Impact of Masticatory Performance and the Tongue-Lip Motor Function on Incident Adverse Health Events in Patients with Metabolic Disease

Abstract

Aim: The present study aimed to determine whether decreased masticatory performance and tongue-lip motor function are associated with an increased incidence of adverse health events in patients with metabolic disease.

Methods: One thousand patients with metabolic diseases including diabetes, dyslipidemia, hypertension, and hyperuricemia were recruited. Masticatory performance was assessed using a gummy jelly test, wherein glucose elution from chewed gummy jelly was measured. The tongue-lip motor function was measured using repeatedly pronounced syllables per second. Their association with the incidence of adverse health events (a composite of all-cause death, cardiovascular disease, bone fracture, malignant neoplasm, pneumonia, and dementia) was investigated using the generalized propensity score (GPS) method.

Results: During a median follow-up period of 36.6 (interquartile range, 35.0–37.7) months, adverse health events were observed in 191 patients. The GPS adjusted dose-response function demonstrated that masticatory performance was inversely associated with the incidence of adverse health events. The 3-year incidence rate was 22.8% (95% confidence interval, 19.0–26.4%) for the lower quartile versus 13.6% (10.5–16.7%) for the upper quartile (P＜0.001). Similarly, the tongue-lip motor function was inversely associated with the incidence of adverse health events, with a 3-year incidence rate of 23.6% (20.0–27.0%) for the lower quartile versus 13.2% (10.4–15.9%) for the upper quartile (P＜0.001).

Conclusions: Decreased masticatory performance and tongue-lip motor function were associated with an increased incidence of adverse health events in patients with metabolic disease.

See editorial vol. 31: 1660-1661

Introduction

A decline in the oral function leads to nutritional imbalance, physical frailty, and sarcopenia^1-3). Since imbalanced nutrition, physical frailty, and sarcopenia are linked to a wide spectrum of health disorders^4-6), a decreased oral function can potentially affect general health conditions⁷⁾. Indeed, several population-based cohorts reported that a decreased number of teeth and self-reported difficulty in mastication were associated with a decline in physical and cognitive function and with an elevated incidence of all-cause, cardiovascular, cancer, and other mortality^8-12). Oral health problems, along with frailty and related health problems, are common in patients with metabolic disease¹³⁾. Oral health has attracted increasing attention in the management of patients with metabolic diseases.

Masticatory performance and the tongue-lip motor function are key components of the oral function and can be measured objectively¹⁴⁾. Mastication is roughly described as the shearing and crushing of food by the teeth, but this mechanical process also requires fine semiautomatic, synchronized motion of the tongue and lips¹⁵⁾. To date, little is known about the individual effects of masticatory performance and the tongue-lip motor function on the prognosis of patients with metabolic diseases.

Aim

The present study aimed to determine whether a decrease in the tongue-lip motor function, as well as masticatory performance, is associated with an increased incidence of adverse health events in patients with metabolic disease.

Methods

Study Population

The present study surveyed incident adverse health events in 1000 patients with metabolic disease who previously participated in the CHEWING ability in patients with LIFEstyle disease (CHEWING-LIFE) study. The details of the CHEWING-LIFE study have been described elsewhere¹⁶⁾. In brief, this cross-sectional study enrolled 1000 adult Japanese patients who were treated for metabolic disease at Shiraiwa Medical Clinic, Kashiwara City, Osaka, Japan, between November 2019 and March 2020. The inclusion criteria were (1) age ≥ 20 years and (2) treatment at the clinic for one or more of the following metabolic diseases: diabetes, dyslipidemia, hypertension, and hyperuricemia. The exclusion criteria were (1) mentally disabled individuals who had difficulty in giving informed consent by themselves and (2) those who were expected to have difficulty in undergoing a series of study examinations. They underwent cross-sectional examination of their masticatory performance and tongue-lip motor function. In this study, data on incident adverse health events after participation in the CHEWING-LIFE study were retrospectively collected from medical records. Data collection was conducted between June and September 2023 to assess the three-year incidence of adverse health events.

This study was performed in accordance with the Declaration of Helsinki and approved by the ethics committee of Osaka University Hospital. Written informed consent was obtained from each participant in the original CHEWING-LIFE study; however, the requirement for informed consent was waived for the present retrospective study.

Definitions of Baseline Characteristics

Body mass index (BMI) was calculated as body weight in kilograms divided by the square of the height in meters. Mean blood pressure was calculated as the diastolic blood pressure plus one-third of the difference between systolic and diastolic blood pressure. Masticatory performance was measured using the gummy jelly test¹⁴⁾. Participants were asked to chew a test glucose-containing gummy jelly for 20 s without swallowing it, and then hold 10 mL of water in their mouth and spit it out together with the jelly. The concentration of glucose eluted from the chewed jelly to water was measured using a Gluco Sensor GS-II (GC Corporation, Tokyo, Japan). A low glucose level indicates insufficient comminution of the jelly and impaired masticatory performance. The tongue-lip motor function was evaluated as oral diadochokinesis¹⁴⁾. Participants were instructed to pronounce each of the syllables /pa/, /ta/, and /ka/ repeatedly for five seconds. The number of syllables produced per second was determined using an automatic counter (Kenkokun Handy, Takei Scientific Instruments Co., Ltd., Niigata, Japan), and the lowest of the three was recorded as the oral diadochokinetic rate. A slower oral diadochokinetic rate indicates a decreased tongue-lip motor function¹⁴⁾. Physical performance was assessed using 5-meter gait speed and handgrip strength. The definitions of the respective metabolic diseases were in accordance with the domestic clinical guidelines^17-20).

Outcome Measure

The outcome measure was the incidence of adverse health events, defined as a composite of all-cause death, cardiovascular disease, bone fracture, malignant neoplasm, pneumonia, and dementia. Cardiovascular diseases included coronary artery disease (myocardial infarction and angina pectoris), cerebrovascular disease (transient ischemic attack, ischemic stroke, and cerebral hemorrhage), aortic dissection, peripheral artery disease, and hospitalization due to heat failure.

Statistical Analysis

Data on baseline characteristics are presented as the mean±standard deviation (SD) or median (interquartile range) for continuous variables and frequency (percentage) for discrete variables, unless otherwise mentioned. Statistical significance was set at P＜0.05, and 95% confidence intervals were reported where appropriate. The association between masticatory performance and incident adverse health events was analyzed using the generalized propensity score (GPS) method, an extension of propensity score methods, to analyze continuous exposure^{21, 22)}. The statistical procedures for the GPS method are as follows:

We first developed an ordinary least squares (OLS) regression model of an exposure of interest (i.e., either masticatory performance or tongue-lip motor function) on baseline covariates. The baseline covariates included in the OLS regression model were age, sex, BMI, smoking, medication use (antidiabetic, antihyperlipidemic, antihypertensive, and antihyperuricemic), cardiovascular disease, history of malignancy, osteoporosis, depression, blood pressure, hemoglobin A1c level, lipid profile, uric acid level, gait speed, and handgrip strength. Since marked sex differences exist in handgrip strength²³⁾, its interaction term with sex was also included. Furthermore, we included masticatory performance and tongue-lip motor function as additional explanatory variables in the OLS regression model of tongue-lip motor function and masticatory performance, respectively, to adjust for each other and assess their independent impact on the outcome.

