Oral frailty assessment links brain changes in healthy older adults and predicts future cognitive decline

doi:10.21203/rs.3.rs-5141042/v1

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Oral frailty assessment links brain changes in healthy older adults and predicts future cognitive decline

https://doi.org/10.21203/rs.3.rs-5141042/v1

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This study investigated the association between oral frailty and regional brain volumes in cognitively unimpaired older adults. We employed a modified version of the Oral Frailty Five-item Checklist (OF-5) and examined its relationship with brain structural changes using magnetic resonance imaging (MRI). A total of 732 participants who were cognitively unimpaired (mean age 70.32 years), from a community-dwelling Japanese cohort, were included in the study. Oral frailty was assessed using the original OF-5, revised OF-5, and novel revised OF-6 classification that incorporated social factors.

Our findings revealed that the revised OF-6 classification demonstrated stronger associations with reduced volumes in multiple brain regions than the original and revised OF-5. Individuals classified as orally frail by the revised OF-6 had significantly lower volumes in areas crucial for cognitive function, including the medial temporal lobe, parahippocampal gyrus, and entorhinal cortex. Furthermore, severe tooth loss (≤9 teeth) and solitary eating were independently associated with lower total brain volume and regional atrophy in areas typically affected by dementia. These findings suggest that when assessed using the revised OF-6 classification, oral frailty may serve as an early indicator of cognitive decline risk in older adults.

This study underscores the potential of comprehensive oral health assessments as noninvasive screening tools for identifying individuals at higher risk of dementia, highlighting the complex interplay between oral health, social engagement, and brain structure and suggesting that maintaining good oral health and social connections may play crucial roles in preserving brain volume in regions critical for cognitive function.

Health sciences/Diseases/Neurological disorders/Dementia

Health sciences/Diseases/Oral diseases

Dementia presents a significant and growing public health challenge, particularly in rapidly aging societies like Japan. Recent projections indicate a substantial increase in dementia prevalence, with an estimated 5.84 million Japanese adults aged ≥ 65 years expected to have dementia by 2040, representing approximately 15% of this age group ¹. The projected rise underscores the urgent need for effective strategies to identify individuals at risk and implement timely interventions. Various predictive models and screening tools have been developed to assess dementia risk, including the ANU-ADRI, LIBRA, and CAIDE risk prediction models, as well as cognitive screening tools like the MMSE, MoCA, and ACE ². Although these tools have shown moderate predictive ability, they face limitations, such as high or unclear risk of bias, moderate to low predictive validity, and lack of external validation ³. For these reasons, accurate, validated, and clinically applicable tools to predict cognitive decline and personalize interventions for individuals at risk of dementia are required.

Recent research has highlighted the potential role of oral frailty in cognitive decline and dementia risk, presenting a novel approach for early detection and intervention. Oral frailty, which is characterized by a decline in oral function and health, has been associated with various adverse health outcomes, including cognitive impairment ⁴. However, the precise mechanisms linking oral frailty to dementia risk remain unclear, and existing assessment tools like the Oral Frailty Five-item Checklist (OF-5) and Oral Frailty Index-8 (OFI-8) have limitations in standardization, subjectivity, and cultural adaptability ⁵. Additionally, the potential synergistic effects of social isolation and oral frailty on brain health represent an understudied area ⁶. Although some studies have shown associations between oral frailty and physical frailty, nutritional status, and fall risk ⁷, the direct relationship between oral frailty and cognitive decline requires further investigation. The lack of longitudinal studies examining the causal relationship between oral frailty and dementia onset, as well as the need for refinement of existing screening tools, represent significant gaps in our current understanding ⁸.

The purpose of this study was to investigate the association between oral frailty, as assessed using a modified version of the Oral Frailty Five-item Checklist (OF-5), and regional brain volumes in cognitively unimpaired older adults. We aimed to address the current knowledge gaps by examining whether specific components of oral frailty are linked to structural brain changes that may precede cognitive decline. Additionally, we will explore the potential of incorporating social isolation factors, particularly solitary eating, into the OF-5 to enhance its predictive capability for risk of cognitive decline. By elucidating these relationships, we aim to contribute to the development of comprehensive and accurate screening tools for the early detection of dementia risk. This study may provide valuable insights into the complex interplay between oral, social, and brain healths in aging populations, potentially informing future preventive strategies and interventions to mitigate cognitive decline in older adults.

