
==== Front
J Korean Med Sci
J Korean Med Sci
JKMS
Journal of Korean Medical Science
1011-8934
1598-6357
The Korean Academy of Medical Sciences

10.3346/jkms.2024.39.e246
Original Article
Neuroscience
Masticatory Function, Sex, and Risk of Dementia Among Older Adults: A Population-Based Cohort Study
https://orcid.org/0000-0002-0195-3490
Oh Dae Jong 1
https://orcid.org/0000-0003-2418-4257
Han Ji Won 23
https://orcid.org/0000-0002-4579-8218
Kim Jun Sung 2
https://orcid.org/0000-0003-0133-5227
Kim Tae Hui 4
https://orcid.org/0000-0003-4555-0105
Kwak Kyung Phil 5
https://orcid.org/0000-0003-2419-7306
Kim Bong Jo 6
https://orcid.org/0000-0001-8196-655X
Kim Shin Gyeom 7
https://orcid.org/0000-0002-6554-4637
Kim Jeong Lan 8
https://orcid.org/0000-0002-1074-8122
Moon Seok Woo 9
https://orcid.org/0000-0002-0396-5284
Park Joon Hyuk 10
https://orcid.org/0000-0001-8057-8723
Ryu Seung-Ho 11
https://orcid.org/0000-0002-2904-8951
Youn Jong Chul 12*
https://orcid.org/0000-0001-8976-8320
Lee Dong Young 313
https://orcid.org/0000-0002-6383-1974
Lee Dong Woo 14
https://orcid.org/0000-0002-5481-4697
Lee Seok Bum 15
https://orcid.org/0000-0002-0828-3557
Lee Jung Jae 15
https://orcid.org/0000-0002-8147-5782
Jhoo Jin Hyeong 16
https://orcid.org/0000-0002-1103-3858
Kim Ki Woong 231718
1 Workplace Mental Health Institute, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
2 Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea.
3 Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea.
4 Department of Psychiatry, Yonsei University Wonju Severance Christian Hospital, Wonju, Korea.
5 Department of Psychiatry, Dongguk University Gyeongju Hospital, Gyeongju, Korea.
6 Department of Psychiatry, Gyeongsang National University School of Medicine, Jinju, Korea.
7 Department of Neuropsychiatry, Soonchunhyang University Bucheon Hospital, Bucheon, Korea.
8 Department of Psychiatry, College of Medicine, Chungnam National University, Daejeon, Korea.
9 Department of Psychiatry, Konkuk University Chungju Hospital, School of Medicine, Konkuk University, Chungju, Korea.
10 Department of Neuropsychiatry, Jeju National University Hospital, Jeju, Korea.
11 Department of Psychiatry, Konkuk University Medical Center, School of Medicine, Konkuk University, Seoul, Korea.
12 Department of Neuropsychiatry, Kyunggi Provincial Hospital for the Elderly, Yongin, Korea.
13 Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea.
14 Department of Psychiatry, Inje University Sanggye Paik Hospital, Seoul, Korea.
15 Department of Psychiatry, Dankook University Hospital, Cheonan, Korea.
16 Department of Psychiatry, Kangwon National University School of Medicine, Chuncheon, Korea.
17 Department of Brain and Cognitive Science, Seoul National University College of Natural Sciences, Seoul, Korea.
18 Institute of Human Behavioral Medicine, Seoul National University Medical Research Center, Seoul, Korea.
Address for Correspondence: Ki Woong Kim, MD, PhD. Department of Neuropsychiatry, Seoul National University Bundang Hospital, 82 Gumi-ro 173-beon-gil, Bundang-gu, Seongnam 13620, Republic of Korea. kwkimmd@snu.ac.kr
*Current affiliation: Hansom Psychiatry Clinic, Yongin, Korea.

23 9 2024
26 7 2024
39 36 e24606 6 2024
14 7 2024
© 2024 The Korean Academy of Medical Sciences.
2024
The Korean Academy of Medical Sciences
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background

A decline in masticatory function may indicate brain dysfunction related to dementia, but the relationship between masticatory function and dementia risk remains unclear. This study aimed to investigate whether masticatory function is associated with the risk of cognitive decline and dementia.

Methods

Data were obtained from the nationwide prospective cohort study of randomly sampled community-dwelling Koreans aged ≥ 60 years. The 5,064 non-demented participants, whose number of chewing cycles per bite was assessed by clinical interview, were followed for 8 years with biennial assessments of cognitive performance and clinical diagnoses of all-cause dementia and Alzheimer’s disease (AD). Structural brain magnetic resonance imaging was collected from a subset of cohort participants and their spouses for imaging analyses.