Using the OLS regression model, we subsequently calculated the GPS as the conditional probability density function of the exposure for the observed baseline covariates. The balancing property of the GPS was checked using the blocking method. Briefly, we divided the study population into four subgroups according to quartiles of the exposure. We thereafter compared baseline covariates between one arbitrary subgroup and the others using t statistics with GPS stratification evaluated at the average of the exposure stratum. An imbalance was suggested when the absolute value of t statistics was greater than 1.96.

Finally, we estimated the dose-response function (DRF) of the exposure for the incidence of adverse health events. For the estimation, we regressed the incidence on the exposure and the estimated GPS (evaluated based on the observed value of the exposure) using the Cox proportional hazards regression model, in which the exposure, GPS, and their interaction term were entered as explanatory variables. Based on the developed Cox proportional hazards regression model, we estimated the incidence corresponding to an arbitrary value of the exposure and the GPS evaluated based on the exposure value of each individual. The DRF was obtained by averaging the estimates of the overall study sample. DRF demonstrates the average response that would occur in the population if “everyone” had an arbitrary value of the exposure. Because DRF was expected to be non-linear, we graphically demonstrated the function along with the (first) derivative of the function. The derivative indicates the slope of the tangent line for any point on the function; a value higher or lower than zero indicates an increasing or decreasing risk of incident adverse health events, respectively (i.e., a positive or inverse association with the incidence), whereas a value equal to zero indicates an unchanged incidence of adverse health events (i.e., no association with the incidence). The difference in the risk of adverse health events between quartiles of the exposure was also calculated from the DRF.

We also performed a sensitivity analysis, excluding patients with a history of cardiovascular or malignant disease. Missing data were addressed by multiple imputations using the chained equations method. In this procedure, we generated ten imputed datasets and combined the results of the analysis according to Rubin’s rule. All statistical analyses were performed using R version 4.1.1 (R Development Core Team, Vienna, Austria).

Results

The baseline characteristics are summarized in Table 1. The mean age of the patients was 66±11 years, and 54.7% were male. The mean measurement of masticatory performance was 11.2±4.1 mmol/L, and that of the tongue-lip motor function was 5.5±1.0 times/sec. The two aspects of masticatory performance were significantly correlated (r=0.24; P＜0.001). During a median follow-up of 36.6 (interquartile range, 35.0–37.7) months, adverse health events were observed in 191 patients.

Table 1.Baseline characteristics of overall population (n = 1000)

Age (years)	66±11
Male sex	547 (54.7%)
BMI (kg/m2)	23.7±3.8
Smoking
Never	479 (47.9%)
Past	361 (36.1%)
Current	160 (16.0%)
Diabetes mellitus	765 (76.5%)
Hypertension	667 (66.7%)
Dyslipidemia	752 (75.2%)
Hyperuricemia	121 (12.1%)
Cardiovascular disease	116 (11.6%)
Malignancy	118 (11.8%)
Osteoporosis	57 (5.7%)
Depression	15 (1.5%)
Antidiabetic medication	670 (67.0%)
Antihyperlipidemic medication	652 (65.2%)
Antihypertensive medication	572 (57.2%)
Antihyperuricemic medication	100 (10.0%)
Systolic blood pressure (mmHg)	133±17
(missing data)	7 (0.7%)
Diastolic blood pressure (mmHg)	77±24
(missing data)	7 (0.7%)
Hemoglobin A1c (%)	6.9±0.9
(mmol/mol)	52±10
(missing data)	91 (9.1%)
LDL cholesterol (mmol/L)	2.68±0.77
(missing data)	774 (77.4%)
HDL cholesterol (mmol/L)	1.47±0.43
(missing data)	198 (19.8%)
Triglycerides (mmol/L)	1.30 (0.95 - 1.85)
(missing data)	197 (19.7%)
Uremic acid (μmol/L)	300±75
(missing data)	225 (22.5%)
Gait speed (m/sec)	1.2±0.3
Handgrip strength (kg)
Males	36.4±7.3
Females	22.0±4.5
Masticatory performance (mmol/L)	11.2±4.1
Tongue-lip motor function (times/sec)	5.5±1.0

BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

The OLS regression analysis demonstrated that masticatory performance was independently associated with the tongue-lip motor function and vice versa (Table 2). Based on the OLS regression results, we developed the respective GPSs for masticatory performance and the tongue-lip motor function. After adjustment of the GPS, absolute values of t statistics were smaller than 1.96 for all covariates (Fig.1), suggesting that the GPS adjustment balanced them properly. The GPS-adjusted DRFs of masticatory performance and tongue-lip motor function for incident adverse health events are illustrated in Fig.2. Generally, both masticatory performance and the tongue-lip motor function were inversely associated with adverse health events. The upper limit of the 95% confidence interval for the derivative was lower than zero (indicating an inverse association with the incidence) between 9.2 and 13.9 mmol/L for masticatory performance and between 4.7 and 6.0 times/sec for the tongue-lip motor function, whereas the 95% confidence interval included zero (suggesting a non-significant association with the incidence) out of the range. For masticatory performance, the 3-year incidence rate of adverse health events was 22.8% (95% confidence interval, 19.0–26.4%) at the lower quartile (8.4 mmol/L) versus 13.6% (10.5–16.7%) at the upper quartile (13.7 mmol/L), with a hazard ratio of 1.76 (95% confidence interval, 1.30–2.39; P＜0.001). For the tongue-lip motor function, it was 23.6% (20.0–27.0%) at the lower quartile (5.0 times/sec) versus 13.2% (10.4–15.9%) at the upper quartile (6.2 times/sec), with a hazard ratio of 1.90 (1.51–2.41; P＜0.001) (Table 3). The corresponding values for each adverse health event component are presented in Tables 4 and 5.