Participant Characteristics

As shown in Table 2, this study included 732 participants with a mean age of 70.32 years (SD 6.49). A total of 56.9% respondents were women. The prevalence of hypertension was 52.4%, and 15.4% of participants had diabetes mellitus. On average, participants completed 11.34 years (SD 2.23) of formal education.

Oral Frailty Assessment

Table 2 presents the results of the oral frailty assessment using three classification methods: OF-5, revised OF-5, and revised OF-6.

The OF-5 classification identified 394 participants who were robust and 338 who had oral frailty. The oral frail group was significantly older (mean age 71.75 vs 69.08 years, p < 0.05) and had lower HDL-C levels (mean 59.50 vs 62.46 mg/dL, p < 0.05) than the robust group. Using the revised OF-5 classification, 467 participants were categorized as robust and 265 had oral frailty. Similarly, the oral frail group was significantly older (mean age 72.25 vs 69.22 years, p < 0.05) and had lower HDL-C levels (mean 59.36 vs 62.08 mg/dL, p < 0.05). The revised OF-6 classification resulted in 615 participants who were robust and 117 who had oral frailty. Moreover, this method found the oral frail group to be significantly older (mean age 73.75 vs 69.66 years, p < 0.05). Uniquely, this classification revealed that the oral frail group had significantly fewer years of formal education (mean 10.78 vs 11.46 years, p < 0.05).

Oral Frailty Components

Table 2 indicates the prevalence of the six oral frailty components across the total sample and within each classification group.

Fewer teeth (≤ 19) were observed in 54.2% of the participants. This prevalence was significantly higher in the oral frail groups across all three classification methods (OF-5: 85.2% vs 27.7%; revised OF-5: 81.1% vs 39.0%; revised OF-6: 90.6% vs 47.3%; all p < 0.05). Severe tooth loss (≤ 9 teeth) affected 32.8% of the total sample, with significantly higher rates in all oral frail groups (OF-5: 53.8% vs 14.7%; revised OF-5: 68.7% vs 12.4%; revised OF-6: 81.2% vs 23.6%; all p < 0.05). Chewing difficulty was reported by 50.4% of participants, with a significantly high prevalence in all oral frail groups (OF-5: 87.3% vs 18.8%; revised OF-5: 88.7% vs 28.7%; revised OF-6: 90.6% vs 42.8%; all p < 0.05). Swallowing difficulty affected 27.3% of the total sample, again with significantly higher rates in all oral frail groups (OF-5: 46.2% vs 11.2%; revised OF-5: 55.5% vs 11.3%; revised OF-6: 72.6% vs 18.7%; all p < 0.05). Dry mouth was experienced by 11.2% of participants, with a significantly high prevalence in all oral frail classifications (OF-5: 21.6% vs 2.3%; revised OF-5: 26.4% vs 2.6%; revised OF-6: 44.4% vs 4.9%; all p < 0.05). Pronunciation difficulty was the least common issue, affecting 0.3% of participants, and there were no significant differences between the robust and oral frail groups according to any classification method. Solitary eating was reported by 17.6% of the total sample. Interestingly, it was significantly high in the oral frail group as classified by the revised OF-6 method (42.7% vs 12.8%, p < 0.05).

These results highlight the complex nature of oral frailty and suggest potential differences in the sensitivity and specificity of the three classification methods employed in this study (Table 2).

