontal disease” were treated as controls.
Genome-wide association and meta-analysis
We examined the associations between genetic variant dosages and the prevalence of dental caries and periodontal disease using a logistic regression model, assuming additive genetic effects. For each population, a genome-wide association study (GWAS) was conducted with adjustments for age, sex, and the first five principal components using PLINK (version 2.00a3). We combined the statistical data from both populations using a fixed-effects model and the inverse-variance weighting method with METAL software [16] (version 2011-03-25).
Genomic heritability was estimated from the GWAS summary statistics using LD Score Regression (LDSC) [17]. The analysis utilized LD score and weight files from the 1000 Genomes Phase 3 East Asian (EAS) population reference. To identify genes significantly associated with the phenotypes, we conducted a gene-based association analysis using MAGMA (Multi-marker Analysis of GenoMic Annotation) [18]. The GWAS summary statistics were annotated with gene information from the GENCODE v19 reference genome [19]. Genes identified with p-value less than 0.001 in the MAGMA analysis were further analyzed for functional annotation and pathway enrichment using Metascape [20].
Additionally, we conducted a meta-analysis with GWAS summary statistics from a previously reported study [8] using a p-value based and sample-size weighting method with the METAL software [16]. We considered the sum of Decayed, Missing, and Filled tooth surfaces (DMFS) and dentures in the previously reported study as indicators of dental caries, whereas periodontitis and loose teeth in the pre-reported study were considered as indicators of periodontal disease. The previous study utilized data from the Gene-Lifestyle Interactions in Dental Endpoints (GLIDE) consortium, UK Biobank (UKB), and BioBank Japan (BBJ). In the GLIDE and BBJ datasets, phenotypes were assessed by trained assessors or dental professionals, while in the UKB dataset, phenotypes were self-reported. Dental caries in GLIDE were evaluated using the DMFS index, resulting in continuous data with a sample size of 26,792. For periodontal disease in GLIDE, there were 17,353 cases and 28,210 controls. In UKB, there were 77,714 cases and 383,317 controls for dental caries, and 18,979 cases and 442,052 controls for periodontal disease. The BBJ dataset did not include dental caries data, but it included 3,219 cases and 209,234 controls for periodontal disease.
The significance threshold in the GWAS was set at a genome-wide significance level of p < 5 × 10⁻⁸ to account for multiple testing. For newly identified loci, region plots were generated using LocusZoom.
Results
Genome-wide association analysis of dental caries and periodontal disease
The characteristics of the participants are presented in Table 1 and S1. This study included 30,859 cases and 14,666 controls for dental caries and 7,059 cases and 38,466 controls for periodontal disease. The number of females in the dental caries group was higher than that in the control group. The proportions of cases of dental caries and periodontal diseases by age group are presented in Table S2. Compared to the 2022 dental survey [21] results in Japan, where over 90% of individuals aged over 30 had experienced dental caries, our study indicates a prevalence of around 70%. Similarly, for periodontal disease, previous surveys reported that over 50% of older age groups were affected, whereas our study indicates a prevalence of about 30%. This discrepancy may be due to the fact that the previous survey involved examinations by dental professionals, whereas our current survey relied on self-reports, potentially leading to an underestimation of cases.
We performed a GWAS for each population and a meta-analysis of dental caries and periodontal disease (Figure S1-S6). We identified several associated loci at the genome-wide suggestive level (p-value < 1 × 10− 5), as shown in Tables S3 and S4. However, no associated loci were found at the genome-wide significance level (p-value < 5 × 10− 8). The top-associated variants were rs140784657 in the intergenic region for dental caries and rs12624579 in the SYNDIG1 intron for periodontal disease. All 47 lead variants significantly associated with dental caries in a pre-reported GWAS [8] showed p-values > 0.05 in our study (Table S5). This discrepancy could be attributed to the smaller sample size in our study and the frequency differences between European and Japanese populations. Similarly, the previously significant intron variant rs12461706 in the SIGLEC5 gene, associated with periodontal disease in the same GWAS [8], was not significant in our study (p-value = 0.466, Table S6). One possible reason for this is the considerable frequency difference of rs12461706 between European and Japanese populations (0.40 and 0.062, respectively).
