Pub Date : 2025-11-27DOI: 10.1177/00220345251385964
J.T. Huh, A. Zheng, B. Kheyfets, M.C. Embree
Osteoarthritis (OA) is a degenerative whole-joint disease affecting more than 500 million people worldwide, characterized by irreversible tissue loss, chronic pain, and physical disability. The pathogenesis of OA is complex, with risk factors such as age, obesity, and injury contributing to a disruption of cartilage homeostasis. Here we focus on the chondrocyte, the sole mature cell type in cartilage, as an active participant in mediating joint demise rather than a mere casualty. In a healthy joint, chondrocytes are quiescent, but in the OA environment, they transition to a dysfunctional, catabolic state, undergoing pathological changes such as hypertrophy and senescence that drive tissue degradation. The canonical Wnt signaling pathway is a critical regulator of cartilage maintenance, and its dysregulation is a key driver of OA progression, making it a prime therapeutic target. However, translating this knowledge into effective disease-modifying OA drugs (DMOADs) has been challenging. While experimental DMOADs have shown promise, many have faced setbacks in clinical trials. These trials underscore the complexity of OA and highlight the critical need for improved trial design that stratifies patients based on disease stage and structural characteristics. Comparing the pathobiology of different joints, such as the knee and temporomandibular joint (TMJ), further reveals how joint-specific differences in biomechanics and cellular composition can dictate therapeutic responses. Among emerging strategies, Wnt-targeted therapies that stabilize the joint microenvironment by suppressing inflammation and promoting chondrocyte survival hold significant promise. This review consolidates the evidence positioning the chondrocyte as an active driver of OA pathogenesis, rather than a passive casualty, to inform future therapeutic development.
{"title":"Osteoarthritis and Chondrocytes: On the Road from Mechanisms to Treatment","authors":"J.T. Huh, A. Zheng, B. Kheyfets, M.C. Embree","doi":"10.1177/00220345251385964","DOIUrl":"https://doi.org/10.1177/00220345251385964","url":null,"abstract":"Osteoarthritis (OA) is a degenerative whole-joint disease affecting more than 500 million people worldwide, characterized by irreversible tissue loss, chronic pain, and physical disability. The pathogenesis of OA is complex, with risk factors such as age, obesity, and injury contributing to a disruption of cartilage homeostasis. Here we focus on the chondrocyte, the sole mature cell type in cartilage, as an active participant in mediating joint demise rather than a mere casualty. In a healthy joint, chondrocytes are quiescent, but in the OA environment, they transition to a dysfunctional, catabolic state, undergoing pathological changes such as hypertrophy and senescence that drive tissue degradation. The canonical Wnt signaling pathway is a critical regulator of cartilage maintenance, and its dysregulation is a key driver of OA progression, making it a prime therapeutic target. However, translating this knowledge into effective disease-modifying OA drugs (DMOADs) has been challenging. While experimental DMOADs have shown promise, many have faced setbacks in clinical trials. These trials underscore the complexity of OA and highlight the critical need for improved trial design that stratifies patients based on disease stage and structural characteristics. Comparing the pathobiology of different joints, such as the knee and temporomandibular joint (TMJ), further reveals how joint-specific differences in biomechanics and cellular composition can dictate therapeutic responses. Among emerging strategies, Wnt-targeted therapies that stabilize the joint microenvironment by suppressing inflammation and promoting chondrocyte survival hold significant promise. This review consolidates the evidence positioning the chondrocyte as an active driver of OA pathogenesis, rather than a passive casualty, to inform future therapeutic development.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"8 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1177/00220345251393865
R.V. Rodrigues, A.P. Manso, R.M. Carvalho
Hybrid layers degrade due to endogenous collagenolytic enzymes and adhesive hydrolysis. Adhesive hydrolysis releases by-products that, along with uncured monomers, may have an adverse effect on collagen fibrils and contribute to the dissolution of the hybrid layer in resin–dentin bonds. The aim of this study was to investigate the effects of methacrylate monomers and corresponding by-products on collagen type I. Tendon fibers (TFs) from mouse tail were incubated with BisGMA 0.1%, BisEMA 0.1%, UDMA 0.1%, HEMA 0.1%, TEG-DMA 0.1%, methacrylic acid 0.025% (MAA), pyruvic acid 0.025% (PA), trypsin as positive control (PC), and water/ethanol as negative control for 1 h, 6 h, 24 h, 72 h, and 7 d. At each period, the specimens were tested mechanically (tensile strength and elastic modulus) and the storage medium tested for hydroxyproline (HPY) release and expressed as percentage of collagen solubilization (%CS). The TFs were morphologically analyzed by a Nikon-Eclipse 80i microscope. Incubation media and time affected TFs in different ways. The incubation of TFs with PA or MAA caused significant damage to the structure, reducing properties and increasing %CS to levels similar or higher than that of trypsin PC. Incubation in the monomers BisGMA 0.1%, BisEMA 0.1%, UDMA 0.1%, HEMA 0.1%, and TEG-DMA 0.1% did not cause hydroxyproline release, and their effects on the TF mechanical properties varied and were possibly related to dehydration. Methacrylate monomers and their by-products can adversely affect TF structure and properties. The findings indicate that collagen degradation in resin–dentin bonds can also be caused by by-products of the adhesive.
