The relationship among edentulism, chronic kidney disease and mortality: Results from the NHANES study(2009-2020)

The relationship among edentulism, chronic kidney disease and mortality: Results from the NHANES study(2009-2020) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The relationship among edentulism, chronic kidney disease and mortality: Results from the NHANES study(2009-2020) Huaxiang Jiang, Liping Yin, Zihao Chen, Zishun Qin, Le Gan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6548683/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Limited research has examined the connection between edentulous jaws and chronic kidney disease (CKD), as well as their implications for mortality rates. This study aims to elucidate the correlation between edentulous jaws and CKD, in addition to exploring the connections between all-cause mortality and CKD mortality in this patient population. Methods: The study analyzed 19,427 patients with varying degrees of tooth loss from the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2020. The endpoints were the mortality by all causes and CKD, determined through the National Death Index (NDI). CKD was calculated based on the eGFR and urinary albumin-to-creatinine ratio. A Logistic regression classification model and interaction test were used to determine the connection between edentulousness and CKD. Kaplan-Meier survival analysis, multivariable Cox regression survival models, and stratified analyses were used to explore the correlation between edentulousness and mortality risk. Results: During a follow-up period encompassing 19,427 persons, a total of 1,579 cases of all-cause mortality were recorded, representing an incidence rate of 8.13%. Among these, 865 cases, accounting for 54.78% of the total mortality, were attributed to chronic kidney disease (CKD). After multivariable-adjusted logistic regression analysis, it was found that the risk of CKD increased by 39% among participants with complete edentulism (OR 1.39, 95% CI 1.17 ~ 1.66, P<0.001). Following multivariable-adjusted Cox regression models, a significant connection was identified between edentulism and the mortality by CKD or all causes. Compared to participants without tooth loss, those with maxillary tooth loss had a significantly increased all-cause mortality rate of 61% (HR 1.61, 95% CI 1.30 ~ 1.98, p < 0.001), and CKD mortality rate was also significantly elevated by 45% (HR 1.45, 95% CI 1.11~1.90, p=0.007); participants with both maxillary and mandibular tooth loss exhibited a significant increase in all-cause mortality rate of 102% (HR 2.02, 95% CI 1.73 ~ 2.35, p<0.001), and CKD mortality rate was significantly increased by 69% (HR 1.69, 95% CI 1.33 ~ 2.14, p<0.001). Conclusion: The complete loss of both maxillary and mandibular dentition not only increases the prevalence of CKD but also elevates the all-cause mortality rate and the mortality rate associated with CKD. Clinical Trial Number: Not applicable. NHANES edentulism CKD relevance mortality Figures Figure 1 Figure 2 Figure 3 Clinical significance This study provides evidence for clinicians that an increased degree of tooth loss is significantly related with the risk of mortality. Therefore, when assessing the survival status of CKD patients, attention ought to be given to the condition of tooth loss and oral health. 1. Introduction CKD is a public health issue globally, with an rising incidence observed worldwide, affecting millions of individuals and posing a serious harm to the health and the quality of life [ 1 – 3 ]. At the same time, it presents significant challenges to healthcare systems and patient prognosis. CKD is characterized by a gradual abnormalities of kidney function and structure over time, over 3 months,leading to a range of complications and increased mortality[ 4 , 5 ]. Early identification and intervention are crucial for managing CKD and improving patient outcomes[ 6 , 7 ]. Tooth loss, particularly complete edentulism, is a common oral health problem whose impact may extend beyond the oral cavity itself. Previous research has indicated a certain relation between oral health status and several chronic diseases, including periodontal disease, cardiovascular diseases, and diabetes [ 8 , 9 ]. Edentulism, as a more severe oral health issue, may affect systemic health through various mechanisms [ 10 ]. On one hand, edentulism may lead to a decline in masticatory function, subsequently affecting nutritional intake and digestion, thereby adversely impacting the body's metabolic regulation and immune system function [ 11 , 12 ]. On the other hand, edentulism may be related to a chronic inflammatory state, which is a common pathophysiological basis for various chronic diseases, including CKD[ 13 , 14 ]. Although previous studies have hinted at a link between oral health and general diseases, few studies have specifically investigated the association between edentulism and CKD[ 15 ], which raises concerns regarding this knowledge gap. Understanding this relationship is essential for developing comprehensive public health strategies that encompass both oral and systemic health. Furthermore, it may provide aware into the mechanisms behind the connection between oral health and kidney health, thereby opening new avenues for prevention and treatment. Therefore, this study utilized a large population sample from the NHANES to investigate the connection between edentulism and CKD, as well as the connection between edentulism and mortality. 2. Materials and methods 2.1 Study Population The NHANES was a nationally representative survey conducted by the National Center for Health Statistics (NCHS) of the USA Centers for Disease Control and Prevention (CDC). Its main purpose was to examine the health and dietary condition of non-institutionalized individuals throughout the United States. Data were obtained in variety of methods, including structured household interviews, visits to mobile inspection centers, and laboratory tests, employing a complicated, stratified, multi-stage probabilistic sampling design. The research protocol had received approval from the NCHS Ethics Review Committee, and all cases had provided written informed consent. The data for the study were obtained from the NHANES database covering five cycles (2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2020). Mortality outcomes were decided by the records from the NDI, with follow-up extending to the last update date. A total of 55,999 participants of all aged 18 years or older were initially included. Individuals were excluded according to the following criteria: (1) missing chronic kidney disease (CKD) data (n = 21,375); (2) lack of tooth count data and those unable to be assessed (n = 2,609); (3) missing mortality data (n = 12,588). Ultimately, this study included 19,427 participants (see Fig. 1 ). 2.2 Exposure variable: edentulous jaw During clinical examinations, trained and calibrated dentists assessed the number of dental arches and noted the count of all teeth in the participants' mouths. The gold standard examination was conducted to observe the on-site operations, with dental checks repeated 20 to 25 times during each visit. If the correlation between raters was not within an acceptable range, on-site retraining would be conducted, and future monitoring of on-site reviewers would be reinforced. Approximately 10% of the examined participants were asked to return for repeat checks. The purpose of these "repeat checks" was to monitor the internal consistency of the reviewers regarding the data collection process. Each dental examiner would undergo a retraining course once a year, also conducted by the gold standard examiner. In this study, participants were specifically classified into four types of dental conditions: 1) Edentulous individuals: at least one tooth present in both the upper and lower jaws, 2) Upper jaw edentulous: all teeth missing in the upper jaw, 3) Lower jaw edentulous: all teeth missing in the lower jaw, 4) Complete edentulous: all teeth missing in both the upper and lower jaws. 2.3 Definition of CKD The eGFR was computed utilizing the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula [ 16 ]. The specific formula is: eGFR = 141 × min (Scr/κ, 1)α × max (Scr/κ, 1)-1.209 × 0.993Age × 1.018 [for females], with Scr representing serum creatinine, κ being 0.7 for females and 0.9 for males, α set at − 0.329 for females and − 0.411 for males, min signifying the lesser of Scr/κ or 1, and max denoting the greater of Scr/κ or 1. In line with previously established guidelines, CKD was defined in the following manner: absence of CKD; presence of CKD, indicated by eGFR30mg/g [ 17 ]. 