Abstract
Background
Falls threaten the physical and mental health of older persons. Studies of the relationship between oral health and falls were fragmented in their selection of oral health indicators. We aimed to analyze whether and to what extent self-reported oral health was associated with falls among community-dwelling older adults in China.
Methods
A cross-sectional study was conducted using data from physical examination and questionnaire survey for older adults in Tianjin, China. 2150 community-dwelling individuals aged 60 and older were included. Self-reported history of falls during the past year and oral health were collected using questionnaires. In the oral health index, fewer missing teeth, wearing dentures, no toothache, no bleeding gums, and brushing teeth at least twice a day were assigned as a score of 1, respectively. Oral health index was calculated by summing the scores of each item and ranged from 0 to 5, with higher scores indicating better oral health status. Logistic regression controlled for potential confounders was employed to analyze the association between oral health and falls.
Results
The prevalence of fall within the previous year was 12.6%. Logistic regression model showed that participants with higher oral health index (≥ 4) had a 26% lower fall rate than those with lower scores (OR = 0.71; 95% CI: 0.52 to 0.97) after adjusting for age, sex, health status and behaviors, and muscle status. Specifically, older adults who had lost 3–5 teeth (OR = 1.72; 95% CI: 1.01 to 2.69) and gingival bleeding (OR = 1.95; 95% CI: 1.20 to 3.17) had a higher fall prevalence rate compared with those losing 2 or fewer teeth and no gingival bleeding, respectively.
Conclusion
Older adults with worse oral health, particularly more missing teeth and gingival bleeding, might be more vulnerable to falls. More attention paid to oral health may be helpful for fall prevention.
Keywords: Falls, Oral health, Community-dwelling, Aged
Introduction
Falls in older adults have become a major public health concern. Previous studies have shown that 28.7% and 27.5% of older adults aged ≥ 65 years in the United States experienced falling at least once in the past year in 2014 and 2018, respectively [1, 2]. In China, a meta-analysis reported that the rate of falls among community-dwelling older persons was 19.3% [3]. Despite the lower incidence, falling in Chinese older persons was still the major cause of injury-related deaths, and the crude falls mortality was 9.55 per 100 000 population in 2016 [4]. In addition, the important impact of falls also included functional deterioration, hospitalization and so on [5], all of which placed significant financial pressure [6]. To reduce the burden of healthcare resulting from falls, strengthening the research on falls among older adults is undoubtedly an urgent issue in China.
Furthermore, older people had prevalent oral health problems, such as tooth loss, edentulism, periodontitis, dry mouth and chewing problems [7, 8]. Oral health was an important predictor of several adverse health-related outcomes including physical frailty, quality of life, functional disability, hospitalization, falls, and mortality in the older adults [9]. Several oral health-related indicators might be related with falls. First, tooth loss and no denture wearing [10] were associated with worse substandard nutritional status through masticatory function [11], which may increase fall rates [12, 13]. Second, periodontitis with gingival bleeding was associated with cognitive decline and dementia [14, 15] and muscle status [16], which may lead to falls [16, 17]. Moreover, older adults with oral pain [18] and frequently teeth brushing were associated with frailty status [19], which was related with a higher fall risk (RR = 1.48, 95% CI: 1.27 to 1.73) [20].
There might be an association between oral health indicators, including tooth loss, denture wearing, oral pain, gingival bleeding, and tooth brushing frequency, with falls under the mechanisms of nutrition, physical balance and control, and frailty. However, previous studies of the relationship between oral health and falls were not comprehensive in their selection of oral health indicators. Integrating oral factors which may be associated with falls could provide insight into the relationship and mechanisms between oral health and falls from the perspective of independent oral health indicator as well as overall oral health status, resulting to recommendation for fall prevention. Moreover, there were fewer studies based on real world data to explore the association between integrated oral health indicators and falls among older adults. Several oral-related indicators, such as maximum occlusal force [21], were difficult to obtain during routine examinations in China. Therefore, the aim of this study was to analyze the association between oral health obtained through accessible self-reported indicators and falls among community-dwelling older adults in Tianjin, China.
