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Please cite this article as an ‘Accepted Article’, doi:10.2188/jea.JE20200476
Received October 8, 2020; accepted January 11, 2021; released online January 30, 2021

Original Article

Title: Longitudinal association between oral status and cognitive decline by fixedeffects analysisnSakura Kiuchi1, Taro Kusama1, Kemmyo Sugiyama1, Takafumi Yamamoto1, UpulsioCooray1, Tatsuo Yamamoto2, Katsunori Kondo3,4, Ken Osaka1, Jun Aida5

1) Department of International and Community Oral Health, Tohoku University,
Graduate School of Dentistry, Sendai, JapanVer2) Department of Disaster Medicine and Dental Sociology, Graduate School of
Dentistry, Kanagawa Dental University, Kanagawa, Japan

3) Department of Social Preventive Medical Sciences, Center for Preventive Medical
Sciences, Chiba University, Chiba, Japaned4) Department of Gerontological Evaluation, Center for Gerontology and Social
Science, National Center for Geriatrics and Gerontology, Aichi, Japan
5) Department of Oral Health Promotion, Graduate School of Medical and Dentalcept

Sciences, Tokyo Medical and Dental University, Tokyo, Japan

Corresponding Author:
Jun Aida

Ac

Department of Oral Health Promotion, Graduate School of Medical and Dental
Sciences, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo
113-8510, Japan
E-mail: aida.ohp@tmd.ac.jp
ORCID: https://orcid.org/0000-0002-8405-9872
Short Title: Oral status and cognitive decline
Numbers of Tables: 3
Figure: 1
Supplementary tables: 5

Copyright © 2021 Sakura Kiuchi et al. This is an open access article distributed under the terms of Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract
Background
Although the feasibility of randomized trials for investigating the long-term associationnbetween oral health and cognitive decline is low, deriving causal inferences fromsioobservational data is challenging. We aimed to investigate the association between poor
oral status and subjective cognitive complaints (SCC) using fixed-effects model toVereliminate the confounding effect of unobserved time-invariant factors.

Methods

We used data from Japan Gerontological Evaluation Study (JAGES) which wasedconducted in 2010, 2013, and 2016. β regression coefficients (95% confidence intervals)
were calculated using fixed-effects models to determine the effect of deteriorating oralptstatus on developing SCC. Onset of SCC was evaluated using the Kihon Checklist-ce

Cognitive function score. Four oral status variables were used: awareness of swallowingAc

difficulty, decline in masticatory function, dry mouth, and number of teeth.

Results

13,594 participants (55.8% women) without SCC at baseline were included. The mean
age was 72.4 (SD = 5.1) for men and 72.4 (SD=4.9) for women. Within the 6-year followup, 26.6% of men and 24.9% of women developed SCC. The probability of developing
SCC was significantly higher when participants acquired swallowing difficulty (β= 0.088;
0.065-0.111 for men, β= 0.077; 0.057-0.097 for women), decline in masticatory function(β=0.039; 0.021-0.057 for men, β= 0.030; 0.013-0.046 for women), dry mouth (β= 0.026;
0.005-0.048 for men, β= 0.064; 0.045-0.083 for women), and tooth loss (β= 0.043; 0.001-

0.085 for men, β= 0.058; 0.015-0.102 for women).

Conclusionssiodevelopment of SCC, which increases the risk for future dementia.

n

The findings suggest that good oral health needs to be maintained to prevent the

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Key words: oral health, number of teeth, oral function, cognitive decline, dementia