Table 2.OLS regression analysis of masticatory performance and tongue-lip motor function on baseline covariates

	Adjusted β in OLS regression model of masticatory performance	Adjusted β in OLS regression model of tongue-lip motor function
Age (years)	-0.026 (P= 0.081)	-0.026 (P＜0.001)
Male sex	1.392 (P= 0.28)	-0.494 (P= 0.070)
BMI (kg/m2)	-0.083 (P= 0.027)	-0.023 (P= 0.005)
Smoking	-0.657 (P= 0.001)	-0.043 (P= 0.33)
Cardiovascular disease	-0.140 (P= 0.73)	-0.058 (P= 0.50)
Malignancy	-0.285 (P= 0.47)	-0.015 (P= 0.86)
Osteoporosis	-0.209 (P= 0.72)	-0.141 (P= 0.25)
Depression	-0.328 (P= 0.75)	-0.119 (P= 0.58)
Antidiabetic medication	-0.111 (P= 0.74)	-0.206 (P= 0.005)
Antihyperlipidemic medication	-0.042 (P= 0.87)	0.113 (P= 0.047)
Antihypertensive medication	-0.294 (P= 0.28)	-0.014 (P= 0.81)
Antihyperuricemic medication	0.040 (P= 0.93)	0.041 (P= 0.65)
Mean blood pressure (mmHg)	-0.008 (P= 0.45)	0.002 (P= 0.43)
Hemoglobin A1c (%)	-0.332 (P= 0.064)	0.024 (P= 0.55)
LDL cholesterol (mmol/L)	0.141 (P= 0.48)	0.056 (P= 0.32)
HDL cholesterol (mmol/L)	0.242 (P= 0.50)	0.001 (P= 0.99)
Log-transformed triglycerides (mmol/L)	0.203 (P= 0.49)	0.038 (P= 0.57)
Uremic acid (μmol/L)	0.002 (P= 0.28)	0.001 (P= 0.052)
Gait speed (m/sec)	0.861 (P= 0.093)	0.374 (P= 0.001)
Handgrip strength (kg)	0.077 (P= 0.088)	0.028 (P= 0.004)
(Handgrip strength)×sex	-0.028 (P= 0.55)	-0.006 (P= 0.56)
Masticatory performance (mmol/L)	N/I	0.029 (P＜0.001)
Tongue-lip motor function (times/sec)	0.639 (P＜0.001)	N/I

Data are adjusted regression coefficients β and P values, derived from the multivariable OLS regression analysis of masticatory performance and tongue-lip motor function, in which all the variables listed in the table except tongue-lip motor function and masticatory performance, respectively, were entered as the explanatory variables. N/I, not included. BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; OLS, ordinary least squares.

Fig.1. Balance of baseline characteristics before (gray circles) and after GPS adjustment (black circles) for masticatory performance (A) and tongue-lip motor function (B)

Data are presented as t statistics between one quartile and the other quartiles. Dotted lines represent t=±1.96; t statistics greater than 1.96 in absolute value, indicate imbalance. GPS, generalized propensity score; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

Fig.2. Incidence of adverse health events corresponding to masticatory performance (A) and tongue-lip motor function (B)

The upper panels show the 3-year incidence rate of adverse health events corresponding to an arbitrary value of masticatory performance or the tongue-lip motor function (bold line) and its 95% confidence interval (dotted lines). The lower panels show the (first) derivative of the log-transformed hazard ratio (i.e., slope of the tangent line) (bold line) and its 95% confidence interval (dotted line).

Table 3.Three-year incidence rate of adverse health events corresponding to quartiles of masticatory performance and tongue-lip motor function

	Lower quartile	Median	Upper quartile
Masticatory performance
3-year incidence rate	22.8% [19.0%–26.4%]	18.0% [14.9%–21.1%]	13.6% [10.5%–16.7%]
Hazard ratio vs. the lower quartile	(Reference)	0.77 [0.64–0.92] (P= 0.005)	0.57 [0.42–0.77] (P＜0.001)
Hazard ratio vs. the median	1.30 [1.08–1.56] (P= 0.005)	(Reference)	0.74 [0.62–0.88] (P= 0.001)
Hazard ratio vs. the upper quartile	1.76 [1.30–2.39] (P＜0.001)	1.36 [1.14–1.62] (P= 0.001)	(Reference)
Tongue-lip motor function
3-year incidence rate	23.6% [20.0%–27.0%]	16.7% [14.0%–19.3%]	13.2% [10.4%–15.9%]
Hazard ratio vs. the lower quartile	(Reference)	0.68 [0.59–0.78] (P＜0.001)	0.53 [0.42–0.66] (P＜0.001)
Hazard ratio vs. the median	1.47 [1.27–1.70] (P＜0.001)	(Reference)	0.77 [0.66–0.90] (P= 0.001)
Hazard ratio vs. the upper quartile	1.90 [1.51–2.40] (P＜0.001)	1.29 [1.11–1.51] (P= 0.001)	(Reference)

Data are generalized propensity score-adjusted estimates [95% confidence intervals] (P values). The first quartile, median and upper quartile of masticatory performance were 8.4, 11.2, and 13.7 mmol/L, whereas those of tongue-lip motor function were 5.0, 5.6, and 6.2 times/sec, respectively.

Table 4.Three-year incidence rate of individual adverse health events corresponding to quartiles of masticatory performance

	Number of observed events	Masticatory performance
		Lower quartile	Median	Upper quartile
All-cause death	11	0.8% [0.1%–1.6%]	0.9% [0.1%–1.6%]	0.7% [-0.0%–1.4%]
Hazard ratio vs. the upper quartile		1.22 [0.34–4.35] (P= 0.76)	1.26 [0.53–3.01] (P= 0.60)	(Reference)
Cardiovascular disease	76	8.8% [6.2%–11.3%]	7.1% [5.0%–9.2%]	6.1% [3.9%–8.3%]
Hazard ratio vs. the upper quartile		1.45 [0.90–2.34] (P= 0.13)	1.17 [0.88–1.54] (P= 0.28)	(Reference)
Bone fracture	38	5.3% [3.3%–7.3%]	3.5% [2.0%–5.0%]	2.0% [0.8%–3.3%]
Hazard ratio vs. the upper quartile		2.63 [1.31–5.31] (P= 0.007)	1.73 [1.15–2.60] (P= 0.008)	(Reference)
Malignant neoplasm	68	7.7% [5.3%–10.0%]	6.6% [4.6%–8.5%]	5.0% [3.0%–7.0%]
Hazard ratio vs. the upper quartile		1.56 [0.94–2.57] (P= 0.083)	1.32 [0.98–1.77] (P= 0.068)	(Reference)
Pneumonia	12	1.6% [0.5%–2.8%]	1.0% [0.2%–1.9%]	0.7% [-0.0%–1.5%]
Hazard ratio vs. the upper quartile		2.21 [0.63–7.77] (P= 0.22)	1.39 [0.68–2.85] (P= 0.36)	(Reference)
Dementia	16	2.6% [1.1%–4.0%]	1.3% [0.3%–2.3%]	0.3% [-0.1%–0.8%]
Hazard ratio vs. the upper quartile		7.79 [2.23–27.29] (P= 0.001)	3.93 [1.57–9.78] (P= 0.003)	(Reference)

Data are generalized propensity score-adjusted estimates [95% confidence intervals] (P values). The first, median, and upper quartiles of masticatory performance were 8.4, 11.2, and 13.7 mmol/L, respectively.