2.4, Mortality data The death data information came from the NDI database, which was managed by the Centers for Disease Control and Prevention [ https://www.cdc.gov/nchs/data-linkage/mortality-public.htm ]. The duration of follow-up was conducted from the date of the initial interview to the time of death or up to December 31, 2019, which was the last time of the NDI database updated. Deaths due to CKD were determined using the International Classification of Diseases, Tenth Revision (ICD-10) codes, specifically N02-N03, N05-N07, and N26. 2.5. Covariates The factors taken into account in the study encompassed age, gender, race (Hispanic American, non-Hispanic white, non-Hispanic black, and other races), cultural difference (no college, college degree or higher), marital condition (living with partner, separated from partner), history of diabetes, and hypertension history. The criteria of these covariates were detailed on the NHANES website of the Centers for Disease Control and Prevention (CDC) [ https://www.cdc.gov/nchs/nhanes/ ]. 2.6. Statistical analysis Baseline characteristics were delineated in relation to dental status. Continuous variables were represented by the Mean ± standard deviation, with comparisons across multiple groups conducted through analysis of variance. Categorical variables were presented as proportions, The p-value of the categorical variable is obtained by using Chi-square test or Fisher exact test. Three sets of logistic regression models (named Model 1, Model 2, and Model 3) were constructed to explore the association between edentlessness and CKD patients. At the same time, three sets of multivariate Cox regression models (also named Model 1, Model 2, and Model 3) were established to measure the degree of association between edentulous jaws and all-cause mortality and CKD-related mortality. Model 1 did not adjust any covariates, Model 2 accounted for gender and age, while Model 3 factored in gender, age, race, and marital condition. Kaplan-Meier survival curves were used to graphically demonstrate the nexus between edentulousness, CKD, and mortality by all causes. Additionally, subgroup analyses was executed based on fully adjusted Cox models to probe into the nexus between edentulousness and mortality across disparate subgroups. All means of analysis were performed using Excel and R software(version 4.3.0). P<0.05 was considered indicative of statistical significance. 3. Results 3.1 Analysis of the Sociodemographic Characteristics of the Study Subjects According to the eligibility criteria of this study, a total of 19,427 individuals were included, with an average age of 47.3 ± 0.32 years. Among the subjects, there were 9,564 males, accounting for 48.78%, and 9,863 females, accounting for 51.22%. Table 1 provides a detailed description of the participants. Among these participants, 3,452 had chronic kidney disease, accounting for 14.73%. There were 17,221 individuals with no missing teeth in both the upper and lower jaws, accounting for 88.64%; 1,248 individuals had complete edentulousness in both jaws, accounting for 6.42%; 890 individuals had missing teeth only in the upper jaw, accounting for 4.58%; and 68 individuals had missing teeth only in the lower jaw, accounting for 0.35%. There were significant distinctions in age, sex, gender, race, race and race of covariates among groups (P 0.05). Table 1 Analysis of basic characteristics and differences (mean error and standard error SE, proportion n and percentage %) Variable Total (n = 19427) no (n = 17221) maxillary dentition missing(n = 890) mandibular teeth missing(n = 68) complete dentition missing(n = 1248) P Age, Mean (SE) 47.35 (0.32) 45.65 (0.30) 63.62 (0.56) 66.97 (1.92) 66.51 (0.60) < .001 Sex, n(%) 0.053 male 9564 (48.78) 8490 (49.05) 411 (43.96) 39 (55.90) 624 (46.77) female 9863 (51.22) 8731 (50.95) 479 (56.04) 29 (44.10) 624 (53.23) Race, n(%) < .001 mexican American 2908 (8.56) 2751 (9.09) 74 (3.25) 6 (5.05) 77 (2.68) other Race 4601 (13.69) 4194 (13.91) 164 (10.73) 16 (14.66) 227 (11.71) non-Hispanic White 7811 (66.84) 6723 (66.31) 412 (72.14) 30 (63.36) 646 (73.15) non-Hispanic Black 4107 (10.91) 3553 (10.69) 240 (13.88) 16 (16.93) 298 (12.46) Education, n(%) < .001 below college 9438 (38.84) 7894 (36.30) 582 (61.02) 53 (80.05) 909 (68.70) college Graduate or above 9989 (61.16) 9327 (63.70) 308 (38.98) 15 (19.95) 339 (31.30) Marital status,n(%) < .001 live together 12108 (65.68) 11024 (66.93) 478 (57.54) 40 (53.26) 566 (48.41) separation 7319 (34.32) 6197 (33.07) 412 (42.46) 28 (46.74) 682 (51.59) Hypertension,n(%) < .001 no 11526 (63.03) 10858 (65.80) 282 (34.11) 25 (35.86) 361 (33.17) yes 7882 (36.97) 6348 (34.20) 605 (65.89) 43 (64.14) 886 (66.83) Diabetes,n(%) < .001 no 15570 (85.50) 14257 (87.21) 548 (70.76) 40 (62.73) 725 (64.76) yes 3564 (14.50) 2726 (12.79) 318 (29.24) 27 (37.27) 493 (35.24) CKD, n(%) < .001 no 15975 (85.27) 14649 (87.18) 570 (68.97) 42 (59.01) 714 (62.37) yes 3452 (14.73) 2572 (12.82) 320 (31.03) 26 (40.99) 534 (37.63) 3.2 The correlation between edentulous jaws and chronic kidney disease As shown in Table 2 , there was a significant positive correlation between edentulous jaws and CKD (P < 0.001). In the univariate analysis, the risk of CKD in patients with maxillary tooth loss was 206% higher than that in patients without tooth loss, while the risk in patients with mandibular tooth loss was 372% higher than that in patients without tooth loss. Additionally, the risk of CKD in patients with both maxillary and mandibular tooth loss is 310% higher than that in patients without tooth loss. Therefore, a multivariate logistic regression model was conducted to further investigate the correlation between CKD and edentulous jaws. After adjusting for covariates such as age, gender, race, and marital status (Model 2), maxillary tooth loss was positively correlated with CKD (OR = 1.35, 95% CI = 1.07 ~ 1.69, P = 0.014), and mandibular tooth loss was significantly associated with CKD (OR = 1.60, 95% CI = 1.07 ~ 1.69, P < 0.001). After adjusting for all covariates (Model 3), the results of Model 3 also suggest this positive correlation, with maxillary tooth loss associated with CKD (OR = 1.21, 95% CI = 0.96 ~ 1.54), mandibular tooth loss associated with CKD (OR = 1.60, 95% CI = 0.79 ~ 3.25), and a significant correlation between both maxillary and mandibular tooth loss and CKD (OR = 1.39, 95% CI = 1.17 ~ 1.66). Table 2 Logistic regression,multi-model strategy Variables Model1 Model2 Model3 OR (95%CI) P OR (95%CI) P OR (95%CI) P Edentulous No 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) maxillary dentition missing 3.06 (2.49 ~ 3.75) < .001 1.35 (1.07 ~ 1.69) 0.014 1.21 (0.96 ~ 1.54) 0.114 mandibular teeth missing 4.72 (2.34 ~ 9.55) < .001 1.82 (0.92 ~ 3.59) 0.092 1.60 (0.79 ~ 3.25) 0.201 complete dentition missing 4.10 (3.40 ~ 4.95) < .001 1.60 (1.34 ~ 1.91) < .001 1.39 (1.17 ~ 1.66) < .001 OR: Odds Ratio, CI: Confidence Interval Model1: Crude Model2: Adjust: sex, age, race, marital status, education Model3: Adjust: sex, age, race, marital status, education, hypertension, diabetes 3.3 Subgroup Analysis of the Association between Edentulism and Chronic Kidney Disease To determine if the link between toothlessness and CKD holds steady throughout the broader populace, we carried out subgroup examinations and interaction assessments factoring in age, sex, racial background, cultural aspects, marital conditions, blood pressure issues, and diabetic status. Our results suggest that this connection isn't constant across all groups. The detailed subgroup analysis exploring the tie between tooth loss and CKD is presented in Fig. 2 . Once we accounted for a range of influencing factors, a notable link between toothlessness and CKD emerged (P < 0.05). Interestingly, the positive tie between missing teeth and CKD isn't swayed by variables such as age, sex, race, cultural nuances, marital situations, high blood pressure, or diabetes. Moreover, no significant interactions were detected, as all interaction P values were above 0.05. 