Systematic review and meta-analysis of risk factors for oral frailty

Abstract

Background:

Oral frailty can lead to complications such as eating disorders and sarcopenia, thereby significantly impairing patients’ health. While numerous studies have been conducted to identify risk factors for oral frailty, the results remain inconsistent due to variability in study populations, and certain risk factors remain controversial.

Methods:

Eight databases, including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI (China National Knowledge Infrastructure), VIP (China Science and Technology Journal Database), CBM (China Biology Medicine disc), and Wanfang Databases, were thoroughly searched from database inception to August 1, 2024 to identify studies reporting risk factors for oral frailty. Odds ratios (OR) and 95% confidence intervals (CI) were pooled using RevMan 5.4 to identify variables influencing the incidence of oral frailty.

Results:

A total of 14 studies involving 12,297 participants were included, among whom 4540 presented with oral frailty. Forty risk factors were extracted from these studies.

Among them, 10 risk factors were eligible for meta-analysis, whereas the remaining 30 were excluded from the analysis due to being reported in fewer than 3 studies. The meta-analysis revealed that nine of the ten risk factors, namely age, gender, smoking, denture use, xerostomia, low oral health awareness, malnutrition, unbalanced diet, and physical frailty, were significantly associated with oral frailty.

Conclusion:

This study identified risk factors for oral frailty and validated several factors already included in existing assessment scales, such as the use of dentures and xerostomia. Additionally, novel factors such as gender and age were identified. Taken together, these results may assist in the timely identification of individuals at risk for oral frailty and prevent its onset, as well as enhance current risk assessment tools.

1. Introduction

Oral frailty (OF) is defined as a steady deterioration in oral characteristics such as tooth count, oral cleanliness, and oral function.^[1] It is generally accompanied by dietary impairment, a decreased interest in dental health, and a decline in psychological and physical well-being. This condition represents a decline in oral function accompanied by a reduction in social adaptability.^[2] Epidemiological studies have reported that the prevalence of OF among older adults ranges between 14 and 44.7%,^[3] and it can lead to complications such as eating difficulties and malnutrition, thereby significantly increasing mortality rates.^[4,5] Additionally, patients with OF frequently require long-term care, which imposes a substantial burden on healthcare systems.^[6] Given that early-stage OF may be preventable through timely and appropriate interventions,^[7] the early identification of risk factors is essential for preventing disease onset.

Numerous studies have been conducted to identify risk factors for OF. Existing studies suggest that risk factors include advanced age, gender, smoking, and malnutrition.^[3,8–10] However, due to variations in research methodologies and study populations, several risk factors remain controversial, and definitive risk factors remain elusive. The Oral Frailty Index-8 (OFI-8), developed by Tomoki Tanaka et al,^[11] is currently the primary tool for assessing the risk of OF. This tool was created through a cohort study involving data derived from 2044 patients. However, during sample collection, 12,000 questionnaires were initially distributed, with only 2044 responses received, resulting in a low response rate. Furthermore, the scale lacks systematic validation of the included risk factors, and its reliability requires further verification.

Systematic reviews are regarded as one of the most important methods in evidence-based medicine and the most robust sources of clinical data.^[12] They enable the synthesis of results from multiple studies, improving the accuracy of effect estimates, identifying and quantifying potential risk factors, and offering targeted strategies for disease prevention. This study employed a systematic review to compile risk factors linked to the development of OF to improve the understanding of modifiable risk factors in patients and to elucidate the underlying pathogenesis. Moreover, the findings of this study are expected to play a decisive role in the development of preventive measures to minimize the risk of OF.

2. Methods

2.1. Search strategy

A comprehensive search was conducted in 8 databases, including PubMed, EMBASE, Web of Science, Cochrane Library, CNKI, VIP, CBM, and Wanfang Database. The search period ranged from the establishment of each database to August 1, 2024. A search strategy that combined subject headings with free-text terms was utilized. Search terms comprised oral frailty, oral weakness, oral fragility, risk factors, and associated factors. A detailed example of the search strategy in PubMed is illustrated in Figure 1.

Figure 1.

Example of PubMed search strategy.

2.2. Inclusion and exclusion criteria

2.2.1. Inclusion criteria

Study Population: Studies enrolling patients diagnosed with oral frailty; Research Design: cross-sectional, case-control, and cohort studies; Data Reporting: Studies reporting ORs and corresponding 95% Cis; Outcome Measures: Studies identifying the presence or absence of oral frailty; Sample Size: Articles involving over fifty subjects; Language: Publications published in English and Chinese.