INTRODUCTION
Approximately 50 million individuals are currently living with dementia
worldwide 1. The number of cases is estimated to rise to 152 million by 2050. A total ofn28.8 million disability-adjusted life years have been attributed to dementia 2. Mildsiocognitive impairment (MCI) has been identified as an important precursor of dementia 3.
MCI is defined as the intermediate state of cognitive function between the changes seenVer

in aging and those fulfilling the criteria for dementia and often Alzheimer’s disease 1,3.
The prevention of MCI could potentially reduce the incidence of dementia in the future.
Recently, several studies have reported an association between poor oral status
and cognitive decline or the onset of dementia 4–8. There are several possible biomedicaledand social mechanisms underlying the association between oral status and cognitive
decline. Vascular dementia, which is caused by cerebrovascular diseases such as stroke,pt

is the second most prevalent type of dementia 9. Periodontal inflammation has beenceassociated with an increased risk of cerebrovascular diseases 10. Furthermore, diabetes isa well-known risk factor for dementia 9; studies have suggested that inflammation causedAcby periodontal disease increases the risk of diabetes 11. Depression is also a risk factor for
dementia, and poor oral health is associated with the onset of depression 12. Reduced
physical activity and social isolation are also well-known risk factors associated with thedevelopment dementia 9. These factors are also possibly associated with the oral statusbecause poor oral conditions can impair social interactions and increase homeboundness
13.
Despite the abundance of possible connections between oral status and cognitive

decline, determining the causal relationships between them with epidemiological studies
is a formidable task. Both oral disease and dementia are chronic diseases with long-term

progression, and experimental studies such as randomized control trials are difficult forinvestigating the relationship. Therefore, any causal inferences have to be drawn fromobservational data 14. However, deducing causal inferences from observational data isnchallenging because of the bias introduced by unmeasured confounders. Recently,sioeconometric methods are being used increasingly in healthcare research to facilitate theacquisition of robust causal inferences 15. Fixed-effects models have the ability toVerdiminish the bias caused by even unmeasured time-invariant confounders 14,16. It is
possible that previous observational studies may have overestimated the association
between oral health and cognitive decline because of the unmeasured confounding bias.
Therefore, we examined the hypothesis that poor oral status could increase the risk ofceData collection

pt

METHODSed

cognitive decline using the fixed-effects model.

This longitudinal study used data from the Japan Gerontological Evaluation StudyAc(JAGES), an ongoing cohort study for community-dwelling older individuals aged 65
years and above and living in Japan 17. We used data from three waves of the survey, i.e.,the data from 2010 were considered as baseline and data from 2013 and 2016 wereconsidered as follow-up. A postal survey was randomly distributed to 95,827 individuals
in 16 Japanese municipalities from August 2010 to January 2012. A total of 62,418
participants responded to the initial (baseline) survey (response rate: 65.1%). Amongthem, 54,529 participants were linked to 2013 and 2016 survey. However, 15,185
participants were lost to follow-up over the 6-year study period (follow-up rate: 72.1%).
A total of 6,148 deaths were observed, and 7,744 participants became ineligible for thesurvey because they moved away from the community dwellings. We focused onparticipants who were functionally independent and did not have subjective cognitive
complaints (SCC) at baseline. Therefore, we excluded 580 functionally dependentnparticipants and 8,450 participants with SCC. After excluding participants with invalidsioresponses for the teeth variable, 13,594 participants were included in this analysis. TheVerflowchart is shown in Figure 1.

The outcome variable

We used SCC as the outcome measure in this study. SCC was determined from threequestions related to cognitive function in the Kihon Checklist (KCL-CF) 18. Tomata et al.

19ed(2017) reported higher KCF-CF score was related to a higher risk of the onset of dementia. Following questions were included in the KCL-CF.

pt

1. “Do your family or your friends point out your memory loss?”ce

2. “Do you make a call by looking up phone numbers?”
3. “Do you find yourself not knowing today’s date?”

Ac

Participants were asked to answer ‘yes’ or ‘no’ to the above-mentioned questions. Answer‘yes’ for the 1st and 3rd questions, and answer ‘no’ for the 2nd question was indicative ofcognitive decline. Participants with at least one response indicating cognitive decline
were considered to have SCC 20.

Explanatory variables
We used swallowing difficulty, decline in masticatory function, dry mouth, and numberof teeth as explanatory variables. Swallowing difficulty, decline in masticatory function,and dry mouth were assessed using responses to the following questions, respectively.

1. “Have you often choked on your tea or soup recently?”
2. “Do you have any difficulties eating tough foods compared to 6 months ago?”