Association of Oral Frailty Components with Regional Brain Volumes

Table 3 presents the association between oral frailty components and regional brain volume in cognitively unimpaired individuals. Participants with ≤ 9 teeth exhibited lower total brain volume (TBV) (58.42%, 95% CI: 58.02–58.82) than those with more teeth (59.12%, 95% CI: 58.85–59.39). In addition, they showed higher white matter hyperintensity (WMH) volume (0.377%, 95% CI: 0.337–0.418 vs 0.300%, 95% CI: 0.273–0.328). The temporal lobe was affected in ≤ 19 and ≤ 9 teeth groups (5.951%, 95% CI: 5.904–5.997 and 5.932%, 95% CI: 5.871–5.993, respectively) compared with those with more teeth (6.041%, 95% CI: 5.991–6.091 and 6.022%, 95% CI: 5.981–6.063). Specific regions with reduced volumes in the ≤ 9 teeth group included the medial temporal lobe (1.823%, 95% CI: 1.801–1.845 vs 1.854%, 95% CI: 1.839–1.869), parahippocampal gyrus (0.227%, 95% CI: 0.222–0.231 vs 0.234%, 95% CI: 0.232–0.237), and fusiform gyrus (1.085%, 95% CI: 1.069–1.101 vs 1.106%, 95% CI: 1.095–1.117).

Participants who reported chewing difficulty had lower amygdala volumes (0.163%, 95% CI: 0.161–0.165 vs 0.166%, 95% CI: 0.164–0.169). Individuals with swallowing difficulty had a higher precuneus cortex volume (1.034%, 95% CI: 1.021–1.048 vs 1.018%, 95% CI: 1.009–1.026). Those experiencing dry mouth had higher WMH volume (0.406%, 95% CI: 0.339–0.474 vs 0.315%, 95% CI: 0.292–0.339), lower transverse temporal cortex volume (0.104%, 95% CI: 0.100–0.108 vs 0.109%, 95% CI: 0.108–0.110), and lower middle temporal gyrus volume (1.204%, 95% CI: 1.175–1.233 vs 1.235%, 95% CI: 1.225–1.245). Participants who reported eating alone had lower volumes in multiple regions. These included TBV (58.28%, 95% CI: 57.75–58.80 vs 59.02%, 95% CI: 58.78–59.27), medial temporal lobe (1.812%, 95% CI: 1.784–1.841 vs 1.851%, 95% CI: 1.838–1.864), fusiform gyrus (1.078%, 95% CI: 1.057–1.099 vs 1.104%, 95% CI: 1.094–1.113), parietal lobe (5.918%, 95% CI: 5.841–5.995 vs 6.020%, 95% CI: 5.984–6.055), precuneus cortex (1.002%, 95% CI: 0.985–1.018 vs 1.027%, 95% CI: 1.019–1.034), occipital lobe (2.375%, 95% CI: 2.338–2.412 vs 2.438%, 95% CI: 2.421–2.455), and hippocampus (0.473%, 95% CI: 0.463–0.482 vs 0.484%, 95% CI: 0.480–0.489). Because of the small sample size (n = 2) for pronunciation difficulty, no meaningful comparisons could be made.

These findings suggest that the components of oral frailty, particularly tooth loss and eating alone, are associated with reduced volumes in multiple brain regions. The associations were most pronounced and widespread for severe tooth loss and solitary eating, potentially indicating their importance as markers of broader health status or as direct contributors to brain health.

Association Between the Oral Frailty Checklist and Regional Brain Volumes

Table 4 presents the association between oral frailty, as assessed by the OF-5, revised OF-5, and revised OF-6 scores, and regional brain volumes in cognitively unimpaired individuals. All values were presented as percentages of estimated total intracranial volume (eTIV) and adjusted for age, sex, educational level, hypertension, diabetes mellitus, and LDL and HDL cholesterol levels.

Using the OF-5 classification, the only significant difference between the oral frail and robust groups was observed in the fusiform gyrus (frail group: 1.087%, 95% CI: 1.074–1.100 vs robust group: 1.109%, 95% CI: 1.097–1.121, p < 0.05). The revised OF-5 classification revealed significant differences in several regions. WMH were higher in the frail group (0.362%, 95% CI: 0.324–0.400) than in the robust group (0.305%, 95% CI: 0.277–0.333, p < 0.05). The medial temporal lobe had a lower volume in the frail group (1.823%, 95% CI: 1.802–1.843) than in the robust group (1.856%, 95% CI: 1.841–1.871, p < 0.05). Moreover, the fusiform gyrus showed reduced volume in the frail group (1.079%, 95% CI: 1.064–1.094) compared with the robust group (1.110%, 95% CI: 1.099–1.121, p < 0.05). Additionally, the pars triangularis showed lower volume in the frail group (0.402%, 95% CI: 0.396–0.409) than in the robust group (0.411%, 95% CI: 0.406–0.416, p < 0.05).

