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Abstract

Aims:

To investigate the association between oral frailty (OF) and continuous glucose monitoring (CGM)-derived metrics in older patients with type 2 diabetes.

Materials and Methods:

This cross-sectional study included 93 older outpatients with type 2 diabetes (age 76.9 ± 4.1 years, 55 men) at the diabetes outpatients’ clinic at the National Center for Geriatrics and Gerontology, Japan. OF was assessed using the Oral Frailty Index-8 (OFI-8). CGM-derived metrics included mean sensor glucose (SG), glucose coefficient of variation, percentage values of time in range (%TIR, 70–180 mg/dL), time above range (%TAR), and time below range (%TBR). Multiple linear regression analyses were performed to examine associations between OF and these CGM metrics, with each metric as the dependent variable and OF or individual OFI-8 subitems as the independent variables.

Results:

In all, 34 older patients (36.6%) had OF. Multiple linear regression analysis found no significant association between OF and CGM-derived metrics or HbA1c. However, the sub-items “How many times do you brush your teeth per day?: <2 times/day” and “Do you visit a dental clinic at least annually?: No” were significantly positively associated with mean SG and %TAR >180 mg/dL, and %TAR >250 mg/dL, and negatively associated with %TIR (P < 0.05).

Conclusions:

Poor oral health behaviors in older patients with type 2 diabetes may be linked to CGM-derived hyperglycemic metrics and %TIR, which are not reflected by the HbA1c value.

Introduction

As individuals age, their risk of periodontal disease increases¹ and masticatory function declines.² In addition, oral frailty (OF)—a condition characterized by mild deterioration of oral function—is prevalent among older adults and is associated with physical frailty and increased risk of mortality.³

Recent evidence highlights a strong relationship between oral health, diabetes, and glycemic control. Studies have reported a higher prevalence of OF in patients with diabetes compared with in those without.^4,5 Furthermore, periodontal disease is not only a risk factor for worsening diabetes⁶ but also a common contributor to OF. Maintaining good oral hygiene may help prevent periodontal disease progression and support glycemic control. In addition, chewing is thought to stimulate insulin secretion and help mitigate postprandial hyperglycemia.⁷

While HbA1c has traditionally been the standard marker of glycemic control, continuous glucose monitoring (CGM), which provides various metrics such as mean glucose levels, time spent in hyperglycemia or hypoglycemia, and glycemic variability, has gained attention for enabling a more comprehensive assessment of glycemic control.⁸ Several studies have demonstrated that CGM-derived metrics are associated with various health outcomes, such as microvascular and macrovascular complications independently of HbA1c.^9–12 However, to our knowledge, no study has yet investigated the relationship between CGM-derived metrics and OF. Therefore, this study aimed to examine the association of OF with CGM-derived metrics and HbA1c in older patients with type 2 diabetes.

Materials and Methods

Design, setting, and participants

This cross-sectional study utilized baseline data from a 2-year longitudinal study^13–15 that investigated the association of CGM-derived metrics with cognitive decline and brain structural alterations in older adults with type 2 diabetes. The inclusion criteria were older outpatients aged 70–85 years who visited the Department of Endocrinology and Metabolism, the National Center for Geriatrics and Gerontology between December 2020 and December 2021; the participants had received a full explanation of this study and agreed to participate. The exclusion criteria were individuals without type 2 diabetes and those with a Montreal Cognitive Assessment-Japanese (MoCA-J) score <17.¹⁶ The ethics committee of the National Center for Geriatrics and Gerontology approved the study protocol (approval no. 1439).

In this study, we included participants who had completed a questionnaire on OF at baseline.

Outcome variables

Glycemic control was assessed using CGM⁸ and HbA1c. CGM was performed for up to 14 days using a FreeStyle Libre Pro (Abbott Japan), which automatically measures blood glucose levels every 15 min without the need for scanning by participants. CGM data were not used for clinical decision-making during the study period and were blinded to both participants and physicians. Mean sensor glucose (SG) and standard deviation (SD) were calculated using EasyGV, version 9.0 software.¹⁷ Blood glucose variability was assessed using the coefficient of variation (%CV), calculated as: 100% × [SD] / [mean SG]. In addition, the percentage values of time in range (%TIR) (70–180 mg/dL), time above range (%TAR) (>180 and >250 mg/dL), and time below range (%TBR) (<70 and <54 mg/dL) were calculated during monitoring.⁸