These questions were also a part of the KCL-CF questionnaire 18.

n

3. “Do you often experience having a dry mouth?”sioThe number of teeth were measured by asking participants, “How manyremaining teeth do you have?”. Participants were asked to choose between 4 categories

Ver(≥20/10-19/1-9/0) in 2010 and 5 categories (≥20/10-19/5-9/1-4/0) in 2013 and 2016. In
this study, 2 category classification for number of teeth (i.e. ≥20/0-19) was used. This
categorization was obtained from a previous study 21. We conducted sensitivity analysisedwith different number of teeth categories (N=14,830).

CovariatesptPotential confounders were selected based on a systematic review 5. Demographic factors

ceincluding age (65-69/70-74/75-79/80-84/≥85 years) and marital status (married, living
together/single, divorced, or widowed) were recorded. Socioeconomic status wasAcevaluated using the equivalent household income (<1.00/1.00-1.99/2.00-2.99/3.003.99/≥4.00 million Japanese Yen) and education level (≤9/10-12/≥13 years of educations).
Previous study which used fixed-effects analysis treated educational level as timeinvariant variable 16, however, we treated it as time-variant because lifelong learning has
become normal in Japan 22. The equivalent household income was calculated by dividing
the annual household income by the square root of the number of family members.
Comorbidities included the presence of hypertension (yes/no) and diabetes mellitus(yes/no). Health behaviors included alcohol consumption (current drinker/pastdrinker/nondrinker), smoking history (current smoker/past smoker/non-smoker), anddaily walking time (<30 minutes/30-59 minutes/60-89 minutes/≥90 minutes). We did not
adjust for the history of stroke, depression, social relationship, and the denture use. We
considered these variables to be mediators between oral status and cognitive declineVer

Statistical analysissioand less communication25, and the denture use26.

nbecause it was reported that poor oral status increased the risk of stroke 23, depression 24

First, the suitability of the fixed-effects models over the random-effects models was
confirmed using the Hausman test 27. A linear probability model with the onset of SCC as
the outcome was used to improve interpretability 28,29. We calculated the β coefficientedwith a 95% confidence interval (95% CI) to investigate how changes in oral status
affected the probability of onset of SCC. The following equation was used to calculatept

the β coefficients.

ce

Subjective cognitive complaint it = β0 + β1 swallowing difficulty it + β2 masticatory
function it + β3 dry mouth it + β4 number of teeth it +βk Xk it + Ai + Uit

Ac

The subjective cognitive complaint (0,1) of individual “i" at a given time “t” is theleft part of this formula. Four time-variant explanatory variables for the ith individual attime t are included on the right side. Xk is the matrix of time-variant confounding
variables. Ai models the fixed-effect, i.e., the effect of unmeasured time-invariant
variables such as personal ability. Uit is an error term. The β coefficient indicated thedegree of the probability of the onset of SCC when oral status increase by one unit, inother words, from 0 (no) to 1 (yes) for oral functions or 0 (≥20 teeth) to 1 (0-19 teeth) for
number of teeth.
We found that 3,732 participants responded with a higher value for the number ofteeth than that of the previous wave. We dropped these variables and performed
multivariate imputations (MI). We also conducted a sensitivity analysis that excluded thedata of participants with 0-19 teeth could not change their dental status over 6 years,nwhich might not have been suitable for the fixed-effects analysis. Therefore, we excludedsiothe data of the participants with 0-19 teeth at baseline for the sensitivity analysis(N=6,666).

Ver

Cognitive decline was found to be affected by sex 30. Therefore, we conducted ananalysis stratified by sex. The oral status variables were assessed using separate models
because they were highly correlated, especially between severe tooth loss and chewing
difficulty 31. Swallowing difficulty, decline in masticatory function, dry mouth, anded

number of teeth were separately included separately with the age variable in model 1.
Marital status, equivalent household income, education level, presence of hypertension,pt

presence of diabetes mellitus, drinking history, smoking history, and walking time werece

added in model 2.

We compensated the missing values with the iterative imputation methodAc(missForest) based on a random forest using Python 32. We conducted MI in a wide data
format in line with the recommendations of Young et al. (2015) 33. We used Stata 15.0
MP (Stata Corporation LP, Texas, USA, Windows version) for all analyses except MI.
The reporting of this study conforms to STROBE guidelines.

