Ted R Mikuls1,2, Clay Walker3, Fang Qiu4, Fang Yu4, Geoffrey M Thiele1,2, Barnett Alfant3, Eric C Li5, Lisa Y Zhao5, Gary P Wang5,6, Susmita Datta7, Jeffrey B Payne1,8. 1. Department of Internal Medicine, College of Medicine, University of Nebraska Medical Center, Omaha, NE, USA. 2. Medicine, Veterans Affairs Nebraska-Western Iowa Health Care System, Omaha, NE, USA. 3. Department of Oral Biology, College of Dentistry, University of Florida, Gainesville, FL, USA. 4. Department of Biostatistics, College of Public Health, University of Nebraska Medical Center, Omaha, NE, USA. 5. Department of Medicine, College of Medicine, University of Florida, Gainesville, FL, USA. 6. Medicine, North Florida/South Georgia Veterans Health System, Gainesville, FL, USA. 7. Department of Biostatistics, College of Public Health & Health Professions College of Medicine, University of Florida, Gainesville, FL, USA. 8. Department of Surgical Specialties, College of Dentistry, University of Nebraska Medical Center, Lincoln, NE, USA.
Abstract
OBJECTIVES: To profile and compare the subgingival microbiome of RA patients with OA controls. METHODS: RA (n = 260) and OA (n = 296) patients underwent full-mouth examination and subgingival samples were collected. Bacterial DNA was profiled using 16 S rRNA Illumina sequencing. Following data filtering and normalization, hierarchical clustering analysis was used to group samples. Multivariable regression was used to examine associations of patient factors with membership in the two largest clusters. Differential abundance between RA and OA was examined using voom method and linear modelling with empirical Bayes moderation (Linear Models for Microarray Analysis, limma), accounting for the effects of periodontitis, race, marital status and smoking. RESULTS: Alpha diversity indices were similar in RA and OA after accounting for periodontitis. After filtering, 286 taxa were available for analysis. Samples grouped into one of seven clusters with membership sizes of 324, 223, 3, 2, 2, 1 and 1 patients, respectively. RA-OA status was not associated with cluster membership. Factors associated with cluster 1 (vs 2) membership included periodontitis, smoking, marital status and Caucasian race. Accounting for periodontitis, 10 taxa (3.5% of those examined) were in lower abundance in RA than OA. There were no associations between lower abundance taxa or other select taxa examined with RA autoantibody concentrations. CONCLUSION: Leveraging data from a large case-control study and accounting for multiple factors known to influence oral health status, results from this study failed to identify a subgingival microbial fingerprint that could reliably discriminate RA from OA patients.
OBJECTIVES: To profile and compare the subgingival microbiome of RApatients with OA controls. METHODS:RA (n = 260) and OA (n = 296) patients underwent full-mouth examination and subgingival samples were collected. Bacterial DNA was profiled using 16 S rRNA Illumina sequencing. Following data filtering and normalization, hierarchical clustering analysis was used to group samples. Multivariable regression was used to examine associations of patient factors with membership in the two largest clusters. Differential abundance between RA and OA was examined using voom method and linear modelling with empirical Bayes moderation (Linear Models for Microarray Analysis, limma), accounting for the effects of periodontitis, race, marital status and smoking. RESULTS: Alpha diversity indices were similar in RA and OA after accounting for periodontitis. After filtering, 286 taxa were available for analysis. Samples grouped into one of seven clusters with membership sizes of 324, 223, 3, 2, 2, 1 and 1 patients, respectively. RA-OA status was not associated with cluster membership. Factors associated with cluster 1 (vs 2) membership included periodontitis, smoking, marital status and Caucasian race. Accounting for periodontitis, 10 taxa (3.5% of those examined) were in lower abundance in RA than OA. There were no associations between lower abundance taxa or other select taxa examined with RA autoantibody concentrations. CONCLUSION: Leveraging data from a large case-control study and accounting for multiple factors known to influence oral health status, results from this study failed to identify a subgingival microbial fingerprint that could reliably discriminate RA from OA patients.
Authors: Menke J de Smit; Poerwati Soetji Rahajoe; Elisabeth Raveling-Eelsing; Paola Lisotto; Hermie J M Harmsen; Nyoman Kertia; Arjan Vissink; Johanna Westra Journal: Front Oral Health Date: 2022-06-16
Authors: Ryanne Lehenaff; Ryan Tamashiro; Marcelle M Nascimento; Kyulim Lee; Renita Jenkins; Joan Whitlock; Eric C Li; Gurjit Sidhu; Susanne Anderson; Ann Progulske-Fox; Michael R Bubb; Edward K L Chan; Gary P Wang Journal: BMC Oral Health Date: 2021-05-08 Impact factor: 2.757
Authors: Kaja Eriksson; Guozhong Fei; Anna Lundmark; Daniel Benchimol; Linkiat Lee; Yue O O Hu; Anna Kats; Saedis Saevarsdottir; Anca Irinel Catrina; Björn Klinge; Anders F Andersson; Lars Klareskog; Karin Lundberg; Leif Jansson; Tülay Yucel-Lindberg Journal: J Clin Med Date: 2019-05-08 Impact factor: 4.241