Table 5.Three-year incidence rate of individual adverse health events corresponding to quartiles of tongue-lip motor function

	Number of observed events	Tongue-lip motor function
		Lower quartile	Median	Upper quartile
All-cause death	11	1.0% [0.2%–1.8%]	0.6% [0.1%–1.1%]	0.3% [-0.1%–0.6%]
Hazard ratio vs. the upper quartile		4.11 [1.37–12.35] (P= 0.012)	2.36 [0.91–6.09] (P= 0.076)	(Reference)
Cardiovascular disease	76	10.4% [7.9%–12.9%]	6.4% [4.6%–8.2%]	5.3% [3.5%–7.0%]
Hazard ratio vs. the upper quartile		2.03 [1.42–2.90] (P＜0.001)	1.22 [0.99–1.52] (P= 0.063)	(Reference)
Bone fracture	38	4.4% [2.7%–6.1%]	3.3% [2.0%–4.5%]	1.9% [0.8%–3.1%]
Hazard ratio vs. the upper quartile		2.31 [1.32–4.04] (P= 0.003)	1.70 [1.09–2.64] (P= 0.018)	(Reference)
Malignant neoplasm	68	8.0% [5.8%–10.2%]	6.4% [4.7%–8.0%]	4.7% [3.0%–6.4%]
Hazard ratio vs. the upper quartile		1.73 [1.19–2.51] (P= 0.004)	1.36 [1.05–1.76] (P= 0.022)	(Reference)
Pneumonia	12	1.3% [0.3%–2.2%]	1.0% [0.3%–1.7%]	0.7% [-0.1%–1.4%]
Hazard ratio vs. the upper quartile		1.93 [0.66–5.60] (P= 0.23)	1.55 [0.68–3.55] (P= 0.29)	(Reference)
Dementia	16	2.1% [0.9%–3.2%]	1.1% [0.3%–1.9%]	0.7% [0.0%–1.4%]
Hazard ratio vs. the upper quartile		2.89 [1.24–6.73] (P= 0.014)	1.54 [0.85–2.79] (P= 0.16)	(Reference)

Data are generalized propensity score-adjusted estimates [95% confidence intervals] (P values). The first, median, and upper quartiles of tongue-lip motor function were 5.0, 5.6, and 6.2 times/sec, respectively.

Broadly similar findings were observed in the sensitivity analysis, excluding patients with a history of cardiovascular disease or malignant disease (Supplementary Tables 1, 2, 3, 4, 5 and Figs.1 and 2).

Supplementary Table 1.Baseline characteristics after excluding patients with a history of cardiovascular disease or malignancy (n = 782)

Age (years)	65±11
Male sex	409 (52.3%)
BMI (kg/m2)	23.8±3.9
Smoking
Never	386 (49.4%)
Past	261 (33.4%)
Current	135 (17.3%)
Diabetes mellitus	595 (76.1%)
Hypertension	492 (62.9%)
Dyslipidemia	572 (73.1%)
Hyperuricemia	85 (10.9%)
Cardiovascular disease	0 (0.0%)
Malignancy	0 (0.0%)
Osteoporosis	40 (5.1%)
Depression	12 (1.5%)
Antidiabetic medication	520 (66.5%)
Antihyperlipidemic medication	491 (62.8%)
Antihypertensive medication	419 (53.6%)
Antihyperuricemic medication	69 (8.8%)
Systolic blood pressure (mmHg)	133±17
(missing data)	8 (1.0%)
Diastolic blood pressure (mmHg)	77±11
(missing data)	8 (1.0%)
Hemoglobin A1c (%)	6.9±0.9
(mmol/mol)	51±10
(missing data)	70 (9.0%)
LDL cholesterol (mmol/L)	2.65±0.76
(missing data)	614 (78.5%)
HDL cholesterol (mmol/L)	1.49±0.44
(missing data)	151 (19.3%)
Triglycerides (mmol/L)	1.27 (0.94–1.84)
(missing data)	150 (19.2%)
Uremic acid (μmol/L)	300±75
(missing data)	182 (23.3%)
Gait speed (m/sec)	1.3±0.3
Handgrip strength (kg)
Males	37.2±7.0
Females	22.3±4.4
Masticatory performance (mmol/L)	11.3±4.2
Tongue-lip motor function (times/sec)	5.6±0.9

BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

Supplementary Table 2.OLS regression analysis of masticatory performance and tongue-lip motor function on baseline covariates after excluding patients with a history of cardiovascular disease or malignancy

	Adjusted β in OLS regression model of masticatory performance	Adjusted β in OLS regression model of tongue-lip motor function
Age (years)	-0.031 (P= 0.066)	-0.025 (P＜0.001)
Male sex	1.849 (P= 0.23)	-0.315 (P= 0.33)
BMI (kg/m2)	-0.074 (P= 0.086)	-0.029 (P= 0.001)
Smoking	-0.577 (P= 0.010)	-0.034 (P= 0.48)
Osteoporosis	-0.362 (P= 0.60)	-0.060 (P= 0.68)
Depression	0.347 (P= 0.77)	-0.082 (P= 0.74)
Antidiabetic medication	-0.039 (P= 0.92)	-0.183 (P= 0.026)
Antihyperlipidemic medication	-0.212 (P= 0.49)	0.110 (P= 0.085)
Antihypertensive medication	-0.264 (P= 0.39)	0.001 (P= 0.99)
Antihyperuricemic medication	-0.299 (P= 0.57)	0.144 (P= 0.19)
Mean blood pressure (mmHg)	-0.020 (P= 0.13)	0.005 (P= 0.075)
Hemoglobin A1c (%)	-0.318 (P= 0.13)	0.000 (P＞0.99)
LDL cholesterol (mmol/L)	0.221 (P= 0.36)	0.054 (P= 0.42)
HDL cholesterol (mmol/L)	0.353 (P= 0.39)	-0.001 (P= 0.99)
Log-transformed triglycerides (mmol/L)	0.447 (P= 0.19)	0.037 (P= 0.61)
Uremic acid (μmol/L)	0.002 (P= 0.51)	0.001 (P= 0.043)
Gait speed (m/sec)	0.975 (P= 0.10)	0.308 (P= 0.013)
Handgrip strength (kg)	0.093 (P= 0.072)	0.033 (P= 0.002)
(Handgrip strength)×sex	-0.043 (P= 0.44)	-0.013 (P= 0.25)
Masticatory performance (mmol/L)	N/I	0.029 (P＜0.001)
Tongue-lip motor function (times/sec)	0.666 (P＜0.001)	N/I

Data are adjusted regression coefficients β and P values, derived from the multivariable OLS regression analysis of masticatory performance and tongue-lip motor function in which all the variables listed in the Table except tongue-lip motor function and masticatory performance, respectively, were entered as the explanatory variables. N/I, not included. BMI, body mass index; HDL, high-density lipoprotein; LDL, low-density lipoprotein; OLS, ordinary least square.