3.4 The Relationship Between Edentulousness and Mortality Rate Among the follow-up of 19,427 individuals, there were 1,579 cases (8.13%) of all-cause mortality, of which 865 individuals (54.78%) died from CKD. The results of the univariate and multivariate Cox proportional hazards models were shown in Table 3 , indicating that age, gender, marital status, hypertension, diabetes, edentulousness, and CKD are risk factors for mortality (P < 0.05). The mortality risk was distinctly increased in older individuals, males, those who are separated, and those with hypertension, diabetes, edentulousness, and CKD. The hazard ratio (HR) for mortality associated with edentulousness was as high as 1.85 (1.57–2.17), while the HR for CKD was 2.23 (1.98–2.51). In the fully adjusted model presented in Table 4 , participants with maxillary edentulousness had a significantly increased mortality rate due to all causes by 61% (HR = 1.61, 95% CI = 1.30–1.98, P < 0.001) compared to those without edentulousness, and the mortality rate due to CKD also significantly increased by 45% (HR = 1.45, 95% CI = 1.11–1.90, P = 0.007); participants with both maxillary and mandibular edentulousness had a significantly increased all-cause mortality rate by 102% (HR = 2.02, 95% CI = 1.73–2.35, P < 0.001), and the CKD mortality rate also significantly increased by 69% (HR = 1.69, 95% CI = 1.33–2.14, P < 0.001). Figure 3 shows the Kaplan-Meier survival analysis, indicating that older age, male gender, complete edentulousness, and the presence of CKD were associated with the highest all-cause mortality rate (log-rank P < 0.001). Table 3 , Results of the univariate and multivariate Cox proportional hazards models Variables β S.E Z P HR (95%CI) β S.E Z P HR (95%CI) Age 0.09 0.00 28.31 < .01 1.09 (1.09–1.10) 0.06 0.00 18.46 < .01 1.07 (1.06–1.07) Sex male 1.00 (Reference) 1.00 (Reference) female -0.20 0.05 -3.82 < .01 0.82 (0.74–0.91) -0.49 0.06 -7.90 < .01 0.61 (0.54–0.69) Race mexican American 1.00 (Reference) 1.00 (Reference) other Race 0.04 0.16 0.22 0.82 1.04 (0.75–1.43) -0.23 0.14 -1.67 0.09 0.79 (0.60–1.04) non-Hispanic White 0.82 0.14 5.71 < .01 2.27 (1.71–3.00) 0.18 0.12 1.43 0.15 1.20 (0.94–1.53) non-Hispanic Black 0.59 0.15 3.91 < .01 1.81 (1.34–2.44) -0.01 0.13 -0.10 0.92 0.99 (0.76–1.28) Marital status live together 1.00 (Reference) 1.00 (Reference) separation 0.65 0.06 10.11 < .01 1.92 (1.69–2.18) 0.50 0.08 6.65 < .01 1.65 (1.42–1.91) Education below college 1.00 (Reference) 1.00 (Reference) college Graduate or above -0.48 0.08 -6.36 < .01 0.62 (0.53–0.72) -0.13 0.07 -1.81 0.07 0.88 (0.76–1.01) Hypertension No 1.00 (Reference) 1.00 (Reference) Yes 1.49 0.07 20.97 < .01 4.45 (3.87–5.11) 0.25 0.08 3.32 < .01 1.29 (1.11–1.49) Diabetes No 1.00 (Reference) 1.00 (Reference) Yes 1.23 0.07 18.67 < .01 3.42 (3.00–3.89) 0.23 0.08 2.98 < .01 1.26 (1.08–1.46) Edentulous No 1.00 (Reference) 1.00 (Reference) maxillary dentition missing 1.52 0.10 14.77 < .01 4.59 (3.75–5.62) 0.42 0.11 3.86 < .01 1.53 (1.23–1.90) mandibular teeth missing 1.87 0.26 7.09 < .01 6.49 (3.87–10.88) 0.42 0.19 2.20 0.03 1.52 (1.05–2.20) complete dentition missing 1.97 0.07 27.51 < .01 7.14 (6.21–8.22) 0.61 0.08 7.49 < .01 1.85 (1.57–2.17) CKD No 1.00 (Reference) 1.00 (Reference) Yes 1.91 0.05 37.95 < .01 6.77 (6.13–7.47) 0.80 0.06 13.25 < .01 2.23 (1.98–2.51) Table 4 , HRs (95% CIs) for mortality according to the edentulous index Edentulous No maxillary dentition missing mandibular teeth missing complete dentition missing HR (95%CI) P HR (95%CI) P HR (95%CI) P HR (95%CI) P All-cause mortality Model1 1.00 (Reference) 4.59 (3.75 5.62) < .001 6.49 (3.87–10.88) < .001 7.14 (6.21–8.22) < .001 Model2 1.00 (Reference) 1.77 (1.44–2.16) < .001 1.91 (1.23–2.97)0.004 2.33 (2.01–2.70) < .001 Model3 1.00 (Reference) 1.61 (1.30–1.98) < .001 1.70 (1.13–2.56)0.012 2.02 (1.73–2.35) < .001 CKD mortality Model1 1.00 (Reference) 2.59 (2.04–3.30) < .001 3.18 (1.41–7.15)0.005 3.25 (2.56–4.12) < .001 Model2 1.00 (Reference) 1.48 (1.15–1.91)0.002 1.88 (0.93–3.80)0.078 1.73 (1.38–2.18) < .001 Model3 1.00(Reference) 1.45 (1.11–1.90)0.007 1.78 (0.88–3.59)0.110 1.69 (1.33–2.14) < .001 HR: Hazard Ratio, CI: Confidence Interval Model1: Crude Model2: Adjust: Sex, Age Model3: Adjust: Sex, Age, Race, Education, Marital status 3.5 Interaction Test of Edentulousness and Mortality Rate In Table 5 , among all patients, the HR for edentulousness and all-cause mortality was 1.34, with a 95% CI of 1.27 to 1.41, and a P<0.001, indicating a significant connection between edentulousness and mortality due to all-cause, with a markedly increased risk of mortality by all causes in edentulous patients. In age stratification, the P-value for the interaction test was less than 0.001, suggesting a significant interaction of age on the connection between edentulousness and all-cause mortality, with notable risk differences across different age groups. In racial stratification, the P-value for the interaction test was 0.001, indicating a significant interaction of race on the relationship between edentulousness and all-cause mortality, with significant risk differences among different racial groups. In stratifications for hypertension and diabetes, the interaction test P-values were < 0.001 and 0.015, respectively, indicating significant interactions of hypertension and diabetes status on the connection between edentulousness and all-cause mortality, with a more pronounced risk in the group without hypertension and diabetes. This suggested that the impact of different population characteristics on the relationship between edentulousness and mortality by all causes varies. In Table 6 , among all patients, the HR for edentulousness and CKD mortality was 1.25, with a 95% CI of 1.15 to 1.35, and a P<0.001, indicating a significant connection between edentulousness and CKD mortality, with a markedly increased risk of CKD mortality in edentulous patients. The above results remained consistent across different subgroups, demonstrating a positive correlation between edentulousness and both types of mortality. Table 5 , Stratified analyses of the connections between edentulous and Mortality due to all causes Variables n (%) HR (95%CI) P P for interaction All patients 19427 (100.00) 1.34 (1.27 ~ 1.41) < .001 sex 0.085 male 9564 (49.23) 1.27 (1.18 ~ 1.36) < .001 female 9863 (50.77) 1.41 (1.31 ~ 1.50) < .001 age < .001 <60 13335 (68.64) 1.67 (1.43 ~ 1.96) < .001 ≥60 6092 (31.36) 1.31 (1.24 ~ 1.38) < .001 race 0.001 mexican American 2908 (14.97) 1.22 (1.01 ~ 1.47) 0.040 other Race 4601 (23.68) 1.67 (1.45 ~ 1.92) < .001 non-Hispanic White 7811 (40.21) 1.34 (1.26 ~ 1.43) < .001 non-Hispanic Black 4107 (21.14) 1.19 (1.07 ~ 1.32) 0.001 Education 0.070 below college 9438 (48.58) 1.32 (1.25 ~ 1.41) < .001 college Graduate or above 9989 (51.42) 1.39 (1.28 ~ 1.51) < .001 Marital status 0.072 live together 12108 (62.33) 1.42 (1.29 ~ 1.56) < .001 separation 7319 (37.67) 1.28 (1.19 ~ 1.37) < .001 Hypertension < .001 No 11526 (59.39) 1.58 (1.38 ~ 1.79) < .001 Yes 7882 (40.61) 1.29 (1.21 ~ 1.37) < .001 Diabetes 0.015 No 15570 (81.37) 1.39 (1.30 ~ 1.49) < .001 Yes 3564 (18.63) 1.28 (1.17 ~ 1.39) < .001 HR: Hazard Ratio, CI: Confidence Interval Table 6 , Stratified analyses of the connections between edentulous and CKD mortality Variables n (%) HR (95%CI) P P for interaction All patients 3096 (100.00) 1.25 (1.15 ~ 1.35) < .001 sex 0.321 male 1321 (42.67) 1.27 (1.12 ~ 1.43) < .001 female 1775 (57.33) 1.22 (1.11 ~ 1.34) < .001 age 0.332 <60 1157 (37.37) 1.43 (0.98 ~ 2.07) 0.062 ≥60 1939 (62.63) 1.23 (1.13 ~ 1.33) < .001 race 0.072 mexican American 400 (12.92) 1.16 (0.96 ~ 1.40) 0.126 other Race 1285 (41.51) 1.25 (1.13 ~ 1.39) < .001 non-Hispanic White 835 (26.97) 1.18 (1.04 ~ 1.33) 0.009 non-Hispanic Black 576 (18.60) 1.44 (1.15 ~ 1.81) 0.001 Education 0.121 below college 1746 (56.40) 1.20 (1.08 ~ 1.33) < .001 college Graduate or above 1350 (43.60) 1.34 (1.21 ~ 1.49) < .001 Marital status 0.056 live together 1668 (53.88) 1.36 (1.19 ~ 1.56) < .001 separation 1428 (46.12) 1.16 (1.05 ~ 1.29) 0.004 Hypertension 0.054 No 908 (29.35) 1.36 (1.15 ~ 1.61) < .001 Yes 2186 (70.65) 1.22 (1.11 ~ 1.35) < .001 Diabetes 0.509 No 1794 (59.05) 1.24 (1.11 ~ 1.39) < .001 Yes 1244 (40.95) 1.24 (1.11 ~ 1.38) < .001 HR: Hazard Ratio, CI: Confidence Interval 4, Discussion This study utilized a large population sample from the NHANES conducted between 2009 and 2020 to explore the relationship between edentulism, CKD, and mortality. It revealed that edentulism was not only a risk factor for increased CKD prevalence but was also closely connected with a significant rise in all-cause mortality and CKD mortality. Compared to previous studies, this research had made beneficial expansions and supplements in several key aspects, further highlighting its significance. 