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Age-group-specific association of oral health and systemic health on cognitive function: a cross-sectional study of Korean elders

BMC Oral Health volume 23, Article number: 997 (2023) Cite this article

Abstract

Background

Although the importance of oral and systemic healthcare for elderly people is increasing owing to the rapid ageing of the population in South Korea, studies on the relationship between oral health, systemic health, and cognitive function, as well as on the prediction of cognitive function by oral and systemic health depending upon age groups are lacking.

Methods

We included 5,975 out of 6,488 participants from the 8th wave of the Korean Longitudinal Study of Aging (KLoSA) panel data, divided the participants into three age groups, and performed a hierarchical multiple linear regression analysis to explain cognitive function with four types of predictors: oral health status, sociodemographic factors, objective health status, and subjective health status.

Results

Oral health status was positively correlated with systemic health status and cognitive function. Of all ages over 54, cognitive function was significantly predicted by oral health variables, such as the number of functional teeth, masticatory ability, and Geriatric Oral Health Assessment Index (GOHAI); sociodemographic variables, such as age, sex, education level, and residence; and systemic health variables, such as diagnosis of diabetes mellitus, cancer or malignant tumours, cerebrovascular disease and rheumatoid arthritis, depressive symptom, and self-rated health status. Oral health variables explained cognitive function differently by age group; GOHAI appeared important predictor in the group aged < 75 years, whereas the number of functional teeth did in the group aged ≥ 75 years. Educational level, masticatory ability, depressive symptoms, and self-rated health status were pivotal factors age-independently.

Conclusions

The general and age-group-specific association between oral health, systemic health, and cognitive function were confirmed, suggesting that age-group-specific oral healthcare should be emphasized for the effective management of systemic and cognitive health in the elderly group.

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Background

By 2050, the share of the global proportion aged > 65 years will reach 16% [1]. South Korea is expected to become a super-aged society by 2025 when the proportion of the elderly aged 65 years or above becomes 20.6% from 17.5% in 2022 [2]. In 2020, 840,000 (10.3%) of the elderly population in Korea were diagnosed with dementia, which will rise to three million (16%) by 2050 [3]. The national cost for the management of dementia in South Korea is expected to reach $65 billion in 2050 from $12.7 billion in 2020 [3]. In the absence of a clear cure for dementia, diagnosis of an early stage of the disease, e.g., mild cognitive impairment (MCI), followed by holistic healthcare to delay the onset of the disease is accepted as the best practice.

Growing evidence emphasizes the link between oral health and cognitive function in elderly people [4,5,6]. Indeed, many of the risk factors of systemic diseases, such as cardiovascular and circulatory diseases [7,8,9], rheumatoid arthritis [10, 11], and Alzheimer’s disease [12], coincide with the ones of oral diseases [13, 14]. Among the variables for oral health, periodontitis [15,16,17], the number of teeth [18,19,20], chewing efficiency [21,22,23], and infection by a complex set of bacterial species [12, 24, 25] have been reported to be correlated with cognitive impairment. Furthermore, several clinical studies have shown that oral rehabilitation [22, 26] and hygienic interventions [27, 28] may be effective in improving cognitive function. Recently, Asher and colleagues conducted a systematic review and meta-analysis of 47 longitudinal studies, published until April 2022, finding that poor periodontal health and tooth loss appear to increase the risk of cognitive impairment [29]. In addition, they also found, albeit with limited evidence, that the extent of tooth loss is proportional to the risk of cognitive impairment; partial tooth loss is important for cognitive decline whereas complete tooth loss is for dementia.

The Korean Longitudinal Study of Aging (KLoSA), launched in 2006, is a biannual nationwide panel survey of people aged 45 and older and their partners, focusing on international data compatibility, cross-disciplinary studies, and socio-economic policy making [30]. When the 7th wave of KLoSA was conducted, oral health-related questionnaires were newly adopted as the growing number of studies had enunciated the importance of oral health in late-life satisfaction with respect to the linkage between oral health and cognitive ability. Of particular, studies using KLoSA have identified irregularity in working hours [31], low life satisfaction [32], decreased chewing function [33], and the risk factors of cardiovascular diseases (CVD), such as age, diabetes, and hypertension [34], are associated with the development of cognitive impairment. It was also reported that depression and cognitive function are associated with oral health-related quality of life in elderly people aged 65 years and over using the 7th wave of KLoSA data [35]. However, a scarce number of studies exploring the association among oral health, systemic health, sociodemographic factors, as well as cognitive function altogether in elderly people have been introduced yet.

Traditionally, 65 years of age has been recognised as the start of the older age eligible for the official retirement based on social and economic feasibility, rather than biological definition [36]. Although age classification for elderly people varies between countries, efforts to re-define the elderly as aged 75 years or over have emerged especially in East Asian countries with increased life expectancy and rapid population ageing [37]. However, no studies have been reported to explain cognitive function by oral health status, systemic health status, and sociodemographic characteristics in different age groups in Korean older adults. Thus, this study, in pursuit of the traditional definition of elders in Korea [38, 39], aimed to identify the different associations between oral or systemic health status and cognitive function in Korean elders by three age groups (the pre-, the early-, and the middle-or-late-elderly), taking into consideration on the age distribution of the 8th KLoSA dataset.

Methods

Data collection and study design

In this study, sample data were collected from the 8th wave of the KLoSA issued by the Korea Employment Information Service (KEIS) in 2020. The first wave of the survey comprised 4,460 men and 5,794 women out of 10,254, randomly selected people aged 45 and over (born before 1961) by stratified multistage probability sampling from nationwide, excluding Jeju Island in 2006 (baseline). In the 5th wave (2014), a sample of 920 people born between 1962 and 1963 was added [30]. So far, 6,488 people have completed the survey every two years until 2020 (8th wave).

The survey was conducted by computer-assisted personal interviewing (CAPI) using a structured questionnaire assessing household background, socio-demographics, family structure, healthcare, employment status, income, assets, and subjective life satisfaction. All interviewers and investigators recruited were not professional medical practitioners, so they had to be managed, and pre-trained to abide by data collection protocols or guidelines provided by KEIS every two years.

Sample data for our cross-sectional secondary analysis study using the 8th wave of the KLoSA were provided as three types, raw data, structurally converted data, and light version data. The KLoSA survey were reviewed and approved by the Institutional Review Board of Statistics Korea (approval number: 336,052) and this secondary analysis study, using the KLoSA data, was also conducted after approval from the Public Institutional Review Board (http://public.irb.or.kr) by the Korea National Institute for Bioethics Policy (approval number: P01-202206-01-003).