Supplementary Table 3.Three-year incidence rate of adverse health events corresponding to quartiles of masticatory performance and tongue-lip motor function after excluding patients with a history of cardiovascular disease or malignancy

	Lower quartile	Median	Upper quartile
Masticatory performance
3-year incidence rate	20.4% [16.4%–24.3%]	15.1% [11.8%–18.3%]	11.2% [8.0%–14.2%]
Hazard ratio vs. the lower quartile	(Reference)	0.72 [0.57–0.90] (P= 0.004)	0.52 [0.36–0.74] (P＜0.001)
Hazard ratio vs. the median	1.39 [1.11–1.74] (P= 0.004)	(Reference)	0.72 [0.59–0.88] (P= 0.001)
Hazard ratio vs. the upper quartile	1.93 [1.35–2.77] (P＜0.001)	1.39 [1.13–1.69] (P= 0.001)	(Reference)
Tongue-lip motor function
3-year incidence rate	21.3% [17.1%–25.3%]	13.5% [10.7%–16.2%]	11.8% [8.9%–14.6%]
Hazard ratio vs. the lower quartile	(Reference)	0.60 [0.48–0.76] (P＜0.001)	0.52 [0.39–0.70] (P＜0.001)
Hazard ratio vs. the median	1.66 [1.31–2.09] (P＜0.001)	(Reference)	0.86 [0.76–0.99] (P= 0.036)
Hazard ratio vs. the upper quartile	1.91 [1.43–2.57] (P＜0.001)	1.16 [1.01–1.32] (P= 0.036)	(Reference)

Data are generalized propensity score-adjusted estimates [95% confidence intervals] (P values). The first quartile, median and upper quartile of masticatory performance were 8.5, 11.4, and 13.8 mmol/L, whereas those of tongue-lip motor function were 5.0, 5.8, and 6.2 times/sec, respectively.

Supplementary Table 4.Three-year incidence rate of individual adverse health events corresponding to quartiles of masticatory performance after excluding patients with a history of cardiovascular disease or malignancy

	Number of observed events	Masticatory performance
		Lower quartile	Median	Upper quartile
All-cause death	8	1.1% [0.1%–2.1%]	0.6% [0.0%–1.3%]	0.4% [0.0%–0.9%]
Hazard ratio vs. the upper quartile		3.04 [0.62–14.8] (P= 0.17)	1.64 [0.67–3.99] (P= 0.28)	(Reference)
Cardiovascular disease	48	7.7% [5.0%–10.4%]	5.4% [3.3%–7.4%]	4.6% [2.4%–6.7%]
Hazard ratio vs. the upper quartile		1.72 [0.95–3.11] (P= 0.072)	1.19 [0.86–1.64] (P= 0.30)	(Reference)
Bone fracture	28	4.8% [2.7%–6.8%]	3.3% [1.7%–4.9%]	1.9% [0.6%–3.2%]
Hazard ratio vs. the upper quartile		2.56 [1.18–5.56] (P= 0.018)	1.76 [1.14–2.73] (P= 0.011)	(Reference)
Malignant neoplasm	42	5.7% [3.4%–8.0%]	5.1% [3.1%–7.0%]	4.0% [2.1%–5.9%]
Hazard ratio vs. the upper quartile		1.45 [0.78–2.68] (P= 0.24)	1.28 [0.90–1.82] (P= 0.17)	(Reference)
Pneumonia	10	1.6% [0.3%–2.9%]	1.3% [0.2%–2.3%]	1.0% [0.0%–2.1%]
Hazard ratio vs. the upper quartile		1.57 [0.43–5.70] (P= 0.49)	1.26 [0.61–2.58] (P= 0.54)	(Reference)
Dementia	10	2.0% [0.2%–3.6%]	0.8% [0.0%–1.7%]	0.2% [0.0%–0.5%]
Hazard ratio vs. the upper quartile		13.1 [2.68–64.3] (P= 0.002)	5.33 [1.25–22.7] (P= 0.024)	(Reference)

Data are generalized propensity score-adjusted estimates [95% confidence intervals] (P values). The first quartile, median and upper quartile of masticatory performance were 8.5, 11.4, and 13.8 mmol/L, respectively.

Supplementary Table 5.Three-year incidence rate of individual adverse health events corresponding to quartiles of tongue-lip motor function after excluding patients with a history of cardiovascular disease or malignancy

	Number of observed events	Tongue-lip motor function
		Lower quartile	Median	Upper quartile
All-cause deat	8	0.5% [0.0%–1.1%]	0.2% [0.0%–0.4%]	0.0% [0.0%–0.1%]
Hazard ratio vs. the upper quartile		18.3 [3.23–103] (P= 0.001)	6.08 [1.54–24.02] (P= 0.010)	(Reference)
Cardiovascular disease	48	9.3% [6.4%–12.1%]	4.7% [2.9%–6.4%]	4.3% [2.4%–6.0%]
Hazard ratio vs. the upper quartile		2.24 [1.39–3.60] (P= 0.001)	1.10 [0.91–1.32] (P= 0.34)	(Reference)
Bone fracture	28	4.4% [2.3%–6.4%]	2.7% [1.4%–4.0%]	1.9% [0.7%–3.1%]
Hazard ratio vs. the upper quartile		2.35 [1.24–4.46] (P= 0.009)	1.42 [0.99–2.03] (P= 0.056)	(Reference)
Malignant neoplasm	42	6.3% [3.9%–8.7%]	4.6% [3.0%–6.3%]	3.9% [2.2%–5.6%]
Hazard ratio vs. the upper quartile		1.62 [0.99–2.65] (P= 0.057)	1.18 [0.94–1.47] (P= 0.15)	(Reference)
Pneumonia	10	1.2% [0.1%–2.2%]	1.0% [0.2%–1.8%]	0.7% [0.0%–1.5%]
Hazard ratio vs. the upper quartile		1.76 [0.53–5.77] (P= 0.35)	1.53 [0.73–3.19] (P= 0.26)	(Reference)
Dementia	10	1.5% [0.3%–2.8%]	0.9% [0.1%–1.7%]	0.6% [0.0%–1.3%]
Hazard ratio vs. the upper quartile		2.59 [0.81–8.25] (P= 0.11)	1.52 [0.78–2.97] (P= 0.22)	(Reference)

Data are generalized propensity score-adjusted estimates [95% confidence intervals] (P values). The first quartile, median and upper quartile of tongue-lip motor function were 5.0, 5.8, and 6.2 times/sec, respectively.

Supplementary Fig.1. Balance of baseline characteristics before (gray circles) and after GPS adjustment (black circles) for masticatory performance (A) and tongue-lip motor function (B)

Data are t statistics between one quartile and the other quartiles. Dotted lines represent t=±1.96; t statistics greater than 1.96 in absolute values indicate imbalance. GPS, generalized propensity score; HDL, high-density lipoprotein; LDL, low-density lipoprotein.

Supplementary Fig.2. Incidence of adverse health events corresponding to masticatory performance (A) and tongue-lip motor function (B)

Upper panels show the 3-year incidence rate of adverse health events corresponding to an arbitrary value of masticatory performance or tongue-lip motor function (bold line) and its 95% confidence interval (dotted lines). Lower panels show the derivative (i.e., slope) of the log-transformed hazard ratio (bold line) and its 95% confidence interval (dotted lines).