1. Supplementation and Expansion of Research Results Previous research had primarily focused on the qualitative connection between edentulism and CKD [ 18 ]. In contrast, this study employed multivariable-adjusted logistic regression and Cox regression models to accurately quantify the increased risks of CKD prevalence, all-cause mortality, and CKD mortality among different types of edentulous patients. For instance, the CKD risk among participants with complete edentulism rose by 39% (OR 1.39, 95% CI = 1.17 ~ 1.66, P<0.001), while the all-cause mortality of participants with both upper and lower jaw edentulism significantly rose by 102% (HR 2.02, 95% CI = 1.73–2.35, P<0.001), and CKD mortality also significantly rose by 69% (HR 1.69, 95% CI = 1.33–2.14, P<0.001). This quantitative analysis provides a more intuitive and precise basis for clinical risk assessment, aiding physicians in formulating personalized prevention and treatment plans based on patients' specific dental loss conditions. Compared to previous studies, this research has made solid strides in the quantification of risk. This study further conducted multidimensional subgroup analyses according to age, gender, race, education condition, marital condition, diabetes, and hypertension history, finding that the positive correlation between edentulism and CKD is not influenced by these confounding factors and remains consistent across different subgroups. This is more comprehensive than some previous researchs that only focused on the correction of a single or a few confounding factors, revealing the universality and stability of the association between edentulism and CKD, thereby enhancing the reliability and generalizability of the research conclusions [ 19 ]. For example, in age stratification, the interaction test P-value was less than 0.001, indicating a significant interaction effect of age on the relationship between edentulism and mortality from all causes, with significant risk differences across different age groups, providing a scientific basis for differentiated oral health and kidney disease prevention strategies targeting different age populations, thus addressing the shortcomings of previous studies in the depth of subgroup analysis. Utilizing Kaplan-Meier survival analysis, this study visually demonstrated the dynamic association between the degree of tooth loss and CKD and mortality, making the research results easier to understand and disseminate. The survival curves clearly showed that older age, male gender, complete edentulism, and the presence of CKD were associated with the highest all-cause mortality (log-rank p < 0.001). This visualization method is relatively rare in previous studies and helps clinicians, public health workers, and patients themselves to more intuitively recognize the impact of edentulism on survival prognosis[ 20 ], thereby increasing the emphasis on oral health and kidney disease prevention and treatment. Compared to previous studies, this research presents results in a more innovative and practical manner. 2. Highlighting the Significance of the Research Precision of Public Health Strategies: The results of this study offered strong support for the formulation of more precise and effective oral health and kidney disease prevention and control strategies in the public health field. Based on the strong association between edentulism, CKD, and mortality, public health departments can combine oral health examinations with kidney disease screenings, conducting joint screening programs for high-risk populations (such as the elderly and edentulous patients) to achieve early detection and early intervention[ 21 ]. At the same time, by strengthening oral health education and raising public awareness of dental care, the incidence of edentulism can be reduced from the source, thereby lowering the prevalence and mortality of CKD, optimizing the allocation of public health resources, and perfecting the overall health level and life expectancy of the population. Compared to previous studies, this research provides more operational strategy recommendations for public health practice. Synergy in Clinical Diagnosis and Treatment Models: In clinical practice, this study encourages physicians to shift their diagnostic and treatment thinking, incorporating oral health as an important component of physical health assessment for patients. During patient consultations, physicians should not only focus on oral symptoms but also be vigilant about the potential kidney disease risks that may be hidden behind edentulism, achieving collaborative diagnosis and treatment among multiple disciplines such as dentistry and nephrology[ 22 ]. For example, when providing oral restoration or treatment for edentulous patients, simultaneous kidney function monitoring can be conducted to timely identify potential kidney issues and intervene; in the management of kidney disease patients, attention should be paid to oral health status, and targeted oral care measures should be implemented to improve patients' quality of life and prognosis. Compared to previous studies, this research provides stronger theoretical support for the transformation of clinical diagnosis and treatment models. Diversification of Scientific Research Directions: This study offers new ideas and directions for further exploring the potential mechanisms linking edentulism with CKD and mortality. Factors such as chewing dysfunction, inadequate nutritional intake, and oral microbiome dysbiosis caused by edentulism may affect kidney health through various pathways, such as triggering chronic inflammatory responses, promoting oxidative stress, and interfering with endocrine metabolism[ 23 ]. Future research can build on this foundation to further explore these potential mechanisms from multiple perspectives, including molecular biology, cell biology, and immunology, laying the foundation for the developing new diagnostic markers, therapeutic targets, and intervention methods, and promoting the interdisciplinary integration and development of related fields. Compared to previous studies, this research expands the scope for scientific inquiry. Transformation of Social Cognition and Health Concepts: At the societal level, the benefits of this study help to break the traditional cognitive limitations of the public regarding oral health, allowing people to fully recognize the close connection and mutual influence between oral health and overall health[ 24 , 25 ]. This will stimulate various sectors of society to pay more attention to oral health, encouraging individuals to develop good oral hygiene habits, such as regular brushing, using dental floss, and undergoing regular oral check-ups. At the same time, it will also promote social forces to actively participate in oral health promotion activities, such as corporate sponsorship of oral health education programs and communities offering free oral screening services, creating a positive atmosphere for society to focus on oral health and maintain overall health, thereby enhancing the health literacy of the entire population. Compared to previous studies, this research has more far-reaching significance in terms of social impact. 