Study parameters

Independent variables

Sociodemographic variables: Sex, age, marital status, educational level, and residence were selected as sociodemographic variables having been reported as denoted factors both associated with oral health status [40, 41] and cognitive impairment [42, 43] in the elderly group. The marital status was classified as “unmarried”, “widowed”, “divorced”, “living apart together”, and “married”. The level of education was divided into “elementary school”, “middle school”, “high school”, and “college” graduates. The residence was classified into “township”, “small city”, and “big city”.

Oral health variables: For oral health, the number of functional teeth, masticatory ability, and the Korean version of GOHAI were used [44] and those variables had all been reported to have bidirectional relation to cognitive function in the literature [45]. Based on data user guidelines by KEIS, responders were asked four items regarding their teeth, which included the number of implant teeth, teeth in dentures, missing teeth to be replaced by implants or dentures, and remaining wisdom teeth. Assuming the basic number of 28 natural teeth, it is stated that the number of natural teeth was counted by summing 28 and the number of remaining wisdom teeth and subtracting the number of missing teeth as well as false teeth, such as implants or dentures. Using responses from four items and the newly created ‘natural teeth’ variable in the sample data, we calculated the number of functional teeth by adding the number of implants to the number of natural teeth and then subtracting the number of wisdom teeth. Masticatory ability was measured using a subjective five-point Likert-type scale (1 = very good chewing, 2 = good chewing, 3 = moderate, 4 = poor chewing, 5 = no chewing at all). The original version of GOHAI, a 12-item measure with a six-point scale scoring system, assesses oral-related pain/discomfort, physical function, and psychosocial function (0 = always, 1 = very often, 2 = often, 3 = occasionally, 4 = rarely, 5 = never). Response from the masticatory ability item was reversely coded in this study.

Systemic health variables: As recent studies have been shedding light on the interaction between oral health and systemic health [46], this study categorized systemic health into objective health status (the number of chronic diseases, the diagnosis of disease, alcohol use habit, and smoking habit) and subjective health status (depressive symptom and self-rated health status) to explore the oral-overall health relationship. For the diagnosis of disease, the responses to individual diagnostic status of ten diseases (hypertension, diabetes mellitus, cancer and malignant tumours, chronic lung disease, liver disease, heart disease, cerebrovascular disease, psychiatric disease, rheumatoid arthritis, and digestive disorders) were dichotomized into “yes” and “no”. The number of chronic diseases was generated as the sum of the number of “yes” responses for the presence of the aforementioned diseases. Alcohol use and smoking habits were classified into “never”, “former”, and “current”. The depressive symptom was measured using the Korean version of the Centre for Epidemiologic Studies Depression Scale (CES-D10) [47]. Self-rated health status by the question ‘How do you evaluate your health status?‘ with a five-point Likert-type scale (1 = very good, 2 = good, 3 = average, 4 = bad, 5 = very bad) was reverse-coded for the analysis.

Dependent variables

Cognitive function variables: The Korean version of the Mini-Mental State Examination (K-MMSE) was used to evaluate the cognitive state of the participants [48, 49]. The K-MMSE, consisting of 19 elements, has scores ranging from 0 to 30 points with higher scores indicating better cognitive function.

Data analysis

In this study, four datasets were prepared for data analysis; one was a total dataset with entire subjects for performing a descriptive analysis, correlation analysis, and hierarchical multiple linear regression modelling based on four conceptual models; three other datasets were the subgroups of the total dataset divided by their ages “under 65”, “between 65 and 74” and “75 and older” that were pre-defined classification of the pre-old, the early-old, and the middle-or-late-old population for conducting descriptive analysis and multiple linear regression analysis [38, 39]. Descriptive statistics were applied to delineate the general characteristics of the study subjects for all variables. To identify the statistically significant age group differences in the values or ratios of discrete and continuous variables, a chi-square test and one-way analysis of variance (ANOVA) were performed, respectively. Welch’s ANOVA was conducted on the variables provided that the homogeneity of variance was not confirmed by Bartlett’s test. With regards to correlation analysis, Pearson’s coefficient was calculated to explore the correlation between continuous variables, otherwise Spearman’s coefficient was computed. A hierarchical multiple linear regression analysis using a total dataset was conducted to explain cognitive function by predictors when building up the model with (a) oral health variables, (b) sociodemographic variables, (c) objective health status variables, and (d) subjective health status variables in sequential order. Additional subgroup analysis results were compared to predict the cognitive function of elderly people by age group while entering all the predictors simultaneously into a multiple linear regression model. All categorical predictors were treated as dummy variables and statistical assumptions were scrutinized before executing regression analysis. Data processing and analysis were implemented in R (version 4.1.0) and the significance level was set to 0.05 for all statistical inferences carried out in the study.

Results

General characteristics of study participants

The general characteristics of the 5,975 subjects, excluding missing values of the dependent variables ​​from the total of 6,488 who completed the survey, are shown in < Table 1>. The average age of the subjects was 70.8 years (32.5%, under 64; 32.2%, 65–74; and 35.3%, ≥ 75), which consisted of 3,468 females (58.0%) and 2,507 males (42.0%). The education level was elementary school graduates (36.9%), middle school graduates (16.9%), high school graduates (33.3%), or university graduates (12.9%). In terms of marital status, the majority of people were married (75.0%) or widowed (21.1%), whereas others remained unmarried (0.9%), divorced (2.5%), or living apart together (0.5%). The residence area was classified as big city (42.3%), small city (34.6%), and township (23.1%). It revealed significant associations between age and sociodemographic characteristics (education level, χ2(6) = 1553.50, p < .001; marital status, χ2(8) = 919.54, p < .001; area of residence, χ2(4) = 107.05, p < .001). On average, the number of functional teeth, masticatory ability and the GOHAI score were 22.8 (SD = 9.5), 3.2 (SD = 0.8), and 35.9 (SD = 7.0), respectively, and there showed statistically significant association of age group on oral health (number of functional teeth, F(2, 3642) = 637.36, p < .001; masticatory ability, F(2, 3974) = 665.40, p < .001; GOHAI, F(2, 3965.8) = 250.98, p < .001). Subjects had an average of 1.3 chronic diseases and had been commonly diagnosed with hypertension (46.0%), rheumatoid arthritis (25.6%), diabetes mellitus (21.0%), and heart disease (9.9%). People had experienced more alcohol use than smoking; 51.5% had alcohol drinking experience (20.5% stopped drinking, and 31.0% currently drinking); 30.8% had smoking experience (22.9% quitted smoking, and 7.9% currently smoking). Regarding subjective health status, the average degree of depressive symptoms and self-rated health status were 1.3 (SD = 1.8), and moderate with 3.0 (SD = 0.8), respectively. The average level of cognitive function was 25.1 (SD = 5.4) and significantly different by age group, F(2, 3718.5) = 802.36, p < .001.