Discussion

Using the GPS method, the present study revealed that decreased masticatory performance and tongue-lip motor function were associated with an increased incidence of adverse health events in patients with metabolic disease. Masticatory performance and tongue-lip motor function were adjusted for each other during the GPS analysis, indicating that these two aspects were independently associated with the outcome.

The association between a decreased oral function and incident adverse health events would be partially mediated by physical frailty and sarcopenia. Masticatory impairment can lead to physical frailty and sarcopenia³⁾. Growing evidence supports that both physical frailty and sarcopenia are associated with an increased risk of various health problems, including metabolic disorders^{24, 25)}, osteoporosis^{26, 27)}, falls^{28, 29)}, altered immune responses^{30, 31)}, and cognitive decline^{32, 33)}. Consequently, individuals with frailty and sarcopenia are highly susceptible to incident cardiovascular diseases^{34, 35)}, fractures^{29, 36)}, cancer^{37, 38)}, pneumonia^{39, 40)}, and dementia^{41, 42)}. Furthermore, individuals with frailty and sarcopenia are at high risk of mortality^{43, 44)}. Taken together, masticatory impairment would reasonably increase the risk of adverse health outcomes via physical frailty and sarcopenia. It is also well known that masticatory impairment disturbs the balance of nutritional intake¹⁾, which would affect adiposity, the cardiovascular risk profile⁴⁵⁾, bone mineral density⁴⁶⁾, inflammation, immunity⁴⁷⁾, cognitive function⁴⁸⁾, and longevity⁴⁹⁾. Moreover, a decreased oral function is associated with an increased risk of social withdrawal or decreased social activities^50-52), which will accelerate cognitive decline. These aspects would also increase the risk of adverse health outcomes. Although the confidence intervals were considerably wide and overlapping, the hazard ratios for some individual adverse health outcomes might differ between masticatory performance and the tongue-lip motor function (Tables 4 and 5). Whereas bone fracture and dementia were significantly associated with both functional items, all-cause death and cardiovascular disease might have a more marked association with the tongue-lip motor function. Future studies are needed to determine the similarities and differences in the prognostic impact between masticatory performance and the tongue-lip motor function.

Masticatory performance is achieved as a result of the coordination of various oral components, including, but not limited to, opposing pairs of properly aligned teeth and fine synchronized motion of the tongue and lips¹⁵⁾. Improper tooth alignment can be addressed by dental, periodontal, and prosthetic treatments, whereas a decreased tongue-lip motor function can be ameliorated by oral motor training programs including articulatory training⁵³⁾. Future clinical trials are warranted to determine whether interventions to restore each component of masticatory performance would reduce the incidence of adverse health events.

Although the present sample size was too small to make a definitive conclusion, DRFs would have some clinical implications for cutoff points. The cutoff point of tongue-lip motor function is proposed to be 6 times/sec, while that of masticatory performance is 5.6 mmol/L (100 mg/dL)¹⁴⁾. As indicated in Fig.2B, the incidence of adverse health outcomes would increases when the tongue-lip motor function fell roughly below its cutoff point. In this sense, the cutoff point would work as a threshold above which the incidence is minimized. In contrast, the cutoff point for masticatory performance seemed to command a different position. As masticatory performance decreased, the incidence of adverse health outcomes gradually increased, reaching a plateau around the cutoff point (Fig.2A). Patients with masticatory performance below the cutoff point would be identified as the group with the highest risk of adverse health outcomes. Future studies with larger sample sizes are needed to arrive at definitive conclusions.

The current study was associated with several limitations. First, this study was not a clinical trial. We adopted the GPS method to address the influence of confounding factors; however, unmeasured confounding factors might have affected the results. Second, although baseline information was prospectively collected in the CHEWING-LIFE study, follow-up data were collected retrospectively in the present study, which would cause a bias that interferes with the relationship between exposures and outcomes. Third, the sample size was limited, especially after excluding patients with a history of cardiovascular disease or malignancy. In the sensitivity analysis in which these patients were excluded, some components of the outcome measure lost statistical significance. Further studies with a sufficient sample size are needed to determine whether the non-significance came from the absence of an association in patients free from a history of cardiovascular disease or malignant disease, or simply from reduced statistical power. Fourth, the study did not include patients who were too vulnerable to undergo examinations. Therefore, the findings of our study cannot be generalized to a wider population, including severely ill patients.

Conclusion

Both decreased masticatory performance and tongue-lip motor function were associated with an increased incidence of adverse health events in patients treated for metabolic diseases.

Acknowledgements and Notice of Grant Support

This study was funded by LOTTE Co., Ltd. (Tokyo, Japan). The sponsor and the funder had no involvement in the study design, collection, analysis, and interpretation of data, writing of the report, or decision to submit the article for publication.

Conflicts of Interest

The authors declared no conflict of interest.