Results

Males who chewed ≥ 30 cycles/bite had faster decline in global cognition and memory function and were at higher risk for incident all-cause dementia (hazard ratio [HR], 2.91; 95% confidence interval [CI], 1.18–7.18) and AD (HR, 3.22; 95% CI, 1.14–9.11) compared to males with less than 10 cycles/bite. Additionally, increased chewing cycles in males were associated with reduced brain volume, particularly in regions involved in compensatory cognitive control of mastication. There was no significant association between chewing cycles and the risk of dementia or brain volume in females.

Conclusion

Older men who frequently chew their meals could be considered a notable population at risk for dementia who should be carefully assessed for their cognitive trajectories.

Graphical Abstract

Masticatory Function
Dementia
Cognitive Decline
Sex Difference
Brain-Stomatognathic System
Seoul National University Bundang Hospital https://dx.doi.org/10.13039/100016275 18-2023-0012 Korea Centers for Disease Control and Prevention https://dx.doi.org/10.13039/501100003669 2019-ER6201-01
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pmcINTRODUCTION

Dementia is the leading cause of disability and mortality worldwide.1 In response to the steady increase in dementia cases and its global burden, the World Health Organization has designated dementia as a public health priority requiring immediate action.1 Early identification of at-risk populations would be critical to reducing the burden of disease and improving the effectiveness of novel dementia intervention strategies.

Changes in masticatory function may be a potential link to the risk of cognitive decline and dementia. Chewing is regulated by top-down control of the brain-stomatognathic system, which coordinates jaw and tongue movements. Compared to younger adults who rely on automatic activation of motor circuits, older adults require greater cognitive effort to monitor and control chewing movements and integrate sensory signals due to decreased occlusal forces and motor function of the tongue and perioral muscles.234 Compensatory cognitive control processing related to aging involves various brain regions, including the prefrontal cortex, cingulate cortex, insula, hippocampus, and basal ganglia.235 A decline in masticatory function, which is a decompensation in the control of chewing movement, may indicate cortical and subcortical brain dysfunction, increasing the risk of dementia.

Although many cross-sectional studies have suggested a link between masticatory dysfunction and cognitive impairment,678910 the association between masticatory function and the risk of dementia remains unclear due to the lack of prospective studies. Only one prospective study has been conducted, which found no association between masticatory dysfunction and the risk of incident dementia.11 However, this study had limitations in using administrative data for the assessment of dementia outcomes. In addition, the relationship between mastication and dementia risk in relation to sex has not been examined, despite evidence that masticatory performance differs between the sexes. Females generally exhibit lower masticatory performance, weaker occlusal force, and less masticatory muscle thickness compared to males.121314 Therefore, the association between masticatory function and the risk of cognitive decline and incident dementia may vary by sex.

This study is aimed to determine whether there is an association between masticatory dysfunction and the risk of cognitive decline, incident dementia, and related structural brain changes, and whether this association varies by sex.

METHODS

Study design, setting, and participants

We gathered data from the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a nationwide prospective cohort study.15 The KLOSCAD study randomly sampled 6,818 Korean adults aged 60 years or older from 13 districts in South Korea using the national residential roster of 2009. Baseline assessment was conducted from November 2010 to October 2012, followed by four biannual assessments until December 2020.

For this study, we identified 6,166 non-demented participants at baseline, after excluding those with neurologic/psychiatric disorders including dementia (n = 442), or peripheral diseases affecting the stomatognathic system, such as oral diseases, pharyngolaryngitis, temporomandibular joint disorders, sinusitis, head and neck neuralgia, dysphagia, or head and neck cancers (n = 17), or those who did not respond to the masticatory function assessment (n = 193). For the longitudinal analysis tracking the risk of cognitive decline and incident dementia, we finally included 5,064 participants after excluding 1,102 who did not respond to all follow-up assessments.

For the cross-sectional brain imaging analyses, we constructed a brain imaging cohort consisting of 650 non-demented older adults: 594 from the KLOSCAD cohort who underwent structural brain magnetic resonance imaging (MRI) once at the baseline or at one of the follow-up assessments (baseline, n = 75; 1st follow-up, n = 15; 2nd follow-up, n = 20; 3rd follow-up, n = 203; and 4th follow-up, n = 281), and 56 from the KLOSCAD spouse cohort who participated as spouses of the original KLOSCAD participants and underwent structural brain MRI between January 2019 and December 2020.16

Cognitive assessments

Geriatric psychiatrists conducted standardized diagnostic interviews as well as physical and neurologic examinations for all participants, utilizing the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet (CERAD) Clinical Assessment Battery.17 A comprehensive neuropsychological battery, the Korean version of the CERAD Neuropsychological Assessment Battery (CERAD-K-N),18 was administered by neuropsychological researchers or trained research nurses. In addition, the researchers performed comprehensive laboratory tests, including complete blood counts, chemistry panels, serologic tests to screen for syphilis, serum levels of folate and vitamin B12, thyroid function tests, and apolipoprotein E genotyping.

