Caries Research最新文献

{"title":"Social Intersectional Differences in Pulpal Clinical Consequences of Untreated Dental Caries among Brazilian Adolescents: Analysis of the SB Brasil 2023 Survey.","authors":"Gabriela Scortegagna de Souza, Maria Laura Braccini Fagundes, Fernando Neves Hugo, Juliana Balbinot Hilgert, Jessye Melgarejo do Amaral Giordani, Orlando Luiz do Amaral Júnior","doi":"10.1159/000553636","DOIUrl":"10.1159/000553636","url":null,"abstract":"<p><strong>Introduction: </strong>The aim of the study was to examine differences in the occurrence of consequences of untreated dental caries (pulpal involvement, ulceration, fistula, and abscess-PUFA) across intersectional social positions defined by sex, skin color, and household income among adolescents.</p><p><strong>Methods: </strong>This study analyzed data from the Brazilian National Oral Health Survey (SB Brasil 2023), using a multistage stratified probability sampling design to represent adolescents aged 15-19 years. Social variables were combined into 8 intersectional groups based on sex, skin color, and family income. Outcomes included PUFA prevalence ≥1 and counts of its components. Survey-adjusted Poisson regression with robust variance and a three-way interaction term was applied.</p><p><strong>Results: </strong>The sample comprised 4,892 adolescents. Compared to high-income White males, prevalence was higher among low-income Black males (PR = 5.93; 95% CI: 2.60-13.51) and low-income Black females (PR = 4.06; 95% CI: 1.85-8.90). The predicted prevalence of PUFA ≥1 ranged from 4.0% (95% CI: 2.0-7.0) among high-income White males to 21.0% (95% CI: 11.0-32.0) among low-income Black males. Among females, prevalence estimates were 15.0% (95% CI: 9.0-20.0) in high-income Black, 16.0% (95% CI: 5.0-28.0) in low-income White, and 18.0% (95% CI: 12.0-24.0) in low-income Black individuals.</p><p><strong>Conclusion: </strong>Marked differences in PUFA occurrence were observed across intersectional social positions, with the highest burden observed among low-income Black adolescents, particularly males for prevalence ratios, although females also presented high absolute prevalence. These findings reinforce the need for policies addressing structural social inequalities in oral health.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-10"},"PeriodicalIF":2.5,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13597073/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148683386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligence Quotient in Children with Dental Fluorosis in Rural Areas of Paraíba.","authors":"Marcos Alexandre Casimiro de Oliveira, Clarissa Lopes Drumond, Alexandre Almeida Júnior, Cláudia Batista Vieira de Lima, Raulison Vieira de Sousa, Jessica Klöckner Knorst, Hemílio Fernandes Campos Coêlho, Fábio Correia Sampaio","doi":"10.1159/cre/abuag002","DOIUrl":"10.1159/cre/abuag002","url":null,"abstract":"<p><strong>Introduction: </strong>Concerns about possible neurotoxicity related to fluoride exposure remain controversial, and evidence is heterogeneous across exposure ranges and study settings. This study evaluated the association between dental fluorosis and intelligence quotient (IQ) among children living in rural areas of Paraíba, Brazil.</p><p><strong>Methods: </strong>A nested case-control study was conducted within a cross-sectional survey. Participants were matched 1:1 by age and sex. IQ was assessed using Raven's Colored Progressive Matrices and dichotomized as lower IQ (levels IV-V) versus higher IQ (levels I-III). Dental caries (DMFT/dmft) and fluorosis (TF index) were assessed after cleaning and drying. Fluoride concentration in drinking water was determined using a fluoride ion-specific electrode. Associations with lower IQ were tested using conditional logistic regression in the matched sample (n = 136) and a sensitivity analysis using logistic regression with cluster-robust standard errors.</p><p><strong>Results: </strong>In crude-matched analyses (68 pairs; n = 136), low family income was associated with lower IQ (odds ratio [OR] = 3.50, 95% confidence interval [95% CI]: 1.15-10.63; p = 0.027), while dental caries showed a borderline association (OR = 1.85, 95% CI: 0.94-3.63; p = 0.075). In the adjusted conditional logistic regression model, associations were not statistically significant (income: adjusted OR = 2.93, 95% CI: 0.89-9.58; p = 0.076; caries: adjusted OR = 1.32, 95% CI: 0.57-3.05; p = 0.514), and dental fluorosis (TF >0) was not associated with lower IQ (adjusted OR = 1.02, 95% CI: 0.45-2.33; p = 0.965). In the sensitivity analysis using the full analytic sample (n = 175) with cluster-robust standard errors clustered at the school level, low income (adjusted OR = 2.64, 95% CI: 1.04-6.71; p = 0.0416) and dental caries (adjusted OR = 2.13, 95% CI: 1.20-3.78; p = 0.0096) were associated with lower IQ, whereas dental fluorosis was not (adjusted OR = 0.89, 95% CI: 0.55-1.45; p = 0.6471).