Oral frailty

OF is a state between robust oral function and its decline, characterized by a gradual decline in oral function, including tooth loss and difficulties in eating and communicating. OF increases the risk of impaired oral functional capacity but can be reversed with proper intervention and treatment. OF was assessed using the Oral Frailty Index-8 (OFI-8), a self-administered questionnaire.¹⁸ It consists of eight sub-items: 1) Do you have any difficulties eating tough foods compared to 6 months ago? (Yes = 2); 2) Have you choked on your tea or soup recently? (Yes = 2); 3) Denture use (Yes = 2); 4) Do you often experience having a dry mouth? (Yes = 1); 5) Do you go out less frequently than you did last year? (Yes = 1); 6) Can you eat hard foods like squid jerky or pickled radish? (No = 1) 7); How many times do you brush your teeth per day? (<2 times/day = 1); and 8) Have you visited a dental clinic at least annually? (No = 1). In this study, a score ≥4 was considered to denote OF. In addition, the presence or absence of the eight sub-items was used in the analysis.

Demographic and clinical variables

Demographic variables included age, sex, and body mass index ([BMI]: weight [kg] divided by height [m] squared). Clinical variables included duration of diabetes, use of insulin, sulfonylureas, biguanides, α-glucosidase inhibitors, thiazolidinediones, dipeptidyl peptidase-4 inhibitors, glucagon-like peptide-1 receptor agonists, and sodium-glucose co-transporter-2 inhibitors, presence or absence of comorbidities (hypertension: systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg; hyperlipidemia: LDL cholesterol ≥140 mg/dL, HDL cholesterol <40 mg/dL, or triglycerides ≥150 mg/dL; chronic kidney disease: glomerular filtration rate <60 mL/min/1.73m² for 3 months or more), years of education, cognitive function, and activities of daily living. Cognitive function was assessed using the MoCA-J, and the level of independence in activities of daily living was assessed using the Barthel Index.

Statistical analysis

For continuous variables, normality analysis was performed using the Shapiro–Wilk test. Continuous variables were presented as mean ± standard deviation if the data were normally distributed, and as median (interquartile range) if the data were not normally distributed. Categorical variables were denoted as number of patients (%) and used to compare individual demographic and clinical variables. The association of OF with CGM-derived metrics and HbA1c was then examined using linear regression analysis, and B values and 95% confidence intervals (95% CIs) were calculated. CGM-derived metrics or HbA1c was included as the dependent variable, while OF or one of its components was included as the independent variable. In accordance with previous studies,^4,5,19,20 age, sex, BMI, insulin/sulfonylurea use, years of education, and cognitive function were selected as confounding variables. P < 0.05 was considered statistically significant. All statistical analyses were performed using the “R” software (version 4.0.0).

Results

A total of 283 outpatients were informed about the study recruitment between December 2020 and December 2021. Among them, 111 older adults received a full explanation of the study, and five declined to participate. Consequently, 106 outpatients provided written informed consent. After excluding participants without type 2 diabetes (n = 4) and those with a MoCA-J score <17 (n = 2), 100 participants were enrolled. In addition, the participants who did not complete the OF questionnaire (n = 7) were excluded, resulting in 93 participants being included in the final analysis (age, 76.9 ± 4.1 years; 55 men; BMI. 23.6 ± 3.0 kg/m²). The characteristics of the participants are presented in Table 1. Of the total participants, 34 patients had OF (36.6%), and the mean values of mean SG (mg/dL), %CV, %TIR, %TAR >250 mg/dL, %TAR >180 mg/dL, %TBR <70 mg/dL, %TBR <54 mg/dL, and HbA1c were 152.7 ± 28.1, 29.2 ± 7.2, 72.6 ± 18.0, 5.6 ± 9.9, 26.4 ± 18.0, 1.1 ± 2.3, 0.1 ± 0.3, and 7.4 ± 0.8 (%), respectively.

Table 1.