Ethical approval
The Ethics Committee of the National Center for Geriatrics and Gerontology(approval number: 992), Chiba University (approval number: 2493) and Tohoku
University Graduate School of Medicine (21-40) provided ethical approval for theJAGES. Participants were notified that participation was voluntary and returning ancompleted survey was considered as consent for enrollment.

RESULTSsioTable 1 presents the baseline characteristics of 13,594 participants (6,006 men
and 7,588 women) before and after MI. The distribution showed similar before and afterVe
r

MI. The mean age of the participants at baseline was 72.4 (SD = 5.1) years for men and
72.4 (SD=4.9) years for women. The age of the study population ranged from 65 to 96
years at baseline. Table 2 shows the cross-tabulation of the onset of SCC in 2016 by oral
status in 2010 and changes in 2010-2016. Within the follow-up period, 26.6% of men anded

24.9% of women developed SCC. Participants who reported comparatively lower levelsof oral status at baseline and no changes in oral status from 2010 to 2016 tended to havept

a higher prevalence of SCC in 2016.

ce

Table 3 presents the sex-stratified results of the fixed-effects models generated
to predict the onset of SCC. The probability of the onset of SCC was significantly higherAc

when participants acquired swallowing difficulty (β= 0.088; 0.065-0.111 for men, β=0.077; 0.057-0.097 for women), decline in masticatory function (β=0.039; 0.021-0.057
for men, β=0.030; 0.013-0.046 for women), dry mouth (β= 0.026; 0.005-0.048 for men,β= 0.064; 0.045-0.083 for women) and tooth loss (β= 0.043; 0.001-0.085 for men, β=0.058; 0.015-0.102 for women). This result can be interpreted as follows. Men who
acquired swallowing difficulty demonstrated 8.8% (95% CI 6.5%-11.1%) higher
probability of developing SCC on average. The probability was the highest among
participants who acquired swallowing difficulty compared to those with a decline inmasticatory function, dry mouth, and tooth loss. All other covariate specific coefficients

and 95% CI are presented in eTable.2. and eTable.3.
In the results of both of the sensitivity analysis with a different number of teeth
categories and without participants with 0-19 teeth at baseline, the β coefficient of tooth

si
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n

loss was slightly larger than the main analysis. (shown in eTable.4. and eTable.5)

DISCUSSIONVer

To the best of our knowledge, this was the first study to examine the effect of a wider
range of oral status variables on SCC using large-panel data, after adjusting forunmeasured time-invariant confounders. The probabilities of the onset of SCC werehigher when participants acquired swallowing difficulty [differences in the onset (%)

edwere 8.8% for men, 7.7% for women], decline in masticatory function (3.9% for men,
3.0% for women), dry mouth (2.6% for men, 6.4% for women), and tooth loss (4.3% forpt

men and 5.8% for women) on average. These results are important for understanding the

19

.

ce

risk of dementia because the SCC was related to the increased risk of incident dementia

Ac

The longitudinal association observed between tooth loss and future dementia inthis study is consistent with the findings of previous studies 5,6. We added robust evidence
obtained from fixed-effects model, which nullified the confounding effect of unmeasured
time-invariant variables. Our findings related to masticatory function were consistent
with those of previous systematic reviews 4,7. Earlier studies have suggested that patientswith dementia tended to have dysphagia 34 and dry mouth 35, however, no study has
examined the longitudinal association between swallowing difficulty or dry mouth andthe onset of SCC among the community-dwelling older adults.
Several plausible mechanisms are underlying the association between poor oralstatus and cognitive decline. First, the deterioration in oral status affects the quality ofsocial activities such as speaking and smiling. Tooth loss and deterioration of oral status
can predict future homeboundness among the older population 13. Physical inactivity andnsocial isolation are major risks for dementia 9. Second, a deteriorating oral condition couldsioincrease the risk of depression due to the lack of social activities 13,36, subsequently
increasing the risk of dementia 9. Third, the decline of oral status could lead toVermalnutrition, which is a possible cause of cognitive decline among older adults 37. Thosewith dry mouth tend to avoid dry food such as bread and vegetable 38. Swallowing
difficulty causes malnutrition because they reduce or alter food intake from solids toliquids 39. A previous study has shown that vitamin deficiency partially explains theedassociation between malnutrition and cognitive decline 40. Furthermore, an association
between nutritional biomarkers at baseline and cognitive decline at follow-up has alsoptbeen reported 41. Fourth, the decline in oral status might be related to cognitive declinecethrough diminished cerebral blood flow. A systematic review that examined the results ofbiological experiments suggested that masticatory function may play a protective role inAc