{"title":"Can Resin Monomers and By-Products Damage Collagen?","authors":"R.V. Rodrigues, A.P. Manso, R.M. Carvalho","doi":"10.1177/00220345251393865","DOIUrl":"https://doi.org/10.1177/00220345251393865","url":null,"abstract":"Hybrid layers degrade due to endogenous collagenolytic enzymes and adhesive hydrolysis. Adhesive hydrolysis releases by-products that, along with uncured monomers, may have an adverse effect on collagen fibrils and contribute to the dissolution of the hybrid layer in resin–dentin bonds. The aim of this study was to investigate the effects of methacrylate monomers and corresponding by-products on collagen type I. Tendon fibers (TFs) from mouse tail were incubated with BisGMA 0.1%, BisEMA 0.1%, UDMA 0.1%, HEMA 0.1%, TEG-DMA 0.1%, methacrylic acid 0.025% (MAA), pyruvic acid 0.025% (PA), trypsin as positive control (PC), and water/ethanol as negative control for 1 h, 6 h, 24 h, 72 h, and 7 d. At each period, the specimens were tested mechanically (tensile strength and elastic modulus) and the storage medium tested for hydroxyproline (HPY) release and expressed as percentage of collagen solubilization (%CS). The TFs were morphologically analyzed by a Nikon-Eclipse 80i microscope. Incubation media and time affected TFs in different ways. The incubation of TFs with PA or MAA caused significant damage to the structure, reducing properties and increasing %CS to levels similar or higher than that of trypsin PC. Incubation in the monomers BisGMA 0.1%, BisEMA 0.1%, UDMA 0.1%, HEMA 0.1%, and TEG-DMA 0.1% did not cause hydroxyproline release, and their effects on the TF mechanical properties varied and were possibly related to dehydration. Methacrylate monomers and their by-products can adversely affect TF structure and properties. The findings indicate that collagen degradation in resin–dentin bonds can also be caused by by-products of the adhesive.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"102 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1177/00220345251392515
J.O. Makanjuola, R.G. Hill, S.A. Niazi, J. Aduse-Opoku, S. Banerji, S. Deb
Conventional bioactive glasses enhance mineralization in glass-ionomer cements (GICs), but their high sodium content adversely affects mechanical performance, limiting clinical utility. To address the limitations of bioactive glass, this study explores a novel sodium-free, fluoride- and magnesium-enriched bioactive glass (J-BAG) in GICs. The mechanical performance, ion release, antibacterial efficacy against multispecies cariogenic biofilms, and mineralization capacity were investigated. The J-BAG (38.3SiO 2 –6P 2 O 5 –43.9CaO–6.8CaF 2 –5MgO) and two experimental ionomer glasses were synthesized using the melt-quench technique. A new ionomer glass, LG99Sr-Mg-Zn, with molar composition (4.5SiO 2 –3Al 2 O 3 –1.5P 2 O 5 –3SrF 2 –0.5SrO–1MgO–0.5ZnO), was developed from a previously established LG99Sr (4.5SiO 2 –3Al 2 O 3 –1.5P 2 O 5 –3SrF 2 –2SrO), with Fuji IX (GC Corporation, Tokyo, Japan) serving as the reference material. Incorporating J-BAG at 5 and 10 weight% into the experimental Mg-Zn and reference GICs resulted in significant improvements in compressive and flexural strength as well as microhardness, compared with unmodified GICs, with further gains noted postmaturation. However, mechanical properties declined at 15% loading, especially with finer J-BAG particles. Therefore, the 5% J-BAG–loaded LG99Sr-Mg-Zn (LG5) and 10% J-BAG–loaded Fuji IX (F10) formulations were selected for further testing. LG5 and F10 exhibited significantly enhanced fluoride release, along with elevated magnesium and zinc release from LG5. Antibacterial assays revealed biofilm inhibition and reduced live biomass ( P < 0.05) compared with controls. Mineralization studies confirmed that higher J-BAG concentrations and longer immersion in media led to greater mineral deposition. In addition, LG5 and F10 facilitated mineral deposition on demineralized dentine surfaces and partially occluded dentinal tubules. In conclusion, the incorporation of J-BAG at 5% in Mg-Zn–fortified GIC and 10% in Fuji IX simultaneously enhanced mechanical properties, ion release, mineralization, and antibacterial performance. This multifunctionality supports the therapeutic and restorative roles of GICs in clinical dentistry. It offers a promising solution for atraumatic restorative treatments and long-term tooth rehabilitation in patients at high risk of caries.