Characteristics of Study Participants (N = 93)

Variable	Mean ± SD/n(%)
Age (years)	76.9 ± 4.1
Male sex	55 (59.1)
Body mass index (kg/m²)	23.7 ± 3.0
Education (years)	11.8 ± 2.5
Diabetes duration (years)	16.3 ± 11.3
Hypertension	45 (48.4)
Dyslipidemia	26 (28.0)
Chronic kidney disease	2 (2.2)
HbA1c (%)	7.4 ± 0.8
Use of antidiabetes drugs
Insulin	19 (20.4)
Sulfonyl urea	26 (28.0)
Biguanides	12 (12.9)
α-Glucosidase inhibitors	6 (6.5)
Thiazolidinediones	3 (3.2)
Dipeptidyl peptidase-4 inhibitors	55 (59.1)
Glucagon-like peptide-1 receptor agonists	6 (6.5)
Sodium-glucose co-transporter-2 inhibitors	28 (30.1)
MoCA-J	23.3 ± 2.7
BI	99.0 ± 3.6
Oral frailty	34 (36.6)
OF1: Do you have any difficulties eating tough foods compared 6 months ago? (Yes)	19 (20.4)
OF2: Have you choked on your tea or soup recently? (Yes)	22 (23.7)
OF3: Denture use (Yes)	56 (60.2)
OF4: Do you often experience having a dry mouth? (Yes)	20 (21.5)
OF5: Do you go out less frequently than you did last year? (Yes)	31 (33.3)
OF6: Can you eat hard foods like squid jerky or pickled radish? (No)	6 (6.5)
OF7: How many times do you brush your teeth per day? (<2 times/day)	29 (31.2)
OF8: Have you visit dental clinic at least annually? (No)	25 (26.9)
CGM metrics
Mean sensor glucose (mg/dL)	152.7 ± 28.1
%CV	29.2 ± 7.2
%TIR	72.6 ± 18.0
%TAR >250 mg/dL	5.6 ± 9.9
%TAR >180 mg/dL	26.4 ± 18.0
%TBR <70 mg/dL	1.1 ± 2.3
%TBR <54 mg/dL	0.1 ± 0.3

BI, Barthel Index; CGM, continuous glucose monitoring; CV, coefficient of variation; MoCA-J, Japanese version of Montreal Cognitive Assessment; OF, oral frailty; SG, sensor glucose; TAR, time above range; TBR, time below range; TIR, time in range.

Univariate linear regression analysis showed no significant association between OF and any of the CGM-derived metrics. However, among the OFI-8 subitems, denture use (OF3) was negatively associated with %TIR. Brushing teeth fewer than twice per day (OF7) was positively associated with mean SG and %TAR >250 mg/dL. In addition, not visiting dental clinics at least annually (OF8) was associated with higher mean SG, %TAR >180 mg/dL, %TAR >250 mg/dL, and HbA1c, while being negatively associated with %TIR.

In the multivariate analysis, OF7 and OF8 remained significantly associated with CGM-derived metrics after adjusting for major confounders. Specifically, OF7 and OF8 were associated with mean SG (OF7; B = 14.1, 95% CI: 2.8 to 25.4, OF8; B = 13.0, 95% CI: 0.9 to 25.2), %TIR (OF7; B = −7.9, 95% CI: −14.9 to −0.9, OF8; B = −8.0, 95% CI: −15.4 to −0.4), %TAR >180 mg/dL (OF7; B = 8.1, 95% CI:1.0 to 15.2, OF8; B = 7.9, 95% CI: 0.3–15.6), and %TAR >250 mg/dL (OF7; B = 4.5, 95% CI: 0.3 to 8.7, OF8; B = 6.3, 95% CI: 1.9 to 10.7). None of the OFI-8 subitems were significantly associated with HbA1c (Table 2).

Table 2.