3. Limitations and Directions for Improvement of the Research Limitations of Causal Relationships: Although this study has attempted to control confounding factors as much as possible through multivariable adjustments and other statistical methods, the analysis design based on cross-sectional data still cannot fully establish causal relationships between edentulism, CKD, and mortality. Future research could use more rigorous designs such as prospective studies or randomized controlled trials to conduct long-range follow-up observations of the occurrence and development of CKD and mortality events in edentulous patients, to more accurately reveal causal links and provide a more solid basis for clinical interventions. Omission of Potential Confounding Factors: Among the covariates included, although common variables for example age, gender, race/ethnicity, cultural difference, marital condition, diabetes, and hypertension history were considered, there may still be some unaccounted potential confounding variables, for example patients' dietary habits, smoking and drinking history, and oral hygiene behaviors, which may have some impact on the research results. Subsequent studies should further refine the selection of covariates and comprehensively collect relevant information to reduce confounding bias and improve the accuracy of research conclusions. Representativeness and Generalizability of Data: Although NHANES data is nationally representative, it is primarily based on the U.S. population, which may somewhat limit the extrapolation of the research results to other countries or regions. Differences in racial composition, lifestyle, and healthcare systems across different countries and regions may all influence the relationship between edentulism, CKD, and mortality. Future research could consider conducting multicenter studies in different countries or regions, incorporating more diverse populations to validate the generalizability of the findings of this study and provide broader references for public health policy formulation and clinical practice on a global scale. Conclusion Edentlessness may be a risk factor for increased prevalence of CKD and may contribute to all-cause and CKD-specific mortality. Declarations Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Availability of data and materials: The datasets used and analyzed during the current study are publicly available from the National Health and Nutrition Examination Survey (NHANES) through the Centers for Disease Control and Prevention (CDC) website. The data files and related documentation can be accessed at the NHANES website (https://www.cdc.gov/nchs/nhanes/index.html) . Competing interests ： None of the authors have any relevant financial relationship(s) with a commercial interest. Fund project: 2023 Jiangxi Province Traditional Chinese Medicine Science and technology project- Mechanism of astragaloside regulating osteogenic differentiation of bone marrow mesenchymal stem cells via SP1/microRNA-107/DKK1 axis(2023Z030). Authors Contributions: Huaxiang Jiang:Conceptualization and original draft；Liping Yin:Writing-review＆editing and Data curation; Zihao Chen : Methodology and Investigation,; Zishun Qin：Project administration and Funding acquisition.Le Gan:Regulation Mapping and Supervision. Acknowledgements: We would like to thank the National Health and Nutrition Examination Survey (NHANES) for providing the data used in this study. Authors: Huaxiang Jiang a,1 , M.M., Resident, [email protected] Liping Yin a,1 , M.M., Resident, [email protected] Zihao Chen a , M.M., Resident, [email protected] Zishun Qin a,b, *,DDS, Ph.D.Professor, [email protected] Le Gan c, *, [email protected] a Department of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi,330000, China b School/Hospital of Stomatology, Lanzhou University, Lanzhou 730000, China c Jinxi County Hospital of Traditional Chinese Medicine 1 This authors contributed to this work equally. *Correspondence Author: Le Gan c, *, [email protected] Zishun Qin a,* ,DDS, Ph.D.Professor, [email protected] Email: [email protected] References Zhao WM, Li XL, Shi R, et al. Global, regional and national burden of CKD in children and adolescents from 1990 to 2019. Nephrol Dial Transplant. 2024;39(8):1268-1278. doi:10.1093/ndt/gfad269 Wang L, Xu X, Zhang M, et al. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med. 2023;183(4):298-310. doi:10.1001/jamainternmed.2022.6817 GBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-733. doi:10.1016/S0140-6736(20)30045-3 Ying M, Shao X, Qin H, et al. Disease Burden and Epidemiological Trends of Chronic Kidney Disease at the Global, Regional, National Levels from 1990 to 2019. Nephron. 2024;148(2):113-123. doi:10.1159/000534071 Evenepoel P, Stenvinkel P, Shanahan C, Pacifici R. Inflammation and gut dysbiosis as drivers of CKD-MBD. Nat Rev Nephrol. 2023;19(10):646-657. doi:10.1038/s41581-023-00736-7 Levin A, Stevens PE. Early detection of CKD: the benefits, limitations and effects on prognosis. Nat Rev Nephrol. 2011;7(8):446-457. Published 2011 Jun 28. doi:10.1038/nrneph.2011.86 Levey AS, Coresh J. Chronic kidney disease. Lancet. 2012;379(9811):165-180. doi:10.1016/S0140-6736(11)60178-5 Sumayin Ngamdu K, Mallawaarachchi I, Dunipace EA, et al. Association Between Periodontal Disease and Cardiovascular Disease (from the NHANES). Am J Cardiol. 2022;178:163-168. doi:10.1016/j.amjcard.2022.05.028 Liccardo D, Cannavo A, Spagnuolo G, et al. Periodontal Disease: A Risk Factor for Diabetes and Cardiovascular Disease. Int J Mol Sci. 2019;20(6):1414. Published 2019 Mar 20. doi:10.3390/ijms20061414 Chatzopoulos GS, Jiang Z, Marka N, Wolff LF. Periodontal Disease, Tooth Loss, and Systemic Conditions: An Exploratory Study. Int Dent J. 2024;74(2):207-215. doi:10.1016/j.identj.2023.08.002 Yeung AWK, Leung WK. Functional Neuroplasticity of Adults with Partial or Complete Denture Rehabilitation with or without Implants: Evidence from fMRI Studies. Nutrients. 2023;15(7):1577. Published 2023 Mar 24. doi:10.3390/nu15071577 Lu EM. The role of vitamin D in periodontal health and disease. J Periodontal Res. 2023;58(2):213-224. doi:10.1111/jre.13083 Kostunov J, Menzel R, Bermejo JL, Rammelsberg P, Giannakopoulos NN, Kappel S. Immediate loading of dental implants in edentulous mandibles using Locator attachments or Dolder bars: A 9-year prospective randomized clinical study. J Clin Periodontol. 2023;50(11):1530-1538. doi:10.1111/jcpe.13857 Tran F, Schirmer JH, Ratjen I, et al. Patient Reported Outcomes in Chronic Inflammatory Diseases: Current State, Limitations and Perspectives. Front Immunol. 2021;12:614653. Published 2021 Mar 18. doi:10.3389/fimmu.2021.614653 D'Aiuto F, Parkar M, Andreou G, et al. Periodontitis and systemic inflammation: control of the local infection is associated with a reduction in serum inflammatory markers. J Dent Res. 2004;83(2):156-160. doi:10.1177/154405910408300214 Levey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011 Sep 20;155(6):408]. Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006 Sun DQ, Jin Y, Wang TY, et al. MAFLD and risk of CKD. Metabolism. 2021;115:154433. doi:10.1016/j.metabol.2020.154433 Delbove T, Gueyffier F, Juillard L, et al. Effect of periodontal treatment on the glomerular filtration rate, reduction of inflammatory markers and mortality in patients with chronic kidney disease: A systematic review. PLoS One. 2021;16(1):e0245619. Published 2021 Jan 22. doi:10.1371/journal.pone.0245619 Ioannidou E, Hall Y, Swede H, Himmelfarb J. Periodontitis associated with chronic kidney disease among Mexican Americans. J Public Health Dent. 2013;73(2):112-119. doi:10.1111/j.1752-7325.2012.00350.x Sharma P, Dietrich T, Ferro CJ, Cockwell P, Chapple IL. Association between periodontitis and mortality in stages 3-5 chronic kidney disease: NHANES III and linked mortality study. J Clin Periodontol. 