A panel of geriatric psychiatrists diagnosed dementia and mild cognitive impairment (MCI) at regular diagnostic meetings, using the criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition19 and the diagnostic criteria of the International Working Group20 respectively. The panel made a diagnosis of Alzheimer’s disease (AD) among participants diagnosed with dementia, based on the criteria of the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association.21 For assessment of cognitive performance, we calculated the CERAD total score (maximum = 100) by the summation of the subsets of CERAD-K-N: word list memory test, word list recall test, word list recognition test, verbal fluency test, 15-item Boston naming test, and constructional praxis test. We also calculated the CERAD memory score (maximum = 50) by the summation of the subsets for verbal memory: word list memory, recall, and recognition tests.

Assessment of masticatory function

We evaluated the number of chewing cycles as a proxy for masticatory function through clinical interviews. The number of chewing cycles required to prepare food for swallowing is the key determinant of masticatory efficiency22 and remains rather constant within an individual.23 In this study, we assessed the number of chewing cycles by asking participants about the mean number of chews per bite of meal before swallowing in their daily lives over the past year. An increased number of chewing cycles indicates a decline in masticatory function, as reported in previous studies.2224 The meal indicated steamed rice, which is a staple food in many Asian countries. Participants were arbitrarily categorized into three groups based on the number of chewing cycles per bite: < 10 cycles/bite, 10–30 cycles/bite, and ≥ 30 cycles/bite.

Assessment of covariates

Trained research nurses collected data of covariates involving demographic (age, sex, and education), lifestyle (current drinking, smoking, and physical activity), clinical factors (body mass index [BMI], atherosclerotic cardiovascular diseases [ASCVD], and depression), and the duration of mealtime. We defined current drinking as the alcohol consumption of more than 7 standard units per week within the past year and current smoking as smoking any amount of tobacco within the past year. We defined less physical activity as < 150 minutes per week of moderate-intensity and < 75 minutes per week of vigorous-intensity aerobic physical activity.25 We evaluated the presence of depression by using the Korean version of Geriatric Depression Scale.26 We also evaluated the mean duration of mealtime past year as follows: less than 10 minutes, higher than 30 minutes, and others.

Acquisition and processing of MRI

For the brain imaging cohort, we performed brain MRI using a 3.0 Tesla Achieva Scanner (Philips Medical Systems; Eindhoven, NL, USA) with a uniform protocol (acquired voxel size = 1.0 × 0.5 × 0.5 mm; echo time = 4.6 ms; repetition time = 8.1 ms; axial-plane acquisition matrix size = 240 × 240 mm; number of excitations = 1; flip angle = 8°). The T1 images were bias-corrected to remove intensity inhomogeneity artifacts by using Statistical Parametric Mapping software version 8 (SPM8; Wellcome Trust Centre for Neuroimaging, London, UK). Subsequently, we resliced the bias-corrected T1 images into isotropic voxels sized 1.0 × 1.0 × 1.0 mm3. We performed cortical reconstruction and volumetric segmentation, automatically segmented whole brain structures, and then obtained parcellated brain masks for each region of interest by using FreeSurfer v6.0 (http://surfer.nmr.mgh.harvard.edu/).

To determine the volumes of white matter hyperintensity (WMH), we utilized a fully automated WMH quantification code of MATLAB 2021a (The MathWorks, Inc., Natick, MA, USA).27 Briefly, 1) we obtained fluid-attenuated inversion recovery (FLAIR) images in all participants and we applied a bias field correction using the SPM8 to correct the low-frequency and to smooth the signal that corrupts the magnetic resonance images; 2) we isolated hyperintense area on the FLAIR images using inhouse MATLAB code; 3) we parcellated brain mask using T1-weighted images using FreeSurfer v6.0 and select white matter regions; 4) from the isolated hyperintense area of the FLAIR images, we overlapped the selected white matter regions and removed the non-overlapping parts. We calculated total volume of WMH, periventricular and deep WMH volumes based on the distance rule.28

Statistical analysis

We compared the baseline characteristics of participants by using the analysis of variance or Student’s t-test for continuous variables and the χ2 test for categorical variables. We analyzed the association of masticatory function with the progression rate of cognitive decline during 8-year follow-up using linear mixed model analyses, including an interaction term between baseline number of chewing cycles and follow-up time as a fixed effect along with random effects for the follow-up time and intercept, and CERAD total and memory scores as dependent variables. The models were fitted using restricted maximum likelihood and adjusted for age, sex, education, lifestyle, BMI, ASCVD, depression, and duration of mealtime. The β coefficients and their standard errors from the models were reported as the results of analyses.