Table 1 Table of descriptive statistics
Full size table

Correlation between oral health, general health, and cognitive function

Correlation analysis between oral health and factors influencing the level of cognitive function in the elderly has represented that oral health had a stronger correlation with sociodemographic characteristics and self-rated health status than with objective health status. Concerning sociodemographic characteristics, oral health was negatively correlated with age, r = −.47 – −.31, while positively correlated with educational level, r = .25 – .35, and with residence area, r = .03 – .06. The result also showed that oral health had a relation with sex, r = −.03, and marital status, r = .18 – .24. When it comes to self-rated health status, self-rated health status was most positively correlated with masticatory ability, r = .45, and depressive symptom was most negatively correlated with the GOHAI score, r = −.28, of all oral health variables. Oral health had a negative correlation with the number of chronic diseases, r = −.30 – −.22 and had a positive correlation with alcohol use, r = .09 – .12. Of the three oral health variables, masticatory ability showed the strongest correlation with cognitive function, r = .42, while GOHAI score did weakest correlation with it, r = .34. The result of the matrix of correlation analysis is summarized in < Table S1>.

Hierarchical multiple linear regression results predicting cognitive function

A hierarchical multiple linear regression was implemented to test the predictions of cognitive function with four blocks of variables. By adding four blocks, each of which were oral health variables, sociodemographic variables, objective health status variables, and self-rated health status, as predictors in four regression models one at a time, changes of variances in the level of cognition were calculated to explore the different effect size of predictions by four models.

Of all statistical assumption testing on regression analysis, homoscedasticity and normality were slightly violated for the skewness of cognitive function; however, the heteroscedasticity issue was offset by additional subgroup analysis by age group and the sample size analysed in the study was large enough, being robust to violation of the normality assumption, to exclude the normality test [50]. Additionally, no multicollinearity was confirmed for any variables by the variance inflation factors (VIF), not exceeding 5 [51], then it gave this study a plausible reason for continuing regression analysis.

The results showed that all four models were statistically significant in predicting cognitive function. The first model itself accounted for 23% of the variance of cognitive function, F(3, 5971) = 609.50, p < .001, R2 = 0.23, and the second model explained 16% more of the variance when sociodemographic variables were included, F(14, 5960) = 270.50, p < .001, R2 = 0.39. The third model including objective health status represented little amount of but still significant 1% improvement of the variance, F(28, 5946) = 143.00, p < .001, R2 = 0.40. Final model with all variables increased by 7% of the variance from the third model, F(30, 5944) = 173.30, p < .001, R2 = 0.47. From the first model to final model, whether adjustment for other variables was applied to or not, three oral health condition remained the statistically significant predictors. Regarding on result from the fourth model in < Table 2>, cognitive function was positively associated with the number of functional teeth, masticatory ability, the GOHAI, education level, the residence area, diagnosis with diabetes mellitus, and with cancer and malignant tumours, and self-self-rated health status. On the other way, cognitive function was negatively associated with age, the number of chronic diseases, diagnosis with cerebrovascular disease, and rheumatoid arthritis, and the level of depressive symptoms. Sex and former alcohol use or smoking experience were related to the cognitive function of the elderly but marital status, diagnosis with hypertension, lung disease, liver disease, heart disease, digestive disorders, and psychiatric diseases were not.

Table 2 Result of hierarchical regression analysis
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Cognitive function by three age groups

Subgroup hierarchical multiple linear regression analyses by three age groups for cognitive function prediction were delivered in this study and the results were suggested in < Table 3> (for detailed results by each group, see < Table S2-S4>).

Table 3 Hierarchical Regression Analysis Result by Age Group
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The number of functional teeth had a positive relation to cognitive function in those aged over 75 years, B = 0.04, p < .001. The more masticatory ability people had, the better cognitive function they retained in all age groups: 45–64 years, B = 0.41, p < .001, 65–74 years, B = 0.39, p = .002, and over 74 years, B = 0.60, p < .001. The high GOHAI score predicted the increased level of cognitive function for those under 65 years of age, B = 0.03, p = .008 and those 65–74 years of age, B = 0.03, p = .019.

Age was found to be a statistically significant predictor of cognitive function both in people aged 65–74 years, B = −0.14, p < .001, and 75 and older, B = −0.32, p < .001. In terms of sex, males over 74 years of age had better cognitive function than females, B = −1.75, p < .001. The education level had a positive association with cognitive function in all age groups, and marital status had a significant association with cognitive function for those under 65 years of age. People living in a large city compared to a township among those aged 65–74 years, B = 0.81, p < .001, and over 74 years, B = 0.55, p = .047, had better cognitive function.

Having been diagnosed with cerebrovascular disease both in the group of 65–74 years of age, and those over 74 years of age had significantly lower cognitive function, B = −1.29, p < .001, and, B = −1.52, p < .001, respectively, than those who have not been with; and people having had rheumatoid arthritis only in the group of aged 65–74 years showed decreased level of cognitive function, B = −0.48, p = .018. Concerning alcohol drinking or smoking habits, the elderly who had experienced but were not currently drinking under the age of 65 and former smokers over the age of 74 both have shown declines in cognitive function, B = −0.47, p = .010. B = −0.75, p = .028.

The degree of depressive symptoms had a negative association with cognitive function in all age groups: under 65 years, B = −0.34, p < .001; 65–74 years, B = −0.62, p < .001; and over 74 years, B = −0.81, p < .001. The association between the self-rated health status and the level of cognitive function has also been verified in all age groups: under 65 years, B = 0.48, p < .001, 65–74 years old, B = 0.85, p < .001, and over 74 years, B = 1.34, p < .001.

Discussion

This study aimed to confirm the association of oral health and systemic health on cognitive function in people aged 55 and over and to investigate the differences by age group using the latest cross-sectional dataset from a biannual longitudinal panel survey in Korea. Oral health accounted for 23.4% of cognitive function and cognitive function was positively correlated with the number of functional teeth, masticatory ability, and the GOHAI score. Notably, when controlling for sociodemographic and systemic health variables, masticatory ability was found to be a significant factor in explaining cognitive function in all age groups aged over 54. However, significant associations between cognitive function and the GOHAI scores were observed in the 55–74 age group, while the number of functional teeth showed significance in the age group aged > 74 years. These results support previous studies showing that oral diseases such as periodontitis [15,16,17], the number of teeth [18,19,20] and chewing efficiency [21,22,23] are associated with cognitive ability in the elderly population. The age-dependency of the oral variables necessitates the implementation of age-group-specific policy-driven systematic oral healthcare services along with proper education programs to ease the socioeconomic and medical burden originating from cognitive decline in older adults.