{"title":"Exploring the Multifunctional Potential of Bioactive Glass-Ionomer Cements","authors":"J.O. Makanjuola, R.G. Hill, S.A. Niazi, J. Aduse-Opoku, S. Banerji, S. Deb","doi":"10.1177/00220345251392515","DOIUrl":"https://doi.org/10.1177/00220345251392515","url":null,"abstract":"Conventional bioactive glasses enhance mineralization in glass-ionomer cements (GICs), but their high sodium content adversely affects mechanical performance, limiting clinical utility. To address the limitations of bioactive glass, this study explores a novel sodium-free, fluoride- and magnesium-enriched bioactive glass (J-BAG) in GICs. The mechanical performance, ion release, antibacterial efficacy against multispecies cariogenic biofilms, and mineralization capacity were investigated. The J-BAG (38.3SiO <jats:sub>2</jats:sub> –6P <jats:sub>2</jats:sub> O <jats:sub>5</jats:sub> –43.9CaO–6.8CaF <jats:sub>2</jats:sub> –5MgO) and two experimental ionomer glasses were synthesized using the melt-quench technique. A new ionomer glass, LG99Sr-Mg-Zn, with molar composition (4.5SiO <jats:sub>2</jats:sub> –3Al <jats:sub>2</jats:sub> O <jats:sub>3</jats:sub> –1.5P <jats:sub>2</jats:sub> O <jats:sub>5</jats:sub> –3SrF <jats:sub>2</jats:sub> –0.5SrO–1MgO–0.5ZnO), was developed from a previously established LG99Sr (4.5SiO <jats:sub>2</jats:sub> –3Al <jats:sub>2</jats:sub> O <jats:sub>3</jats:sub> –1.5P <jats:sub>2</jats:sub> O <jats:sub>5</jats:sub> –3SrF <jats:sub>2</jats:sub> –2SrO), with Fuji IX (GC Corporation, Tokyo, Japan) serving as the reference material. Incorporating J-BAG at 5 and 10 weight% into the experimental Mg-Zn and reference GICs resulted in significant improvements in compressive and flexural strength as well as microhardness, compared with unmodified GICs, with further gains noted postmaturation. However, mechanical properties declined at 15% loading, especially with finer J-BAG particles. Therefore, the 5% J-BAG–loaded LG99Sr-Mg-Zn (LG5) and 10% J-BAG–loaded Fuji IX (F10) formulations were selected for further testing. LG5 and F10 exhibited significantly enhanced fluoride release, along with elevated magnesium and zinc release from LG5. Antibacterial assays revealed biofilm inhibition and reduced live biomass ( <jats:italic toggle=\"yes\">P</jats:italic> < 0.05) compared with controls. Mineralization studies confirmed that higher J-BAG concentrations and longer immersion in media led to greater mineral deposition. In addition, LG5 and F10 facilitated mineral deposition on demineralized dentine surfaces and partially occluded dentinal tubules. In conclusion, the incorporation of J-BAG at 5% in Mg-Zn–fortified GIC and 10% in Fuji IX simultaneously enhanced mechanical properties, ion release, mineralization, and antibacterial performance. This multifunctionality supports the therapeutic and restorative roles of GICs in clinical dentistry. It offers a promising solution for atraumatic restorative treatments and long-term tooth rehabilitation in patients at high risk of caries.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"118 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1177/00220345251383827
F.M. Saavedra, L. Fischer, P.D. Bittner-Eddy, K.F. Johnstone, J.M. Stolley, D.W. Williams, M.C. Herzberg, M. Costalonga
Dysbiotic subgingival biofilms initiate periodontitis, while the consequential destruction of periodontal tissues results from a dysregulated local immune response. Interstitial CD4 + T cells play a crucial role in orchestrating periodontal inflammation. Upon activation, CD4 + T cells express CD69 receptors, which can influence their migration patterns, phenotype, and function during inflammation. Here, we report that in the absence of CD69, memory CD4 + T cells (mCD4 + T cells) derived from gingival and cervical lymph nodes (cLNs) display an increased proinflammatory phenotype. Following in vitro activation, negative-selected mCD4 + T cells from cLNs of CD69 KO mice showed enhanced expression of interleukin (IL)–17A ( P = 0.0043) and interferon-γ ( P = 0.0479). Although comparable to untreated wild-type (WT) mice in the absence of disease, CD69-deficient mice showed augmented alveolar bone loss and a greater interstitial inflammatory cell infiltrate after 7 d of ligature-induced experimental periodontitis. Furthermore, gingival CD4 + T cells derived from mice lacking CD69 produced significantly higher levels of IL-17A compared with WT animals. 16S rRNA gene sequencing and bioinformatics analyses of the subgingival microbiota associated with ligatures indicated that the absence of CD69 in the host significantly shaped the composition of the periodontitis-associated biofilm. Therefore, our data suggest that CD69 receptors play a regulatory role in both the cellular and microbial microenvironments associated with periodontitis.