Association between OFand CGM-Derived Metrics, HbA1c

	CV	Mean SG	%TIR	%TAR > 250 mg/dL	%TAR > 180 mg/dL	%TBR <70 mg/dL	%TBR < 54 mg/dL	HbA1c
	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)	B (95% CI)	B (95%CI)
OF
Model 1	−1.5 (−4.6 to 1.5)	6.3 (−5.7 to 18.4)	−4.2 (−11.9 to 3.5)	1.4 (−2.8 to 5.7)	4.8 (−2.9 to 12.5)	−0.7 (−1.7 to 0.3)	−0.1 (−0.2 to 0.1)	0.1 (−0.3 to 0.4)
Model 2	−0.9 (−3.5 to 1.7)	1.7 (−9.0 to 12.4)	−1.8 (−8.3 to 4.8)	0.4 (−3.6 to 4.3)	2.1 (−4.6 to 8.8)	−0.5 (−1.5 to 0.5)	−0.1 (−0.2 to 0.04)	−0.05 (−0.4 to 0.3)
OF1: Do you have any difficulties eating tough foods compared 6 months ago? (Yes)
Model 1	−3.5 (−7.1 to 0.2)	−3.9 (−18.3 to 10.5)	2.8 (−6.5 to 12.0)	−1.8 (−6.9 to 3.2)	−2.0 (11.2 to 7.2)	−0.8 (−2.0 to 0.4)	−0.1 (−0.3 to 0.1)	−0.2 (−0.6 to 0.2)
Model 2	−0.4 (−3.5 to 2.8)	−1.1 (−14.0 to 11.8)	−1.0 (−8.9 to 7.0)	0.2 (−4.6 to 4.9)	0.9 (−7.2 to 9.0)	−0.1 (−1.3 to 1.1)	−0.1 (−0.2 to 0.1)	−0.03 (−0.4 to 0.4)
OF2: Have you choked on your tea or soup recently? (Yes)
Model 1	−0.6 (−4.1 to 2.9)	−0.1 (−13.8 to 13.6)	−1.1 (−9.8 to 7.7)	−0.01 (−4.8 to 4.8)	1.6 (−7.1 to 10.4)	−0.7 (−1.8 to 0.5)	−0.04 (−0.2 to 0.1)	−0.1 (−0.5 to 0.4)
Model 2	1.3 (−1.8 to 4.3)	3.0 (−9.5 to 15.4)	−3.5 (−11.1 to 4.2)	2.0 (−2.5 to 6.6)	3.8 (−3.9 to 11.6)	−0.5 (−1.6 to 0.7)	−0.1 (−0.2 to 0.1)	0.03 (−0.3 to 0.4)
OF3: Denture use (Yes)
Model 1	2.9 (−0.1 to 5.8)	7.1 (−4.7 to 18.9)	−7.6 (−15.1 to −0.2) ^*	2.7 (−1.4 to 6.8)	7.1 (−0.3 to 14.6)	0.7 (−0.3 to 1.6)	0.1 (−0.1 to 0.2)	0.2 (−0.2 to 0.5)
Model 2	0.8 (−1.9 to 3.5)	−4.5 (−15.6 to 6.5)	0.2 (−6.6 to 7.0)	−1.0 (−5.1 to 3.1)	−0.4 (−7.4 to 6.5)	0.4 (−0.7 to 1.4)	0.1 (−0.1 to 0.2)	−0.2 (−0.5 to 0.1)
OF4: Do you often experience having a dry mouth? (Yes)
Model 1	−0.6 (−4.2 to 3.1)	−6.6 (−20.7 to 7.4)	3.5 (−5.5 to 12.5)	−1.5 (−6.4 to 3.5)	−3.5 (−12.5 to 5.6)	−0.1 (−1.3 to 1.1)	−0.1 (−0.2 to 0.1)	−0.2 (−0.6 to 0.2)
Model 2	0.1 (−3.0 to 3.2)	−5.0 (−17.6 to 7.6)	2.0 (−5.8 to 9.7)	−0.8 (−5.4 to 3.8)	−2.2 (−10.1 to 5.7)	0.1 (−1.1 to 1.2)	−0.1 (−0.2 to 0.1)	−0.2 (−0.6 to 0.2)
OF5: Do you go out less frequently than you did last year? (Yes)
Model 1	−1.0 (−3.9 to 2.4)	−6.5 (−18.8 to 5.7)	3.6 (−4.2 to 11.5)	−0.1 (−4.4 to 4.2)	−3.8 (−11.6 to 4.1)	0.3 (−0.7 to 1.4)	0.03 (−0.1to 0.2)	−0.2 (−0.6 to 0.2)
Model 2	−0.7 (−3.3 to 1.9)	−9.1 (−19.5 to 1.4)	5.0 (−1.4 to 11.4)	−0.8 (−4.7 to 3.1)	−5.2 (−11.8 to 1.3)	0.5 (−0.5 to 1.4)	0.1 (−0.1to 0.2)	−0.3 (−0.6 to 0.1)
OF6: Can you eat hard foods like squid jerky or pickled radish? (No)
Model 1	2.0 (−4.0 to 8.1)	2.2 (−21.5 to 25.9)	−5.1 (−20.2 to10.0)	−0.7 (−9.0 to 7.6)	4.1 (−11.0 to 19.2)	0.9 (−1.1 to 2.9)	−0.1 (−0.3 to 0.2)	0.1 (−0.6 to 0.8)
Model 2	1.3 (−3.6 to 6.3)	−2.3 (−22.5 to 17.9)	−1.9 (−14.3 to 10.5)	−2.1 (−9.4 to 5.3)	1.1 (−11.5 to 13.7)	0.7 (−1.1 to 2.6)	−0.1 (−0.3 to 0.2)	−0.04 (−0.6 to 0.6)
OF7: How many times do you brush your teeth per day? (<2 times/day)
Model 1	−1.6 (−4.8 to 1.6)	14.3 (2.1 to 26.5) ^*	−7.2 (−15.1 to 0.6)	4.5 (0.2 to 8.8) ^*	7.8 (−0.1 to 15.6)	−0.6 (−1.7 to 0.4)	0.04 (−0.1to 0.2)	0.3 (−0.1 to 0.6)
Model 2	−0.2 (−3.0 to 2.7)	14.1 (2.8 to 25.4) ^*	−7.9 (−14.9 to −0.9) ^*	4.5 (0.3 to 8.7) ^*	8.1 (1.0 to 15.2) ^*	−0.2 (−1.3 to 0.9)	0.03 (−0.1to 0.2)	0.3 (−0.1 to 0.6)
OF8: Have you visit dental clinic at least annually? (No)
Model 1	0.8 (−2.5 to 4.2)	17.8 (5.3 to 30.4) ^**	−11.4 (−19.4 to −3.3) ^**	7.6 (3.3 to 11.9) ^**	11.4 (3.8 to 19.5) ^**	−0.2 (−1.2 to 0.9)	−0.03 (−0.2 to 0.1)	0.5 (0.1 to 0.9) ^**
Model 2	0.2 (−2.9 to 3.3)	13.0 (0.9 to 25.2) ^*	−8.0 (−15.4 to −0.4) ^*	6.3 (1.9 to 10.7) ^**	7.9 (0.3 to 15.6) ^*	−0.1 (−1.2 to 1.1)	−0.1 (−0.2 to 0.1)	0.4 (−0.01 to 0.7)