We analyzed the association of masticatory function with the risks of incident all-cause dementia and AD using Cox proportional hazard analyses adjusted for age, sex, education, lifestyle, BMI, ASCVD, depression, and duration of mealtime. To identify the sex-dimorphic associations of masticatory function with the progression of cognitive decline and the risk of incident dementia, we also conducted all the analyses above after stratification by sex.

To analyze the cross-sectional association of masticatory function with brain volume measures in the brain imaging cohort by sex, we used generalized linear model analyses adjusted for age, education, lifestyle, BMI, ASCVD, depression, and duration of mealtime. All variables, including masticatory function and covariates, were collected at the same assessment wave as the imaging acquisition. Dependent variables included 1) global volumes, 2) lobar volumes, 3) AD signature regions volume (medial temporal lobe, inferior temporal gyrus, angular gyrus, supramarginal gyrus, superior parietal lobule, and superior and middle frontal gyri),29 4) aging-related masticatory regions (anterior cingulate cortex, insula, superior and middle frontal gyri, and superior parietal lobule),35 and 5) WMH volumes. Before entering into the models, all dependent variables were residualized to total cranial volume30 and standardized by calculating z-score as the original value minus participants mean divided by the standard deviation. The volumes of WMH were log-transformed before residualizing due to long-tailed distributions.

All statistical analyses were performed using IBM SPSS Statistics, version 21.0 (IBM Corporation, Armonk, NY, USA).

Ethics statement

All participants were fully informed about the study protocol and provided written informed consent. This study protocol was reviewed and approved by the Institutional Review Board of the Seoul National University Bundang Hospital (No. B-0912-089-010).

RESULTS

The 5,064 participants without dementia, whose characteristics were described in Supplementary Table 1, were followed up for 5.8 ± 2.4 years. Table 1 presents the baseline characteristics of the participants based on the number of chewing cycles and sex. Participants with ≥ 30 cycles/bite were more likely to be male, older, more educated, had higher BMI, longer mealtime, and lower depression rates compared to those with < 10 cycles/bite. Male participants were older and more educated than females, with higher alcohol consumption, smoking rates, physical activity levels, BMI, and ASCVD rates. However, they reported lower depression rates, shorter meal times, and lower MCI rates. The number of chewing cycles was similar between sexes.

Table 1 Comparison of baseline characteristics by the number of chewing cycles and sex

Variables	No. of chewing cycles (cycles/bite)	Sex	
< 10a (n = 1,755)	10–30b (n = 3,015)	≥ 30c (n = 294)	P value	Post-hoc	Male (n = 2,195)	Female (n = 2,869)	P value	
Age, yr	69.3 ± 6.2	69.7 ± 6.4	70.8 ± 7.1	0.001	A < C	69.3 ± 6.2	69.9 ± 6.5	0.001	
Female	1,022 (58.2)	1,698 (56.3)	149 (50.7)	0.045	A > C	-	-	-	
Education, yr	7.8 ± 5.1	8.7 ± 5.4	8.6 ± 5.5	< 0.001	A < B, C	10.8 ± 4.8	6.6 ± 4.9	< 0.001	
Current drinkinga	256 (14.7)	424 (14.1)	37 (12.6)	0.626	-	659 (30.1)	58 (2.0)	< 0.001	
Current smokingb	216 (12.3)	336 (11.1)	32 (10.9)	0.451	-	515 (23.5)	69 (2.4)	< 0.001	
Less physical activityc	1,282 (73.6)	2,191 (73.2)	214 (73.0)	0.952	-	1,354 (62.1)	2,333 (81.9)	< 0.001	
BMI, kg/m2	24.5 ± 3.0	23.9 ± 2.9	23.6 ± 2.8	< 0.001	A > B, C	23.9 ± 2.8	24.2 ± 3.1	0.002	
ASCVD	270 (15.4)	403 (13.4)	42 (14.3)	0.150	-	348 (15.9)	367 (12.8)	0.002	
GDS score	10.5 ± 6.6	9.4 ± 6.3	9.0 ± 6.5	< 0.001	A > B, C	8.7 ± 6.1	10.6 ± 6.6	< 0.001	
No. of chewing cycles, cycles/bite	5.9 ± 1.3	13.6 ± 4.5	33.6 ± 6.3	< 0.001	A < B < C	12.4 ± 7.7	12.0 ± 8.1	0.058	
Duration of average mealtime				< 0.001				0.004	
	Less than 10 minutes	1,138 (64.9)	825 (27.4)	9 (3.1)	A < B < C	909 (41.4)	1,063 (37.1)	
	Higher than 30 minutes	35 (2.0)	429 (14.2)	137 (46.6)		262 (11.9)	339 (11.8)	
Mild cognitive impairment	457 (26.0)	842 (27.9)	86 (29.3)	0.279	-	538 (24.5)	847 (29.5)	< 0.001	
CERAD total score	62.1 ± 12.7	63.3 ± 13.3	63.0 ± 14.6	0.008	A < B	65.1 ± 11.9	61.2 ± 13.9	< 0.001	
CERAD memory score	28.3 ± 7.1	28.9 ± 7.3	29.1 ± 7.4	0.018	A < B	28.7 ± 6.8	28.7 ± 7.5	0.756	
Values are presented as mean ± standard deviation or number (%).