</p><p><strong>Conclusion: </strong>Socioeconomic disadvantage and dental caries showed the most consistent associations with lower IQ, whereas dental fluorosis, used as an indirect marker of earlier fluoride exposure during tooth development, was not independently associated with IQ status after adjustment when comparing lower IQ versus higher IQ.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1"},"PeriodicalIF":2.5,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148677188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Socioeconomic Disparities in Comorbidity of Dental Caries and Metabolic Syndrome in Norwegian Adults: A Cross-Sectional Study Using HUNT4 Survey Data.","authors":"Lina Stangvaltaite-Mouhat, Rasa Skudutyte-Rysstad, Ólöf Anna Steingrimsdóttir, Abhijit Sen","doi":"10.1159/000553481","DOIUrl":"10.1159/000553481","url":null,"abstract":"<p><strong>Introduction: </strong>Dental caries is considered the most prevalent noncommunicable disease worldwide, while metabolic syndrome (MetS) is another noncommunicable condition with an increasing global burden. These conditions are caused by common risk factors, such as lifestyle and diet, which are shaped by overarching socioeconomic determinants. The aim of this study was to assess socioeconomic disparities in caries/MetS comorbidity.</p><p><strong>Methods: </strong>This cross-sectional study included data from adults participating in the population-based Trøndelag Health Study (HUNT4, 2017-2019). Inclusion criteria were dentate 35 years or older participants in Oral Health Examination having records of socioeconomic determinants. Socioeconomic determinants included self-reported educational level (3 categories) and household income (3 categories). The outcome was a comorbidity of untreated caries (number of teeth with clinically and radiographically detected cavitation in dentine, D₃T) and MetS (defined by the Joint Interim Statement). A modified Poisson regression analysis with robust error variances was used to estimate the prevalence ratio (PR) and the corresponding 95% confidence interval (CI). Sensitivity analyses were performed.</p><p><strong>Results: </strong>Out of 7,347, 3,840 (52%) participants were included (55% women, the mean age of the participants 57.4 years; standard deviation 12.3). Neither dental caries nor MetS were recorded in 1,052 (27%) participants, while 887 (23%) experienced caries/MetS comorbidity. Caries/MetS comorbidity was associated with the educational level in a gradient: basic and middle educational level (vs. higher), PR 1.47 (95% CI: 1.22-1.79) and PR 1.29 (95% CI: 1.15-1.47), respectively. Income gradient was also observed: lower and medium income level (vs. higher) PR 1.46 (95% CI: 1.19-1.80) and PR 1.23 (95% CI: 1.03-1.48), respectively. The sensitivity analyses yielded results of similar strength.</p><p><strong>Conclusion: </strong>A socioeconomic gradient was observed in caries/MetS comorbidity in adult Norwegian population. This finding supports integrated public health interventions targeting both health conditions simultaneously, particularly among populations with lower educational levels aiming to decrease overall burden of these conditions and reduce socioeconomic disparities. Oral health services should be a part of the Universal Health Coverage to reduce financial barriers to access. Further research in other contexts and longitudinal studies as well as public health intervention studies are warranted.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-10"},"PeriodicalIF":2.5,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13585419/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148652875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for Radiographic Caries Detection in Dental Education: A Systematic Review with Functional Meta-Synthesis.","authors":"Carlos Martin Ardila, Anny M Vivares-Builes, Eliana Pineda-Velez","doi":"10.1159/cre/abuag001","DOIUrl":"10.1159/cre/abuag001","url":null,"abstract":"<p><strong>Introduction: </strong>Dental caries remains a major global oral health burden, and accurate radiographic detection of carious lesions is a core competency in dental education. Artificial intelligence (AI), particularly machine learning and deep learning systems, has shown promising diagnostic performance in caries detection on dental radiographs. However, evidence on how AI influences dental students' diagnostic performance and learning remains fragmented and methodologically heterogeneous. This systematic review aimed to synthesize student-based evidence on the role of AI in radiographic caries detection within dental education.</p><p><strong>Methods: </strong>A systematic review with functional meta-synthesis was conducted following PRISMA 2020 guidance and a preregistered PROSPERO protocol. PubMed, Embase, Scopus, and SciELO were searched up to April 30, 2026. Eligible studies involved undergraduate or postgraduate dental students performing radiographic caries detection or classification tasks using AI-assisted decision support, AI-based training, or AI system versus student benchmarking. Risk of bias was assessed using an adapted QUADAS-2 framework, and certainty of evidence was evaluated using GRADE. Quantitative findings were interpreted descriptively because methodological heterogeneity precluded pooled effect estimation.