{"title":"CD69 Regulates Gingival Inflammation and Microbiome in Periodontitis","authors":"F.M. Saavedra, L. Fischer, P.D. Bittner-Eddy, K.F. Johnstone, J.M. Stolley, D.W. Williams, M.C. Herzberg, M. Costalonga","doi":"10.1177/00220345251383827","DOIUrl":"https://doi.org/10.1177/00220345251383827","url":null,"abstract":"Dysbiotic subgingival biofilms initiate periodontitis, while the consequential destruction of periodontal tissues results from a dysregulated local immune response. Interstitial CD4 <jats:sup>+</jats:sup> T cells play a crucial role in orchestrating periodontal inflammation. Upon activation, CD4 <jats:sup>+</jats:sup> T cells express CD69 receptors, which can influence their migration patterns, phenotype, and function during inflammation. Here, we report that in the absence of CD69, memory CD4 <jats:sup>+</jats:sup> T cells (mCD4 <jats:sup>+</jats:sup> T cells) derived from gingival and cervical lymph nodes (cLNs) display an increased proinflammatory phenotype. Following in vitro activation, negative-selected mCD4 <jats:sup>+</jats:sup> T cells from cLNs of CD69 <jats:sup>KO</jats:sup> mice showed enhanced expression of interleukin (IL)–17A ( <jats:italic toggle=\"yes\">P</jats:italic> = 0.0043) and interferon-γ ( <jats:italic toggle=\"yes\">P</jats:italic> = 0.0479). Although comparable to untreated wild-type (WT) mice in the absence of disease, CD69-deficient mice showed augmented alveolar bone loss and a greater interstitial inflammatory cell infiltrate after 7 d of ligature-induced experimental periodontitis. Furthermore, gingival CD4 <jats:sup>+</jats:sup> T cells derived from mice lacking CD69 produced significantly higher levels of IL-17A compared with WT animals. 16S rRNA gene sequencing and bioinformatics analyses of the subgingival microbiota associated with ligatures indicated that the absence of CD69 in the host significantly shaped the composition of the periodontitis-associated biofilm. Therefore, our data suggest that CD69 receptors play a regulatory role in both the cellular and microbial microenvironments associated with periodontitis.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"35 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145609841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1177/00220345251387660
J.J. Wong, O. Urquhart, A. Carrasco-Labra, E.F. Schisterman, M. Glick
Administrative health care data offer unique opportunities to investigate relationships between oral and systemic diseases. However, these data sources introduce methodological challenges that can compromise causal inference. This article demonstrates how, in the context of claims databases, selection bias (i.e., arising from restricting analyses to individuals with both dental and medical insurance) creates a collider structure that can distort estimates of periodontal treatment effects on systemic disease outcomes. Drawing on causal inference theory, we distinguish between confounding (resulting from common causes) and selection bias (resulting from common effects) and demonstrate how directed acyclic graphs (DAGs) can identify these biases and inform rigorous analytical strategies. Therefore, the goal of this article is to demonstrate how selection and confounding biases in administrative health care claims data can compromise causal inference in periodontal–systemic disease research and to introduce methodological approaches for addressing these threats. Our review of 7 studies investigating periodontal–systemic disease associations using claims data reveals methodological gaps in addressing selection bias in the current literature. Moreover, through a numerical example, we illustrate how selection bias can not only distort but also potentially reverse observed associations, producing contradictory clinical recommendations. To address these methodological threats, we introduce established causal inference strategies, referencing implementation tutorials: for confounding, we reference G-methods (G-formula, inverse probability weighting) and stratification-based approaches (regression, matching); for selection bias, we reference inverse probability of selection weighting approaches when data on nonselected individuals are available. To improve methodological rigor in oral–systemic research, we advocate for (1) routine use of DAGs with freely available software, (2) application of bias-correction techniques using established statistical packages, and (3) transparent reporting of bias assessment procedures. Strengthening causal inference methodology in dental research is paramount to building a robust evidence base on periodontal–systemic relationships that supports clinical decision making and integration of oral health into broader health care frameworks.
{"title":"Addressing Selection and Confounding Biases in Dental Claims Data: A Causal Inference Framework for Periodontal–Systemic Disease Research","authors":"J.J. Wong, O. Urquhart, A. Carrasco-Labra, E.F. Schisterman, M. Glick","doi":"10.1177/00220345251387660","DOIUrl":"https://doi.org/10.1177/00220345251387660","url":null,"abstract":"Administrative health care data offer unique opportunities to investigate relationships between oral and systemic diseases. However, these data sources introduce methodological challenges that can compromise causal inference. This article demonstrates how, in the context of claims databases, selection bias (i.e., arising from restricting analyses to individuals with both dental and medical insurance) creates a collider structure that can distort estimates of periodontal treatment effects on systemic disease outcomes. Drawing on causal inference theory, we distinguish between confounding (resulting from common causes) and selection bias (resulting from common effects) and demonstrate how directed acyclic graphs (DAGs) can identify these biases and inform rigorous analytical strategies. Therefore, the goal of this article is to demonstrate how selection and confounding biases in administrative health care claims data can compromise causal inference in periodontal–systemic disease research and to introduce methodological approaches for addressing these threats. Our review of 7 studies investigating periodontal–systemic disease associations using claims data reveals methodological gaps in addressing selection bias in the current literature. Moreover, through a numerical example, we illustrate how selection bias can not only distort but also potentially reverse observed associations, producing contradictory clinical recommendations. To address these methodological threats, we introduce established causal inference strategies, referencing implementation tutorials: for confounding, we reference G-methods (G-formula, inverse probability weighting) and stratification-based approaches (regression, matching); for selection bias, we reference inverse probability of selection weighting approaches when data on nonselected individuals are available. To improve methodological rigor in oral–systemic research, we advocate for (1) routine use of DAGs with freely available software, (2) application of bias-correction techniques using established statistical packages, and (3) transparent reporting of bias assessment procedures. Strengthening causal inference methodology in dental research is paramount to building a robust evidence base on periodontal–systemic relationships that supports clinical decision making and integration of oral health into broader health care frameworks.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"27 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-26DOI: 10.1177/00220345251388340
D.T. Graves, M.A. Levine, S. Aldosary, R.T. Demmer
Diabetes mellitus (DM) and periodontitis share a complex, bidirectional relationship, with each condition exacerbating the other. Diabetes, particularly when poorly controlled, significantly increases the risk, severity, and progression of periodontitis. The biological mechanisms involved are complex and numerous. Hyperglycemia in diabetes is linked to oral microbial dysbiosis, which is in turn associated with increased inflammation, epithelial barrier dysfunction, impaired neutrophil and macrophage function, altered T-cell profiles, and cytokine imbalance, collectively fostering chronic inflammation and immune dysregulation. Moreover, diabetes alters bone metabolism, promoting osteoclastogenesis and reducing reparative bone regeneration by limiting coupled bone formation through an effect on growth factor production, mesenchymal stems cells, and osteoblasts. Conversely, periodontitis is strongly linked to poor glycemic control. Clinical studies and longitudinal meta-analyses report consistent positive associations, while randomized controlled trials show that periodontal therapy reduces HbA1c by ~0.43%. Emerging evidence suggests that periodontitis and oral preclinical dysbiosis contribute to diabetogenesis, although causality remains uncertain. Periodontitis may drive metabolic dysfunction through several biological mechanisms. The dysbiotic oral microbiome and subsequent periodontitis may promote systemic inflammation and subsequent insulin resistance and glucose intolerance. Moreover, oral dysbiosis may deplete nitrate-reducing taxa and impair nitric oxide pathways, which has relevance to both periodontal and cardiometabolic health. Accordingly, periodontal treatment in diabetic populations has shown potential health care savings. Nevertheless, trials assessing the influence of periodontitis treatment on systemic outcomes consistently show significant treatment heterogeneity, which requires explication in future studies. This review underscores the systemic implications of periodontitis in diabetes and highlights the value of integrating periodontal care into diabetes management. A better understanding of the shared pathophysiology between these diseases supports interdisciplinary approaches and points toward novel preventive and therapeutic strategies targeting inflammation, microbial balance, and host response modulation to jointly benefit periodontal and cardiometabolic health.