BMI = body mass index, ASCVD = atherosclerotic cardiovascular diseases, GDS = Geriatric Depression Scale, CERAD = Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet.

aDrinking more than 7 standard units per week within the past year.

bSmoking of any amount within the past year.

cNot engaging in at least 75 minutes of vigorous-intensity nor 150 minutes of moderate-intensity physical activity per week.

In linear mixed model analyses adjusted for age, sex, education, lifestyle, BMI, ASCVD, depression, and duration of mealtime, participants who had ≥ 30 cycles/bite showed a faster decrease in CERAD total score and memory score, at 0.511 points/year and 0.633 points/year respectively, compared to those who had less than 10 cycles/bite (Table 2). The association between the number of chewing cycles and the rate of cognitive decline differed by sex (P < 0.001). Male participants with ≥ 30 cycles/bite exhibited 0.611 points/year and 0.855 points/year faster decreases in CERAD total score and memory score, respectively, compared to those with less than 10 cycles/bite. These associations were less pronounced in female participants.

Table 2 Association between the number of chewing cycles and the progression rate of cognitive decline during 8-year follow-up

Variables	β estimates	Standard error	P value	
Total (N = 5,064)				
	CERAD total score				
		10–30 cycles/bite (vs. < 10)	−0.233	0.094	0.013	
		≥ 30 cycles/bite (vs. < 10)	−0.511	0.200	0.011	
	CERAD memory score				
		10–30 cycles/bite (vs. < 10)	−0.124	0.057	0.030	
		≥ 30 cycles/bite (vs. < 10)	−0.633	0.123	< 0.001	
Male (n = 2,195)				
	CERAD total score				
		10–30 cycles/bite (vs. < 10)	−0.180	0.137	0.191	
		≥ 30 cycles/bite (vs. < 10)	−0.611	0.286	0.033	
	CERAD memory score				
		10–30 cycles/bite (vs. < 10)	−0.081	0.085	0.341	
		≥ 30 cycles/bite (vs. < 10)	−0.855	0.177	< 0.001	
Female (n = 2,869)				
	CERAD total score				
		10–30 cycles/bite (vs. < 10)	−0.277	0.128	0.031	
		≥ 30 cycles/bite (vs. < 10)	−0.440	0.278	0.114	
	CERAD memory score				
		10–30 cycles/bite (vs. < 10)	−0.157	0.077	0.042	
		≥ 30 cycles/bite (vs. < 10)	−0.454	0.170	0.007	
Age, education, lifestyle, body mass index, atherosclerotic cardiovascular disease, depression, and duration of mealtime were adjusted as covariates. Statistical values with P < 0.05 are reported in bold.

CERAD = Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet.

In Cox proportional hazard model analyses adjusted for covariates, it was found that the association between the number of chewing cycles and the risk of incident dementia was only present in males. Male participants who had ≥ 30 cycles/bite had a 2.9-fold higher risk of incident all-cause dementia than those who had less than 10 cycles/bite. An increase of five cycles/bite was associated with a 16% increased risk of incident all-cause dementia in males. Male participants with a chewing rate of ≥ 30 cycles/bite had a 3.2-fold higher risk of incident AD compared to those with less than 10 cycles/bite. An increase of five cycles/bite was associated with a 23% increased risk of incident AD in males. In contrast, there was no significant association between the number of chewing cycles and the risks of all-cause dementia and AD in females (Table 3).