We estimated the SNP-based heritability (h²) of our phenotypes from the GWAS summary statistics using linkage-disequilibrium score regression (LDSR) [17]. The total observed-scale heritability was found to be h2 = 0.0258 ± 0.0155 for dental caries and h2 = 0.0519 ± 0.0182 for periodontal disease. We conducted a gene-based association analysis using MAGMA [18] (Table S7 and S8). In the gene-based test, only the association between ADGRL2 and dental caries had a q-value of false discovery rate (FDR) < 0.05. Genes with p-values < 0.001 in the MAGMA analysis were further examined using Metascape [20] for functional annotation and pathway enrichment. Functional annotation revealed several enriched biological processes and pathways (refer to Table S9 and S10).
Subsequently, a meta-analysis was performed using GWAS summary statistics from both our study and a pre-reported study [8]. We identified two novel loci associated with dental caries (Figs. 1a and 2a and b; Table 2 and S11). The lead variants of the associated loci were intron variant rs10974056 in GLI-Similar 3 (GLIS3) and intron variant rs4801882 in Sialic Acid-Binding Immunoglobulin-Type Lectin 5 (SIGLEC5). However, for periodontal disease, only previously known associated loci were identified (Fig. 1b, Table S12). The locus associated with periodontal disease on chromosome 19 was not replicated in the Japanese population (our study and Biobank Japan (BBJ) in the previously reported study). This indicates that this locus may only be associated with periodontal disease in European populations.
Discussion
In GWAS of dental caries in the present study, no loci reached the genome-wide significance level; however, through a meta-analysis of past large-scale studies, we identified two novel loci associated with dental caries. The lead variants of the associated loci were intron variant rs10974056 in GLIS3 and intron variant rs4801882 in SIGLEC5. Glis3 protein, a Krüppel-like zinc finger transcription factor, plays a critical role in the regulation of pancreatic β-cell development and insulin gene expression in mice [22]. In humans, GLIS3 gene polymorphism is associated with diabetes [23]; additionally, diabetes can increase the risk of developing dental caries and periodontal disease owing to reduced salivary secretion [24]. GLIS3 expression has been reported to be associated with dental anomalies, although not statistically significant25. Therefore, the association between GLIS3 and dental caries identified in this study can be considered robust. SIGLEC5 is a protein expressed on the surface of leukocytes that recognizes glycans containing sialic acid, playing a role in suppressing immune response [26]. Research indicates that bacteria with sialic glycans can bind to SIGLEC5, potentially evading immune response [27]. SIGLEC5 gene polymorphisms have been associated with periodontitis in both European and East Asian populations in previous studies28,29. R. Mueller et al. suggested that intron variants in SIGLEC5 gene influence its expression [30]. We identified a novel locus associated with dental caries, specifically intron variant rs4801882 in SIGLEC5. This SNP is also associated with periodontal disease (see Table S5). Considering this information, rs4801882 may influence the expression level of SIGLEC5 protein, potentially disrupting the oral bacterial ecosystem and affecting the survival of bacteria with sialic acid.
For periodontal disease, we were unable to identify any novel loci, and only one significant locus was not replicated in the Japanese population. However, previous studies have reported that the heritability of periodontitis is approximately 50%31,32. The reasons for this discrepancy might include the small effect of a single variant, the possibility that rare variants not targeted in our study are strongly associated, genetic and environmental differences between ethnicities, and inaccuracies in self-reporting because individuals do not realize that they have periodontal diseases. Considering these possibilities, further detailed investigations are necessary to clarify the association between genetic factors and periodontal disease.
Our study had some limitations. Our phenotypic data were collected via web-based self-reports, which may be subject to recall bias. Furthermore, relying on self-reports means that the severity of symptoms was not assessed, treating both severe and mild cases equally.
Conclusion
We identified two novel loci associated with dental caries: the lead variants were intron variant rs10974056 in GLIS3 and intron variant rs4801882 in SIGLEC5. However, we did not identify any novel loci for periodontal disease, and only one significant locus did not replicate in the Japanese population. These findings are expected to contribute to understanding the mechanisms underlying dental caries and periodontal disease.
Data availability
The published article and its additional files include all the data analyzed in this study. Summary statistics of the GWAS analyses have been deposited in Zenodo (https://doi.org/10.5281/zenodo.10906386). All other data are available from the corresponding author upon reasonable request.