</p><p><strong>Results: </strong>Nine studies involving 537 dental students were included. Functional synthesis identified three roles of AI: AI-assisted decision support, AI-based training scaffold, and AI system performance benchmark. AI-assisted interpretation was associated with improved lesion detection in some studies, particularly sensitivity, although specificity and overall diagnostic balance varied. Pre-post-AI-based training studies suggested short-term improvements, but effects were inconsistent and not clearly superior to alternative educational approaches. Benchmarking studies showed that AI systems performed comparably to or better than students in some settings, but several included very small student comparator groups. Substantial heterogeneity was observed in study design, imaging modality, AI systems, reference standards, diagnostic thresholds, and outcome reporting. Risk of bias ranged from low to high, although most studies were judged as low or moderate risk. Certainty of evidence was very low across exploratory comparative outcome domains.</p><p><strong>Conclusion: </strong>AI may support radiographic caries detection education, particularly as decision support or structured training under controlled conditions. However, current evidence mainly reflects short-term performance effects and does not establish durable learning gains, educational superiority, or transferability to clinical practice.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1"},"PeriodicalIF":2.5,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148599254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prognostic Clinical Predictive Models for Dental Caries Using Artificial Intelligence: Methodological Considerations.","authors":"Sergio E Uribe, Alonso Carrasco-Labra, Falk Schwendicke, Ilze Maldupa","doi":"10.1159/000553309","DOIUrl":"10.1159/000553309","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) is currently used to develop clinical predictive models for dental caries. However, most prognostic models lack key methodological components. Few reach clinical practice, and fewer demonstrate clinical benefit.</p><p><strong>Summary: </strong>This narrative methodological review examines considerations for developing, validating, and implementing AI-based caries prognostic models. Based on established prediction model frameworks (TRIPOD+AI, PROBAST+AI, and PROGRESS), we contextualize eight critical phases for caries-specific application. Each phase addresses challenges specific to caries, e.g., clustering of teeth within patients, dominance of baseline caries experience as a predictor, and frequent absence of external validation. Addressing these phases reduces bias, improves reproducibility, and supports meaningful evaluation of clinical impact. AI-based prognostic models should serve as decision-support tools that inform clinician and patient choices rather than substitutes for clinical judgment.</p><p><strong>Key messages: </strong>(i) Most AI-based caries prognostic models are never implemented and fewer of those implemented demonstrate clinical benefit. Methodological deficiencies explain this gap. (ii) Caries prognostic research faces field-specific challenges such as retrospective design, clustering of teeth within patients, and dominance of baseline caries experience. Valid caries prognostic models require attention to eight phases, from problem selection through deployment and ongoing monitoring. (iii) Better discrimination (higher AUC) does not indicate clinical usefulness. Calibration and decision curve analysis are essential yet frequently absent from published studies. (iv) Researchers should report regulatory considerations and plan prospective impact assessment from study inception. Models generating risk estimates that influence clinical decisions qualify as software as a medical device, requiring regulatory compliance and demonstrated clinical impact before deployment.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-16"},"PeriodicalIF":2.5,"publicationDate":"2026-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13485288/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148469225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance of Advanced Large Language Models in Caries Risk Assessment and Preventive Decision-Making: A Multidimensional Evaluation of Five Chatbots.","authors":"Berkant Sezer, Tuğba Aydoğdu","doi":"10.1159/000553557","DOIUrl":"10.1159/000553557","url":null,"abstract":"<p><strong>Introduction: </strong>Large language models (LLMs) have recently been integrated into dental practice to support clinical reasoning and preventive decision-making. This study compared the performance of 5 advanced chatbots - ChatGPT-5, Claude 4.5 Sonnet, Gemini 2.5 Pro, LLaMA 3.1, and Mistral 7B - in providing evidence-based responses for caries risk assessment and preventive management in pediatric cases.