The revised OF-6 classification demonstrates the most widespread differences. TBV was lower in the frail group (58.34%, 95% CI: 57.77–58.91) than in the robust group (58.99%, 95% CI: 58.75–59.23, p < 0.05). WMH volume was higher in the frail group (0.400%, 95% CI: 0.343–0.458) than in the robust group (0.311%, 95% CI: 0.287–0.335, p < 0.05). The medial temporal lobe had reduced volume in the frail group (1.795%, 95% CI: 1.764–1.827) compared with the robust group (1.853%, 95% CI: 1.840–1.866, p < 0.05). The entorhinal cortex, parahippocampal gyrus, fusiform gyrus, and middle temporal gyrus showed significantly lower volumes in the frail group than in the robust group (p < 0.05 for all).

These findings suggest that oral frailty is associated with reduced volumes in multiple brain regions, particularly in the temporal lobe. The revised OF-6 classification seems the most sensitive for detecting associations between oral frailty and reduced brain volumes across a wide range of regions.

This study examined the association between oral frailty, as assessed using the Oral Frailty 5-item Checklist (OF-5) and its modified versions, and regional brain volumes in cognitively unimpaired older adults. Our findings reveal two significant outcomes: the revised OF-6 classification demonstrated a stronger association with reduced volumes in multiple brain regions compared with the original and revised OF-5 classifications, particularly in areas crucial for cognitive function—such as the medial temporal lobe, parahippocampal gyrus, and entorhinal cortex. Severe tooth loss (≤ 9 teeth) and solitary eating were independently associated with lower TBV and regional atrophy in areas typically affected by dementia. These results provide novel insights into the potential link between oral health and brain structural changes, suggesting that oral frailty may serve as an early indicator of cognitive decline risk in older adults.

Our first key finding revealed that the revised OF-6 classification demonstrated a stronger association with reduced volumes in multiple brain regions compared with the original and revised OF-5 classifications, particularly in areas crucial for cognitive function—such as the medial temporal lobe, parahippocampal gyrus, and entorhinal cortex. This finding is particularly significant considering the established importance of these brain regions in cognitive function and their role in the early stages of dementia. For instance, the entorhinal cortex is one of the earliest brain regions affected in Alzheimer’s disease, and its atrophy is closely correlated with changes in cognitive function and disease progression (Supplemental Table 1) ^9,10. Similarly, the parahippocampal gyrus, along with other medial temporal lobe structures, exhibits significant atrophy in individuals with dementia and is considered an early biomarker of Alzheimer’s disease (Supplemental Table 1) ^11,12. Our revised OF-6 classification showed a stronger association with reduced volumes in these critical areas, suggesting that it can be a sensitive tool for detecting early signs of cognitive decline risk. This finding underscores the potential of oral health assessments, particularly the revised OF-6, as noninvasive and easily administrable screening methods for identifying individuals at high risk of developing dementia. Moreover, the findings highlight the intricate relationship between oral health and brain structure, suggesting that maintaining good oral health may play a crucial role in preserving brain volume in regions critical for cognitive function.