Abbreviations
- BBJ:
-
Biobank Japan
- DMFS:
-
Decayed, Missing, and Filled tooth surfaces
- DTC:
-
direct-to-consumer
- GLIDE:
-
Gene-Lifestyle Interactions in Dental Endpoints
- GWAS:
-
genome-wide association study
- UKB:
-
UK Biobank
References
Watson S, McGowan L, McCrum LA, Cardwell CR, McGuinness B, Moore C, et al. The impact of dental status on perceived ability to eat certain foods and nutrient intakes in older adults: cross-sectional analysis of the UK National Diet and Nutrition Survey 2008–2014. Int J Behav Nutr Phys Act. 2019;16(1):43. https://doi.org/10.1186/s12966-019-0803-8
Sheiham A, Steele JG, Marcenes W, Finch S, Walls AW. The impact of oral health on stated ability to eat certain foods; findings from the National Diet and Nutrition Survey of Older people in Great Britain. Gerodontology. 1999;16(1):11–20. https://doi.org/10.1111/j.1741-2358.1999.00011.x
Watanabe Y, Okada K, Kondo M, Matsushita T, Nakazawa S, Yamazaki Y. Oral health for achieving longevity. Geriatr Gerontol Int. 2020;20(6):526–38. https://doi.org/10.1111/ggi.13921
Hakeem FF, Bernabé E, Sabbah W. Association between oral health and frailty: a systematic review of longitudinal studies. Gerodontology. 2019;36(3):205–15. https://doi.org/10.1111/ger.12406
Loesche WJ. Role of Streptococcus mutans in human dental decay. Microbiol Rev. 1986;50(4):353–80. https://doi.org/10.1128/mr.50.4.353-380.1986
Murakami S, Mealey BL, Mariotti A, Chapple ILC. Dental plaque-induced gingival conditions. J Clin Periodontol. 2018;45(Suppl 20):S17–27. https://doi.org/10.1111/jcpe.12937
Van Dyke TE, Bartold PM, Reynolds EC. The Nexus between Periodontal inflammation and Dysbiosis. Front Immunol. 2020;11:511. https://doi.org/10.3389/fimmu.2020.00511
Shungin D, Haworth S, Divaris K, Agler CS, Kamatani Y, Keun Lee M, et al. Genome-wide analysis of dental caries and periodontitis combining clinical and self-reported data. Nat Commun. 2019;10:2773. https://doi.org/10.1038/s41467-019-10630-1
Second-generation PLINK. rising to the challenge of larger and richer datasets | GigaScience. Oxford Academic. https://academic.oup.com/gigascience/article/4/1/s13742-015-0047-8/2707533. Accessed November 17, 2021.
Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D et al. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses: The American Journal of Human Genetics. https://www.cell.com/ajhg/fulltext/S0002-9297(07)61352-4. Accessed November 17, 2021.
Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9. https://doi.org/10.1038/ng1847
International HapMap Consortium. The International HapMap Project. Nature. 2003;426(6968):789–96. https://doi.org/10.1038/nature02168
Loh PR, Danecek P, Palamara PF, Fuchsberger C, Reshef A, Finucane YK. Reference-based phasing using the Haplotype Reference Consortium panel. Nat Genet. 2016;48(11):1443–8. https://doi.org/10.1038/ng.3679
Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nat Genet. 2016;48(10):1284–7. https://doi.org/10.1038/ng.3656
1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. https://doi.org/10.1038/nature15393
Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–1. https://doi.org/10.1093/bioinformatics/btq340
Bulik-Sullivan BK, Loh PR, Finucane HK, et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–5. https://doi.org/10.1038/ng.3211
de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11(4):e1004219. https://doi.org/10.1371/journal.pcbi.1004219
Frankish A, Diekhans M, Ferreira AM, et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 2019;47(D1):D766–73. https://doi.org/10.1093/nar/gky955
Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523. https://doi.org/10.1038/s41467-019-09234-6
Ministry of Health, Labour and Welfare. Overview of the Results of the 2022 Survey on Dental Diseases. 2022. Accessed June 25, 2024. https://www.mhlw.go.jp/toukei/list/dl/62-17b_r04.pdf