Materials and methods
Study design and population
A cross-sectional study was carried out from May to July in three community health service centers of Tianjin, China. Physical examination and questionnaire survey were conducted during annual health check-ups. The selection criteria were as follows: (1) aged 60 years and above; (2) living in the community; (3) able to understand and answer the questions in the questionnaire; (4) able to recall history of falls in the past year. Participants aged less than 60 years old, without complete information of falls within the previous year and oral health were excluded. A total of 2150 individuals were enrolled for the subsequent analysis (Fig. 1).
Fig. 1.
Flow diagram of the study enrollment
Individual history of falls and oral health index
We investigated history of falls by asking “Have you had incident falls in the past year?”. The oral health index consisted of three components (dental health, periodontal condition and oral health behaviors) and were assessed with five items (tooth loss, denture, toothache, gingival bleeding, and brushing frequency), all of which were dichotomous. (1) Tooth loss was assessed with the number of missing teeth, which was categorized based on the median value. Less than or equal to the median value of missing teeth was assigned as 1 point, otherwise 0 point. (2) Wearing denture or without tooth loss was scored as 1 point, otherwise 0 point. (3) Toothache was obtained by the question “In the last six months, have you had frequent toothaches while eating?”. No toothache was recorded as 1 point, otherwise 0 point. (4) Gingival bleeding was obtained by asking “Do you often experience gingival bleeding?”, and no gingival bleeding was scored as 1 point, otherwise 0 point. (5) Brushing teeth at least twice a day was scored as 1 point, otherwise 0 point. The total scores of oral health index ranged from 0 to 5. A score of four or higher was considered as a healthier oral status. Based on the median value, the cut-off score of four or higher was considered as a healthier oral status.
Covariates
This study utilized a structured questionnaire to gather data on demographic status (age gender), health-related behaviors (exercise, smoking, drinking, social participation, body mass index (BMI)), and health status (hypertension, diabetes, stroke, dyslipidemia, hearing, vision, the number of prescription drugs, cognitive function, nutritional status, and self-rated health). Additionally, muscle status, including calf circumference, upper arm circumference, grip strength and 5-time chair stand test (5-CST), were examined by trained interviewers.
In terms of health-related behaviors, smoking was divided into “No smoking”, “Smoking” and “Cessation”. Drinking and exercise and were both divided into “Never”, “Sometimes” and “Everyday” based on frequency. Body mass index was calculated using measured height and weight with the following formula: BMI (kg/m2) = weight (kg)/height (m).2. Social participation was defined as participation in at least one of the following categories: (1) interaction with friends; (2) play Mahjong/chess/cards or attending a community club; (3) go to the park or other places to dance, fitness, practice qigong, etc.; (4) participate in community organization activities/volunteer/charity activities; (5) offer free help to your relatives, friends or neighbors; (6) go to school/take a training course; (7) surf the Internet/invest in stocks; (8) other social events [22].
Regarding to health status, (1) Hypertension was defined as self-reported physician-diagnosis or receiving antihypertensive treatments or systolic blood pressure (SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg [23]. (2) Diabetes was assessed by self-reported physician-diagnosis or fasting plasma glucose (FPG) ≥ 7.0 mmol/L [24]. (3) Dyslipidemia was defined as total cholesterol (TC) ≥ 6.2 mmol/L or high-density lipoprotein cholesterol (HDL-C) < 1.0 mmol/L or low-density lipoprotein cholesterol (LDL-C) ≥ 4.1 mmol/or triglycerides (TG) ≥ 2.3 mmol/L [25]. (4) Stroke was ascertained with self-reported physician-diagnosis. (5) Vision and hearing status were both self-reported and divided into “Good”, “Average”, and “Poor”. (6) The number of prescription drugs was obtained by asking “How many prescription drugs do you take daily?”, which was categorized as < 3, 3–6, and ≥ 6. (7) Cognitive function was assessed using Ascertain dementia 8-item questionnaire (AD8), and the cut-off value was 2 for discriminating nondementia from very mild dementia among older adults [26]. (8) The mini-nutritional assessment short-form (MNA-SF) was used to evaluate nutritional status, individuals with the score of ≤ 11 were under possible malnutrition or malnutrition status [27]. (9) Self-rated health was obtained by the overall evaluation of health status for the elderly with response options ranging from “Excellent”, “Good”, “Fair” to “Poor”, which was a comprehensive health indicator and related to all-cause mortality among Chinese older adults [28].