Our second significant finding revealed that severe tooth loss (≤ 9 teeth) and solitary eating were independently associated with lower TBV and regional atrophy in areas affected by dementia. This observation aligns with and expands previous research. For instance, even in cognitively normal individuals, tooth loss was associated with parahippocampal gyrus atrophy and increased WMH volume, both of which are characteristics of dementia ¹³. The link between severe tooth loss and brain atrophy can be explained by several mechanisms. Chronic low-grade inflammation resulting from poor oral health, particularly periodontitis and tooth loss, can contribute to neuroinflammation and subsequent brain atrophy ^14,15. Oral pathogens and their byproducts can enter the bloodstream, triggering a systemic inflammatory response ^16,17. Furthermore, periodontal disease increases the production of proinflammatory cytokines, which have been linked to accelerated brain aging and increased risk of neurodegenerative diseases ¹⁸. Concurrently, nutritional deficiencies resulting from impaired masticatory function and altered dietary patterns in orally frail individuals may exacerbate brain atrophy ^13,19. Severe tooth loss can lead to reduced intake of essential nutrients crucial for brain health, such as vitamins (vitamins A, E, B1, B6, β-carotene, and folic acid) and antioxidants ²⁰. Furthermore, the association between solitary eating and brain atrophy highlights the potential impact of social isolation on brain health. This finding is particularly relevant because the growing body of evidence linking social isolation to cognitive decline and brain atrophy ⁶. The synergistic effects of severe tooth loss and solitary eating on brain volume suggest a complex interplay between oral health, social engagement, and cognitive function. These results underscore the importance of maintaining oral health and social connections among older adults as potential strategies for preserving brain volume and thus cognitive function. Moreover, the study suggested that interventions targeting oral health and social engagement could have far-reaching implications for cognitive health in aging populations.

The findings of this study have significant implications for clinical practice and public health policy. First, the study suggested that oral health assessments, particularly the revised OF-6, could potentially serve as an early screening tool for cognitive decline risk. This approach is particularly valuable given its noninvasive nature and relative ease of administering oral health assessments compared with complex neurological tests. The strong association between oral frailty and reduced brain volumes in regions critical for cognitive function underscores the potential of oral health as a window into overall brain health. This finding aligns with the growing recognition of oral health as an integral component of general health and well-being ⁴. Second, our findings highlight the importance of maintaining good oral health and social connections among older adults as potential strategies for preserving brain volume and cognitive function, suggesting that interventions targeting oral health and social engagement can have far-reaching implications for cognitive health in aging populations. From a public health perspective, these results support the integration of oral health care into broader health care strategies for older adults. Further, they emphasize the need for policies that promote social engagement among the elderly, particularly those at risk of isolation. Furthermore, our findings contribute to the growing body of evidence supporting a multidimensional approach to cognitive health in aging, encompassing traditional cognitive interventions and oral health care and social support ²¹. Future research should focus on longitudinal studies to establish causal relationships between oral frailty, brain atrophy, and cognitive decline, as well as intervention studies to determine whether improving oral health and increasing social engagement can slow or prevent brain atrophy in older adults.

In summary, our study provides compelling evidence of an association between oral frailty and reduced brain volumes in regions critical for cognitive function. The revised OF-6 classification demonstrated superior sensitivity in detecting these associations compared with the original and revised OF-5, particularly in the medial temporal lobe, parahippocampal gyrus, and entorhinal cortex. Additionally, severe tooth loss and solitary eating were independently associated with lower TBV and regional atrophy. These findings underscore the potential of oral health assessments as early indicators of cognitive decline risk and highlight the complex interplay between oral health, social engagement, and brain structure. However, the limitations of this study should be acknowledged. First, as a cross-sectional study, we could not establish causal relationships between oral frailty and brain atrophy. Longitudinal studies are needed to determine whether oral frailty precedes or follows brain volume changes. Second, although we controlled for several confounding factors, other unmeasured variables may have influenced the observed associations. Future research should address these limitations by conducting longitudinal studies to track changes in oral health, brain volume, and cognitive function over time. In addition, intervention studies are needed to determine whether improving oral health and increasing social engagement can slow or prevent brain atrophy in older adults. Further investigation into the mechanisms linking oral health to brain structure, such as inflammatory processes and nutritional pathways, is important. Finally, validating the revised OF-6 in diverse populations and settings is crucial to establish its broad applicability as a screening tool for cognitive decline risk.