Kang HS, Kim YS, ZeRuth G, et al. Transcription factor Glis3, a novel critical player in the regulation of pancreatic beta-cell development and insulin gene expression. Mol Cell Biol. 2009;29(24):6366–79. https://doi.org/10.1128/MCB.01259-09
Wen X, Yang Y. Emerging roles of GLIS3 in neonatal diabetes, type 1 and type 2 diabetes. J Mol Endocrinol. 2017;58(2):R73–85. https://doi.org/10.1530/JME-16-0232
Leite RS, Marlow NM, Fernandes JK, Hermayer K. Oral health and type 2 diabetes. Am J Med Sci. 2013;345(4):271–3. https://doi.org/10.1097/MAJ.0b013e31828bdedf
Alotaibi RN, Howe BJ, Moreno Uribe LM, Ramirez CV, Restrepo C, Deleyiannis FWB, et al. Multivariate GWAS of Structural Dental Anomalies and Dental Caries in a multi-ethnic cohort. Front Dent Med. 2021;2:771116. https://doi.org/10.3389/fdmed.2021.771116
Vuchkovska A, Glanville DG, Scurti GM, et al. Siglec-5 is an inhibitory immune checkpoint molecule for human T cells. Immunology. 2022;166(2):238–48. https://doi.org/10.1111/imm.13470
Carlin AF, Chang YC, Areschoug T, et al. Group B Streptococcus suppression of phagocyte functions by protein-mediated engagement of human Siglec-5. J Exp Med. 2009;206(8):1691–9. https://doi.org/10.1084/jem.20090691
Munz M, Willenborg C, Richter GM, Jockel-Schneider Y, Graetz C, Staufenbiel I, et al. A genome-wide association study identifies nucleotide variants at SIGLEC5 and DEFA1A3 as risk loci for periodontitis. Hum Mol Genet. 2017;26(13):2577–88. https://doi.org/10.1093/hmg/ddx151
Tong H, Wei Z, Yin J, Zhang B, Zhang T, Deng C, et al. Genetic susceptibility of common polymorphisms in NIN and SIGLEC5 to chronic periodontitis. Sci Rep. 2019;9(1):2088. https://doi.org/10.1038/s41598-019-38632-5
Mueller R, Chopra A, Dommisch H, Schaefer AS. Periodontitis Risk variants at SIGLEC5 impair ERG and MAFB binding. J Dent Res. 2022;101(5):551–8. https://doi.org/10.1177/00220345211049984
Michalowicz BS, Diehl SR, Gunsolley JC, Sparks BS, Brooks CN, Koertge TE, et al. Evidence of a substantial genetic basis for risk of adult periodontitis. J Periodontol. 2000;71(11):1699–707. https://doi.org/10.1902/jop.2000.71.11.1699
Divaris K, Monda KL, North KE, Olshan AF, Reynolds LM, Hsueh WC, et al. Exploring the genetic basis of chronic periodontitis: a genome-wide association study. Hum Mol Genet. 2013;22(11):2312–24. https://doi.org/10.1093/hmg/ddt065
Acknowledgements
We are grateful to “Genequest ALL” and “Euglena MyHealth” for their participation in this study.
Funding
This work was supported by internal funding from the Lion Corporation.
Author information
Authors and Affiliations
Contributions
S.N. is an employee of Genequest Inc., K.S. is a board member of Genequest Inc., S.M. is an employee of Lion Corporation. H.K. has no competing interests.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was approved by the Ethics Committee of Genequest Inc. (2020-1002-4).
All participants provided written informed consent for the general use of their genetic and questionnaire data for research purposes. Prior to participation, detailed objectives of the study were sent to the participants, and an additional study-specific agreement was obtained through an opt-out approach.
Consent for publication
Not applicable.
Competing interests
S.N. is an employee of Genequest Inc., K.S. is a board member of Genequest Inc., S.M. is an employee of Lion Corporation. H.K. has no competing interests.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Nogawa, S., Morishita, S., Saito, K. et al. Genome-wide association meta-analysis identifies two novel loci associated with dental caries. BMC Oral Health 24, 1003 (2024). https://doi.org/10.1186/s12903-024-04799-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12903-024-04799-1