2016;43(2):104-113. doi:10.1111/jcpe.12502 Fujimoto P, Wong KA, Kataoka-Yahiro M. Behind the Smile: Detecting Chronic Kidney Disease Through Oral Health Screenings. Hawaii J Health Soc Welf. 2024;83(9):260-262. doi:10.62547/VYCO2960 Sezer B, Kaya R, Kodaman Dokumacıgil N, et al. Assessment of the oral health status of children with chronic kidney disease. Pediatr Nephrol. 2023;38(1):269-277. doi:10.1007/s00467-022-05590-6 Kinane DF, Stathopoulou PG, Papapanou PN. Periodontal diseases. Nat Rev Dis Primers. 2017;3:17038. Published 2017 Jun 22. doi:10.1038/nrdp.2017.38 Žmavc JB, Verdenik M, Skomina Z, Ihan Hren N. Tooth Loss and Systemic Diseases in the Slovenian Elderly Population: A Cross-Sectional Study of the Associaton Between Oral and Systemic Health. Zdr Varst. 2024;63(3):142-151. Published 2024 Jun 14. doi:10.2478/sjph-2024-0019 Issrani R, Reddy J, Dabah THE, et al. Exploring the Mechanisms and Association between Oral Microflora and Systemic Diseases. Diagnostics (Basel). 2022;12(11):2800. Published 2022 Nov 15. doi:10.3390/diagnostics12112800 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Dec, 2025 Read the published version in BMC Oral Health → Version 1 posted Editorial decision: Revision requested 27 Jun, 2025 Reviews received at journal 24 Jun, 2025 Reviews received at journal 22 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers agreed at journal 16 Jun, 2025 Reviewers invited by journal 03 Jun, 2025 Editor assigned by journal 13 May, 2025 Submission checks completed at journal 13 May, 2025 First submitted to journal 28 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6548683","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466008909,"identity":"3c3fe5ec-f7c3-453d-a56d-9ffb271e1aa8","order_by":0,"name":"Huaxiang Jiang","email":"","orcid":"","institution":"Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Huaxiang","middleName":"","lastName":"Jiang","suffix":""},{"id":466008911,"identity":"09d71b07-e4a2-4ca3-86c6-1c9b8ceff46f","order_by":1,"name":"Liping Yin","email":"","orcid":"","institution":"Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Yin","suffix":""},{"id":466008912,"identity":"0b527e84-d560-4b88-9121-cd669bbd9bc9","order_by":2,"name":"Zihao Chen","email":"","orcid":"","institution":"Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Zihao","middleName":"","lastName":"Chen","suffix":""},{"id":466008914,"identity":"2b9e3013-6ef3-4d3b-b54d-cf3f8a69fb01","order_by":3,"name":"Zishun Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIie3QsQrCMBCA4YRAu1zbNQV9h0hABAVfJUVoF3F2cAgU0tHXcVQO6lJxdXVx1rGLWh1ERGJHh/xww8F9yxHicv1jrBmqRxD5r7UdSTtx3pqQJ8GRwLYk3AZ4qVcMJPonTubDRPu7tZXEGCoZVB70kaScVFmiYaasRCCICTXwICWnBhPNQfwkm9pwkDk1nF7bkZ4OjADBmMebP/wmMYJkgVHA0WMDVWbSwNROwn0lL7W5jaOiOB7Oi2F36Vd2Qtbvi2rGs99/EpfL5XJ96w6JgDy8ZjnEAAAAAABJRU5ErkJggg==","orcid":"","institution":"Second Affiliated Hospital of Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Zishun","middleName":"","lastName":"Qin","suffix":""},{"id":466008916,"identity":"8ed8e6ae-79ed-4429-9808-2a9f823e372a","order_by":4,"name":"Le Gan","email":"","orcid":"","institution":"Second Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Le","middleName":"","lastName":"Gan","suffix":""}],"badges":[],"createdAt":"2025-04-28 14:08:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6548683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6548683/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12903-025-07166-w","type":"published","date":"2025-12-01T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":84219124,"identity":"9c2f1e45-f69c-4c44-8ef7-02a04d4eeb0e","added_by":"auto","created_at":"2025-06-09 11:22:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282156,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the participants being analyzed.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6548683/v1/f80dbc6729c1a83040ff1760.png"},{"id":84219125,"identity":"c2e69c81-e7c0-419c-8515-f4d6956698f2","added_by":"auto","created_at":"2025-06-09 11:22:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":177395,"visible":true,"origin":"","legend":"\u003cp\u003esubgroup analysis of the relationship between edentulous jaw and CKD.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6548683/v1/84fb68bb61ff13b70adba616.png"},{"id":84220251,"identity":"b7defd12-5038-4ddf-b7e0-865a1d12e9cf","added_by":"auto","created_at":"2025-06-09 11:30:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":124802,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of survival curves (KM) for gender (1 stands for male, 2 stands for female), age (1 means less than 60 years old, 2 means 60 years or older), edentulous jaw (0 represents no, 1 represents maxillary dentition missing, 2 represents mandibular teeth missing, 3 represents complete dentition missing), and CKD (0 represents no, 1 represents yes).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6548683/v1/e4c590668c2d04eadd3ab4fb.png"},{"id":97723882,"identity":"f2460950-4cf6-431c-aaa3-96f61bcfd397","added_by":"auto","created_at":"2025-12-08 16:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2287698,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6548683/v1/2633a450-3317-4a3a-985b-239847d9799b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The relationship among edentulism, chronic kidney disease and mortality: Results from the NHANES study(2009-2020)","fulltext":[{"header":"Clinical significance","content":"\u003cp\u003eThis study provides evidence for clinicians that an increased degree of tooth loss is significantly related with the risk of \u0026nbsp; mortality. Therefore, when assessing the survival status of CKD patients, attention ought to be given to the condition of tooth loss and oral health.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eCKD is a public health issue globally, with an rising incidence observed worldwide, affecting millions of individuals and posing a serious harm to the health and the quality of life [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At the same time, it presents significant challenges to healthcare systems and patient prognosis. CKD is characterized by a gradual abnormalities of kidney function and structure over time, over 3 months,leading to a range of complications and increased mortality[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Early identification and intervention are crucial for managing CKD and improving patient outcomes[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTooth loss, particularly complete edentulism, is a common oral health problem whose impact may extend beyond the oral cavity itself. Previous research has indicated a certain relation between oral health status and several chronic diseases, including periodontal disease, cardiovascular diseases, and diabetes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Edentulism, as a more severe oral health issue, may affect systemic health through various mechanisms [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. On one hand, edentulism may lead to a decline in masticatory function, subsequently affecting nutritional intake and digestion, thereby adversely impacting the body's metabolic regulation and immune system function [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. On the other hand, edentulism may be related to a chronic inflammatory state, which is a common pathophysiological basis for various chronic diseases, including CKD[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Although previous studies have hinted at a link between oral health and general diseases, few studies have specifically investigated the association between edentulism and CKD[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which raises concerns regarding this knowledge gap. Understanding this relationship is essential for developing comprehensive public health strategies that encompass both oral and systemic health. Furthermore, it may provide aware into the mechanisms behind the connection between oral health and kidney health, thereby opening new avenues for prevention and treatment.\u003c/p\u003e \u003cp\u003eTherefore, this study utilized a large population sample from the NHANES to investigate the connection between edentulism and CKD, as well as the connection between edentulism and mortality.