Study Population

This report describes an ongoing study aimed at investigating cognitive deterioration among older Japanese adults. Dementia screening was conducted in a population-based, longitudinal cohort of individuals within a specific age range. A detailed description of the data has been previously published ¹³. This study was conducted in Nakajima, which is located in Nanao City, Ishikawa Prefecture, Japan. The research methodology has been previously described ^13,22,23. Between 2016 and 2018, 2,454 inhabitants aged ≥ 60 (representing 92.9% of the total demographics within this age bracket) participated in the study. All participants in this study were community-dwelling older adults. Institutionalized individuals were included in the sample. We excluded individuals who had no brain magnetic resonance imaging (MRI) examinations (n = 1,159), dental examinations (N = 302), hemorrhagic and/or ischemic stroke lesions on MRI regardless of the presence or absence of neurological symptoms (n = 69), no questionnaire filled out (N = 5). Stroke lesions (hemorrhagic and/or ischemic) were identified by two trained neuroradiologists who were blinded to the clinical information. To determine whether oral frailty is a risk factor for cognitive impairment in cognitively unimpaired older adults, we excluded patients with dementia (n = 24) and MCI (n = 163). Finally, data from 732 individuals were included in the analysis. The regional brain volumes normalized to the estimated total intracranial volume (eTIV) across these excluded cognitive function groups (MCI, and dementia) were presented in Supplemental Table 1.

Ethics Statements

This study complied with the principles outlined in the Declaration of Helsinki, and all methodologies were approved by the Kanazawa University Medical Ethics Review Committee (Approval Number 2185) and the Ryukyu University Medical Ethics Review Committee (Approval Number 2153). Written informed consent was obtained from all participants.

Assessment of Oral Frailty.

This study evaluated oral frailty and its phenotype based on six criteria: tooth count, chewing ability, swallowing function, oral moisture, pronunciation clarity, and eating companionship. Data were collected from 2016 to 2018, and oral frailty was assessed using the original and revised OF-5 ^7,24.

A dentist conducted comprehensive dental examinations for all participants, adhering to the Third National Health and Nutrition Examination Survey guidelines ²⁵. Tooth categories included healthy, carious, and treated teeth (encompassing crowned, inlay, and abutment teeth for prosthetic devices), as well as fully erupted third molars. Excluded were unerupted or congenitally absent teeth, root remnants, and excessively mobile teeth. The study defined “fewer teeth” as a count below 19 or 9 (1 point). Chewing difficulty was assessed using responses to the self-reported question “Do you have difficulty eating hard foods compared to six months ago?” (“Yes” = 1 point). Swallowing difficulty was assessed using responses to the self-reported question “Do you sometimes choke on tea, soup, etc.?” (“Yes” = 1 point). Dry mouth was assessed using responses to the self-reported question “Do you feel dry mouth?” (“Yes” = 1 point). Pronunciation difficulty was assessed using responses to the self-reported question “Do you have trouble pronouncing words clearly in everyday conversation?” (“Yes” = 1 point). Solitary eating was assessed using responses to the self-reported question “How many people do you eat with?” (“Alone” = 1 point). Based on these criteria, we classified oral frailty into three groups based on the OF-5, revised OF-5, and revised OF-6 checklists (Table 1).

Magnetic Resonance Imaging Analysis

Structural MRI was conducted using a 1.5-T system (ECHELON RX; Hitachi, Japan). A detailed description of brain MRI data acquisition and processing has been previously published ¹³. Three-dimensional volumetric acquisition of T1-weighted turbo field echo images was conducted according to the brain MRI protocol for the Alzheimer’s Disease Neuroimaging Initiative study ²⁶ (Echo time/Repetition time, 4.0/9.2 ms; flip angle, 8°; Field of View, 240 mm; acquisition matrix, 192 × 192; number of slices, 170; voxel size, 0.9375 × 0.9375 mm²; slice thickness, 1.2 mm). All T1-structural images were analyzed using FreeSurfer version 5.3 (http://surfer.nmr.mgh.harvard.edu) ²⁷ at Tohoku University and preprocessed according to the standard method. Volumes of the areas of interest were created using FreeSurfer ²⁸. The 37 areas were calculated as the sum of the volumes of the right and left sides of each area. The TBV was calculated by summing the white and gray matter volumes. WMHs are lesions that appear as regions of decreased signal intensity on T1-weighted MRI. These are considered manifestations of small vessel ischemic disease. Among the volumes of parcels, the volumes of the following brain regions were used as outcomes: cortical volumes in the lobes (frontal, temporal, parietal, and occipital), insula, cingulum, hippocampus, and amygdala. The estimated total intracranial volume (eTIV) was used to normalize the volumetric value.