References

• 1)  N’Gom P I and Woda A: Influence of impaired mastication on nutrition. J Prosthet Dent, 2002; 87: 667-673
• 2)  Kimble R, Papacosta AO, Lennon LT, Whincup PH, Weyant RJ, Mathers JC, Wannamethee SG and Ramsay SE: The Relationship of Oral Health with Progression of Physical Frailty among Older Adults: A Longitudinal Study Composed of Two Cohorts of Older Adults from the United Kingdom and United States. J Am Med Dir Assoc, 2023; 24: 468-474 e463
• 3)  Tanaka T, Takahashi K, Hirano H, Kikutani T, Watanabe Y, Ohara Y, Furuya H, Tetsuo T, Akishita M and Iijima K: Oral Frailty as a Risk Factor for Physical Frailty and Mortality in Community-Dwelling Elderly. J Gerontol A Biol Sci Med Sci, 2018; 73: 1661-1667
• 4)  Dorner B and Friedrich EK: Position of the Academy of Nutrition and Dietetics: Individualized Nutrition Approaches for Older Adults: Long-Term Care, Post-Acute Care, and Other Settings. J Acad Nutr Diet, 2018; 118: 724-735
• 5)  Lee L, Heckman G and Molnar FJ: Frailty: Identifying elderly patients at high risk of poor outcomes. Can Fam Physician, 2015; 61: 227-231
• 6)  Yuan S and Larsson SC: Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism, 2023; 144: 155533
• 7)  Do KY and Moon S: Relationship between Subjective Oral Discomfort and Health-Related Quality of Life in the South Korean Elderly Population. Int J Environ Res Public Health, 2020; 17:
• 8)  Cho HA and Shin H: Effects of tooth loss on functional impairment in elderly: A secondary data analysis of elderly cohort in the South Korea. Int J Dent Hyg, 2022; 20: 534-542
• 9)  Darnaud C, Thomas F, Danchin N, Boutouyrie P and Bouchard P: Masticatory Capacity and Mortality: The Preventive and Clinical Investigation Center (IPC) Cohort Study. J Dent Res, 2020; 99: 152-158
• 10)  Liu J, Zong X, Vogtmann E, Cao C, James AS, Chan AT, Rimm EB, Hayes RB, Colditz GA, Michaud DS, Joshipura KJ, Abnet CC and Cao Y: Tooth count, untreated caries and mortality in US adults: a population-based cohort study. Int J Epidemiol, 2022; 51: 1291-1303
• 11)  Okura M, Ogita M and Arai H: Are Self-Reported Masticatory Ability and Regular Dental Care Related to Mortality? J Nutr Health Aging, 2020; 24: 262-268
• 12)  Laudisio A, Gemma A, Fontana DO, Rivera C, Bandinelli S, Ferrucci L and Incalzi RA: Self-Reported Masticatory Dysfunction and Mortality in Community Dwelling Elderly Adults: A 9-Year Follow-Up. J Am Geriatr Soc, 2016; 64: 2503-2510
• 13)  Souza ML, Massignan C, Glazer Peres K and Aurelio Peres M: Association between metabolic syndrome and tooth loss: A systematic review and meta-analysis. J Am Dent Assoc, 2019; 150: 1027-1039 e1027
• 14)  Minakuchi S, Tsuga K, Ikebe K, Ueda T, Tamura F, Nagao K, Furuya J, Matsuo K, Yamamoto K, Kanazawa M, Watanabe Y, Hirano H, Kikutani T and Sakurai K: Oral hypofunction in the older population: Position paper of the Japanese Society of Gerodontology in 2016. Gerodontology, 2018; 35: 317-324
• 15)  Orchardson R and Cadden SW: Mastication and swallowing: 1. Functions, performance and mechanisms. Dent Update, 2009; 36: 327-330, 332-324, 337
• 16)  Takahara M, Shiraiwa T, Maeno Y, Yamamoto K, Shiraiwa Y, Yoshida Y, Nishioka N, Katakami N, Matsuoka TA and Shimomura I: Association of obesity, diabetes, and physical frailty with dental and tongue-lip motor dysfunctions in patients with metabolic disease. Obes Res Clin Pract, 2021; 15: 243-248
• 17)  Araki E, Goto A, Kondo T, Noda M, Noto H, Origasa H, Osawa H, Taguchi A, Tanizawa Y, Tobe K and Yoshioka N: Japanese Clinical Practice Guideline for Diabetes 2019. Diabetol Int, 2020; 11: 165-223
• 18)  Okamura T, Tsukamoto K, Arai H, Fujioka Y, Ishigaki Y, Koba S, Ohmura H, Shoji T, Yokote K, Yoshida H, Yoshida M, Deguchi J, Dobashi K, Fujiyoshi A, Hamaguchi H, Hara M, Harada-Shiba M, Hirata T, Iida M, Ikeda Y, Ishibashi S, Kanda H, Kihara S, Kitagawa K, Kodama S, Koseki M, Maezawa Y, Masuda D, Miida T, Miyamoto Y, Nishimura R, Node K, Noguchi M, Ohishi M, Saito I, Sawada S, Sone H, Takemoto M, Wakatsuki A and Yanai H: Japan Atherosclerosis Society (JAS) Guidelines for Prevention of Atherosclerotic Cardiovascular Diseases 2022. J Atheroscler Thromb, 2024; 31: 641-853
• 19)  Umemura S, Arima H, Arima S, Asayama K, Dohi Y, Hirooka Y, Horio T, Hoshide S, Ikeda S, Ishimitsu T, Ito M, Ito S, Iwashima Y, Kai H, Kamide K, Kanno Y, Kashihara N, Kawano Y, Kikuchi T, Kitamura K, Kitazono T, Kohara K, Kudo M, Kumagai H, Matsumura K, Matsuura H, Miura K, Mukoyama M, Nakamura S, Ohkubo T, Ohya Y, Okura T, Rakugi H, Saitoh S, Shibata H, Shimosawa T, Suzuki H, Takahashi S, Tamura K, Tomiyama H, Tsuchihashi T, Ueda S, Uehara Y, Urata H and Hirawa N: The Japanese Society of Hypertension Guidelines for the Management of Hypertension (JSH 2019). Hypertens Res, 2019; 42: 1235-1481
• 20)  Hisatome I, Ichida K, Mineo I, Ohtahara A, Ogino K, Kuwabara M, Ishizaka N, Uchida S, Kurajoh M, Kohagura K, Sato Y, Taniguchi A, Tsuchihashi T, Terai C, Nakamura T, Hamaguchi T, Hamada T, Fujimori S, Masuda I, Moriwaki Y and T. Y: Japanese Society of Gout and Uric & Nucleic Acids 2019 Guidelines for Management of Hyperuricemia and Gout 3rd edition. Gout Uric Nucleic Acids, 2022; 44: sp1-40
• 21)  Hirano K. and G.W. Imbens “The Propensity Score with Continuous Treatments”, in A. Gelman and X. Meng (eds.), Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, Wiley 2004
• 22)  Horie K, Takahara M, Nakama T, Tanaka A, Tobita K, Hayakawa N, Mori S, Iwata Y and Suzuki K: Impact of Postoperative Lumen Gain on the Reduction of Restenosis Risk after Endovascular Treatment using Drug-coated Balloon for Femoropopliteal Lesions Assessed by Intravascular Ultrasound. J Atheroscler Thromb, 2023; 30: 1142-1151
• 23)  Chen LK, Woo J, Assantachai P, Auyeung TW, Chou MY, Iijima K, Jang HC, Kang L, Kim M, Kim S, Kojima T, Kuzuya M, Lee JSW, Lee SY, Lee WJ, Lee Y, Liang CK, Lim JY, Lim WS, Peng LN, Sugimoto K, Tanaka T, Won CW, Yamada M, Zhang T, Akishita M and Arai H: Asian Working Group for Sarcopenia: 2019 Consensus Update on Sarcopenia Diagnosis and Treatment. J Am Med Dir Assoc, 2020; 21: 300-307 e302
• 24)  Ramsay SE, Arianayagam DS, Whincup PH, Lennon LT, Cryer J, Papacosta AO, Iliffe S and Wannamethee SG: Cardiovascular risk profile and frailty in a population-based study of older British men. Heart, 2015; 101: 616-622
