| Literature DB >> 35883642 |
Kotomi Sakai1,2, Enri Nakayama3, Daisuke Yoneoka4,5,6,7, Nobuo Sakata2,8, Katsuya Iijima9,10, Tomoki Tanaka9, Kuniyoshi Hayashi11, Kunihiro Sakuma12, Eri Hoshino1.
Abstract
Studies investigating the associations of oral function and dysphagia with frailty and sarcopenia in community-dwelling older adults are increasing; however, they have not been systematically summarized. We conducted a systematic review to investigate these associations. We searched electronic databases and synthesized relevant data using conventional (frequentist-style) and Bayesian meta-analyses. Twenty-four studies were found to be eligible for our review, including 20 cross-sectional and four prospective cohort studies. Older adults with frailty or sarcopenia had lower tongue pressure, according to the results of conventional meta-analysis (mean difference [95% confidence interval or credible interval]: -6.80 kPa [-10.22 to -3.38] for frailty and -5.40 kPa [-6.62 to -4.17] for sarcopenia) and Bayesian meta-analysis (-6.90 kPa [-9.0 to -4.8] for frailty, -5.35 kPa [-6.78 to -3.89] for sarcopenia). People with frailty had a higher odds ratio (OR) for dysphagia according to the results of conventional meta-analysis (3.99 [2.17 to 7.32]) and Bayesian meta-analysis (1.38 [0.77 to 1.98]). However, the results were inconclusive for people with sarcopenia. A prospective association could not be determined because of the lack of information and the limited number of studies. Decreased oral function and dysphagia can be important characteristics of frailty and sarcopenia in community-dwelling older adults.Entities:
Keywords: aging; dysphagia; frailty; oral function; sarcopenia
Mesh:
Year: 2022 PMID: 35883642 PMCID: PMC9316124 DOI: 10.3390/cells11142199
Source DB: PubMed Journal: Cells ISSN: 2073-4409 Impact factor: 7.666
Study characteristics.
| Country | Study Year | Study Design | Mean Age (SD) of All Participants | Number of Participants (Men, %) | Prevalence of Frailty and Sarcopenia, | |
|---|---|---|---|---|---|---|
| Cha et al., 2019 | Seongnam, Korea | 2005–2006 | Cross-sectional | 76.6 (5.8) | 236 (114, 48.3) | Pre-frailty: NR |
| Chang et al., 2011 | Taipei, Taiwan | NR | Cross-sectional | 71.1 (3.8) | 275 (127, 46.2) | Pre-frailty: 161 (58.5) |
| Chen et al., 2020 | Taipei, Taiwan | NR | Cross-sectional | 75.1 (5.8) | 94 (26, 27.7) | Pre-frailty: NR |
| Horibe et al., 2018 | Tokyo, Japan | 2014 | Cross-sectional | 72.7 (5.2) | 659 (264, 40.1) | Pre-frailty: 220 (33.4) |
| Horibe et al., 2018 | Tokyo, Japan | 2013–2015 | Prospective cohort | NR | 418 (175, 41.9) | Pre-frailty: NR |
| Iwasaki et al., 2018 | Niigata, Japan | 2003–2008 | Prospective cohort | 75 (0) | 322 (181, 56.2) | Pre-frailty: NR |
| Kera et al., 2017 | Tokyo, Japan | 2011–2013 | Cross-sectional | NR | 1380 (592, 42.9) | Pre-frailty: NR |
| Kugimiya et al., 2021 | Tokyo, Japan | 2018 | Cross-sectional | 76.7 (8.4) | 871 (268, 30.8) | Pre-frailty: NR |
| Machida et al., 2017 | NR, Japan | NR | Cross-sectional | NR | 197 (97, 49.2) | Pre-frailty: NR |
| Molfenter et al., 2018 | New York, U.S. | NR | Cross-sectional | 76.9 (7.1) | 44 (21, 47.7) | Pre-frailty or frailty: 26 (59.1) |
| Motokawa 2018 | Saitama, Japan | 2014 | Cross-sectional | 69.6 (NR) | 283 (121, 42.8) | Pre-frailty: 165 (58.3) |
| Murakami et al., 2014 | Tokyo, Japan | 2012 | Cross-sectional | 73.0 (5.1) | 761 (314, 41.3) | Pre-frailty: NR |
| Nishida et al., 2021 | Niigata, Japan | 2018–2019 | Cross-sectional | 77.3 (6.6) | 320 (52, 16.3) | Pre-frailty: 154 (48.1) |
| Nakamura et al., 2021 | Kagoshima, Japan | 2018 | Cross-sectional | 74.9 (6.29) | 832 (303, 36.4) | Pre-frailty: NR |