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003eThe NHANES was a nationally representative survey conducted by the National Center for Health Statistics (NCHS) of the USA Centers for Disease Control and Prevention (CDC). Its main purpose was to examine the health and dietary condition of non-institutionalized individuals throughout the United States. Data were obtained in variety of methods, including structured household interviews, visits to mobile inspection centers, and laboratory tests, employing a complicated, stratified, multi-stage probabilistic sampling design. The research protocol had received approval from the NCHS Ethics Review Committee, and all cases had provided written informed consent.\u003c/p\u003e \u003cp\u003eThe data for the study were obtained from the NHANES database covering five cycles (2009\u0026ndash;2010, 2011\u0026ndash;2012, 2013\u0026ndash;2014, 2015\u0026ndash;2016, and 2017\u0026ndash;2020). Mortality outcomes were decided by the records from the NDI, with follow-up extending to the last update date. A total of 55,999 participants of all aged 18 years or older were initially included. Individuals were excluded according to the following criteria: (1) missing chronic kidney disease (CKD) data (n\u0026thinsp;=\u0026thinsp;21,375); (2) lack of tooth count data and those unable to be assessed (n\u0026thinsp;=\u0026thinsp;2,609); (3) missing mortality data (n\u0026thinsp;=\u0026thinsp;12,588). Ultimately, this study included 19,427 participants (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Exposure variable: edentulous jaw\u003c/h2\u003e \u003cp\u003e During clinical examinations, trained and calibrated dentists assessed the number of dental arches and noted the count of all teeth in the participants' mouths. The gold standard examination was conducted to observe the on-site operations, with dental checks repeated 20 to 25 times during each visit. If the correlation between raters was not within an acceptable range, on-site retraining would be conducted, and future monitoring of on-site reviewers would be reinforced. Approximately 10% of the examined participants were asked to return for repeat checks. The purpose of these \"repeat checks\" was to monitor the internal consistency of the reviewers regarding the data collection process. Each dental examiner would undergo a retraining course once a year, also conducted by the gold standard examiner.\u003c/p\u003e \u003cp\u003eIn this study, participants were specifically classified into four types of dental conditions: 1) Edentulous individuals: at least one tooth present in both the upper and lower jaws, 2) Upper jaw edentulous: all teeth missing in the upper jaw, 3) Lower jaw edentulous: all teeth missing in the lower jaw, 4) Complete edentulous: all teeth missing in both the upper and lower jaws.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Definition of CKD\u003c/h2\u003e \u003cp\u003eThe eGFR was computed utilizing the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The specific formula is: eGFR\u0026thinsp;=\u0026thinsp;141 \u0026times; min (Scr/κ, 1)α\u0026thinsp;\u0026times;\u0026thinsp;max (Scr/κ, 1)-1.209 \u0026times; 0.993Age \u0026times; 1.018 [for females], with Scr representing serum creatinine, κ being 0.7 for females and 0.9 for males, α set at \u0026minus;\u0026thinsp;0.329 for females and \u0026minus;\u0026thinsp;0.411 for males, min signifying the lesser of Scr/κ or 1, and max denoting the greater of Scr/κ or 1. In line with previously established guidelines, CKD was defined in the following manner: absence of CKD; presence of CKD, indicated by eGFR\u0026lt;60mL/min/1.73m2 or UACR\u0026gt;30mg/g [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4, Mortality data\u003c/h2\u003e \u003cp\u003eThe death data information came from the NDI database, which was managed by the Centers for Disease Control and Prevention [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/data-linkage/mortality-public.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/data-linkage/mortality-public.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. The duration of follow-up was conducted from the date of the initial interview to the time of death or up to December 31, 2019, which was the last time of the NDI database updated. Deaths due to CKD were determined using the International Classification of Diseases, Tenth Revision (ICD-10) codes, specifically N02-N03, N05-N07, and N26.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Covariates\u003c/h2\u003e \u003cp\u003eThe factors taken into account in the study encompassed age, gender, race (Hispanic American, non-Hispanic white, non-Hispanic black, and other races), cultural difference (no college, college degree or higher), marital condition (living with partner, separated from partner), history of diabetes, and hypertension history. The criteria of these covariates were detailed on the NHANES website of the Centers for Disease Control and Prevention (CDC) [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e \u003cp\u003eBaseline characteristics were delineated in relation to dental status. Continuous variables were represented by the Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, with comparisons across multiple groups conducted through analysis of variance. Categorical variables were presented as proportions, The p-value of the categorical variable is obtained by using Chi-square test or Fisher exact test. Three sets of logistic regression models (named Model 1, Model 2, and Model 3) were constructed to explore the association between edentlessness and CKD patients. At the same time, three sets of multivariate Cox regression models (also named Model 1, Model 2, and Model 3) were established to measure the degree of association between edentulous jaws and all-cause mortality and CKD-related mortality. Model 1 did not adjust any covariates, Model 2 accounted for gender and age, while Model 3 factored in gender, age, race, and marital condition. Kaplan-Meier survival curves were used to graphically demonstrate the nexus between edentulousness, CKD, and mortality by all causes. Additionally, subgroup analyses was executed based on fully adjusted Cox models to probe into the nexus between edentulousness and mortality across disparate subgroups.\u003c/p\u003e \u003cp\u003eAll means of analysis were performed using Excel and R software(version 4.3.0). P\u0026lt;0.05 was considered indicative of statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of the Sociodemographic Characteristics of the Study Subjects\u003c/h2\u003e \u003cp\u003eAccording to the eligibility criteria of this study, a total of 19,427 individuals were included, with an average age of 47.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32 years. Among the subjects, there were 9,564 males, accounting for 48.78%, and 9,863 females, accounting for 51.22%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a detailed description of the participants. Among these participants, 3,452 had chronic kidney disease, accounting for 14.73%. There were 17,221 individuals with no missing teeth in both the upper and lower jaws, accounting for 88.64%; 1,248 individuals had complete edentulousness in both jaws, accounting for 6.42%; 890 individuals had missing teeth only in the upper jaw, accounting for 4.58%; and 68 individuals had missing teeth only in the lower jaw, accounting for 0.35%. There were significant distinctions in age, sex, gender, race, race and race of covariates among groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while there were no significant distinctions in gender of covariates (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of basic characteristics and differences (mean error and standard error SE, proportion n and percentage %)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;19427)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eno\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;17221)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emaxillary dentition missing(n\u0026thinsp;=\u0026thinsp;890)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003emandibular teeth missing(n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ecomplete dentition missing(n\u0026thinsp;=\u0026thinsp;1248)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Mean (SE)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47.35 (0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45.65 (0.