2.2.2. Exclusion criteria

Full text was not available; Duplicate publications (only one version was retained); Theses, case reports, conference abstracts, and animal research; Incomplete data.

2.3. Literature screening and data extraction

All references were imported into EndNote X9 to exclude duplicates. The articles were independently screened by 2 researchers (YL and XS) based on the inclusion and exclusion criteria. Disagreements were arbitrated and resolved by a third researcher (TW). Variables such as the first author, year of publication, country, study location, sample size, study design, and risk factors (only those reported in two or more studies were included), were extracted from eligible studies. For studies performing both univariate and multivariate analyses, the results from multivariate logistic regression (ORs and 95% CIs) were recorded. If multivariate analysis was not conducted, the ORs and 95% CIs from the univariate logistic regression were documented.

2.4. Quality assessment of studies

The quality of the included studies was independently assessed by 2 researchers (YL and XS). The Newcastle-Ottawa Scale (NOS), which evaluates the quality of studies in 3 domains, namely exposure assessment, group comparability, and study group selection, was employed for case-control and cohort research.^[13] Low quality is denoted by a score of 0 to 4, moderate quality by a score of 5 to 6, and high quality by a score of 7 to 9. The risk of bias suggested by the Agency for Healthcare Research and Quality (AHRQ) was applied to analyze cross-sectional studies.^[14] This tool assigns a score ranging from 0 to 11, where 0 to 3 represents low quality, 4 to 7 represents moderate quality, and 8 to 11 represents high quality.

2.5. Statistical analysis

Review Manager 5.4 was used to analyze the collected OR values and 95% CI for risk variables. Only risk factors reported in at least 3 different studies were included in the analysis to ensure accurate pooled effect sizes. I² and P values were calculated to assess heterogeneity; low heterogeneity was indicated by an I² < 50% and a P > .10, which led to the adoption of a fixed-effects model. Significant heterogeneity was reflected by an I² > 50% or P < .10, in which case the source was identified using a stepwise exclusion method. A random-effects model was applied in cases when the source of heterogeneity could not be identified. The symmetry of the funnel plot distribution was used to quantify publication bias in meta-analyses of ten or more trials.

3. Results

3.1. Literature search and screening results

Database searches yielded a total of 3609 studies. After excluding 568 duplicate studies using EndNote X9, 3021 irrelevant papers were further excluded based on titles and abstracts. Ultimately, 14 studies were retained after applying the inclusion and exclusion criteria. The detailed screening process is displayed in Figure 2.

Figure 2.

Literature screening process.

3.2. Study characteristics and risk of bias assessment

A total of 4540 patients with oral frailty were identified among the 12,297 participants across 14 studies. One of these was a cohort study, whereas the remaining thirteen were cross-sectional studies. Four studies were carried out in Japan, and ten were conducted in China. Quality assessment indicated that all 13 cross-sectional studies achieved AHRQ scores > 4 points, indicating high quality. Additionally (Table S1, Supplemental Digital Content 1), the NOS score of the cohort study was 6 (Table S2, Supplemental Digital Content 2). A summary of the basic characteristics of the included studies is listed in Table 1.

Table 1.

Basic characteristics of included studies.

First author	Year	Study Country	Experimental Site	Study Design	Included Number	Number of Patients	Risk Factors	NOS/AHRQ
Yiran Wu[15]	2024	China	Wuhu City	Cross-sectional	388	118	①②⑤⑧	6
Lin Wang[16]	2023	China	Luzhou City	Cross-sectional	223	132	①③	5
Hangjia Tu[17]	2023	China	Hangzhou City	Cross-sectional	204	69	①②④⑤⑥⑦⑩	5
Ji Tang[18]	2023	China	Guizhou Province	Cross-sectional	1298	580	①②⑧	5
Fang Pan[19]	2024	China	Sichuan Province	Cross-sectional	200	102	①③⑦	6
Yi Li[20]	2024	China	Jiangsu Province	Cross-sectional	207	133	①③④⑥⑩	7
Lingmei Jiao[21]	2023	China	Haikou City	Cross-sectional	270	68	①⑨	7
Jingyi Wei[22]	2024	China	Sichuan Province	Cross-sectional	348	108	①⑤	7
Junkai Dou[23]	2024	China	Luan City	Cross-sectional	238	98	⑤	7
Yueheng Yin[3]	2024	China	Nanjing City	Cross-sectional	310	214	①③④⑩	8
Tatsuo Yamamoto[8]	2022	Japan	Kanagawa Prefecture	Cohort study	595	142	①	6
Miku Izutsu[9]	2023	Japan	Hokkaido	Cross-sectional	238	150	⑥⑨	8
Koichiro Irie[10]	2024	Japan	Kanagawa Prefecture	Cross-sectional	5051	1418	①②⑦⑧	5
Sayuri Nakagawa[24]	2024	Japan	Tottori Prefecture	Cross-sectional	2727	1208	①②③④⑨	6