{"title":"Understanding the Periodontitis–Diabetes Linkage: Mechanisms and Evidence","authors":"D.T. Graves, M.A. Levine, S. Aldosary, R.T. Demmer","doi":"10.1177/00220345251388340","DOIUrl":"https://doi.org/10.1177/00220345251388340","url":null,"abstract":"Diabetes mellitus (DM) and periodontitis share a complex, bidirectional relationship, with each condition exacerbating the other. Diabetes, particularly when poorly controlled, significantly increases the risk, severity, and progression of periodontitis. The biological mechanisms involved are complex and numerous. Hyperglycemia in diabetes is linked to oral microbial dysbiosis, which is in turn associated with increased inflammation, epithelial barrier dysfunction, impaired neutrophil and macrophage function, altered T-cell profiles, and cytokine imbalance, collectively fostering chronic inflammation and immune dysregulation. Moreover, diabetes alters bone metabolism, promoting osteoclastogenesis and reducing reparative bone regeneration by limiting coupled bone formation through an effect on growth factor production, mesenchymal stems cells, and osteoblasts. Conversely, periodontitis is strongly linked to poor glycemic control. Clinical studies and longitudinal meta-analyses report consistent positive associations, while randomized controlled trials show that periodontal therapy reduces HbA1c by ~0.43%. Emerging evidence suggests that periodontitis and oral preclinical dysbiosis contribute to diabetogenesis, although causality remains uncertain. Periodontitis may drive metabolic dysfunction through several biological mechanisms. The dysbiotic oral microbiome and subsequent periodontitis may promote systemic inflammation and subsequent insulin resistance and glucose intolerance. Moreover, oral dysbiosis may deplete nitrate-reducing taxa and impair nitric oxide pathways, which has relevance to both periodontal and cardiometabolic health. Accordingly, periodontal treatment in diabetic populations has shown potential health care savings. Nevertheless, trials assessing the influence of periodontitis treatment on systemic outcomes consistently show significant treatment heterogeneity, which requires explication in future studies. This review underscores the systemic implications of periodontitis in diabetes and highlights the value of integrating periodontal care into diabetes management. A better understanding of the shared pathophysiology between these diseases supports interdisciplinary approaches and points toward novel preventive and therapeutic strategies targeting inflammation, microbial balance, and host response modulation to jointly benefit periodontal and cardiometabolic health.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"3 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145599756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1177/00220345251383863
J S Patel,E Dinh
Despite well-established connections between oral and systemic health, electronic health records (EHRs) and electronic dental records (EDRs) remain largely siloed due to infrastructural and interoperability challenges. This separation limits interdisciplinary care and data-driven research to generate practice-based evidence. We developed and validated 4 algorithmic frameworks specifically designed to link EHR with EDR across nonintegrated systems. Using data from more than 1.7 million medical records and 222,480 dental records spanning a 10-y period at Temple University, we evaluated 4 linkage strategies: (1) direct Social Security number matching, (2) unweighted similarity scoring, (3) weighted average similarity scoring, and (4) a probabilistic expectation-conditional maximization classification model. We compared these approaches using expert-reviewed validation of 1,000 candidate record pairs and selected optimal similarity thresholds for high-fidelity linkages. Our weighted average similarity algorithm demonstrated the best performance with 100% specificity (correctly avoiding false matches), 99% sensitivity (correctly identifying all true matches), and 99% accuracy (proportion of all correct linkages out of total comparisons) at the threshold of 0.82 for successfully linking 121,771 unique patients and 144,229 patients' linkage with 96% sensitivity, 78% specificity, and 89% accuracy. After linking the datasets, the completeness of key patient demographic information significantly improved, with missing race data reduced from 79% to 11% and missing ethnicity data from 82% to 17%. We designed the algorithm to be transparent and vendor neutral, making it potentially adaptable to any institution or practice regardless of their existing EHR/EDR systems. This provides a foundation for developing a clinical decision support systems that facilitate real-time health information exchange, supporting safer dental procedures, timely medical referrals, and integrative research. Our findings provide a critical bridge between medicine and dentistry, which have remained largely divorced from each other. Future work will focus on multi-institutional validation, implementation, and integration into routine clinical workflows.