Table 3 Association between the number of chewing cycles and the risk of incident dementia

Variables	All-cause dementia	Alzheimer’s disease	
No. of cases/total (%)	HR (95% CI)	P value	No. of cases/total (%)	HR (95% CI)	P value	
Total (N = 5,064)							
	10–30 cycles/bite (vs. < 10)	168/3,015 (5.6)	1.26 (0.90–1.75)	0.175	140/3,015 (4.6)	1.38 (0.95–1.99)	0.089	
	≥ 30 cycles/bite (vs. < 10)	22/294 (7.5)	1.45 (0.83–2.52)	0.192	17/294 (5.8)	1.57 (0.85–2.91)	0.151	
	Per 5 cycles-increase	-	1.07 (0.98–1.17)	0.134	-	1.09 (0.99–1.19)	0.093	
Male (n = 2,195)							
	10–30 cycles/bite (vs. < 10)	66/1,317 (5.0)	1.85 (1.00–3.42)	0.051	49/1,317 (3.7)	1.84 (0.90–3.74)	0.095	
	≥ 30 cycles/bite (vs. < 10)	11/145 (7.6)	2.91 (1.18–7.18)	0.021	8/145 (5.5)	3.22 (1.14–9.11)	0.027	
	Per 5 cycles-increase	-	1.16 (1.01–1.34)	0.036	-	1.23 (1.05–1.44)	0.010	
Female (n = 2,869)							
	10–30 cycles/bite (vs. < 10)	102/1,698 (6.0)	1.08 (0.73–1.60)	0.710	91/1,698 (5.4)	1.25 (0.81–1.93)	0.317	
	≥ 30 cycles/bite (vs. < 10)	11/149 (7.4)	0.97 (0.46–2.04)	0.939	9/149 (6.0)	1.10 (0.49–2.43)	0.823	
	Per 5 cycles-increase	-	1.02 (0.91–1.14)	0.725	-	1.02 (0.91–1.15)	0.729	
Age, education, lifestyle, body mass index, atherosclerotic cardiovascular disease, depression, and duration of mealtime were adjusted as covariates. Statistical values with P < 0.05 are reported in bold.

HR = hazard ratio, CI = confidence interval.

The participant characteristics of the brain imaging cohort and its comparison with the original cohort are described in Supplementary Table 1. The results of the generalized linear model analyses indicate that an increase in chewing cycles was associated with decreased global brain volumes, including total brain volume, cortical gray matter and white matter, and temporal and occipital lobes in males (Table 4). Among males with MCI, an increase in chewing cycles was associated with a decrease in global volumes of cortical gray matter and regional volumes, including the frontal lobe, hippocampus, AD signature regions, and aging-related masticatory regions. In contrast, there were no significant associations observed between chewing cycles and brain volumes in females.

Table 4 Association between the number of chewing cycles and the brain volume measures in the brain imaging cohort

Variables	Males, total	Males with MCI	Females, total	Females with MCI	
β (SE)	P value	β (SE)	P value	β (SE)	P value	β (SE)	P value	
Global volumes									
	Total brain volume	−0.118 (0.052)	0.023	−0.206 (0.102)	0.043	−0.012 (0.043)	0.789	0.015 (0.094)	0.876	
	Total gray matter	−0.061 (0.052)	0.245	−0.264 (0.127)	0.038	−0.022 (0.045)	0.627	0.016 (0.098)	0.871	
	Cortical gray matter	−0.094 (0.054)	0.079	−0.276 (0.123)	0.025	−0.028 (0.045)	0.532	0.038 (0.093)	0.679	
	Left cortical gray matter	−0.082 (0.054)	0.130	−0.211 (0.125)	0.092	−0.029 (0.044)	0.506	0.035 (0.092)	0.705	
	Right cortical gray matter	−0.106 (0.054)	0.051	−0.377 (0.124)	0.007	−0.026 (0.045)	0.564	0.041 (0.094)	0.657	
	Subcortical gray matter	0.005 (0.055)	0.922	−0.133 (0.123)	0.281	0.014 (0.045)	0.764	0.030 (0.090)	0.739	
	Cerebellar gray matter	0.081 (0.056)	0.149	−0.056 (0.128)	0.660	0.007 (0.050)	0.894	−0.074 (0.092)	0.422	
	Total white matter	−0.153 (0.055)	0.005	−0.147 (0.111)	0.187	0.003 (0.044)	0.940	−0.039 (0.088)	0.660	
	Cerebral white matter	−0.156 (0.055)	0.005	−0.148 (0.112)	0.186	0.000 (0.044)	0.991	−0.044 (0.087)	0.613	
	Left cerebellar white matter	−0.002 (0.054)	0.965	−0.037 (0.118)	0.754	0.054 (0.051)	0.283	0.048 (0.101)	0.637	
	Right cerebellar white matter	−0.077 (0.056)	0.171	−0.071 (0.105)	0.495	0.025 (0.050)	0.617	0.018 (0.102)	0.858	
Lobar volumes									
	Frontal lobe	−0.079 (0.055)	0.148	−0.296 (0.118)	0.012	−0.059 (0.046)	0.192	0.025 (0.089)	0.782	
	Parietal lobe	−0.060 (0.055)	0.271	−0.119 (0.114)	0.297	0.008 (0.047)	0.871	0.044 (0.101)	0.664	
	Temporal lobe	−0.095 (0.058)	0.098	−0.209 (0.141)	0.136	−0.060 (0.044)	0.166	−0.057 (0.097)	0.558	
	Occipital lobe	−0.127 (0.055)	0.020	−0.242 (0.112)	0.030	0.063 (0.048)	0.187	0.145 (0.094)	0.123	
Regions of interest volumes									
	Hippocampus	−0.057 (0.057)	0.317	−0.230 (0.117)	0.050	0.025 (0.043)	0.560	0.038 (0.088)	0.666	
	AD signature regionsa	−0.042 (0.056)	0.459	−0.215 (0.117)	0.066	−0.057 (0.044)	0.194	−0.084 (0.095)	0.373	
	Aging-related masticatory regionsb	−0.042 (0.054)	0.434	−0.255 (0.117)	0.029	−0.041 (0.047)	0.385	0.073 (0.091)	0.425	
WMH volumes									
	Total WMH volume	0.017 (0.053)	0.749	0.131 (0.119)	0.271	0.014 (0.050)	0.785	0.071 (0.082)	0.386	
	Periventricular WMH volume	0.018 (0.053)	0.740	0.138 (0.118)	0.241	0.013 (0.050)	0.795	0.068 (0.081)	0.404	
	Deep WMH volume	−0.006 (0.055)	0.910	−0.025 (0.107)	0.818	−0.019 (0.053)	0.718	−0.109 (0.101)	0.282	
Age, education, lifestyle, body mass index, atherosclerotic cardiovascular disease, depression, and duration of mealtime were adjusted as covariates. Statistical values with P < 0.1 are reported in bold.