Oral variables correlated with the number of chronic diseases and the diagnosis of specific chronic diseases, including hypertension, diabetes mellitus, chronic lung disease, heart disease, cerebrovascular disease, psychiatric disease, and rheumatoid arthritis. By contrast, no correlations were found with cancer or malignant tumours, liver disease, or digestive disease. Regarding health-related behaviours, alcohol use experience but not smoking was found to be correlated with oral health status. These results were partially supported by previous studies reporting that dental conditions such as periodontitis, tooth loss, and caries have correlations with diabetes mellitus, cardiovascular disease, and cerebrovascular disease [52, 53]. Some of the findings, however, are inconsistent with previous studies showing that periodontal disease including tooth loss is associated with an increased risk of cancers [54, 55] and that smoking increases the risk of tooth loss and edentulism [56, 57].

The discoveries on the positive association between cognitive function with diabetes or cancer in this study deviate from conventional findings – diabetes predicting increased incidence of cognition loss or abnormality [58, 59] and commonly reported cancer-related cognitive impairment [60,61,62]. However, recent studies have reported similar results to those found in our research. One retrospective study with amnestic cognitive impairment subjects in Chile and other population-based US cohort study with 14,583 participants conducted from 1998 to 2014 have shown epidemiological negative associations between cancer and cognitive decline. These two studies supported the hypothesis of a biological inverse mechanism between carcinogenesis and neurodegeneration [63, 64]. Apart from the biological explanation, we posit that the findings could be ascribed to the management of a healthy lifestyle. The concept of a healthy lifestyle perspective may align with the previous literature exploring the association of sustaining physical activity [65,66,67], high-quality sleep [68,69,70,71], a healthy dietary pattern adherence with [72, 73] cognitive decline attenuation or cognitive function enhancement with cancer or diabetes. When delving into the relationships encompassing oral health, systemic health, and cognition, it would be essential to adopt a multifaceted approach and to incorporate variables of healthy lifestyle and behaviours into the in-depth interpretation.

Depression and self-rated health status also appear to have a significant correlation with oral health, supporting previous studies that oral health is inversely related to depression [74,75,76]. The significant correlation between masticatory ability and self-rated health status is self-explanatory in that masticatory movement is important for eating enjoyment without limiting the variety of food and nutrition supply [77]. Thus, it is suggested that for proper management of depression in the elderly group, a holistic healthcare program comprising not only psychological care but also oral care with proper diet and lifestyle is needed.

Previously, cognitive functions have been reported to be lower in people with lower socioeconomic status (SES), increasing age, and in women [78,79,80,81,82]. This study also showed that cognitive function declined more severely in women, less-educated people, and people not living in a big city, with age being the most influential variable. Notably, a recent analysis of two UK-based prospective cohort studies, including 15,924 participants from the English Longitudinal Study of Ageing (ELSA) and the Whitehall II study, revealed that disparities between sexes in education yielded sex differences in cognitive outcomes in older adults [83]. The seminal study also found that women, with evidence of a slower memory decline, have higher fluency scores than men in the high-education group. Thus, the sex differences in cognitive functions in the current study should be interpreted with social conditions in which the degree of inequality in education increases with age. It is also supported by the literature indicating the connection between early SES and a healthy lifestyle mediated by educational attainment, which in turn predicts cognitive abilities in later life stages [84,85,86]. Not reported as main findings in this study, other SES variables such as income or assets exhibited a significant association with oral health as well as cognitive function. Previous studies explained the impact of childhood family income on brain development and cognitive performance and the association between wealth and adverse alterations in physical, social, emotional, and cognitive levels in later stages of life [87, 88]. Therefore, economic status should be additionally considered as another significant factor to explain cognitive function in the elderly.

This study suggested evidence showing the association between oral health, systemic health, and cognitive function comprehensively and additional age-specific characteristics in predicting cognitive function. There were several limitations to be discussed for designing further study. First, the data collection procedure for panel data might raise potential concerns regarding measurement error. This panel data was gathered based on a self-reported survey without the aid of objective examination or professional medical staff. The accuracy of diagnosis information relied on the respondents’ memory, and the quantification of the number of teeth was based on self-counting rather than test-based measurement, all of which could lead to less precise data collection. Like other surveys using the CAPI system, interviewer bias or interviewer-respondent interaction bias may not have been eliminated. Even though all investigators had been well-trained by KEIS before the survey, it might have been challenging to secure the same investigators or equally qualified ones in every wave of the survey. This could potentially influence the responses obtained from participants and eventually affect the accuracy of the result.

Second, this research may encounter biased estimation and interpretation issues by the operationalization of the definition and analytical techniques. We primarily defined functional teeth based on previous literature [89, 90], emphasizing their role in mastication capability. It has been finalized the concept of functional teeth after conducting a correlation analysis between several possible combinations with five types of ‘the number of teeth’ variables collectively in the given dataset and cognitive function – selecting the one from the highest correlation coefficient value. Both considering theory-driven and data-driven operationalization could establish more robust and generalizable findings; however, there remained instability, subjectivity, and difficulties in reproducibility or generalization issues caused by data-oriented processing.

Another issue that may hamper the interpretation of the results pertains to the use of an unweighted regression analysis. In the context of survey data, not considering the complexities of the sampling design would result in a biased estimation of the coefficient affecting statistical significance tests and misleading conclusions. The last issue which requires cautious interpretation is the analytical technique used in this study. Despite having access to panel data that could track individuals over time, the analysis was limited to the use of cross-sectional data. It has not been verified that the causality between predictors – oral health, systemic health, sociodemographic characteristics – and cognitive function, and the identification of moderating or mediating effects among the study variables explaining cognitive function.

Taking the limitations affecting the reliability and validity of study results into account for future studies, it is necessary to employ a longitudinal analysis approach for investigating the directionality of the effects among oral, systemic, and cognitive health variables. It is also possible to design further research to explore the intricate association between oral or systemic health status and cognitive functioning while considering the lifestyle factors and SES for suggesting ground evidence on healthy late-life experience.

Conclusions

This study conducted an integrated analysis of the relationship between oral health, systemic health, and cognitive function in the elderly population. Hierarchical analysis results revealed the relatively prominent importance of oral health for cognitive function among individuals above the age of 54. Each oral variable such as the number of functional teeth, masticatory ability, and GOHAI, however, differently represented the association on cognitive function by the pre-, the early-, and the middle-to-late-old groups: masticatory ability commonly related to cognitive ability in all groups, self-perceived oral health in the pre- and the early elderly groups, and the number of functional teeth in the middle-to-late elderly group.