{"title":"LinkMD: Linking Medical and Dental Records with 4 Linking Algorithms.","authors":"J S Patel,E Dinh","doi":"10.1177/00220345251383863","DOIUrl":"https://doi.org/10.1177/00220345251383863","url":null,"abstract":"Despite well-established connections between oral and systemic health, electronic health records (EHRs) and electronic dental records (EDRs) remain largely siloed due to infrastructural and interoperability challenges. This separation limits interdisciplinary care and data-driven research to generate practice-based evidence. We developed and validated 4 algorithmic frameworks specifically designed to link EHR with EDR across nonintegrated systems. Using data from more than 1.7 million medical records and 222,480 dental records spanning a 10-y period at Temple University, we evaluated 4 linkage strategies: (1) direct Social Security number matching, (2) unweighted similarity scoring, (3) weighted average similarity scoring, and (4) a probabilistic expectation-conditional maximization classification model. We compared these approaches using expert-reviewed validation of 1,000 candidate record pairs and selected optimal similarity thresholds for high-fidelity linkages. Our weighted average similarity algorithm demonstrated the best performance with 100% specificity (correctly avoiding false matches), 99% sensitivity (correctly identifying all true matches), and 99% accuracy (proportion of all correct linkages out of total comparisons) at the threshold of 0.82 for successfully linking 121,771 unique patients and 144,229 patients' linkage with 96% sensitivity, 78% specificity, and 89% accuracy. After linking the datasets, the completeness of key patient demographic information significantly improved, with missing race data reduced from 79% to 11% and missing ethnicity data from 82% to 17%. We designed the algorithm to be transparent and vendor neutral, making it potentially adaptable to any institution or practice regardless of their existing EHR/EDR systems. This provides a foundation for developing a clinical decision support systems that facilitate real-time health information exchange, supporting safer dental procedures, timely medical referrals, and integrative research. Our findings provide a critical bridge between medicine and dentistry, which have remained largely divorced from each other. Future work will focus on multi-institutional validation, implementation, and integration into routine clinical workflows.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"7 1","pages":"220345251383863"},"PeriodicalIF":7.6,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-18DOI: 10.1177/00220345251385966
S Warnakulasuriya,P Ramos-García,M Á González-Moles
The mouth is referred to as "the mirror of health and disease in the body." This review critically examines the comorbidity between systemic diseases and oral lichen planus, an autoimmune disorder affecting the oral mucosa with malignant potential and of high worldwide prevalence. Research has indicated that patients with oral lichen planus are significantly predisposed to diabetes mellitus (pooled proportion [PP] = 9.77%, odds ratio [OR] = 1.64, P < 0.001), Hashimoto thyroiditis (PP = 8.60%, OR = 2.2, P < 0.001), hypothyroidism (PP = 8.14%, OR = 1.65, P = 0.02), hyperthyroidism (PP = 2.84%, OR = 2.11, P = 0.007), celiac disease (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis C (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis B (PP = 3.90%, OR = 1.62, P = 0.02), steatohepatitis (PP = 7.06%, OR = 5.71, P = 0.05), liver cirrhosis (PP = 4.27%, OR = 5.8, P = 0.002), depression (PP = 31.19%, OR = 6.15, P < 0.001), anxiety (PP = 54.76%, OR = 3.51, P < 0.001), and stress (PP = 41.10%, OR = 3.64, P = 0.005). A good knowledge of these associations may assist primary care physicians, dentists, and other oral health professionals involved in the management of patients with oral lichen planus since many patients may be unaware of these associations and could have an impact on their general health. Some of these diseases, such as diabetes, have a role in the development of oral lichen planus. In addition, most of these comorbidities act as risk factors for cancer of different locations: liver, thyroid, small intestine, and the oral cavity. Current evidence indicates a high prevalence and a higher risk of systemic diseases in patients with oral lichen planus compared with the general population. Future research is recommended to increase our knowledge of pathobiology and clinical management of these associations.