① = Age, ② = Female, ③ = Smoking, ④ = Frailty, ⑤ = Education Level, ⑥ = Artificial Tooth, ⑦ = Oral Health Self-Efficacy, ⑧ = Diet type, ⑨ = Malnutrition, ⑩ = Dry Mouth.

AHRQ = Agency for Healthcare Research and Quality, NOS = Newcastle-Ottawa scale.

3.3. Risk factors

Forty risk factors were extracted from the 14 included studies. A meta-analysis was conducted for 10 risk factors (those examined in more than 2 studies). The findings of the meta-analysis are detailed in Table 2.

Table 2.

Summary of risk factors.

Risk factors	Number of included studies	Statistical Method	Pooled effects Level of evidence OR [95%CI]	P value	I2 (%)
Age[3,8,10,15–22,24]	12	Random	1.98 (1.58, 2.48)	<.00001	97
Female[10,15,17,18,24]	5	Fixed	1.53 (1.27, 1.85)	<.00001	32
Smoking[3,16,19,20,24]	4	Fixed	5.22 (2.81, 9.72)	<.00001	2
Frailty[3,17,20,24]	4	Fixed	14.36 (4.60, 44.81)	<.00001	0
Education Level[15,17,22,23]	4	Fixed	0.55 (0.38, 0.80)	.002	36
Artificial Tooth[9,17,20]	3	Fixed	8.89 (2.92, 27.04)	.0001	0
Oral Health Self-Efficacy[10,16,19]	3	Fixed	12.53 (6.91, 22.74)	<.00001	0
Malnutrition[9,21,24]	3	Fixed	2.76 (1.55, 4.92)	.0006	0
Diet Type[10,15,18]	3	Fixed	2.15 (1.52, 3.05)	<.0001	14
Dry Mouth[8,17,20]	3	Fixed	5.76 (2.62, 12.67)	<.0001	0

CI = confidence interval, OR = odds ratio.

3.3.1. Basic information

Age: Twelve studies^{[3,8,10,15–22,24]} investigated the association between advanced age and the occurrence of OF. Significant heterogeneity was observed across the studies (P < .00001, I² = 97%). Sequentially excluding individual studies did not assist in identifying the source of heterogeneity. Thus, a random-effects model was applied, which that older individuals were at a higher risk for OF. Detailed results are depicted in Figure 3. The Egger’s test was conducted, given that age was reported in more than 10 studies, which yielded a P-value of .1075, suggesting no significant publication bias. Sensitivity analysis confirmed the robustness of the conclusions. Details are presented in Figure 4.

Figure 3.

Forest map of risk factors for oral frailty: (A) Age, (B) Female, (C) Educational Level.

Figure 4.

Funnel plot assessing publication bias for advanced age as a risk factor for oral frailty.

Gender: Five studies^{[10,15,17,18,24]} discussed the influence of gender on OF occurrence. Similarly, significant heterogeneity was noted across these studies (P < .00001, I² = 76%). The exclusion of studies undertaken by Wu Yiran et al^[15] and Koichiro et al^[10] significantly reduced heterogeneity. Following their exclusion (P < .00001, I² = 32%), a fixed-effects model revealed that females were more likely to develop OF. Details are shown in Figure 3.

Educational Level: Four studies^{[15,17,22,23]} investigated the relationship between educational level and OF occurrence. Significant heterogeneity was observed across the studies (P < .00001, I² = 77%). Upon the sequential exclusion of individual studies, the removal of the Wu Yiran et al^[23] study significantly reduced heterogeneity. The fixed-effects model revealed a significant correlation between higher educational levels and a lower risk of OF following the exclusion of this specific study (P = .002, I² = 36%). Details are shown in Figure 3.

3.3.2. Lifestyle habits

Smoking: Five studies^{[3,16,19,20,24]} investigated the correlation between smoking and OF incidence. Significant heterogeneity was detected across these studies (P < .00001, I² = 68%). However, the removal of studies conducted by Yin et al^[3] and Nakagawa et al^[24] markedly reduced heterogeneity through the sequential exclusion of individual studies. After excluding these trials (P < .00001, I² = 2%), a fixed-effects model revealed that smokers were at a higher risk for OF. Details are delineated in Figure 5.