Regarding the relationship between systemic health status and elderly cognitive function, positive health perceptions were associated with better cognitive ability in all groups, but in the early elderly, cognitive decline was assocated with cerebrovascular disease or rheumatoid arthritis. This study confirmed the strong evidence of educational level relating to cognitive function in elderly individuals. It also revealed that middle-to-late elderly females exhibited a lower level of cognitive function compared to males of the same age group. Furthermore, age and residence area size seemed to have a significant association with cognitive function from the onset of the early elderly period.

Importantly, this study provides essential groundwork for establishing a holistic approach to cognition healthcare policy tailored to the elderly population while considering different age groups and various factors including oral health, systemic health, and sociodemographic features.

Data Availability

The KLoSA data with deleted personal information and the detailed survey design are publicly available after member registration at https://survey.keis.or.kr/eng/myinfo/login.jsp. Gateway to Global Aging site (https://g2aging.org/?section=study&studyid=5) also contains significant information on KLoSA including codebooks, harmonizing code and other useful resources.

References

  1. United Nations Department of Economic and Social Affairs PD. : World Population Prospects 2022: Summary of Results. In: UN DESA/POP/2022/TR/NO 3 2022.

  2. Projected Population by Age (Korea) [https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1BPA001&conn_path=I2&language=en]

  3. Dementia NIo. Korean Dementia Observatory 2021. In. National Institute of Dementia; 2022.

  4. Wu B, Fillenbaum GG, Plassman BL, Guo L. Association between Oral Health and cognitive status: a systematic review. J Am Geriatr Soc. 2016;64(4):739–51.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Dhingra K, Grimm W-D, Chaudhari PK, Verma F. Does periodontal Disease elevate the risk of Alzheimer’s Disease and mild cognitive impairment? Evid Based Dent. 2021;22(4):123–5.

    Article  PubMed  Google Scholar 

  6. Kim D-H, Han G-S. The relationship between periodontal Disease and cognitive impairment in older adults of Korea. Spec Care Dentist. 2022;42(2):170–6.

    Article  PubMed  Google Scholar 

  7. Larvin H, Kang J, Aggarwal VR, Pavitt S, Wu J. Risk of incident Cardiovascular Disease in people with periodontal Disease: a systematic review and meta-analysis. Clin Exp Dent Res. 2021;7(1):109–22.

    Article  PubMed  Google Scholar 

  8. Van Dyke TE, Kholy KE, Ishai A, Takx RAP, Mezue K, Abohashem SM, Ali A, Yuan N, Hsue P, Osborne MT, et al. Inflammation of the periodontium associates with risk of future cardiovascular events. J Periodontol. 2021;92(3):348–58.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Panezai J, Ghaffar A, Altamash M, Åberg M, Van Dyke TE, Larsson A. Engström P-E: Periodontal Disease augments Cardiovascular Disease risk biomarkers in rheumatoid arthritis. Biomedicines. 2022;10(3):714.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Choi YY, Lee KH. Periodontitis as a risk factor for rheumatoid arthritis: a matched-cohort study. Int Dent J. 2021;71(6):516–21.

    Article  PubMed  PubMed Central  Google Scholar 

  11. González DA, Bianchi ML, Salgado PA, Armada M, Seni S, Isnardi CA, Citera G, Ferrary T, Orman B. Disease activity and subcutaneous nodules are associated to severe periodontitis in patients with rheumatoid arthritis. Rheumatol Int. 2022;42(8):1331–9.

    Article  PubMed  Google Scholar 

  12. Mao S, Huang C-P, Lan H, Lau H-G, Chiang C-P, Chen Y-W. Association of periodontitis and oral microbiomes with Alzheimer’s Disease: a narrative systematic review. J Dent Sci 2022.

  13. Schliephake H. The role of systemic Diseases and local conditions as risk factors. Periodontol 2000. 2022;88(1):36–51.

    Article  PubMed  Google Scholar 

  14. Hajishengallis G. Interconnection of periodontal Disease and comorbidities: evidence, mechanisms, and implications. Periodontol 2000. 2022;89(1):9–18.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Naorungroj S, Schoenbach VJ, Beck J, Mosley TH, Gottesman RF, Alonso A, Heiss G, Slade GD. Cross-sectional associations of oral health measures with cognitive function in late middle-aged adults: a community-based study. J Am Dent Assoc. 2013;144(12):1362–71.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Iwasaki M, Yoshihara A, Kimura Y, Sato M, Wada T, Sakamoto R, Ishimoto Y, Fukutomi E, Chen W, Imai H, et al. Longitudinal relationship of severe periodontitis with cognitive decline in older Japanese. J Periodontal Res. 2016;51(5):681–8.

    Article  PubMed  Google Scholar 

  17. Oral Health Status in Subjects with Amnestic Mild Cognitive Impairment and Alzheimer’s Disease: Data from the Zabút Aging Project. J Alzheimers Dis 2022, 87:173–183.

  18. Yoo J-J, Yoon J-H, Kang M-J, Kim M, Oh N. The effect of missing teeth on Dementia in older people: a nationwide population-based cohort study in South Korea. BMC Oral Health. 2019;19(1):61.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Matsuyama Y, Fujiwara T, Murayama H, Machida M, Inoue S, Shobugawa Y. Differences in brain volume by tooth loss and cognitive function in older Japanese adults. Am J Geriatr Psychiatry 2022.

  20. Zhang XM, Wu X, Chen W. The Association between Number of Teeth and Cognitive Frailty in older adults: a cross-sectional study. J Nutr Health Aging. 2022;26(5):430–8.

    Article  PubMed  Google Scholar 

  21. Shin H-E, Chang I-J, Cho M-J, Song K-B, Choi Y-H. Association between masticatory ability, oral health-related quality of life and cognitive function in the elderly population using structural equation modeling. J Korean Acad Oral Health. 2018;42(4):159–66.

    Article  Google Scholar 

  22. Mizutani S, Egashira R, Yamaguchi M, Tamai K, Yoshida M, Kato T, Umezaki Y, Aoki H, Naito T. Changes in oral and cognitive functions among older Japanese dental outpatients: a 2-year follow-up study. J Oral Rehabil. 2021;48(10):1150–9.

    Article  PubMed  Google Scholar 

  23. Kim M-S, Han D-H. Does reduced chewing ability efficiency influence cognitive function? Results of a 10-year national cohort study. Medicine 2022, 101(25).

  24. Yang I, Arthur RA, Zhao L, Clark J, Hu Y, Corwin EJ, Lah J. The oral microbiome and inflammation in mild cognitive impairment. Exp Gerontol. 2021;147:111273.