maintaining cognitive function 7. Finally, the inflammatory pathway of periodontal
diseases might be related to cognitive decline 42. Cytokines produced by periodontal
inflammation could enter the systemic circulation, which may contribute to cognitive
decline 42.

We provided evidence of the importance of maintaining good oral health as
implication. Public health interventions that focus on the prevention of dental caries andperiodontal diseases, which are the major causes of tooth loss 43, are essential. These
interventions are also aid in maintaining masticatory function 31. The use of several
medications can lead to dry mouth, which is a concern in the medical field 44. Therefore,reducing any unnecessary medications is required via cooperation with medical doctors.
This study has several strengths. First, we used data from a large-scale cohortfrom rural and urban areas of Japan. Moreover, the follow-up rate for all participants wasnrelatively high (72.1%). Second, we used fixed-effects analysis, which enabled thesioreduction of the bias introduced by unobserved time-invariant confounders, such asindividual personalities or abilities for keeping oral hygiene 27. The limitation of the studyVeris that the fixed-effects model cannot control for unobserved time-variant variables 27.
However, we adjusted the models using various time-variant covariates. Second, fixedeffects models cannot determine reverse causality 27. However, we restricted the target
population to individuals without SCC at baseline. Therefore, it is unlikely thatedparticipants with cognitive impairment would have poor oral status. Third, we used a selfreported questionnaire that relied on subjective answers. Tomata et al mentioned onlypt

KCF-CF score is not sufficient for predicting dementia due to a false-negativity rate ofce

KCF-CF 19. Therefore, more validated questionnaire would be expected. For explanatory
variables, the self-reported number of teeth is a reliable measurement 45 and several

Acstudies commonly used 21,24. We assessed the swallowing difficulty by obtaining a single
question. Currently, EAT-10, the validated self-reported questions were introduced 46.
Although a single question was used in several studies 47–49, future studies should consider
multiple questionnaires such as EAT-10. Additionally, we asked the participants about dry
mouth by a single question. A visual analog scale measure would be a better predictor for
detecting dry mouth 50.
Fourth, our data excluded participants who were certified as having dementia by
the city government or died during the follow-up year. We think that the longitudinalassociation between oral health and the onset of SCC would have been stronger if we had

included individuals with dementia or who died in our analysis.

CONCLUSIONn

The decline in oral status (swallowing difficulty, decline in masticatory function, drysiomouth, and tooth loss) was significantly associated with the onset of SCC after adjustingfor time-invariant confounding factors. The present findings show the importance ofVerkeeping good oral health for preventing SCC, which is related with higher risk ofdementia.

Authors’ contributions:ed

Conflicts of interest: The authors have no conflicts of interest to declare.

ptConceptualization: SK and JA. Data curation: SK and JA. Formal analysis: SK, TK, UC,

ce

JA. Writing-original draft: SK and JA. Writing-review& editing: TK, KS, TY, UC, TY,
KK, KO. Supervision: KS, TY, TY, KK, KO, JA. All authors read and approved the finalAcversion of manuscript.