Figure 5.

Forest map of risk factors for oral frailty: (A) Smoking, (B) Dietary Habits.

Dietary Habits: Three studies^[10,15,18] investigated the effect of imbalanced diets on OF occurrence. Significant heterogeneity was observed across the studies (P < .00001, I² = 67%). Sequential exclusion of individual studies demonstrated that the removal of the study by Yin et al^[3] significantly reduced heterogeneity. Following the exclusion of this study, a fixed-effects model indicated that an imbalanced diet was associated with an increased risk of developing OF (P < .0001, I² = 14%). Details are presented in Figure 5.

3.3.3. Medical interventions

Dentures: The effect of denture use on the occurrence of OF was examined in 3 studies.^[9,17,20] Significant heterogeneity was observed across these studies (P < .0001, I² = 82%), which was significantly decreased by excluding the study conducted by Tu Hangjia et al.^[17] As anticipated, a fixed-effects model unveiled that wearing dentures was linked to a higher risk of OF (P < .0001, I² = 0%). Details are shown in Figure 6.

Figure 6.

Forest map of denture use as a risk factor for oral frailty.

3.3.4. Preexisting conditions

Physical Frailty: Four studies^[3,17,20,24] investigated the association between physical frailty and OF. Significant heterogeneity was identified among the trials (P < .00001, I² = 83%). The sequential exclusion of studies included in this analysis significantly decreased heterogeneity upon the removal of the studies carried out by Yin et al^[3] and Nakagawa et al.^[24] As presented in Figure 7, using a fixed-effect model and excluding these trials demonstrated that physical frailty was associated with a significantly increased risk of OF (P < .0001, I² = 0%).

Figure 7.

Forest map of risk factors for oral frailty: (A) Physical Frailty, (B) Malnutrition, (C) Dry Mouth.

Malnutrition: Three studies^[9,21,24] addressed the impact of malnutrition on the development of OF. Significant heterogeneity was noted among the trials (P < .0001, I² = 87%). Sequential exclusion of the included studies revealed a significant reduction in heterogeneity upon the removal of the study by Sayuri et al.^[24] As shown in Figure 7, using a fixed-effect model and excluding this trial indicated that malnutrition was associated with an increased risk of OF (P = .0006, I² = 0%).

Dry Mouth: Three studies^[8,17,20] explored the association between xerostomia and the development of OF. Significant heterogeneity was observed across the studies (P = .0001, I² = 77%). Upon sequentially exclusion of the included studies, heterogeneity significantly decreased following the exclusion of the Yamamoto et al study.^[8] Excluding this study (P < .0001, I² = 0%) and using a fixed-effect model suggested that dry mouth significantly increased the risk of OF. See Figure 7 for details.

3.3.5. Medical scores

Oral Health Self-Efficacy: Three studies^[10,16,19] investigated the influence of oral self-efficacy on the occurrence of OF. Notably, substantial heterogeneity was detected across the studies (P < .00001, I² = 93%). By sequentially removing studies from the analysis, a marked reduction in heterogeneity was observed upon excluding the study conducted by Koichiro et al.^[10] As presented in Figure 8, excluding this study (P < .00001, I² = 0%) and using a fixed-effect model indicated that lower oral self-efficacy scores were associated with a higher risk of OF.

Figure 8.

Forest map of oral health self-efficacy as a risk factor for oral frailty.

4. Discussion

The present study investigated risk factors for OF. After examining 14 publications, 40 risk factors were identified, and 10 of them were eligible for meta-analysis. Considering that fewer than 3 studies reported the remaining 30 risk factors, they were not investigated in this study. Risk factors for OF primarily encompass demographic characteristics, lifestyle habits, medical interventions, preexisting diseases, and medical scores.