| Serra-Prat et al., 2012 | Barcelona, Spain | NR | Cross-sectional | 78.2 (5.6) | 254 (136, 53.5) | Pre-frailty: NR |
| Suzuki et al., 2018 | NR, Japan | NR | Cross-sectional | (Median [IQR]: 81.0 [75.0–85.0]) | 245 (0, 0) | Pre-frailty: NR |
| Satake et al., 2019 | Aomori, Japan | 2016 | Cross-sectional | 74.4 (7.8) | 467 (173, 37.0) | Pre-frailty: NR |
| Shimazaki et al., 2020 | Aichi, Japan | 2018 | Cross-sectional | NR | 978 (463, 47.3) | Pre-frailty: 295 (30.2) |
| Takeuchi et al., 2022 | Okayama, Japan | 2017–2021 | Prospective cohort | 71.9 (5.4) | 97 (34, 35.1) | Pre-frailty: NR |
| Tanaka et al., 2018 | Chiba, Japan | 2012–2016 | Prospective cohort | 73.0 (5.5) | 2011 (NR, 50) | Pre-frailty: NR |
| Watanabe et al., 2017 | Aichi, Japan | 2011–2012 | Cross-sectional | 72.1 (5.6) | 4720 (2274, 48.2) | Pre-frailty: 2691 (57.0) |
| Weiss 2021 | Israel | NR | Cross-sectional | NR | 180 (74, 41.1) | Pre-frailty: 67 (37.2) |
| Yoshida et al., 2022 | Kyoto, Japan | 2019 | Cross-sectional | 75.0 (NR) | 340 (69, 20.3) | Pre-frailty: 155 (46.8) |
| Yamanashi et al., 2018 | Nagasaki, Japan | 2014–2015 | Cross-sectional | 72.8 (7) | 1603 (650, 40.5) | Pre-frailty: 605 (37.7) |
NR, not reported; SD, standard deviation; IQR, interquartile range.
Items assessed and tools used in studies.
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| Cha et al., 2019 | ◯ | ◯ | ||||||||
| Chang et al., 2011 | ◯ | ◯ | ||||||||
| Chen et al., 2020 | ◯ | ◯ | ◯ | |||||||
| Horibe et al., 2018 a | ◯ | ◯ | ||||||||
| Horibe et al., 2018 b | ◯ | ◯ | ||||||||
| Iwasaki et al., 2018 | ◯ | ◯ | ||||||||
| Kera 2016 | ◯ | ◯ | ||||||||
| Kugimiya et al., 2021 | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | |||
| Machida et al., 2017 | ◯ | ◯ | ◯ | |||||||
| Molfenter et al., 2018 | ◯ | ◯ | ||||||||
| Motokawa 2018 | ◯ | ◯ | ||||||||
| Murakami et al., 2014 | ◯ | ◯ | ||||||||
| Nakamura et al., 2021 | ◯ | ◯ | ◯ | ◯ | ◯ | |||||
| Nishida et al., 2021 | ◯ | ◯ | ||||||||
| Serra-Prat 2012 | ◯ | ◯ | ||||||||
| Suzuki et al., 2018 | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ||||
| Satake et al., 2019 | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ||||
| Shimazaki et al., 2020 | ◯ | ◯ | ◯ | ◯ | ◯ | |||||
| Takeuchi et al., 2022 | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | |||
| Tanaka et al., 2018 | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ||
| Watanabe et al., 2017 | ◯ | ◯ | ◯ | ◯ | ◯ | |||||
| Weiss 2021 | ◯ | ◯ | ||||||||
| Yoshida et al., 2022 | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ◯ | ||
| Yamanashi et al., 2018 | ◯ | ◯ |
AWGS, Working Group for Sarcopenia; CHS, Cardiovascular Health Study; J-CHS, Japanese version of the CHS; EAT-10, 10-item Eating Assessment Tool; KCL, Kihon Checklist; SSA, Standardized Swallowing Assessment; TWST, Timed Water Swallow Test; VF, videofluoroscopy; WST, Water Swallowing Test; V-VST, Volume-Viscosity Swallow Test. Note: The blank columns indicate “not-assessed”. Overall tongue-lip motor function refers to the combined assessments of /pa/, /ta/, and /ka/.
Figure 1Comparison of tongue pressure between individuals with and without frailty [19,20,21].
Figure 2Comparison of tongue pressure between individuals with and without sarcopenia [20,25].
Figure 3Comparison of occlusal force between individuals with and without sarcopenia [20,25,31].
Figure 4Comparison of presence of dysphagia between individuals with and without frailty [22,23,32,33,34,35,36].
Figure 5Comparison of presence of dysphagia between individuals with and without sarcopenia [22,38].