</p><p><strong>Methods: </strong>Twenty-five validated, case-based questions were developed in accordance with internationally recognized pediatric and preventive dentistry guidelines. Responses were evaluated by 6 pediatric dentistry experts for accuracy, completeness, relevance, clarity, and usefulness using Likert-type scales. Response time, word count, and linguistic readability characteristics (Flesch Reading Ease Score and Flesch-Kincaid Grade Level) were additionally analyzed to compare textual complexity across chatbot-generated responses. Data normality was assessed using the Shapiro-Wilk test; parametric tests (ANOVA with Bonferroni correction) or nonparametric tests (Kruskal-Wallis with Dunn's post hoc) were applied as appropriate.</p><p><strong>Results: </strong>Statistically significant differences were observed across all qualitative criteria, including accuracy, completeness, relevance, clarity, and usefulness (p < 0.001). ChatGPT-5 consistently ranked among the top-performing models, showing balanced and high-quality responses across domains, while Claude 4.5 Sonnet achieved the highest accuracy and completeness scores. Gemini 2.5 Pro produced the fastest responses (p < 0.001), whereas Claude 4.5 Sonnet generated the longest and most linguistically complex outputs. Readability metrics also differed significantly among models (p < 0.001), with Mistral 7B and LLaMA 3.1 showing the highest readability.</p><p><strong>Conclusions: </strong>All evaluated chatbots generated generally relevant responses for caries risk assessment and preventive counseling; however, substantial inter-model differences were observed in qualitative performance, linguistic complexity, and response characteristics. Occasional inconsistencies and outdated content highlight the need for cautious interpretation and further externally validated evaluation before broader clinical implementation.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-10"},"PeriodicalIF":2.5,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148418953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Caries Risk Assessment: Evaluating the Current Status of Caries Management by Risk Assessment and Cariogram with Large Language Models.","authors":"Şevval Çakıcı, Sümeyra Akkoç","doi":"10.1159/000553200","DOIUrl":"10.1159/000553200","url":null,"abstract":"<p><strong>Introduction: </strong>Caries risk assessment (CRA) is essential for individualized caries management in early childhood, but existing tools are difficult to standardize in routine practice. The ability of large language model (LLM)-based artificial intelligence (AI) systems to recognize and correctly apply validated CRA tools remains unclear. This study aimed to evaluate the ability of ChatGPT and Google Gemini, LLM-based AI systems, to apply and interpret CRA tools in early childhood under different prompting conditions.</p><p><strong>Methods: </strong>Thirty standardized clinical vignettes of children aged 0-5 years were evaluated using Caries Management by Risk Assessment (CAMBRA) and the Cariogram by ChatGPT-5.1 (OpenAI, San Francisco, CA, USA) and Google Gemini-3.0 Pro (Google LLC, Mountain View, CA, USA) under three prompting conditions: unguided, guideline-informed, and guideline-only. An expert panel provided reference classifications. AI outputs were assessed for categorical agreement, mean absolute error (MAE), quality, accuracy, and readability. MAE differences were analyzed using the Friedman test and mixed-design repeated-measures analysis of variance (ANOVA), while quality, accuracy, and readability were analyzed using repeated-measures ANOVA. Reliability was assessed using intraclass correlation coefficients (ICCs).</p><p><strong>Results: </strong>Inter- and intra-rater reliability were high (ICC = 0.89-0.93). Reference CAMBRA and Cariogram classifications showed a moderate correlation (ρ = 0.509; n = 30). MAE differed significantly across AI model-prompting condition combinations for both tools (p < 0.001). No significant difference was observed between AI models for CAMBRA, whereas ChatGPT showed lower MAE than Gemini for the Cariogram (p = 0.002). Guideline-informed and guideline-only prompting significantly reduced MAE and improved quality and accuracy compared with the unguided condition (p < 0.001).</p><p><strong>Conclusion: </strong>LLM-based AI systems can support CRA, particularly when guideline-based prompting is used. However, performance depends on the CRA tool and AI model, and numerical or algorithmic components, such as those in the Cariogram, remain challenging.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-15"},"PeriodicalIF":2.5,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148327007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Driven Decision Thresholds in Cariology: A Systematic Review of Lesion Stage Detection on Bitewing Radiographs.","authors":"Seyed Ahmad Banihashem Rad, Negar Azami, Guglielmo Campus, Fernando Neves Hugo, Marcela Esteves-Oliveira","doi":"10.1159/000552757","DOIUrl":"10.1159/000552757","url":null,"abstract":"<p><strong>Introduction: </strong>This systematic review evaluates the stage-specific diagnostic accuracy of artificial intelligence (AI) models for caries detection and compares their performance with human examiners.