Herein, advanced age was identified as a risk factor for oral frailty (OR 1.98; 95% CI 1.58, 2.48; P < .00001). Kugimiya et al^[25] reported that the incidence of oral frailty among elderly individuals aged 65 years and above is 22.5%. With aging, the physiological functions of the oral cavity progressively decline, leading to reduced saliva secretion and gingival atrophy, ultimately culminating in xerostomia and gum recession. In turn, this diminishes oral resistance and self-cleaning ability.^[19] Liu et al^[26] observed significant changes in the oral microbiota with increasing age, with a steady increase in the number of pathogenic bacteria. Under the dual influence of decreased immunity and increased pathogenic bacteria, the likelihood of developing oral diseases is heightened. In elderly females, estrogen levels also impact oral health. The gums, oral mucosa, and salivary glands contain estrogen receptors. A decline in estrogen levels promotes gum atrophy and concurrently decreases salivary secretion, resulting in a thinner and atrophic oral mucosa.^[27] Moreover, lower estrogen levels can lead to calcium loss in bones, leading to alveolar bone osteoporosis and atrophy, caries, periodontal disease, and other conditions,^[27] ultimately leading to the occurrence of oral frailty. Gender discrimination persists in some countries. Soares et al^[28] evinced that women experience twice the incidence of oral health problems compared to men due to gender-based disparities. Such discrimination not only impedes women’s timely access to dental care but also leaves them insufficient time and energy to prioritize oral health as they shoulder a disproportionate share of domestic responsibilities. Consequently, women frequently delay or forego essential oral examinations and treatments,^[29] rendering them more vulnerable to aggravated oral health conditions. It is worth noting that tooth loss increases with age. When the number of functional dentition falls below 20,^[30] chewing function is impaired, which can have a serious impact on quality of life and dietary intake.^[31] Dentures are necessary for patients with tooth loss, considering that they assist in mastication and concurrently improve oral comfort.^[32] However, denture use can also lead to oral frailty (OR 8.89; 95% CI 2.92, 27.04; P < .00001). Douglas et al.^[33] pointed out that the incidence of oral inflammation in patients wearing dentures is as high as 40 to 65%. Patients with dentures experience faster bone tissue resorption, which results in a suboptimal fit between the tissue surface of the denture base and the supporting mucosa, thereby driving the colonization of oral fungi. Furthermore, inadequate denture cleaning hygiene and food impaction are frequently observed among patients using dentures.^[17] Inadequate long-term denture hygiene increases the risk of fungal infections, leading to a significantly elevated incidence of oral frailty. Denture wearers can significantly reduce the risk of oral fungal infections by implementing key preventive measures. These include daily cleaning of the denture using a soft brush and denture cleanser, removing the denture overnight to allow for mucosal rest, and periodic disinfection using solutions such as chlorhexidine to minimize microbial colonization.^[34] Besides, preventing food accumulation beneath the denture base and attending regular professional checkups for denture adjustment are crucial for maintaining optimal fit and eliminating gaps conducive to fungal growth.

Smoking is the second leading risk factor for death globally^[35] and is associated with multiple diseases. Noteworthily, earlier studies have identified smoking as a risk factor for oral frailty (OR 5.22; 95% CI 2.81, 9.72; P < .00001). Kim et al^[36] demonstrated that exposure to secondhand smoke increases the risk of periodontal disease by 1.57-fold. Toxic compounds in cigarettes disrupt the ecological balance of the oral microbiome by promoting unstable bacterial growth in biofilms, increasing salivary acidity, reducing oxygen levels, altering bacterial adhesion to the mucosa, inducing antibiotic resistance, and interfering with host immune cell functions.^[37] Additionally, cigarette smoke increases the proportion of osteoclasts, leading to significant bone loss.^[38,39] At the same time, nicotine can inhibit immune responses via nicotinic acetylcholine receptors, predisposing individuals to periodontal disease.^[40] The interplay between these factors collectively contributes to the occurrence of oral frailty. Therefore, adopting effective oral hygiene practices and smoking cessation are vital for significantly lowering the incidence of oral diseases.^[41] This highlights that quitting smoking serves not only as a foundational strategy for preserving oral health but also as an essential step toward safeguarding overall health.