Association between oral function and dysphagia at baseline and frailty, sarcopenia, and disability at follow-up.
| Follow-Up Period | Assessed Items at Baseline | Frailty | Sarcopenia | Disability | |
|---|---|---|---|---|---|
| Horibe et al., 2018 | 2 years | Occlusal force | Adjusted OR (95% CI) of low occlusal force for frailty progression (from healthy to pre-frail, from pre-frail to frail, or from healthy to frail): 1.00 (0.99 to 1.00) | NR | NR |
| Iwasaki et al., 2018 | 5 years | Occlusal force | Adjusted HRs (95% CI) of low occlusal force: 2.78 (1.15 to 6.72) | NR | NR |
| Takeuchi et al., 2022 | 2 years | Tongue-lip motor function: ODK for /ta/ | Adjusted OR (95% CI) of ODK for /ta/: 1.85 (1.02 to 3.35). | NR | NR |
| Tanaka et al., 2018 | Frailty and sarcopenia: 2 years | Occlusal force | Low maximum occlusal force: 29% ( | Low maximum occlusal force: 26% ( | Low maximum occlusal force: 32% ( |
NR, not reported; OR, odds ratio; CI, confidence interval; HR, hazards ratio; ODK, oral diadochokinesis. Note: Types of models and covariates in the models used in the studies. Iwasaki et al., 2018: Covariates; sex, depression, diabetes; Eichner index model: Cox proportional-hazards regression analysis. Horibe et al., 2018b: Covariates; age, sex, number of teeth, hand grip, walking speed, Mini-Mental State Examination score, self-reported depression scale, skeletal muscle mass index, number of medications taken; Model: binomial logistic regression analysis (forced entry method). Takeuchi et al., 2022: Covariates; number of teeth, clinical attachment level, /ta/ sound, /ka/ sound; Model: binomial logistic regression analysis (variable reduction method). Tanaka et al., 2018: Covariates: age, sex, body mass index, chronic conditions, depressive symptoms, cognitive function, living arrangement, yearly income, and smoking behavior; Model: Cox proportional-hazards regression analysis. Low indicates less than the first quantile of the data. Percentage indicates the proportion of individuals with low function who developed an outcome.
Quality assessment for each study using the National Institutes of Health’s Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | D | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Cha et al., 2019 | * | * | * | * | ? | NA | NA | NA | * | NA | * | ? | NA | NA | ✢ |
| Chang et al., 2011 | * | * | * | * | ? | NA | NA | * | * | NA | CD | ? | NA | NA | ✢ |
| Chen et al., 2020 | * | * | * | * | * | NA | NA | NA | * | NA | * | ? | NA | NA | ✢ |
| Horibe et al., 2018 a | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Horibe et al., 2018 b | * | * | * | * | ? | * | * | NA | * | − | * | ? | − | CD | → |
| Iwasaki et al., 2017 | * | * | * | * | ? | * | * | * | * | − | * | ? | * | CD | → |
| Kera 2016 | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Kugimiya et al., 2021 | * | * | * | * | ? | NA | NA | NA | * | NA | * | ? | NA | NA | ✢ |
| Machida et al., 2017 | * | * | * | * | ? | NA | NA | NA | * | NA | NA | ? | NA | NA | ✢ |
| Molfenter et al., 2018 | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Motokawa 2018 | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Murakami et al., 2014 | * | * | * | * | ? | NA | NA | NA | * | NA | * | ? | NA | NA | ✢ |
| Nishida et al., 2021 | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Nakamura et al., 2021 | * | * | ? | * | ? | NA | NA | − | * | NA | * | * | NA | NA | ✢ |
| Serra-Prat 2012 | * | * | * | * | * | NA | NA | − | − | NA | * | ? | NA | NA | ✢ |
| Suzuki et al., 2018 | * | * | ? | * | ? | NA | NA | NA | * | NA | * | ? | NA | NA | ✢ |
| Satake et al., 2019 | * | * | ? | * | ? | NA | NA | − | * | NA | * | ? | NA | NA | ✢ |
| Shimazaki et al., 2020 | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Takeuchi et al., 2022 | * | * | * | * | − | * | * | NA | * | * | * | ? | * | CD | → |
| Tanaka et al., 2018 | * | * | * | * | ? | * | * | NA | * | * | * | ? | * | CD | → |
| Watanabe et al., 2017 | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Weiss 2021 | * | − | ? | ? | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Yamanashi et al., 2018 | * | * | * | * | ? | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
| Yoshida et al., 2022 | * | * | ? | * | * | NA | NA | * | * | NA | * | ? | NA | NA | ✢ |
*, yes; −, no; ?, not reported; ✢, cross-sectional study; →, prospective cohort study; NA, not applicable; CD, cannot determine; D, study design. Criterion 1: Was the research question or objective in this paper clearly stated? Criterion 2: Was the study population clearly specified and defined? Criterion 3: Was the participation rate of eligible persons at least 50%? Criteria 4: Were all subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for participation in the study prespecified and applied uniformly to all participants? Criterion 5: Was sample size justification, power description, or variance and effect estimates provided? Criterion 6: For the analyses in this study, were the exposure(s) of interest measured before the outcome(s) being measured? Criterion 7: Was the timeframe sufficient for association between exposure and outcome to become evident, if it existed? Criterion 8: For exposures that can vary in amount or level, did the study examine different levels of exposure as related to the outcome (e.g., categories of exposure or exposure measured as continuous variables)? Criterion 9: Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? Criterion 10: Was the exposure(s) assessed more than once over time? Criterion 11: Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? Criterion 12: Were the outcome assessors blinded to the exposure status of participants? Criterion 13: Was the patient lost to follow-up after a baseline of 20% or less? Criterion 14: Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)?