With regards to muscle status, (1) The grip strength of each hand was measured twice using dynamometer with the arms extended in a standing position. The maximum value of four measurements was used to analyze. (2) Upper arm and calf circumference were measured with a tape when the participant was sitting with their arms hanging loosely. Tape around the thickest part of the arm biceps and calf to get upper arm and calf circumference. Both circumferences were measured twice, and the average results were used. (3) The 5-CST, as one of the most evidence-supported functional measures to predict falls, was measured as the time taken to stand up and sit down five times from a straight-backed armchair as quickly as possible with arms folded across their chest [29]. And less than 12 s was considered to be normal according to the Asian Working Group for Sarcopenia 2019 (AWGS 2019) criteria [30].
Statistical analysis
Continuous variables were described by mean ± SD or median (IQR), and categorical variables were described by counts with percentages. Student’s t-test, Willcoxon rank sum test, and Chi-square tests were applied to compare characteristics between different groups as appropriate. Binary logistic regression models were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) between oral health index and its individual indicators and falls. Model 1 was a crude model. In model 2 to model 4, the covariates including social demographics, health status and behavior, and muscle status has been added in turns. Model 5 was constructed to explore the independent effects of each oral health indicator on falls through mutually adjustment for oral health indicators. The variance inflation factor (VIF) was used to test for multicollinearity. The VIF values for each variable were less than or equal to 5 and assumption without multicollinearity was not violated. The dose relationship between oral health index and falls was further assessed by the restricted cubic splines. The causal mediation analysis with 1000 bootstrap resampling and covariates adjustment was conducted using the “mediation” R package to examine whether self-rated health, the number of prescription drugs, cognitive function, nutritional status, and muscle status mediated the association between oral health and falls in older adults. In addition, we did two sensitivity analyses. Firstly, the missing proportions of baseline data were under 3%, except for AD8 (24.2%) as shown in Supplemental Table 1. Therefore, the sensitivity analysis was conducted by including participants without missing data on AD8 to assess the robustness of the findings. Secondly, health status covariates including hypertension, diabetes, stroke, dyslipidemia, hearing, and vision were replaced with self-rated health to further analyze the relationship between oral health index and falls. All statistical analyses were conducted using SAS (SAS Institute Inc., Cary, NC, USA.) and R (Version 4.2.1). All two-sided p < 0.05 were considered statistically significant.
Table 1.