</p><p><strong>Methods: </strong>Following PRISMA 2020 guidelines, 4 databases (PubMed/Scopus/Embase/Web of Science) were searched up to October 2025. Nineteen studies using bitewing radiographs and reporting at least 1 diagnostic metric stratified by lesion stage (E₁-D₃, or equivalent) were included. Data extraction included model architecture, dataset characteristics, annotation, and stage-specific outcomes. Risk of bias was assessed using QUADAS-AI. Due to heterogeneity in staging systems (binary, ICDAS, ICCMS) and reported metrics, a narrative synthesis was conducted.</p><p><strong>Results: </strong>AI models, especially YOLO variants, U-Net architectures, and ResNet classifiers, demonstrated consistently higher sensitivity (0.56-0.96) than human examiners (0.21-0.83) for early enamel lesions. Across the 19 included studies, 17 reported stage-specific outcomes for enamel and 18 for dentin lesions, with 10 studies providing direct AI-human comparisons. For moderate and advanced dentin lesions, AI performance was comparable to that of human examiners, with strong F1-scores (0.63-0.93), high area under the curve (0.73-0.97), and low false-negative rates, although specificity was less consistently reported. QUADAS-AI identified a high risk of bias in at least 1 domain in 12 studies due to patient selection, insufficient blinding, and limited external validation.</p><p><strong>Conclusion: </strong>AI systems show potential as adjunctive tools for stage-specific caries detection on bitewing radiographs. Their use may be helpful for preliminary screening and flagging of possible early enamel lesions, but performance remains variable and reflects agreement with expert radiographic interpretation rather than validation against biological truth. Evidence certainty was limited by heterogeneity and methodological bias. Standardized reporting, external validation, and unified lesion depth criteria are required before clinical integration.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-22"},"PeriodicalIF":2.5,"publicationDate":"2026-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148276193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What Is Dental Caries - and Why We Need Fluoride.","authors":"Livia M A Tenuta","doi":"10.1159/000552843","DOIUrl":"10.1159/000552843","url":null,"abstract":"","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-5"},"PeriodicalIF":2.5,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13322400/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148210361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Does Adolescent Obesity Influence Caries Increment among Young Adults? A 5-Year Cohort Study in Southern Brazil.","authors":"Mariana Bezerra Monteiro, Letícia Donato Comim, Nathália Costa de Castro, Maísa Mantelli Soares, Aline Sobreira Bezerra, Julio Eduardo do Amaral Zenkner, Luana Severo Alves","doi":"10.1159/000552810","DOIUrl":"10.1159/000552810","url":null,"abstract":"<p><strong>Introdution: </strong>The aim of this prospective cohort study was to investigate whether obesity during adolescence influences caries increment among southern Brazilian young adults over a 5-year period.</p><p><strong>Methods: </strong>In 2018, 1,197 adolescents attending public and private high schools in Santa Maria, southern Brazil, were examined, of whom 570 were reassessed after 5 years (retention rate: 47.6%). Baseline data collection included questionnaires on sociodemographic and behavioral variables, anthropometric measurements (height and weight), and clinical oral examinations (gingival bleeding index and Decayed, Missing, or Filled Surfaces [DMFS] index). At follow-up, the DMFS index was recorded again. Multilevel Poisson regression models were used to assess the influence of weight status (normal, overweight, or obese) on caries increment. Incidence risk ratio (IRR) and 95% confidence interval (CI) were estimated.</p><p><strong>Results: </strong>At baseline, 16.3% of participants were overweight (n = 92) and 11% were obese (n = 62). The overall caries increment in this sample of young adults was 1.79 surfaces (95% CI = 1.31-2.26). In the final model, after adjusting for age, family income, toothbrushing frequency, frequency of consumption of sugary food, and baseline caries experience, obese participants had a twofold higher risk of an additional carious surface compared to participants of normal weight (IRR = 2.32, 95% CI = 1.39-3.86, p = 0.001).</p><p><strong>Conclusion: </strong>This study showed that obesity during adolescence was a risk factor for caries increment among young adults from southern Brazil.</p>","PeriodicalId":9620,"journal":{"name":"Caries Research","volume":" ","pages":"1-9"},"PeriodicalIF":2.5,"publicationDate":"2026-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148052472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}