• 25)  Zhang H, Lin S, Gao T, Zhong F, Cai J, Sun Y and Ma A: Association between Sarcopenia and Metabolic Syndrome in Middle-Aged and Older Non-Obese Adults: A Systematic Review and Meta-Analysis. Nutrients, 2018; 10:
• 26)  Li G, Thabane L, Papaioannou A, Ioannidis G, Levine MA and Adachi JD: An overview of osteoporosis and frailty in the elderly. BMC Musculoskelet Disord, 2017; 18: 46
• 27)  Yu X, Sun S, Zhang S, Hao Q, Zhu B, Teng Y, Long Q, Li S, Lv Y, Yue Q, Lu S and Teng Z: A pooled analysis of the association between sarcopenia and osteoporosis. Medicine (Baltimore), 2022; 101: e31692
• 28)  Cheng MH and Chang SF: Frailty as a Risk Factor for Falls Among Community Dwelling People: Evidence From a Meta-Analysis. J Nurs Scholarsh, 2017; 49: 529-536
• 29)  Yeung SSY, Reijnierse EM, Pham VK, Trappenburg MC, Lim WK, Meskers CGM and Maier AB: Sarcopenia and its association with falls and fractures in older adults: A systematic review and meta-analysis. J Cachexia Sarcopenia Muscle, 2019; 10: 485-500
• 30)  Luo OJ, Lei W, Zhu G, Ren Z, Xu Y, Xiao C, Zhang H, Cai J, Luo Z, Gao L, Su J, Tang L, Guo W, Su H, Zhang ZJ, Fang EF, Ruan Y, Leng SX, Ju Z, Lou H, Gao J, Peng N, Chen J, Bao Z, Liu F and Chen G: Multidimensional single-cell analysis of human peripheral blood reveals characteristic features of the immune system landscape in aging and frailty. Nat Aging, 2022; 2: 348-364
• 31)  Nelke C, Dziewas R, Minnerup J, Meuth SG and Ruck T: Skeletal muscle as potential central link between sarcopenia and immune senescence. EBioMedicine, 2019; 49: 381-388
• 32)  Fabricio DM, Chagas MHN and Diniz BS: Frailty and cognitive decline. Transl Res, 2020; 221: 58-64
• 33)  Peng TC, Chen WL, Wu LW, Chang YW and Kao TW: Sarcopenia and cognitive impairment: A systematic review and meta-analysis. Clin Nutr, 2020; 39: 2695-2701
• 34)  Damluji AA, Chung SE, Xue QL, Hasan RK, Moscucci M, Forman DE, Bandeen-Roche K, Batchelor W, Walston JD, Resar JR and Gerstenblith G: Frailty and cardiovascular outcomes in the National Health and Aging Trends Study. Eur Heart J, 2021; 42: 3856-3865
• 35)  Gao K, Cao LF, Ma WZ, Gao YJ, Luo MS, Zhu J, Li T and Zhou D: Association between sarcopenia and cardiovascular disease among middle-aged and older adults: Findings from the China health and retirement longitudinal study. EClinicalMedicine, 2022; 44: 101264
• 36)  Kojima G: Frailty as a predictor of fractures among community-dwelling older people: A systematic review and meta-analysis. Bone, 2016; 90: 116-122
• 37)  Mak JKL, Kuja-Halkola R, Wang Y, Hagg S and Jylhava J: Can frailty scores predict the incidence of cancer? Results from two large population-based studies. Geroscience, 2023; 45: 2051-2064
• 38)  Celis-Morales CA, Welsh P, Lyall DM, Steell L, Petermann F, Anderson J, Iliodromiti S, Sillars A, Graham N, Mackay DF, Pell JP, Gill JMR, Sattar N and Gray SR: Associations of grip strength with cardiovascular, respiratory, and cancer outcomes and all cause mortality: prospective cohort study of half a million UK Biobank participants. BMJ, 2018; 361: k1651
• 39)  Iwai-Saito K, Shobugawa Y, Aida J and Kondo K: Frailty is associated with susceptibility and severity of pneumonia in older adults (A JAGES multilevel cross-sectional study). Sci Rep, 2021; 11: 7966
• 40)  Altuna-Venegas S, Aliaga-Vega R, Maguina JL, Parodi JF and Runzer-Colmenares FM: Risk of community-acquired pneumonia in older adults with sarcopenia of a hospital from Callao, Peru 2010-2015. Arch Gerontol Geriatr, 2019; 82: 100-105
• 41)  Borges MK, Canevelli M, Cesari M and Aprahamian I: Frailty as a Predictor of Cognitive Disorders: A Systematic Review and Meta-Analysis. Front Med (Lausanne), 2019; 6: 26
• 42)  Esteban-Cornejo I, Ho FK, Petermann-Rocha F, Lyall DM, Martinez-Gomez D, Cabanas-Sanchez V, Ortega FB, Hillman CH, Gill JMR, Quinn TJ, Sattar N, Pell JP, Gray SR and Celis-Morales C: Handgrip strength and all-cause dementia incidence and mortality: findings from the UK Biobank prospective cohort study. J Cachexia Sarcopenia Muscle, 2022; 13: 1514-1525
• 43)  Fan Y, Huang J, Zhou Y, Gu X and Gong D: Does the Impact of Frailty on All-Cause Mortality in Older Persons Differ between Women and Men? A Meta-Analysis. J Am Med Dir Assoc, 2021; 22: 606 e601-606 e606
• 44)  Liu P, Hao Q, Hai S, Wang H, Cao L and Dong B: Sarcopenia as a predictor of all-cause mortality among community-dwelling older people: A systematic review and meta-analysis. Maturitas, 2017; 103: 16-22
• 45)  Fushida S, Kosaka T, Nakai M, Kida M, Nokubi T, Kokubo Y, Watanabe M, Miyamoto Y, Ono T and Ikebe K: Lower Masticatory Performance Is a Risk for the Development of the Metabolic Syndrome: The Suita Study. Front Cardiovasc Med, 2021; 8: 752667
• 46)  New SA, Bolton-Smith C, Grubb DA and Reid DM: Nutritional influences on bone mineral density: a cross-sectional study in premenopausal women. Am J Clin Nutr, 1997; 65: 1831-1839
• 47)  Pansarasa O, Pistono C, Davin A, Bordoni M, Mimmi MC, Guaita A and Cereda C: Altered immune system in frailty: Genetics and diet may influence inflammation. Ageing Res Rev, 2019; 54: 100935
• 48)  Lee JY, Han K, Han E, Kim G, Cho H, Kim KJ, Lee BW, Kang ES, Cha BS, Brayne C and Lee YH: Risk of Incident Dementia According to Metabolic Health and Obesity Status in Late Life: A Population-Based Cohort Study. J Clin Endocrinol Metab, 2019; 104: 2942-2952
• 49)  Yu Z, Kong D, Peng J, Wang Z and Chen Y: Association of malnutrition with all-cause mortality in the elderly population: A 6-year cohort study. Nutr Metab Cardiovasc Dis, 2021; 31: 52-59
• 50)  Mikami Y, Watanabe Y, Motokawa K, Shirobe M, Motohashi Y, Edahiro A, Nakajima J, Osuka Y, Inagaki H, Fujiwara Y, Shinkai S and Awata S: Association between decrease in frequency of going out and oral function in older adults living in major urban areas. Geriatr Gerontol Int, 2019; 19: 792-797
• 51)  Park HN and Yoon JY: Relationship between oral health and social activity among community-dwelling older adults in Korea: Focusing on the mediating effect of depressive symptoms. Geriatr Gerontol Int, 2022; 22: 766-772
• 52)  Reisine ST: Dental health and public policy: the social impact of dental disease. Am J Public Health, 1985; 75: 27-30
• 53)  Hatanaka Y, Furuya J, Sato Y, Uchida Y, Osawa T, Shichita T, Suzuki H and Minakuchi S: Impact of oral health guidance on the tongue-lip motor function of outpatients at a dental hospital. Gerodontology, 2022; 39: 83-89