Characteristics of community-dwelling older adults
| Characteristics | Overall | Non-fall | Fall | p |
|---|---|---|---|---|
| Numbers | 2150 | 1880 | 270 | |
| Age (year), mean ± SD | 69.1 ± 5.8 | 68.8 ± 5.7 | 70.9 ± 6.4 | < 0.001 |
| Age group, n (%) | < 0.001 | |||
| 60–69 | 1199(55.8) | 1078(57.4) | 121(44.8) | |
| 70–79 | 857(39.9) | 730(38.8) | 127(47.0) | |
| ≥ 80 | 94(4.4) | 72(3.8) | 22(8.2) | |
| Male, n (%) | 957(44.5) | 864(46.0) | 93(34.4) | < 0.001 |
| Exercise, n (%) | 0.064 | |||
| Never | 574(26.7) | 486(25.8) | 88(32.6) | |
| Sometimes | 107(5.0) | 95(5.1) | 12(4.4) | |
| Everyday | 1469(68.3) | 1299(69.1) | 170(63.0) | |
| Smoking, n (%) | 0.610 | |||
| No | 1433(66.6) | 1246(66.3) | 187(69.3) | |
| Yes | 511(23.8) | 451 (24.0) | 60(22.2) | |
| Cessation | 206(9.6) | 183(9.7) | 23(8.5) | |
| Drinking, n (%) | 0.213 | |||
| Never | 1575(73.3) | 1398(72.7) | 218(78.1) | |
| Sometimes | 201(9.3) | 182(9.7) | 19(7.0) | |
| Everyday | 374 (17.4) | 332(17.7) | 42(15.6) | |
| Social participation, n (%) | 1839(85.5) | 1617(86.0) | 222(82.2) | 0.098 |
| BMI (kg/m2)a, mean ± SD | 25.9 ± 3.6 | 25.9 ± 3.5 | 26.1 ± 3.7 | 0.455 |
| Hypertension, n (%) | 1405(65.4) | 1219(64.8) | 186(68.9) | 0.191 |
| Diabetes, n (%) | 525(24.6) | 447(23.8) | 81(30.0) | 0.026 |
| Stroke, n (%) | 30(1.4) | 22(1.2) | 8(3.0) | 0.019 |
| Dyslipidemia, n (%) | 909(42.6) | 803(43.0) | 106(39.6) | 0.281 |
| Hearing, n (%) | 0.016 | |||
| Good | 1128(52.5) | 1007(53.6) | 121(44.8) | |
| Average | 630(29.3) | 543(28.9) | 87(32.2) | |
| Poor | 391(18.2) | 329(17.5) | 62(23.0) | |
| Vision, n (%) | < 0.001 | |||
| Good | 1179(54.8) | 1064(56.6) | 115(42.6) | |
| Average | 576(26.8) | 494(26.3) | 82(30.4) | |
| Poor | 395(18.4) | 322(17.1) | 73(27.0) | |
| Self-rated health, n (%) | < 0.001 | |||
| Excellent | 167(7.8) | 156(8.3) | 11(4.1) | |
| Good | 1452(67.5) | 1293(68.8) | 159(58.9) | |
| Fair | 432(20.1) | 355(18.9) | 77(28.5) | |
| Poor | 99(4.6) | 76(4.0) | 23(8.5) | |
| The number of prescription drugs, n (%) | < 0.001 | |||
| < 3 | 1620(75.6) | 1431(76.3) | 189(70.5) | |
| 3–6 | 447(20.8) | 389(20.7) | 58(21.6) | |
| > 6 | 77(3.6) | 56(3.0) | 21(7.8) | |
| AD8b, mean ± SD | 0.1 ± 0.7 | 0.1 ± 0.6 | 0.2 ± 0.8 | 0.361 |
| AD8b, n (%) | 0.368 | |||
| Normal | 1579(96.9) | 1365(97.1) | 214(96.0) | |
| Mild dementia | 50(3.1) | 41(2.9) | 9(4.0) | |
| MNA-SFc, mean ± SD | 16.0 ± 1.5 | 16.1 ± 1.4 | 15.8 ± 1.6 | 0.028 |
| MNA-SFc, n (%) | 0.115 | |||
| Normal | 2072(98.8) | 1818(99.0) | 254(97.7) | |
| Possible malnutrition/Malnutrition | 25(1.2) | 19(1.0) | 6(2.3) | |
| Calf circumference (cm), mean ± SD | 36.0 ± 3.3 | 36.0 ± 3.3 | 35.6 ± 3.4 | 0.099 |
| Upper arm circumference (cm), mean ± SD | 28.8 ± 2.9 | 28.8 ± 2.9 | 28.8 ± 3.0 | 0.852 |
| Grip strength (kg), mean ± SD | 25.5 ± 8.8 | 26.0 ± 8.5 | 22.1 ± 8.7 | < 0.001 |
| 5CSTd < 12 s, n (%) | 1027(47.9) | 930(49.6) | 97(36.1) | < 0.001 |