Cognitive Status

Diagnosis of dementia was based on the Diagnostic and Statistical Manual of Mental Disorders, third edition, revised (DSM-III-R) ²⁹, whereas diagnosis of MCI was established according to the International Working Group on general criteria for MCI ³⁰, which stated that (1) persons should be judged as abnormal using other modalities besides not fulfilling the DSM-III-R dementia criteria, (2) functional activities of the person are mainly preserved or at least impairment is minimal, and (3) the person should have evidence of cognitive decline, either by self-assessment and/or the use of an informative report in conjunction with deficits on objective cognitive tasks. Among participants without dementia, a Clinical Dementia Rating score of 0.5 was used as the objective cognitive impairment value to denote cognitive and functional impairment consistent with MCI. A detailed description of the data has been previously published ¹³.

Statistical Analysis

To compare participant characteristics, we used one-way analysis of variance for mean calculations of continuous variables and chi-square test for categorical variables. A two-tailed p-value < 0.05 indicates statistical significance. Analysis of covariance (ANCOVA) was employed to estimate and compare multivariable-adjusted values and their 95% confidence intervals for cognitive status, residual tooth number, and denture inclusion. In the multivariable-adjusted analysis, age, sex, educational level, hypertension, diabetes mellitus, and LDL and HDL cholesterol levels were analyzed as covariates. Bonferroni correction was applied to multiple comparisons, with FWE-corrected p-values < 0.05 considered statistically significant. The SPSS software suite (version 26; SPSS Inc., Chicago, IL, USA) was used for all statistical analyses.

Data Availability Statement

Because of the inclusion of sensitive medical information that could compromise participant privacy, the data analyzed in this study cannot be made publicly available. However, anonymized data can be accessed upon reasonable request and with permission from the corresponding author.

Acknowledgments

We thank the emeritus professor Masahito Yamada (Kudanzaka Hospital, Tokyo, Japan). We wish to thank Professor Toshiharu Ninomiya (Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan), and Professor Yasuyuki Taki (Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan) for data analysis and all residents of Nakajima for their participation in this study. We would also like to thank Enago (www.enago.jp) for language editing assistance.

This study was supported by JSPS KAKENHI (21K17195 to M.I.J.) and the 8020 Research Grant for fiscal year 2024 from the 8020 Promotion Foundation (24-2-03 to H.N.). The sponsor had no role in the design and conduct of the study, collection, management, analysis, and interpretation of the data, or preparation, review, or approval of the manuscript.

Author Contributions

M.M., H.N: Study concept, design, Data acquisition, analysis, interpretation, Writing—original draft, Writing—review & editing, Project administration, Supervision; M.N.S., M.I.J., K.I., T.K., S.T., R.S., N.M., and M.I.: Data acquisition, interpretation, Writing — review & editing; S.K., and K.O: Writing—review & editing, Project administration. H.N. and K.O: Contributed equally as corresponding authors for this work. All authors have read and agreed to the submission and publication of this manuscript.

Competing Interests

The authors declare no competing interests.

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Tables 1 to 4 are available in the Supplementary Files section

No competing interests reported.

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Oral frailty assessment links brain changes in healthy older adults and predicts future cognitive decline

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Abstract

INTRODUCTION

RESULTS

Participant Characteristics

Oral Frailty Assessment

Oral Frailty Components

Association of Oral Frailty Components with Regional Brain Volumes

Association Between the Oral Frailty Checklist and Regional Brain Volumes

DISCUSSION

METHODS

Study Population

Ethics Statements

Magnetic Resonance Imaging Analysis

Cognitive Status

Statistical Analysis

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

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