Funding

This study used data from JAGES (the Japan Gerontological Evaluation Study). This
study was supported by Grant-in-Aid for Scientific Research from JSPS (Japan Society
for the Promotion of Science) KAKENHI Grant Number (JP15H01972, JP 16H05556,
JP19H03860, JP19H03861, 20H00557), Health Labour Sciences Research Grant (H28Choju-Ippan-002, H30-Jyunkankinado-Ippan-004), Japan Agency for Medical Researchand Development(AMED) (JP17dk0110017, JP18dk0110027, JP18ls0110002,

JP18le0110009, JP20dk0110034, JP20dk0110037), Open Innovation Platform withEnterprises, Research Institute and Academia (OPERA, JPMJOP1831) from the Japan
Science and Technology (JST), a grant from Innovative Research Program on SuicidenCountermeasures (1-4), a grant from Sasakawa Sports Foundation, a grant from JapansioHealth Promotion & Fitness Foundation, a grant from Chiba Foundation for Health
Promotion & Disease Prevention, the 8020 Research Grant for fiscal 2019 from the 8020

Ver

Promotion Foundation (adopted number: 19-2-06), a grant from Niimi University(1915010), grants from Meiji Yasuda Life Foundation of Health and Welfare and the
Research Funding for Longevity Sciences from National Center for Geriatrics anded

Gerontology (29-42, 30-22, 20-19) and grant from Futoku foundation.

AppendixpteTable.1. Descriptive baseline characteristics of the participants who did not have any

cesubjective cognitive complaint (2010) with missing values (N=13,594)
eTable.2. Probability of the onset of subjective cognitive complaints (95% confidenceAcinterval) by oral status among men using fixed-effects linear regression analysis from thedata of 2010, 2013, 2016 panels (N= 6,006)
eTable.3. Probability of the onset of subjective cognitive complaints (95% confidence
interval) by oral status among women using fixed-effects linear regression analysis fromthe data of 2010, 2013, 2016 panels (N= 7,588)
eTable.4. Probability of the onset of subjective cognitive complaints (95% confidence
interval) by oral status with different number of teeth categories using fixed-effects linear
regression analysis from the data of 2010, 2013, 2016 panels (N=14,830)
eTable.5. Probability of the onset of subjective cognitive complaints (95% confidenceinterval) by oral status without participants who responded with 0-19 teeth at baseline
using fixed-effects linear regression analysis from the data of 2010, 2013, 2016 panels

Ac

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(N=6,666)

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Figure Legends

Ac

ce

pt

ed

Ve
r

si
o

n

Figure 1. The flowchart of the participants for the analysis

nThe baseline survey was
conducted in 2010 (n=95,827)

sio2010 JAGES
surveyThe participants who responded
questionnaire (n=62,418,
response rate: 65.1%)

Valid response (n=54,529)

Ver

Invalid ID, sex, age (n=7,889)

ed

Loss to follow up (n=15,185)

Figure 1.

Excluded due to death from 10 to 16 survey(n=6,148)
Excluded due to certified for support or long-termcare from 10 to 16 survey (n=7,744）
Excluded due to functionally dependent atbaseline (n= 580)
Excluded due to having subjective cognitive
complaint (SCC) in 2010 (n= 8,450)

The people who participated infollow up survey (n=16,422)

Ac

2013, 2016
JAGES
surveycept

Valid response (n=39,344,
follow-up rate:72.1%)

Excluded due to invalid
response for teeth (n=2,828)
Analyzed population(n=13,594)

Table 1: Descriptive baseline characteristics of the participants who did not have any
subjective cognitive complaint (2010) (N=13,594)
Men (N=6,006) Women (N=7,588)%

Dry mouth

89.0

Yes

11.0

10.6

No

80.1

80.5

Yes

19.9

19.5

85.3

86.4

14.7

13.6

49.1

49.0

0-19

50.9

51.0

65-69

35.1

33.9

70-74

33.5

35.6

75-79

20.9

21.3

80-84

8.4

7.5

≥85

2.0

1.7

11.0

35.2

89.0

64.8

<1.00

6.8

14.3

1.00-1.99

33.8

34.1

2.00-2.99

29.0

27.5

3.00-3.99

17.9

13.6

≥4.00

12.5

10.4

≤9

37.0

46.5

10-12

35.4

38.7

≥13

27.5

14.7

No
Yes
≥20

ed

Number of teeth
Covariatescept

Age (years)

Ac

Marital status

Income (million Japanese yen)

Educational level (years)