| Tooth loss, median (IQR) | 5.0(2.0,16.0) | 4.0(1.0,15.0) | 5.0(2.0,21.0) | 0.010 |
| Tooth loss, n (%) | 0.006 | |||
| 0–2 | 720(33.5) | 652(34.6) | 68(25.2) | |
| 3–5 | 450(20.9) | 381(20.3) | 69(25.6) | |
| 6–16 | 459(21.4) | 404(21.5) | 55(20.4) | |
| ≥ 17 | 521(24.2) | 443(23.6) | 78(28.9) | |
| Denture, n (%) | 0.072 | |||
| No | 814(37.9) | 700(37.2) | 114(42.2) | |
| Yes | 986(45.9) | 862(45.9) | 124(45.9) | |
| No tooth loss | 350(16.2) | 318(16.9) | 32(11.9) | |
| Toothache, n (%) | 314(14.6) | 264(14.0) | 50(18.5) | 0.051 |
| Gingival bleeding, n (%) | 206(9.6) | 169(9.0) | 37(13.7) | 0.014 |
| Brushing ≥ 2 times per day, n (%) | 1176(54.7) | 1028(54.7) | 148(54.8) | 0.967 |
| Oral health index, median (IQR) | 4.0(3.0,4.0) | 4.0(3.0,4.0) | 3.0(3.0,4.0) | 0.004 |
aBMI: body mass index
bAD8: ascertain dementia 8-item questionnaire
cMNA-SF: mini-nutritional assessment short-form
d5CST: 5-time chair stand test
Results
Characteristics of study participants
A total of 2150 older adults were included in this study, and 12.6% had a history of fall at least once in the past year. The baseline characteristics are summarized in Table 1. The mean age was 69.1 years old, and 44.5% were males. The participants who had fallen in the past year were older, with the majority being female, and had a higher prevalence of diabetes, stroke, and poor hearing and vision statuses. In addition, they lost more teeth, and had a higher proportion of gingival bleeding and lower oral health index (Table 1). Comparison of baseline characteristics between healthy and unhealthy oral groups was shown in Supplemental Table 2.
Table 2.
Associations between each indicator in oral health index and falls among community-dwelling older adults
| Oral health | Numbers | Faller (%) | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
|---|---|---|---|---|---|---|---|
| Tooth loss | 2150 | 270(14.6) | 1.01(1.00,1.02)* | 1.00(0.99,1.02) | 0.99(0.98,1.01) | 0.99(0.98,1.01) | 1.00(0.98,1.02) |
| Tooth lossa | |||||||
| 3–5 | 450 | 69(15.3) | 1.74(1.21,2.48)* | 1.65(1.15,2.37)* | 1.91(1.24,2.94)* | 1.83(1.18,2.84)* | 1.72(1.01,2.69)* |
| 6–16 | 459 | 55(12.0) | 1.30(0.90,1.90) | 1.17(0.80,1.72) | 1.24(0.78,1.96) | 1.27(0.80,2.01) | 1.30(0.82,2.06) |
| ≥ 17 | 521 | 78(15.0) | 1.69(1.19,2.39)* | 1.28(0.88,1.85) | 1.13(0.73,1.75) | 1.12(0.72,1.74) | 1.24(0.78,1.96) |
| Dentureb | |||||||
| Yes | 986 | 124(12.6) | 0.88(0.67,1.16) | 0.79(0.60,1.04) | 0.72(0.52,1.00) | 0.73(0.52,1.01) | 0.75(0.52,1.08) |
| No tooth loss | 350 | 32(9.1) | 0.62(0.41,0.94)* | 0.69(0.45,1.05) | 0.81(0.49,1.33) | 0.83(0.50,1.37) | 0.81(0.48,1.35) |
| Toothacheb | |||||||
| Yes | 314 | 50(15.9) | 1.39(0.99,1.94) | 1.38(0.98,1.93) | 1.23(0.82,1.84) | 1.20(0.78,1.80) | 1.02(0.66,1.56) |
| Gingival bleedingb | |||||||
| Yes | 206 | 37(18.0) | 1.61(1.10,2.35)* | 1.79(1.21,2.64)* | 2.03(1.28,3.23)* | 2.00(1.25,3.19)* | 1.95(1.20,3.17)* |
| Brushing frequencyc | |||||||