{"title":"Oral Lichen Planus and Systemic Diseases.","authors":"S Warnakulasuriya,P Ramos-García,M Á González-Moles","doi":"10.1177/00220345251385966","DOIUrl":"https://doi.org/10.1177/00220345251385966","url":null,"abstract":"The mouth is referred to as \"the mirror of health and disease in the body.\" This review critically examines the comorbidity between systemic diseases and oral lichen planus, an autoimmune disorder affecting the oral mucosa with malignant potential and of high worldwide prevalence. Research has indicated that patients with oral lichen planus are significantly predisposed to diabetes mellitus (pooled proportion [PP] = 9.77%, odds ratio [OR] = 1.64, P < 0.001), Hashimoto thyroiditis (PP = 8.60%, OR = 2.2, P < 0.001), hypothyroidism (PP = 8.14%, OR = 1.65, P = 0.02), hyperthyroidism (PP = 2.84%, OR = 2.11, P = 0.007), celiac disease (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis C (PP = 7.14%, OR = 4.09, P < 0.001), hepatitis B (PP = 3.90%, OR = 1.62, P = 0.02), steatohepatitis (PP = 7.06%, OR = 5.71, P = 0.05), liver cirrhosis (PP = 4.27%, OR = 5.8, P = 0.002), depression (PP = 31.19%, OR = 6.15, P < 0.001), anxiety (PP = 54.76%, OR = 3.51, P < 0.001), and stress (PP = 41.10%, OR = 3.64, P = 0.005). A good knowledge of these associations may assist primary care physicians, dentists, and other oral health professionals involved in the management of patients with oral lichen planus since many patients may be unaware of these associations and could have an impact on their general health. Some of these diseases, such as diabetes, have a role in the development of oral lichen planus. In addition, most of these comorbidities act as risk factors for cancer of different locations: liver, thyroid, small intestine, and the oral cavity. Current evidence indicates a high prevalence and a higher risk of systemic diseases in patients with oral lichen planus compared with the general population. Future research is recommended to increase our knowledge of pathobiology and clinical management of these associations.","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"138 1","pages":"220345251385966"},"PeriodicalIF":7.6,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145545353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-14DOI: 10.1177/00220345251368276
C. Neurath, H. Limeback, C.V. Howard
{"title":"Letter to the Editor, “Early Childhood Exposures to Fluorides and Cognitive Neurodevelopment: A Population-Based Longitudinal Study”","authors":"C. Neurath, H. Limeback, C.V. Howard","doi":"10.1177/00220345251368276","DOIUrl":"https://doi.org/10.1177/00220345251368276","url":null,"abstract":"","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"33 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145509216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-03DOI: 10.1177/00220345251382452
R. O’Kane, D. Stonehouse-Smith, L.C.U. Ota, R. Patel, N. Johnson, C. Slipper, J. Seehra, S.N. Papageorgiou, M.T. Cobourne
Accurate clinical records are fundamental to dental practice. Automatic speech recognition (ASR) has the capacity to convert spoken clinical language into written text within the electronic health record; however, the accuracy of ASR in natural language processing for clinical dentistry remains uncertain. The aim of this study was to investigate the transcriptional accuracy of ASR systems using orthodontic clinical records as the experimental model. Specifically, we used 4 commercial ASR systems (Heidi Health, DigitalTCO, Dragon Medical One, Dragon Professional Anywhere), 5 application programming interfaces (Amazon, Google, Speechmatics, Whisper, GPT4oTranscribe), and a 2-stage pipeline coupling GPT4oTranscribe with the GPT4o large language model (LLM) for generative error correction (GPT4oTranscribeCorrected). Orthodontic diagnostic and treatment planning summaries ( <jats:italic toggle="yes">n</jats:italic> = 200; 10 subject domains; 43,408 words; 6 h of audio) were narrated and recorded for analysis. The primary outcome was domain word error rate (DWER), which investigates clinical terminological transcription errors against the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) database. Secondary outcomes included nondomain WER (N-DWER), lexical accuracy (Recall-Oriented Understudy for Gisting Evaluation [ROUGE] score), semantic similarity (Bidirectional Encoder Representations from Transformers [BERT] and Bidirectional and Auto-Regressive Transformer [BART] scores), hallucinations (transcribed text not in the spoken input), and qualitative error analysis. GPT4oTranscribeCorrected was transcriptionally most accurate (DWER = 3.5%; WER = 3.7%), with DWER decreasing by 54.9% versus GPT4oTranscribe. Heidi Health was the highest-performing commercial system (DWER = 6.2%; WER = 5.4%), with Dragon Professional Anywhere being the worst (WER = 33.9%). All systems were less accurate with technical vocabulary (DWER > N-DWER; <jats:italic toggle="yes">P</jats:italic> < 0.001), except GPT4oTranscribeCorrected. Significant differences were seen across systems for ROUGE, BERT, and BART scores ( <jats:italic toggle="yes">P</jats:italic> < 0.001). Based on post hoc pairwise comparisons, GPT4oTranscribeCorrected performed best and Dragon Professional Anywhere was consistently worst for lexical and semantic errors. Hallucinations were absent except for Whisper ( <jats:italic toggle="yes">n</jats:italic> = 57) and DigitalTCO ( <jats:italic toggle="yes">n</jats:italic> = 1). Across systems, background noise increased DWER and WER ( <jats:italic toggle="yes">P</jats:italic> < 0.001). Importantly, clinically significant errors were seen with all systems, ranging from 2% to 66% (GPT4oTranscribeCorrected clean; Dragon Medical One background noise, respectively). Variation in narrator accent had no effect in clean conditions ( <jats:italic toggle="yes">P</jats:italic> = 0.65) and a small effect with background noise ( <jats:italic toggle="y
