Effects of early tooth loss on chronic stress and progression of neuropathogenesis of Alzheimer’s disease in adult Alzheimer’s model AppNL-G-F mice
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ABSTRACT:
SUBMITTER: Ochi S
PROVIDER: S-EPMC10925668 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:Background/purposeVascular factor is an important risk factor in the process of cognitive impairment or dementia. Tooth loss could cause impairments of spatial learning and memory in mice, and nitric oxide (NO) and its synthase might be involved in the process. The objectives of this study were to investigate and compare the behavioral impairments between the Wistar rats with tooth loss and those with chronic ischemia and to determine the changes in nitric oxide (NO) and its synthases under those two conditions.Materials and methodsThe Morris water maze was used to test the spatial learning and memory abilities in the Wistar rats 8 weeks after the molar extraction procedure and the occlusion of 2 blood vessels to produce cerebral ischemia. The changes in NO and its synthases were evaluated using the Griess assay, Western blotting, and immunohistochemistry.ResultsSimilar impairments in the spatial learning and memory of Wistar rats were found after tooth loss and the induction of cerebral ischemia. The levels of NO and iNOS in the rat hippocampus increased, and the levels of eNOS decreased. Conclusion: For Wistar rats, the results of cognitive impairments related to tooth loss and those that occur due to chronic cerebral ischemia were statistically not significant and that NO, iNOS and eNOS in the hippocampus are involved in both cases.
| S-EPMC7109480 | biostudies-literature
Project description:BACKGROUND:Current periodontal disease taxonomies have limited utility for predicting disease progression and tooth loss; in fact, tooth loss itself can undermine precise person-level periodontal disease classifications. To overcome this limitation, the current group recently introduced a novel patient stratification system using latent class analyses of clinical parameters, including patterns of missing teeth. This investigation sought to determine the clinical utility of the Periodontal Profile Classes and Tooth Profile Classes (PPC/TPC) taxonomy for risk assessment, specifically for predicting periodontal disease progression and incident tooth loss. METHODS:The analytic sample comprised 4,682 adult participants of two prospective cohort studies (Dental Atherosclerosis Risk in Communities Study and Piedmont Dental Study) with information on periodontal disease progression and incident tooth loss. The PPC/TPC taxonomy includes seven distinct PPCs (person-level disease pattern and severity) and seven TPCs (tooth-level disease). Logistic regression modeling was used to estimate relative risks (RR) and 95% confidence intervals (CI) for the association of these latent classes with disease progression and incident tooth loss, adjusting for examination center, race, sex, age, diabetes, and smoking. To obtain personalized outcome propensities, risk estimates associated with each participant's PPC and TPC were combined into person-level composite risk scores (Index of Periodontal Risk [IPR]). RESULTS:Individuals in two PPCs (PPC-G: Severe Disease and PPC-D: Tooth Loss) had the highest tooth loss risk (RR = 3.6; 95% CI = 2.6 to 5.0 and RR = 3.8; 95% CI = 2.9 to 5.1, respectively). PPC-G also had the highest risk for periodontitis progression (RR = 5.7; 95% CI = 2.2 to 14.7). Personalized IPR scores were positively associated with both periodontitis progression and tooth loss. CONCLUSIONS:These findings, upon additional validation, suggest that the periodontal/tooth profile classes and the derived personalized propensity scores provide clinical periodontal definitions that reflect disease patterns in the population and offer a useful system for patient stratification that is predictive for disease progression and tooth loss.
| S-EPMC6125718 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2021-09-03 | GSE168431 | GEO
Project description: Not available
| S-EPMC10763304 | biostudies-literature
Project description:Extant anurans (frogs and toads) exhibit reduced dentition, ranging from a lack of mandibular teeth to complete edentulation, as observed in the true toads of the family Bufonidae. The evolutionary time line of these reductions remains vague due to a poor fossil record. Previous studies have demonstrated an association between the lack of teeth in edentulous vertebrates and the pseudogenization of the major tooth enamel gene amelogenin (AMEL) through accumulation of deleterious mutations and the disruption of its coding sequence. In this study, we have harnessed the pseudogenization of AMEL as a molecular dating tool to correlate loss of dentition with genomic mutation patterns during the rise of the family Bufonidae. Specifically, we have utilized AMEL pseudogenes in three members of the family as a tool to estimate the putative date of edentulation in true toads. Comparison of AMEL sequences from Rhinella marina, Bufo gargarizans and Bufo bufo, with nine extant, dentulous frogs, revealed mutations confirming AMEL inactivation in Bufonidae. AMEL pseudogenes in modern bufonids also exhibited remarkably high 86-93% sequence identity among each other, with only a slight increase in substitution rate and relaxation of selective pressure, in comparison with functional copies in other anurans. Moreover, using selection intensity estimates and synonymous substitution rates, analysis of functional and pseudogenized AMEL resulted in an estimated inactivation window of 46-60 million years ago in the lineage leading to modern true toads, a time line that coincides with the rise of the family Bufonidae.
| S-EPMC8699095 | biostudies-literature
Project description:Charcot-Marie-Tooth disease type 2A (CMT2A) is an axonal neuropathy caused by mutations in the mitofusin 2 (MFN2) gene. MFN2 mutations result in profound mitochondrial abnormalities, but the mechanism underlying the axonal pathology is unknown. Sterile α and Toll/IL-1 receptor motif-containing 1 (SARM1), the central executioner of axon degeneration, can induce neuropathy and is activated by dysfunctional mitochondria. We tested the role of SARM1 in a rat model carrying a dominant CMT2A mutation (Mfn2H361Y) that exhibits progressive dying-back axonal degeneration, neuromuscular junction (NMJ) abnormalities, muscle atrophy, and mitochondrial abnormalities - all hallmarks of the human disease. We generated Sarm1-KO (Sarm1-/-) and Mfn2H361Y Sarm1 double-mutant rats and found that deletion of Sarm1 rescued axonal, synaptic, muscle, and functional phenotypes, demonstrating that SARM1 was responsible for much of the neuropathology in this model. Despite the presence of mutant MFN2 protein in these double-mutant rats, loss of SARM1 also dramatically suppressed many mitochondrial defects, including the number, size, and cristae density defects of synaptic mitochondria. This surprising finding indicates that dysfunctional mitochondria activated SARM1 and that activated SARM1 fed back on mitochondria to exacerbate the mitochondrial pathology. As such, this work identifies SARM1 inhibition as a therapeutic candidate for the treatment of CMT2A and other neurodegenerative diseases with prominent mitochondrial pathology.
| S-EPMC9711878 | biostudies-literature
Project description:The aim of this study was to develop and validate a predictive early tooth loss multivariable model for periodontitis patients before periodontal treatment. A total of 544 patients seeking periodontal care at the university dental hospital were enrolled in the study. Teeth extracted after periodontal diagnosis and due to periodontal reasons were recorded. Clinical and sociodemographic variables were analyzed, considering the risk of short-term tooth loss. This study followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines for development and validation, with two cohorts considered as follows: 455 patients in the development phase and 99 in the validation phase. As a result, it was possible to compute a predictive model based on tooth type and clinical attachment loss. The model explained 25.3% of the total variability and correctly ranked 98.9% of the cases. The final reduced model area under the curve (AUC) was 0.809 (95% confidence interval (95% CI): 0.629-0.989) for the validation sample and 0.920 (95% CI: 0.891-0.950) for the development cohort. The established model presented adequate prediction potential of early tooth loss due to periodontitis. This model may have clinical and epidemiologic relevance towards the prediction of tooth loss burden.
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