| ≥ 2 times per day | 1176 | 148(12.6) | 1.00(0.78,1.30) | 0.95(0.73,1.24) | 0.87(0.64,1.19) | 0.90(0.66,1.24) | 0.94(0.68,1.30) |
Model 1: Crude model. Model 2: Adjusted for age and sex. Model 3: Health status (hypertension, diabetes, stroke, dyslipidemia, hearing, vision, AD8, the number of prescription drug, MNA-SF) and health behaviors (exercise, smoking, drinking, social participation, BMI) were added to model 2. Model 4: Adjusted variables in model 3 and muscle status (calf circumference, upper arm circumference, grip strength, 5CST). Model 5: Adjusted variables in model 4 and other indicators of oral health index, and each variance inflation factor (VIF) < 3
areference: 0–2
breference: No
creference: < 2 times per day; *: p < 0.05
Relationship between oral health and falls
Higher number of missing teeth was significantly associated with higher fall rate in the non-adjusted model. Compared with the older adults losing 2 or fewer teeth, 72% higher fall rate was found among those losing 3–5 teeth after adjusted for confounders. Older adults with gingival bleeding had 95% higher rate of falls than those without gingival bleeding. However, denture wearing, toothache, and brushing frequency were not found to be associated with the prevalence of falls (Table 2).
Table 3 showed the relationship between oral health index and falls. Higher continuous oral health index was significantly associated with lower fall rate in the crude model. After adjusting for confounders, the rate of falls decreased by 16% for each point increase in the oral health index. After categorizing the oral health index using the median value of 4 points, participants with higher scores showed a 29% lower prevalence of falls. As shown in Fig. 2, the association between the oral health index and falls did not show a non-linear trend (pnon-linear = 0.414). Additionally, the mediation analysis indicated no mediating effects of cognitive function, nutritional status, or muscle status in the relationship between oral health index and falls (Supplemental Table 3).
Table 3.
Association between oral health index and falls among community-dwelling older adults
| Model | Oral health index (continuous) |
Oral health index (reference: < 4) |
||
|---|---|---|---|---|
| OR(95%CI) | p | OR(95%CI) | p | |
| Model 1 | 0.82(0.72,0.94) | 0.003 | 0.70(0.54,0.90) | 0.006 |
| Model 2 | 0.83(0.72,0.95) | 0.006 | 0.70(0.54,0.91) | 0.008 |
| Model 3 | 0.83(0.71,0.98) | 0.024 | 0.70(0.51,0.95) | 0.021 |
| Model 4 | 0.84(0.72,0.99) | 0.012 | 0.71(0.52,0.97) | 0.031 |
Model 1: Crude model. Model 2: Adjusted for age and sex. Model 3: Health status (hypertension, diabetes, stroke, dyslipidemia, hearing, vision, AD8, the number of prescription drugs, MNA-SF) and health behaviors (exercise, smoking, drinking, social participation, BMI) were added to model 2. Model 4: Adjusted variables in model 3 and muscle status (calf circumference, upper arm circumference, grip strength, 5CST)
Fig. 2.