    Article  PubMed  Google Scholar 

  25. Tuganbaev T, Yoshida K, Honda K. The effects of oral microbiota on health. Sci (1979). 2022;376(6596):934–6.

    Google Scholar 

  26. Hedberg L, Ekman U, Nordin LE, Smedberg J-I, Skott P, Seiger Å, Sandborgh-Englund G, Westman E, Kumar A, Trulsson M. Cognitive changes and neural correlates after oral rehabilitation procedures in older adults: a protocol for an interventional study. BMC Oral Health. 2021;21(1):297.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Matsubara C, Shirobe M, Furuya J, Watanabe Y, Motokawa K, Edahiro A, Ohara Y, Awata S, Kim H, Fujiwara Y, et al. Effect of oral health intervention on cognitive decline in community-dwelling older adults: a randomized controlled trial. Arch Gerontol Geriatr. 2021;92:104267.

    Article  PubMed  Google Scholar 

  28. Schwahn C, Frenzel S, Holtfreter B, Van der Auwera S, Pink C, Bülow R, Friedrich N, Völzke H, Biffar R, Kocher T, et al. Effect of periodontal treatment on preclinical Alzheimer’s Disease—results of a trial emulation approach. Alzheimers Dement. 2022;18(1):127–41.

    Article  PubMed  Google Scholar 

  29. Asher S, Stephen R, Mäntylä P, Suominen AL, Solomon A. Periodontal health, cognitive decline, and Dementia: a systematic review and meta-analysis of longitudinal studies. J Am Geriatr Soc. 2022;70(9):2695–709.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Shin C. Korean Longitudinal Study of Ageing. In: Encyclopedia of Gerontology and Population Aging edn. Edited by Gu D, Dupre ME. Cham: Springer International Publishing; 2019: 1–4.

  31. Association between Changes in the Regularity of Working Hours and Cognitive Impairment in Middle-Aged and Older Korean Workers: The Korean Longitudinal Study of Aging, 2008–2018. Int J Environ Res Public Health 2022, 19(7):4161.

  32. Zhu X, Luchetti M, Aschwanden D, Sesker AA, Stephan Y, Sutin AR, Terracciano A. Satisfaction with life and risk of Dementia: findings from the Korean Longitudinal Study of Aging. The Journals of Gerontology: Series B 2022.

  33. Kim M-S, Oh B, Yoo JW, Han D-H. The association between mastication and mild cognitive impairment in Korean adults. Medicine. 2020;99(23):e20653.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Boo YY, Jutila O-E, Cupp MA, Manikam L, Cho S-I. The identification of established modifiable mid-life risk factors for Cardiovascular Disease which contribute to cognitive decline: Korean longitudinal study of aging (KLoSA). Aging Clin Exp Res. 2021;33(9):2573–86.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Kim S-M, Kim Y-I. The Effect of Depression and cognitive function on the geriatric oral Health Assessment Index in the Elderly. J Dent Hyg Sci. 2022;22(2):67–74.

    Article  Google Scholar 

  36. Should Age Matter? How. 65 Came to Be Old and Old Came to Be Ill [https://origins.osu.edu/article/should-age-matter-how-65-came-be-old-and-old-came-be-ill?language_content_entity=en].

  37. Ouchi Y, Rakugi H, Arai H, Akishita M, Ito H, Toba K, Kai I. Society obotJCoJG, definition JGSot, elderly cot: redefining the elderly as aged 75 years and older: proposal from the Joint Committee of Japan Gerontological Society and the Japan Geriatrics Society. Geriatr Gerontol Int. 2017;17(7):1045–7.

    Article  PubMed  Google Scholar 

  38. Lee SB, Oh JH, Park JH, Choi SP, Wee JH. Differences in youngest-old, middle-old, and oldest-old patients who visit the emergency department. Clin Exp Emerg Med. 2018;5(4):249–55.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lee H-Y, Kondo N, Oh J. Medical expenditure and unmet need of the pre-elderly and the elderly according to job status in Korea: are the elderly indeed most vulnerable? PLoS ONE. 2018;13(3):e0193676.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Andrade FBd, Antunes JLF, PRBd SJ, Lima-Costa MF. Oliveira Cd: life course socioeconomic inequalities and oral health status in later life: ELSI-Brazil. Rev Saude Publica. 2019;52(Suppl 2):7s.

    Article  Google Scholar 

  41. do Amaral Júnior OL, Braccini Fagundes ML, Menegazzo GR. Melgarejo do Amaral Giordani J: individual social capital and dental pain in older adults. Gerodontology. 2022;39(3):320–6.

    Article  PubMed  Google Scholar 

  42. White L, Ingraham B, Larson E, Fishman P, Park S, Coe NB. Observational study of patient characteristics associated with a timely diagnosis of Dementia and mild cognitive impairment without Dementia. J Gen Intern Med. 2022;37(12):2957–65.

    Article  PubMed  Google Scholar 

  43. Chen Z-C, Wu H, Wang X-D, Zeng Y, Huang G, Lv Y, Niu J, Meng X, Cai P, Shen L et al. Association between marital status and cognitive impairment based on a cross-sectional study in China. Int J Geriatr Psychiatry 2022, 37(1).

  44. Sun-Jung S, Se-Hwan J. A Korean version of the geriatric oral Health Assessment Index (GOHAI) in elderly populations: validity and reliability. J Korean Acad Oral Health. 2011;35(2):187–95.

    Google Scholar 

  45. Nangle MR, Manchery N. Can chronic oral inflammation and masticatory dysfunction contribute to cognitive impairment? Curr Opin Psychiatry. 2020;33(2):156–62.

    Article  PubMed  Google Scholar 

  46. Villa A, da Costa J, Duong M-L, Frazier K, Urquhart O. Oral-systemic health considerations in dental settings: an American Dental Association Clinical Evaluators Panel survey. J Am Dent Assoc. 2022;153(4):388–389e382.

    Article  PubMed  Google Scholar 

  47. CHO MJ, KIM KH. Use of the Center for epidemiologic studies Depression (CES-D) Scale in Korea. J Nerv Ment Dis. 1998;186(5):304–10.

    Article  PubMed  Google Scholar 

  48. Kang YW, Na DL, Hahn SH. A validity study on the Korean mini-mental state examination (K-MMSE) in Dementia patients. J Korean Neurol Assoc. 1997;15(2):300–8.

    Google Scholar 

  49. Kim TH, Jhoo JH, Park JH, Kim JL, Ryu SH, Moon SW, Choo IH, Lee DW, Yoon JC, Do YJ, et al. Korean version of Mini Mental Status Examination for Dementia Screening and its’ short form. Psychiatry Investig. 2010;7(2):102–8.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Schmidt AF, Finan C. Linear regression and the normality assumption. J Clin Epidemiol. 2018;98:146–51.