Healthy dietary habits play a critical role in promoting general well-being; conversely, poor eating habits can lead to the development of various diseases.^[42] Herein, an unbalanced diet was identified as a risk factor for oral frailty (OR 1.53; 95% CI 1.27, 1.85; P < .00001). It is worthwhile emphasizing that long-term dietary imbalance can lead to deficiencies or excesses of certain nutrients. For example, prolonged lack of intake of high-protein foods such as meat can result in malnutrition, leading to a decline in the function of multiple organs and causing damage to the mucous membranes of the oral cavity and digestive tract.^[43] Meanwhile, decreased concentrations of lysozyme and IgA in saliva reduce immunity,^[44] thereby increasing the risk of oral frailty. At the same time, inadequate consumption of vegetables and fruit can result in deficiencies of vitamins and other nutrients. Chapple et al^[45] delineated that deficiencies in vitamins B, C, D, and K increase the risk of caries and periodontal disease. Minerals such as magnesium, calcium, and phosphorus constitute the central components of teeth; long-term insufficient intake of these minerals can lead to tooth loosening and premature loss,^[46] increasing the risk of oral frailty. Thus, maintaining a balanced diet is instrumental for preventing oral frailty. The development of oral frailty further compromises food intake, exacerbating nutrient deficiencies and eventually culminating in physical frailty.^[5] Indeed, an earlier study implied that oral frailty can lead to physical frailty.^[47] In the present study, physical frailty was identified as a significant risk factor for oral frailty (OR 14.36; 95% CI 4.60, 44.81; P < .00001). Frail individuals typically exhibit symptoms such as reduced mobility, fatigue, and muscle weakness, which result in diminished social interactions. In turn, sustained social withdrawal can lead to the deterioration of tongue and pharyngeal muscles,^[48] contributing to impairments in swallowing and mastication.^[49] More importantly, most frail individuals struggle to maintain consistent oral hygiene and seek timely dental care,^[3] thereby increasing the risk of oral frailty. The development of oral frailty exacerbates eating difficulties, resulting in malnutrition, which in turn further worsens physical frailty.^[50] Thus, early detection and intervention for physical frailty are essential for reducing the incidence of oral frailty.

Research also indicates that lower oral health self-efficacy is a critical risk factor for oral frailty (OR 12.53; 95% CI 6.91, 22.74; P < .00001). The confidence and ability of an individual to maintain and improve their oral health is known as oral health self-efficacy,^[16] with lower scores indicating a weaker perceived ability to maintain oral health. Allen et al^[51] signaled that older adults with low self-efficacy are more susceptible to oral diseases. Birgitta et al^[52] further demonstrated that individuals with low self-efficacy often lack awareness of oral health and generally do not maintain proper oral hygiene practices. Ascribed to their insufficient knowledge of oral care and lack of emphasis on oral health, these individuals are more prone to developing oral frailty. Numerous studies have established that higher levels of self-efficacy can promote the adoption of behaviors beneficial to oral health.^[53] Targeted interventions can effectively enhance personal self-efficacy,^[54] thereby improving oral health status and preventing the onset of oral frailty.^[55]

In addition to identifying new risk factors such as age, gender, and smoking, this study validated risk factors already incorporated into current oral frailty assessment scales. These results enable prompt adoption of preventive interventions by providing medical practitioners with a comprehensive understanding of oral frailty(Table S3, Supplemental Digital Content 3).

Nevertheless, several important limitations of this study merit Acknowledgments. To begin, the analysis of certain risk factors was constrained by the limited number of available studies addressing these variables. Secondly, the exclusive focus on studies conducted in China and Japan may have introduced potential geographic bias and restricted the generalizability of findings to other populations. Thirdly, inconsistent age standards across the included studies impeded the ability to establish uniform age thresholds for analysis. Lastly, while most studies identified risk factors, the absence of specific diagnostic criteria in these articles compromised the interpretability and comparability of the results.

5. Conclusion

This meta-analysis investigated risk factors associated with oral frailty. The findings revealed that these risk factors can be categorized into 5 main domains: demographic characteristics, lifestyle habits, medical interventions, past medical history, and clinical assessment scores. Additionally, this study corroborated that factors such as denture use and xerostomia, as included in the OFI-8 scale, are significant risk factors. Additionally, it identified new risk factors not included in the scale, including age, gender, smoking, malnutrition, physical frailty, dietary habits, and oral health self-efficacy. Taken together, these findings are expected to contribute to the prevention of oral frailty, improvement of current risk assessment tools, and reduction of oral frailty prevalence. However, due to the limited number of included studies, many risk factors remain unverified. Further studies are warranted to validate these findings to provide healthcare professionals with a more comprehensive understanding, aiding in the development of preventive strategies for oral frailty.

Author contributions

Writing – original draft: Dajin Li, Ting Wang.

Writing – review & editing: Dajin Li, Ting Wang.

Investigation: Min You, Yan Rong, Ting Wang.

Methodology: Min You, Yan Rong.

Resources: Yan Rong, Xiaoli Sun.

Data curation: Yi Liu, Xiaoli Sun.

Formal analysis: Yi Liu, Xiaoli Sun.

Software: Yi Liu, Xiaoli Sun.

Funding acquisition: Ting Wang.