P < 0.05.

P < 0.01.

Model 1: Crude model.

Model 2: Adjusted for age, sex, years of education, body mass index, use of insulin, use of sulphonyl urea, and MoCA-J scores.

CV, coefficient of variation; MoCA-J, Japanese version of Montreal Cognitive Assessment; OF, oral frailty; SG, sensor glucose; TAR, time above range; TBR, time below range; TIR, time in range.

Discussion

In this study, we investigated the relationship between OF and CGM-derived metrics in older patients with type 2 diabetes. OF was not associated with any CGM-derived metrics. However, poor oral health behaviors—such as brushing teeth infrequently and not visiting the dentist regularly—were associated with hyperglycemic metrics, including high mean SG and %TAR and low %TIR, but not HbA1c.

In a previous study of older adults in rural areas, approximately 23% of the participants, with an age profile similar to the participants in this study, exhibited OF.²¹ In contrast, the prevalence of OF among participants with diabetes in this study was 37%, which is higher than that in individuals without diabetes, a trend consistent with that reported in previous studies.⁴ Contrary to expectations, no association was found between OF and CGM-derived metrics in older patients with type 2 diabetes. However, OF was found to be associated with poor oral health behaviors.

Several potential mechanisms may explain the link between oral health behaviors and hyperglycemic outcomes. Individuals who brush their teeth fewer than twice a day or rarely visit a dentist often show low adherence to oral hygiene and oral health care practices. Such individuals are also more likely to demonstrate low adherence to other health-related behaviors,²² suggesting an overall lack of health consciousness that could contribute directly to poor glycemic control. This association is likely attributable not only to poor adherence but also to periodontal disease and oral function, both of which are closely linked to oral health behavior. Individuals with poor oral hygiene practices, such as infrequent tooth brushing, are at higher risk of developing periodontal disease.²³ Several studies have demonstrated that greater periodontal disease severity is associated with worsening glycemic control,^6,24 and low-grade inflammation caused by periodontal disease has been reported to contribute to insulin resistance.²⁵ Moreover, periodontal treatment in patients with type 2 diabetes and severe periodontal disease has been shown to improve glycemic control.^26,27 Collectively, these findings suggest that poor oral health behaviors may promote chronic inflammation, such as periodontitis, which in turn may exacerbate hyperglycemia.