    Article  PubMed  Google Scholar 

  51. James G, Hastie T, Witten D, Tibshirani R. An introduction to statistical learning with applications in R. 2nd ed. Springer; 2021.

  52. Seitz MW, Listl S, Bartols A, Schubert I, Blaschke K, Haux C, Zande MMVD. Current knowledge on correlations between highly prevalent Dental conditions and Chronic Diseases: an Umbrella Review. Prev Chronic Dis. 2019;16:E132.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Falcao A, Bullón P. A review of the influence of periodontal treatment in systemic Diseases. Periodontol 2000. 2019;79(1):117–28.

    Article  PubMed  Google Scholar 

  54. Abnet CC, Qiao Y-L, Dawsey SM, Dong Z-W, Taylor PR, Mark SD. Tooth loss is associated with increased risk of total death and death from upper gastrointestinal cancer, Heart Disease, and Stroke in a Chinese population-based cohort. Int J Epidemiol. 2005;34(2):467–74.

    Article  PubMed  Google Scholar 

  55. Michaud DS, Liu Y, Meyer M, Giovannucci E, Joshipura K. Periodontal Disease, tooth loss, and cancer risk in male health professionals: a prospective cohort study. Lancet Oncol. 2008;9(6):550–8.

    Article  PubMed  PubMed Central  Google Scholar 

  56. Krall EA, Dawson-Hughes B, Garvey AJ, Garcia RI. Smoking, Smoking Cessation, and tooth loss. J Dent Res. 1997;76(10):1653–9.

    Article  PubMed  Google Scholar 

  57. Carson SJ, Burns J. Impact of smoking on tooth loss in adults. Evid Based Dent. 2016;17(3):73–4.

    Article  PubMed  Google Scholar 

  58. Biessels GJ, Deary IJ, Ryan CM. Cognition and Diabetes: a lifespan perspective. Lancet Neurol. 2008;7(2):184–90.

    Article  PubMed  Google Scholar 

  59. Xue M, Xu W, Ou Y-N, Cao X-P, Tan M-S, Tan L, Yu J-T. Diabetes Mellitus and risks of cognitive impairment and Dementia: a systematic review and meta-analysis of 144 prospective studies. Ageing Res Rev. 2019;55:100944.

    Article  PubMed  Google Scholar 

  60. Boykoff N, Moieni M, Subramanian SK. Confronting chemobrain: an in-depth look at survivors’ reports of impact on work, social networks, and health care response. J Cancer Surviv. 2009;3(4):223–32.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Ahles TA, Root JC, Ryan EL. Cancer- and cancer treatment-associated cognitive change: an update on the state of the science. J Clin Oncol. 2012;30(30):3675–86.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Lange M, Joly F, Vardy J, Ahles T, Dubois M, Tron L, Winocur G, De Ruiter MB, Castel H. Cancer-related cognitive impairment: an update on state of the art, detection, and management strategies in cancer survivors. Ann Oncol. 2019;30(12):1925–40.

    Article  PubMed  Google Scholar 

  63. Ospina-Romero M, Abdiwahab E, Kobayashi L, Filshtein T, Brenowitz WD, Mayeda ER, Glymour MM. Rate of memory change before and after Cancer diagnosis. JAMA Netw Open. 2019;2(6):e196160–0.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Castillo-Passi RI, Vergara RC, Rogers NK, Ponce DP, Bennett M, Behrens MI. Cancer History is Associated with slower speed of Cognitive decline in patients with amnestic cognitive impairment. J Alzheimers Dis. 2022;87:1695–711.

    Article  PubMed  Google Scholar 

  65. Colberg SR, Somma CT, Sechrist SR. Physical activity participation may Offset some of the negative impact of Diabetes on cognitive function. J Am Med Dir Assoc. 2008;9(6):434–8.

    Article  PubMed  Google Scholar 

  66. Salerno EA, Culakova E, Kleckner AS, Heckler CE, Lin P-J, Matthews CE, Conlin A, Weiselberg L, Mitchell J, Mustian KM, et al. Physical activity patterns and relationships with cognitive function in patients with Breast Cancer before, during, and after chemotherapy in a prospective, nationwide study. J Clin Oncol. 2021;39(29):3283–92.

    Article  PubMed  PubMed Central  Google Scholar 

  67. Cai Y-H, Wang Z, Feng L-Y, Ni G-X. Effect of Exercise on the cognitive function of older patients with type 2 Diabetes Mellitus: a systematic review and Meta-analysis. Front Hum Neurosci 2022, 16.

  68. Saetung S, Nimitphong H, Siwasaranond N, Sumritsopak R, Jindahra P, Krairit O, Thakkinstian A, Anothaisintawee T, Reutrakul S. The relationship between sleep and cognitive function in patients with prediabetes and type 2 Diabetes. Acta Diabetol. 2018;55(9):917–25.

    Article  PubMed  Google Scholar 

  69. Carroll JE, Small BJ, Tometich DB, Zhai W, Zhou X, Luta G, Ahles TA, Saykin AJ, Nudelman KNH, Clapp JD, et al. Sleep disturbance and neurocognitive outcomes in older patients with Breast cancer: Interaction with genotype. Cancer. 2019;125(24):4516–24.

    Article  PubMed  Google Scholar 

  70. Brzecka A, Madetko N, Nikolenko NV, Ashraf MG, Ejma M, Leszek J, Daroszewski C, Sarul K, Mikhaleva ML, Somasundaram GS, et al. Sleep disturbances and cognitive impairment in the course of type 2 Diabetes-A possible link. Curr Neuropharmacol. 2021;19(1):78–91.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Drijver AJ, Oort Q, Otten R, Reijneveld JC, Klein M. Is poor sleep quality associated with poor neurocognitive outcome in cancer survivors? A systematic review. J Cancer Surviv 2022.

  72. Coro DG, Hutchinson AD, Banks S, Coates AM. Diet and cognitive function in cancer survivors with cancer-related cognitive impairment: a qualitative study. Eur J Cancer Care (Engl). 2020;29(6):e13303.

    Article  PubMed  Google Scholar 

  73. Kössler T, Weber KS, Wölwer W, Hoyer A, Strassburger K, Burkart V, Szendroedi J, Roden M, Müssig K, Roden M, et al. Associations between cognitive performance and Mediterranean dietary pattern in patients with type 1 or type 2 Diabetes Mellitus. Nutr Diabetes. 2020;10(1):10.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Park SJ, Ko KD, Shin S-I, Ha YJ, Kim GY, Kim HA. Association of oral health behaviors and status with depression: results from the Korean National Health and Nutrition Examination Survey, 2010. J Public Health Dent. 2014;74(2):127–38.