准确的临床记录是牙科实践的基础。自动语音识别（ASR）能够在电子健康记录中将口头临床语言转换为书面文本；然而，在临床牙科的自然语言处理中，ASR的准确性仍然不确定。本研究的目的是利用正畸临床记录作为实验模型来研究ASR系统的转录准确性。具体来说，我们使用了4个商业ASR系统（Heidi Health, DigitalTCO, Dragon Medical One, Dragon Professional Anywhere）， 5个应用程序编程接口（Amazon， b谷歌，Speechmatics, Whisper, gpt40transcripte），以及一个2级管道耦合gpt40transcripte与gpt40large language model (LLM)，用于生成纠错（gpt40transcribeccorrected）。对正畸诊疗计划总结（n = 200, 10个学科领域，43408个单词，6小时音频）进行叙述和记录以供分析。主要结果是领域词错误率（DWER），调查临床术语转录错误对医学临床术语系统化命名法（SNOMED-CT）数据库。次要结果包括非领域WER （N-DWER）、词汇准确性（面向记忆的替代评价评分[ROUGE]）、语义相似性（来自变形金刚的双向编码器表征[BERT]和双向和自回归变形金刚[BART]评分）、幻觉（语音输入中没有转录的文本）和定性错误分析。gpt40transcribeccorrected在转录上最准确（DWER = 3.5%; WER = 3.7%），与gpt40transcribe相比，DWER降低了54.9%。Heidi Health是表现最好的商业系统（WER = 6.2%; WER = 5.4%）， Dragon Professional Anywhere表现最差（WER = 33.9%）。所有系统在技术词汇方面的准确性都较低（DWER > N-DWER; P < 0.001），除了gpt40transcribe corrected。ROUGE、BERT和BART评分在不同系统之间存在显著差异（P < 0.001）。基于事后两两比较，gpt40transcribeccorrected表现最好，而Dragon Professional Anywhere在词汇和语义错误方面一直表现最差。除Whisper （n = 57）和DigitalTCO （n = 1）外，无幻觉。在各个系统中，背景噪声增加了DWER和WER （P < 0.001）。重要的是，所有系统的临床显著误差都在2%至66%之间（分别为gpt40、transcribeccorrected clean和Dragon Medical One背景噪声）。叙述者口音的变化在清洁条件下没有影响（P = 0.65），背景噪音的影响很小（P = 0.001）。ASR系统提供个位数的转录错误率，特别是当结合基于llm的校正时，但临床显著的错误仍然存在。当使用当前的ASR系统时，临床记录的验证是必不可少的。
{"title":"Transcription Accuracy of Automatic Speech Recognition for Orthodontic Clinical Records","authors":"R. O’Kane, D. Stonehouse-Smith, L.C.U. Ota, R. Patel, N. Johnson, C. Slipper, J. Seehra, S.N. Papageorgiou, M.T. Cobourne","doi":"10.1177/00220345251382452","DOIUrl":"https://doi.org/10.1177/00220345251382452","url":null,"abstract":"Accurate clinical records are fundamental to dental practice. Automatic speech recognition (ASR) has the capacity to convert spoken clinical language into written text within the electronic health record; however, the accuracy of ASR in natural language processing for clinical dentistry remains uncertain. The aim of this study was to investigate the transcriptional accuracy of ASR systems using orthodontic clinical records as the experimental model. Specifically, we used 4 commercial ASR systems (Heidi Health, DigitalTCO, Dragon Medical One, Dragon Professional Anywhere), 5 application programming interfaces (Amazon, Google, Speechmatics, Whisper, GPT4oTranscribe), and a 2-stage pipeline coupling GPT4oTranscribe with the GPT4o large language model (LLM) for generative error correction (GPT4oTranscribeCorrected). Orthodontic diagnostic and treatment planning summaries ( <jats:italic toggle=\"yes\">n</jats:italic> = 200; 10 subject domains; 43,408 words; 6 h of audio) were narrated and recorded for analysis. The primary outcome was domain word error rate (DWER), which investigates clinical terminological transcription errors against the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) database. Secondary outcomes included nondomain WER (N-DWER), lexical accuracy (Recall-Oriented Understudy for Gisting Evaluation [ROUGE] score), semantic similarity (Bidirectional Encoder Representations from Transformers [BERT] and Bidirectional and Auto-Regressive Transformer [BART] scores), hallucinations (transcribed text not in the spoken input), and qualitative error analysis. GPT4oTranscribeCorrected was transcriptionally most accurate (DWER = 3.5%; WER = 3.7%), with DWER decreasing by 54.9% versus GPT4oTranscribe. Heidi Health was the highest-performing commercial system (DWER = 6.2%; WER = 5.4%), with Dragon Professional Anywhere being the worst (WER = 33.9%). All systems were less accurate with technical vocabulary (DWER > N-DWER; <jats:italic toggle=\"yes\">P</jats:italic> < 0.001), except GPT4oTranscribeCorrected. Significant differences were seen across systems for ROUGE, BERT, and BART scores ( <jats:italic toggle=\"yes\">P</jats:italic> < 0.001). Based on post hoc pairwise comparisons, GPT4oTranscribeCorrected performed best and Dragon Professional Anywhere was consistently worst for lexical and semantic errors. Hallucinations were absent except for Whisper ( <jats:italic toggle=\"yes\">n</jats:italic> = 57) and DigitalTCO ( <jats:italic toggle=\"yes\">n</jats:italic> = 1). Across systems, background noise increased DWER and WER ( <jats:italic toggle=\"yes\">P</jats:italic> < 0.001). Importantly, clinically significant errors were seen with all systems, ranging from 2% to 66% (GPT4oTranscribeCorrected clean; Dragon Medical One background noise, respectively). Variation in narrator accent had no effect in clean conditions ( <jats:italic toggle=\"yes\">P</jats:italic> = 0.65) and a small effect with background noise ( <jats:italic toggle=\"y","PeriodicalId":15596,"journal":{"name":"Journal of Dental Research","volume":"12 1","pages":""},"PeriodicalIF":7.6,"publicationDate":"2025-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145427794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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