MCI = mild cognitive impairment, SE = standard error, AD = Alzheimer’s disease, WMH = white matter hyperintensity.

aSummation of the volumes of medial temporal lobe, inferior temporal gyrus, angular gyrus, supramarginal gyrus, superior parietal lobule, and superior and middle frontal gyri.

bSummation of the volumes of anterior cingulate cortex, insula, superior and middle frontal gyri, and superior parietal lobule.

DISCUSSION

This study demonstrated that an increase in chewing cycles is associated with a rapid cognitive decline and an increased risk of all-cause dementia and AD in older male adults. The increase in chewing cycles is also linked to global and regional brain atrophies in males.

Our findings suggest that frequent chewing may not only reflect masticatory dysfunction, but also compensatory behavior for underlying dementia pathology. The afferent signals from the peripheral sensory apparatus directly stimulate the brain through the sensory feedback circuitry during the chewing movement. Previous experimental studies have shown that chewing gum can increase cerebral blood flow and brain activity in the hippocampus and related regions.3132 Additionally, gum-chewing has been found to enhance cognitive performance, such as sustained attention and psychomotor speed, in experimental settings.3334 Therefore, frequent and vigorous chewing during daily meals may be an adaptive effort to cope with dementia progression by enhancing mastication-induced brain activation through peripheral stimulation.

Our findings are inconsistent with those of a previous prospective cohort study of Japanese older adults, which found no significant association between masticatory dysfunction and the risk of incident dementia.11 This discrepancy may be attributed to the difference in the method used to assess masticatory function. In the previous study, masticatory function was assessed by asking a question, “How is your ability to chew?” Moreover, the previous study is subject to a significant limitation, the use of claim-based administrative data for the assessment of dementia, which may be susceptible to classification bias. Based on the clinical diagnosis of dementia and the neuropsychological tests administered to every single participant, the present study provides compelling evidence for a linkage between mastication and dementia risk, thereby overcoming the limitations of previous work.

This study found that an increase in chewing cycles was associated with reduced brain volumes in regions related to the compensatory cognitive control of mastication, particularly in the frontal lobe. Previous neuroimaging studies have shown that brain regions related to masticatory performance vary by age. In younger adults, masticatory function depends on the connectivity between the somatosensory cortex and cerebellum. However, the masticatory performance of older adults depends on the activation of intra-cortical and cortical-subcortical networks centered on the prefrontal cortex, which is required for the cognitive control of chewing movements.23535 Our findings suggest that the structural changes in the frontal cortex and related networks are the core common pathology linking masticatory and cognitive decline in older adults. The findings pertaining to diminished volumes of the frontal lobe are also in accordance with previous studies that have reported an association between enhanced masticatory performance and atrophic changes in the premotor cortex in older adults.3637

The noteworthy finding of this study is that the association between frequent chewing and increased dementia risk was significant only in males. However, the study did not provide a clear explanation for this sex difference. In general, males tend to chew faster and with greater force than females, despite both sexes having a similar number of chewing cycles.3839 As males have thicker masticatory muscle and stronger bite force compared to females throughout their lifetime,121314 they may compensate for the decline in top-down motor control by increasing their chewing cycles more easily. Females may use other adaptive strategies due to weaker masticatory muscles and bite force, such as increasing the duration of opening and closing times and chewing cycles, as observed earlier in life.3839 Further research is needed to clarify sex differences in various chewing parameters and to replicate the relationship between chewing patterns and dementia risk.