Dose–response relationship between oral health index and falls among community-dwelling older adults. Legends: The red line indicated the relationship between oral health index and falls, and the light red ranges indicated 95% confidence intervals (CIs)
Sensitivity analysis
In Supplementary Table 4, the analysis restricted to participants with complete AD8 data showed no substantial change in the association between falls and the oral health index. Health status covariates including hypertension, diabetes, stroke, dyslipidemia, hearing, and vision were replaced with self-rated health to further analyzing the relationship between oral health index and falls and the association between falls and oral health index did not change materially (Supplemental Table 5).
Discussion
This cross-sectional study examined the association between self-reported oral health index and falls in Chinese community-dwelling older adults. After adjusting for relevant variables, the higher oral health index score was associated with lower prevalence of falls. Similarly, the participants with an oral health index score of 4 and higher showed a 26% lower prevalence of falls than their counterparts. The number of missing teeth and gingival bleeding were associated with a higher prevalence of falls.
The strengths of this study included the large sample size, population-based sampling and abundant covariates factors adjusted, such as muscle status. In addition, evaluating the oral health index is a simple task without using specific device, which means that it is possible to assess in settings such as dental clinics or health examinations. However, this study also has several limitations. First, this study was a cross-sectional survey, which means that we cannot confirm the cause and effect between oral health index and falls. Second, five common and easily accessible indicators were selected to access oral health, but other oral health related factors such as chewing ability, swallowing ability [31], and dry mouth [9] were not included in this study. In addition, falls and oral health measurement relied on self-reported, which may lead to recall bias or underreporting. As a self-reported oral health assessment tool, Oral Frailty Index-8 could effectively identify individuals at risk of oral frailty and functional disability [31].
In this study, 12.6% older adults had fallen in the past year, lower than the reported rates in a systematic review among Chinese older adults (14.7%—34.0%) [4]. Enrolled participants in our study were interviewed in community center and fewer survey respondents were aged 80 years and above. Older adults who experienced fractures due to falls in the previous year were unlikely to be recruited for the study, which may underestimate the association between oral health and falls. In addition, our study indicated that tooth loss and gingival bleeding were associated with a higher prevalence of falls. Similar to previous literature, tooth loss was also associated with incident falls [12]. It has been shown that loss of proprioceptive sensation from the periodontal ligament due to tooth loss would lead to postural instability in the upright position [32, 33], further increasing the risk of falls. In addition, tooth loss and gingival bleeding are common symptoms of periodontitis. Gingival bleeding might be accompanied by tooth loss in older adults, and the underlying periodontitis might lead to a higher fall risk. Furthermore, our study showed that older adults with more missing teeth had higher risk for falls then those with 0–2 missing teeth, but the association was only significant for individuals with 3–5 missing teeth. In the further analysis, we found that denture wearing rate in groups with 6–16 and ≥ 17 tooth loss was higher than individuals with 3–5 tooth loss (Supplemental Table 6). Mochida et al. reported that older females having ≤ 9 teeth without dentures had a 46% higher risk of falls than those having 20 or more teeth. However, the fall risk decreased among older adults with 9 or fewer teeth but wore dentures [12]. A prospective cohort study also showed that fall risks for older adults who had 19 or fewer teeth with and without dentures were 36% and 150% higher than those having 20 or more teeth, respectively [34]. Therefore, denture wearing may be a compensation factor for high fall risk cause by tooth loss and the underlying mechanisms require further investigation.