As presented in Table 2, the responses to masticatory-related questions (OF1 and OF6) were not significantly associated with glycemic control. However, these items assessed subjective difficulty in mastication, which may not fully capture objective masticatory function.²⁸ Individuals with poor oral health behaviors are more likely to experience tooth loss due to periodontal disease or caries,²² which can affect objective masticatory ability, such as occlusal force and chewing efficiency function.²⁹ Experimental evidence suggests that mastication plays a role in suppressing postprandial blood glucose elevation and promoting insulin secretion.⁷ In addition, studies have reported that tooth loss is associated with elevated fasting blood glucose levels and higher carbohydrate intake.^30,31 Thus, inadequate mastication resulting from tooth loss, swallowing difficultly, or a preference for soft foods, may represent an intermediate pathway linking OF to glycemic control. Because this study did not include objective measures of mastication, further research incorporating such assessments is needed.

The strength of this study lies in it being, to our knowledge, the first to investigate the relationship between OF and glycemic control using CGM-derived metrics. While some studies have explored the relationship between OF and glycemic control based on HbA1c, the number of published studies remains limited, and no definitive conclusions have been reached.³² Our study showed no significant association between OF and HbA1c (Table 2), suggesting that oral health behavior, a subcomponent of OF, is associated with hyperglycemic metrics derived from CGM. This clinically significant finding cannot be captured by HbA1c alone. The results of this study suggest that oral health behavioral interventions may contribute to diabetes management, and that integrated diabetes care models could be further enhanced with the inclusion of dental professionals.

This study has several limitations. First, since this was a cross-sectional study, we could not establish causality. Longitudinal studies are needed to prove causation and determine directionality. Second, unmeasured confounders may have been present. While potential confounders were considered during the hypothesis-generation phase, adherence to diabetes treatment and dietary patterns could not be assessed and therefore were not included as adjustment variables. Although years of education were used as a proxy for adherence, other unaccounted confounders may have influenced the results. Third, the sample size of this study was small, and participants were recruited from a single center in Japan, which may limit both its statistical power and generalizability. Finally, this study relied on self-reported behaviors and objective oral health measures, such as the number of teeth, periodontal status, and masticatory function, could not be assessed. While these are mentioned as potential intermediate factors in the discussion, the actual factors remain unknown. Further investigations, including objective assessments of the oral cavity, oral hygiene, and oral function, are needed.

Conclusions

In older patients with type 2 diabetes, OF was not associated with glycemic control indices. However, poor oral health behaviors, such as brushing fewer times than twice a day and not visiting a dentist more than once a year—sub-items of OF—may be associated with hyperglycemic metrics and %TIR derived from CGM.

Footnotes

Acknowledgments

The authors want to thank the BioBank at the National Center for Geriatrics and Gerontology for controlling the quality of the clinical data.

Authors’ Contributions

J.N.: Conceptualization, methodology, writing—original draft, visualization; T.Su.: Software, validation, formal analysis, investigation, data curation, funding acquisition; T.O.: Investigation, resources; K.U.: Formal analysis, investigation; M.Mu.: Writing—review and editing; Y.K.: Writing—review and editing; Y.Y.: Writing—review and editing; M.Mo.: Writing—review and editing; Y.N.: Writing—review and editing; H.S.: Writing—review and editing; S.N.: Writing—review and editing; S.K.: Investigation, resources; H.T.: Investigation, resources; T.Sa.: Supervision, project administration, funding acquisition.

Author Disclosure Statement

The authors have no conflicts of interest to declare.

Funding Information

This work was supported by JSPS KAKENHI (grant numbers: 21K17675, 24K20127) from the Japan Society for the Promotion of Science and The Research Funding for Longevity Sciences (grant numbers: 20–22, 22–2, and 22–23) from the National Center for Geriatrics and Gerontology. The funders played no role in the article preparation.

Abbreviations Used

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Association Between Oral Frailty and Metrics Derived from Continuous Glucose Monitoring in Older Patients with Type 2 Diabetes

Abstract

Aims:

Materials and Methods:

Results:

Conclusions:

Introduction

Materials and Methods

Design, setting, and participants

Outcome variables

Oral frailty

Demographic and clinical variables

Statistical analysis

Results

Discussion

Conclusions

Footnotes

Acknowledgments

Authors’ Contributions

Author Disclosure Statement

Funding Information

Abbreviations Used

References