Previous studies on the link between mastication and cognition have mainly focused on the association of tooth loss with the risk of dementia. Animal studies have found a decrease in the neurogenesis of the hippocampal dentate gyrus and a decrease in brain-derived neurotrophic factor levels in mice with molar extraction.4041 In humans, while some longitudinal studies have reported a significant association between tooth loss and dementia risk,42434445 many others have not found such a link.114647484950 These mixed findings may be partially attributable to the fact that the loss of natural teeth is not a direct marker of masticatory function per se, nor of the functional level of cognitive motor control, despite its importance as a risk factor for masticatory dysfunction.5152 According to this study, the number of chewing cycles during daily meals may be a useful indicator of real-world masticatory motor control functional status compared to tooth loss, which warrants further investigation.

This study has several limitations. First, the number of chewing cycles was not measured using objective methods such as electromyography and video monitoring. Relying on self-reported numbers of chewing cycles may introduce classification bias due to the possibility of incorrect recall and the absence of standardized data for the number of chewing cycles during daily meals. Second, several covariates such as the number of natural teeth, periodontitis, and other oral health-related factors were not identified by dental examinations. Third, the number of incident cases of all-cause dementia and AD was relatively small, which could weaken the statistical power of the proportional hazard analysis. Furthermore, an analysis of the association between mastication and dementia other than AD was not feasible due to the insufficient number of cases with rare types of dementia. Fourth, the results from the brain imaging cohort must be interpreted with caution due to the lack of representativeness of this sample and the discrepancy in demographic and clinical characteristics in comparison to the original KLOSCAD cohort. Finally, these findings should be limited to Korean community-dwelling older adults. Our findings may vary among populations with different dietary cultures or those who are institutionalized.

Despite its limitations, this study has the strength of analyzing data from a large population-based prospective cohort to firstly demonstrate a strong association between masticatory dysfunction and the risks of all-cause dementia and AD. Community-resident older adults, particularly males, with masticatory dysfunction should be provided with regular dental examinations and timely screening for cognitive decline. Multidisciplinary care, including comprehensive assessments in primary care and public services, can contribute to the early identification of individuals who may be at risk for dementia.

SUPPLEMENTARY MATERIAL

Supplementary Table 1

Comparison of characteristics between original cohort and brain imaging cohort

Funding: This research was supported by grant number 18-2023-0012 from Seoul National University Bundang Hospital Research Fund and grant number 2019-ER6201-01 from the Research of Korea Centers for Disease Control and Prevention.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions: Conceptualization: Oh DJ, Han JW, Kim KW.

Data curation: Oh DJ, Han JW, Kim JS, Kim TH, Kwak KP, Kim BJ, Kim SG, Kim JL, Moon SW, Park JH, Ryu SH, Youn JC, Lee DY, Lee DW, Lee SB, Lee JJ, Jhoo JH, Kim KW.

Formal analysis: Oh DJ, Han JW, Kim JS, Kim TH, Kwak KP, Kim BJ, Kim SG, Kim JL, Moon SW, Park JH, Ryu SH, Youn JC, Lee DY, Lee DW, Lee SB, Lee JJ, Jhoo JH, Kim KW.

Funding acquisition: Kim KW.

Investigation: Oh DJ, Han JW, Kim JS, Kim TH, Kwak KP, Kim BJ, Kim SG, Kim JL, Moon SW, Park JH, Ryu SH, Youn JC, Lee DY, Lee DW, Lee SB, Lee JJ, Jhoo JH, Kim KW.

Methodology: Oh DJ, Han JW, Kim JS, Kim TH, Kwak KP, Kim BJ, Kim SG, Kim JL, Moon SW, Park JH, Ryu SH, Youn JC, Lee DY, Lee DW, Lee SB, Lee JJ, Jhoo JH, Kim KW.

Validation: Oh DJ, Han JW, Kim KW.

Writing - original draft: Oh DJ, Kim KW.

Writing - review & editing: Oh DJ, Han JW, Kim JS, Kim TH, Kwak KP, Kim BJ, Kim SG, Kim JL, Moon SW, Park JH, Ryu SH, Youn JC, Lee DY, Lee DW, Lee SB, Lee JJ, Jhoo JH, Kim KW.
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