The increased risk of falls due to poor oral health may be linked by several conceivable pathways. First, poor oral health can change food choices and have a negative impact on food intake [35], which lead to suboptimal nutritional status [11], which may increase the risk of falls [13]. Specifically, poor oral health, such as the number of natural antagonistic teeth [36] and the occlusal state [37], has an impact on chewing function. The deterioration in chewing function may increase the risk of decline in dietary variety [38], vitamin D intake and calcium intake and malnutrition [39], which may increase the risk of falls [40]. Kawashima et al. found that the participants with low masticatory function had lower intake of calcium and vitamin D [39]. And the older women with 94–143 nmol/L serum 25-hydroxy-vitamin D had 42% lower fall risk compared to 36–68 nmol/L group [41]. Second, poor oral health appears to increase the risk of both cognitive decline and dementia [14, 15], which may lead to higher risk of falls [17] and fall more often [42]. Lin et al. reported that severe deterioration of oral health, such as severe periodontitis or extensive tooth loss, was associated with cognitive dysfunction, and the oral-cognitive pathways were complex [43]. Chinese older adults with worse cognition level or mild cognitive impairment (MCI) status had a higher fall risk [17]. Third, previous studies have shown that there is a link between poor oral health and muscle status [16], which has positive association with falls and fractures in older adults [44]. Poor oral health may lead to chronic low-grade inflammatory state through periodontal disease, which is a well-known risk factor for sarcopenia [16]. Specifically, periodontitis can increase the inflammatory factors such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) [45]. And a prospective population-based study suggested that higher levels of IL-6 and C reactive protein (CRP) increased the risk of muscle status loss [46]. Lastly, poor oral health, such as tooth loss, oral pain and periodontal disease, was associated with frailty [18, 47, 48], and the frailty status may lead to a higher fall risk in a meta-analysis [20]. However, the mediating pathways of nutritional status, cognition, and muscle status in the relationship between oral health and falls in older adults were not found in our study. However, no significant mediating effects for cognitive function, nutritional status, and muscle status in the relationship between oral health index and falls were observed in our study. This may be due to the fact that our study was a cross-sectional study,and the AD8 questionnaire had shortcomings in recognizing common cognitive problems (mild cognitive impairment [49] etc.) among older adults.
Improving oral health, especially for the number of missing teeth and gingival bleeding, might contribute to mitigate fall risk among older adults. Targeted and effective prevention strategies for keeping healthy oral state could contribute to reduce the risk of falls for older adults. Future follow-up cohort studies using objective and validated oral health assessment tools should be conducted to reduce bias and clarify the causal relationship. Moreover, validated tools for timely identifying cognitive impairment, such as minimum mental state examination (MMSE), and more suitable nutrition and muscle assessment would be reconsidered in future follow-up to explore the possible biological pathways.
Conclusions
Oral health was closely related to fall among community-dwelling Chinese older adults. Especially, the number of missing teeth and gingival bleeding were significantly association with falls. The oral health index might improve the accuracy of the fall risk assessments in dental clinics or health examinations. Future studies are needed to further understand whether improvement of oral health can decrease the risk of falls.
Acknowledgements
Not applicable.
Abbreviations
- BMI
Body mass index
- 5CST
5-Time chair stand test
- AD8
Ascertain dementia 8-item questionnaire
- MNA-SF
Mini-nutritional assessment short-form
- VIF
Variance inflation factor
Authors’ contributions
W.L.L. contributed to the study concept and design. H.Z., S.W. and J.S. were responsible for data collection. J.Y.W. and B.R.S. were responsible for data analysis and interpretation. J.Y.W. wrote the manuscript and corrected the manuscript for important intellectual content under the supervision of J.S., W.L.L., Y.W., Y.J.C. and B.R.S. All authors read and approved the final manuscript.
Funding
This work was supported by Tianjin Health Research Project (Grant No. JWJ2023MS057).
Data availability
Data for this study could be obtained from the corresponding author, and could be accessed by requesting.
Declarations
Ethics approval and consent to participate
Our study was approved by the ethics committee of Tianjin Medical University (TMUhMEC20240002). Our study also included a statement on informed consent from participants.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Jingyue Wang, Boran Sun, Jian Sun and Wenli Lu contributed equally to this work.
Contributor Information
Jian Sun, Email: 174597726@qq.com.
Wenli Lu, Email: luwenli@tmu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data for this study could be obtained from the corresponding author, and could be accessed by requesting.