89.4

si
o

Decline in masticatory function

No

Ve
r

Swallowing difficulty%nExplanatory variable

Single,Divorced,
WidowedMarried,Living together

Drinking history

54.4

Yes

43.2

45.6

No

85.9

90.8

Yes

14.1

9.2

Current drinker

61.8

16.7

Past drinker

4.2

Non-drinker

34.0

82.5

Current smoker

17.8

2.7

Ve
r

Smoking history

56.8

Total

55.4

4.2

Non-smoker

26.8

93.0

<30 minutes

24.2

28.6

30-59 minutes

37.2

37.7

60-89 minutes

19.0

15.9

≥90 minutes

19.6

17.7

100.0

100.0

ce

pt

Note: The descriptive characteristics shown here used imputed datasets.

Ac

0.8

Past smokered

Walking timenDiabetes mellitus

No

si
o

Hypertension

Table 2: The cross-tabulation of the onset of subjective cognitive complaint in 2016 by oral healthstatus in 2010 and changes in 2010-2016 (N=13,594)

Total

No

Yes

Total

No

Yes

N

%

%

N

%

No

5,345

74.4

25.6

6,786

75.9

24.1

Yes

661

64.8

si
o

%

35.2

802

68.5

31.5

No

4,813

75.4

24.6

6,105

76.7

23.3

Yes

1,193

65.1

34.9

1,483

68.7

31.3

No

5,124

75.1

24.9

6,555

76.2

23.8

Yes

882

63.3

36.7

1,033

68.5

31.5

function

Dry mouth
Number of teethed

Decline in masticatoryVer

Oral status in 2010
Swallowing difficulty

Women

n

Men

≥20

2,949

75.8

24.2

3,717

77.1

22.9

0-19

3,057

71.0

29.0

3,871

73.2

26.8

Improve

287

67.6

32.4

346

73.4

26.6

No change

5,092

75.5

24.5

6,407

76.6

23.4

Decline

627

58.4

41.6

835

64.9

35.1

Improve

448

71.4

28.6

522

71.6

28.4

No change

4,555

74.7

25.3

5,874

76.6

23.4

Decline

1,003

68.3

31.7

1,192

69.6

30.4

Improve

432

66.7

33.3

409

71.6

28.4

No change

4,945

74.9

25.1

6,270

76.6

23.4

Decline

629

66.1

33.9

909

66.3

33.7

No change

5,613

73.4

26.6

7,243

75.2

24.8

Decline

393

73.0

27.0

345

73.0

27.0

6,006

73.4

26.6

7,588

75.1

24.9

Changes in oral status in 2010-2016

cept

Swallowing difficulty

Decline in masticatory

Ac

function

Dry mouth

Number of teeth
Total

Table 3: Probability of the onset of subjective cognitive complaints (95% confidence
interval) by oral status using fixed-effects linear regression analysis from the data of 2010,2013, 2016 panels (N=13,594)
Model 1Model 2

95% CIβ

95% CI

0.089(0.066-0.112)***0.088(0.065-0.111)***

0.037(0.019-0.055)***0.039(0.021-0.057)***

Dry mouth

0.025(0.003-0.046)*

0.026(0.005-0.048)*

Tooth lossa

0.042(0.000-0.084)*

0.043(0.001-0.085)*

0.077(0.058-0.097)***0.077(0.057-0.097)***function

Swallowing difficulty
Decline in masticatory
function

Tooth lossa

0.030(0.014-0.046)***0.030(0.013-0.046)***0.065(0.047-0.084)***0.064(0.045-0.083)***(0.019-0.106)**

0.058(0.015-0.102)**pt

Dry mouthed

Women (N=7,588)sioDecline in masticatoryVer

Swallowing difficultynβMen (N=6,006)

0.062

ce

Model 1: Swallowing difficulty, decline in masticatory function, dry mouth and tooth loss
were separately included in the models with age.
Model 2: Model 1 + marital status, income, education level, hypertension, diabetesAc

mellitus, drinking history, smoking history, and walking time.
aReference category: ≥20

Abbreviations: CI, confidence interval*p< 0.05**p< 0.01***p<0.001