    Article  PubMed  Google Scholar 

  75. Barbosa ACS, Pinho RCM, Vasconcelos MMVB, Magalhães BG, dos Santos MTBR. De França Caldas Júnior A: Association between symptoms of depression and oral health conditions. Spec Care Dentist. 2018;38(2):65–72.

    Article  PubMed  Google Scholar 

  76. Park HN, 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(9):766–72.

    Article  PubMed  Google Scholar 

  77. Kato Y, Kikutani T, Tohara T, Takahashi N, Tamura F. Masticatory movements and food textures in older patients with eating difficulties. Gerodontology. 2022;39(1):90–7.

    Article  PubMed  Google Scholar 

  78. Brayne C, Calloway P. The Association of Education and Socioeconomic Status with the Mini Mental State Examination and the clinical diagnosis of Dementia in Elderly people. Age Ageing. 1990;19(2):91–6.

    Article  PubMed  Google Scholar 

  79. Kim H, Lee S, Ku BD, Ham SG, Park W-S. Associated factors for cognitive impairment in the rural highly elderly. Brain Behav. 2019;9(5):e01203.

    Article  PubMed  PubMed Central  Google Scholar 

  80. Resende EdPF, Llibre Guerra JJ, Miller BL. Health and Socioeconomic Inequities as contributors to Brain Health. JAMA Neurol. 2019;76(6):633–4.

    Article  Google Scholar 

  81. Levine DA, Gross AL, Briceño EM, Tilton N, Giordani BJ, Sussman JB, Hayward RA, Burke JF, Hingtgen S, Elkind MSV, et al. Sex differences in Cognitive decline among US adults. JAMA Netw Open. 2021;4(2):e210169–9.

    Article  PubMed  PubMed Central  Google Scholar 

  82. Nooyens ACJ, Wijnhoven HAH, Schaap LS, Sialino LD, Kok AAL, Visser M, Verschuren WMM, Picavet HSJ, van Oostrom SH. Sex differences in cognitive functioning with aging in the Netherlands. Gerontology. 2022;68(9):999–1009.

    Article  PubMed  Google Scholar 

  83. Bloomberg M, Dugravot A, Dumurgier J, Kivimaki M, Fayosse A, Steptoe A, Britton A, Singh-Manoux A, Sabia S. Sex differences and the role of education in cognitive ageing: analysis of two UK-based prospective cohort studies. The Lancet Public Health. 2021;6(2):e106–15.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Zhang Z, Liu H, Choi S-w. Early-life socioeconomic status, adolescent cognitive ability, and cognition in late midlife: evidence from the Wisconsin Longitudinal Study. Soc Sci Med. 2020;244:112575.

    Article  PubMed  Google Scholar 

  85. Bertola L, Benseñor IM, Barreto SM, Giatti L, Moreno AB, Viana MC, Lotufo PA, Suemoto CK. Early life socioeconomic status predicts cognition regardless of education level. Eur J Neurol. 2021;28(12):3972–8.

    Article  PubMed  Google Scholar 

  86. Wang X, Bakulski KM, Paulson HL, Albin RL, Park SK. Associations of healthy lifestyle and socioeconomic status with cognitive function in U.S. older adults. Sci Rep. 2023;13(1):7513.

    Article  PubMed  PubMed Central  Google Scholar 

  87. Steptoe A, Zaninotto P. Lower socioeconomic status and the acceleration of aging: An outcome-wide analysis. Proceedings of the National Academy of Sciences 2020, 117(26):14911–14917.

  88. Tomasi D, Volkow ND. Associations of family income with cognition and brain structure in USA children: prevention implications. Mol Psychiatry. 2021;26(11):6619–29.

    Article  PubMed  PubMed Central  Google Scholar 

  89. Watanabe Y, Hirano H, Arai H, Morishita S, Ohara Y, Edahiro A, Murakami M, Shimada H, Kikutani T, Suzuki T. Relationship between Frailty and oral function in Community-Dwelling Elderly adults. J Am Geriatr Soc. 2017;65(1):66–76.

    Article  PubMed  Google Scholar 

  90. Watanabe Y, Okada K, Kondo M, Matsushita T, Nakazawa S, Yamazaki Y. Oral health for achieving longevity. Geriatr Gerontol Int. 2020;20(6):526–38.

    Article  PubMed  Google Scholar 

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Acknowledgements

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Funding

This work was supported in part by the Korea Biobank Network program run by the Korea Disease Control and Prevention Agency (KBN4-A04-03).

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Authors and Affiliations

  1. Apple Tree Institute of Biomedical Science, Apple Tree Medical Foundation, 1450 Jungang-ro, Goyang-si, Gyeonggi-do, Republic of Korea

    Jae-Eun Sin, Hye-Sung Kim & Inseong Hwang

  2. Apple Tree Dental Hospital, Apple Tree Medical Foundation, 1450 Jungang-ro, Goyang-si, Gyeonggi-do, Republic of Korea

    Jae-Eun Sin, Hye-Sung Kim & Inseong Hwang

  3. DOCSmedi OralBiome Co., Ltd, 143 Gangseong-ro, Goyang-si, Gyeonggi-do, Republic of Korea

    Miwha Noh

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  1. Jae-Eun Sin
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Contributions

J.-E.S. contributed to methodology, analysis, writing and critical revision. H.-S.K. contributed to conceptualization and methodology. I.H. contributed to conceptualization, original draft preparation, critical revision, final editing and supervision. M.N. was involved in conceptualization, acquisition of data, initial analysis, original draft preparation and supervision.

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Correspondence to Inseong Hwang or Miwha Noh.

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All methods for the KLoSA survey were reviewed and approved by the Institutional Review Board of Statistics Korea (approval number: 336052) and conducted in accordance with the guidelines and regulations under Article 18 of the Statistics Act. Trained surveyors collected data by conducting computer-assisted face-to-face interviews with participants who provided written informed consent. This secondary analysis study, using the KLoSA panel data, was conducted after approval from the Public Institutional Review Board (http://public.irb.or.kr) run by the Korea National Institute for Bioethics Policy (approval number: P01-202206-01-003).

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Sin, JE., Kim, HS., Hwang, I. et al. Age-group-specific association of oral health and systemic health on cognitive function: a cross-sectional study of Korean elders. BMC Oral Health 23, 997 (2023). https://doi.org/10.1186/s12903-023-03724-2

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BMC Oral Health

ISSN: 1472-6831

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