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.62 (0.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e66.97 (1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e66.51 (0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9564 (48.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8490 (49.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e411 (43.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39 (55.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e624 (46.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9863 (51.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8731 (50.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e479 (56.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e29 (44.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e624 (53.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2908 (8.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2751 (9.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74 (3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6 (5.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77 (2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4601 (13.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4194 (13.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e164 (10.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (14.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e227 (11.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7811 (66.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6723 (66.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e412 (72.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30 (63.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e646 (73.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4107 (10.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3553 (10.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e240 (13.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (16.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e298 (12.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebelow college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9438 (38.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7894 (36.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e582 (61.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53 (80.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e909 (68.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecollege Graduate or\u003c/p\u003e \u003cp\u003eabove\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9989 (61.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9327 (63.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e308 (38.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15 (19.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e339 (31.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elive together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12108 (65.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11024 (66.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e478 (57.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (53.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e566 (48.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eseparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7319 (34.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6197 (33.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e412 (42.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28 (46.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e682 (51.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11526 (63.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10858 (65.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e282 (34.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25 (35.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e361 (33.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7882 (36.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6348 (34.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e605 (65.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43 (64.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e886 (66.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes,n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15570 (85.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14257 (87.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e548 (70.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40 (62.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e725 (64.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3564 (14.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2726 (12.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e318 (29.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27 (37.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e493 (35.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD, n(%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15975 (85.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14649 (87.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e570 (68.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42 (59.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e714 (62.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3452 (14.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2572 (12.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e320 (31.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26 (40.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e534 (37.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The correlation between edentulous jaws and chronic kidney disease\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, there was a significant positive correlation between edentulous jaws and CKD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the univariate analysis, the risk of CKD in patients with maxillary tooth loss was 206% higher than that in patients without tooth loss, while the risk in patients with mandibular tooth loss was 372% higher than that in patients without tooth loss. Additionally, the risk of CKD in patients with both maxillary and mandibular tooth loss is 310% higher than that in patients without tooth loss. Therefore, a multivariate logistic regression model was conducted to further investigate the correlation between CKD and edentulous jaws. After adjusting for covariates such as age, gender, race, and marital status (Model 2), maxillary tooth loss was positively correlated with CKD (OR\u0026thinsp;=\u0026thinsp;1.35, 95% CI\u0026thinsp;=\u0026thinsp;1.07\u0026thinsp;~\u0026thinsp;1.69, P\u0026thinsp;=\u0026thinsp;0.014), and mandibular tooth loss was significantly associated with CKD (OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI\u0026thinsp;=\u0026thinsp;1.07\u0026thinsp;~\u0026thinsp;1.69, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for all covariates (Model 3), the results of Model 3 also suggest this positive correlation, with maxillary tooth loss associated with CKD (OR\u0026thinsp;=\u0026thinsp;1.21, 95% CI\u0026thinsp;=\u0026thinsp;0.96\u0026thinsp;~\u0026thinsp;1.54), mandibular tooth loss associated with CKD (OR\u0026thinsp;=\u0026thinsp;1.60, 95% CI\u0026thinsp;=\u0026thinsp;0.79\u0026thinsp;~\u0026thinsp;3.25), and a significant correlation between both maxillary and mandibular tooth loss and CKD (OR\u0026thinsp;=\u0026thinsp;1.39, 95% CI\u0026thinsp;=\u0026thinsp;1.17\u0026thinsp;~\u0026thinsp;1.66).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression,multi-model strategy\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEdentulous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaxillary dentition missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.06 (2.49\u0026thinsp;~\u0026thinsp;3.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.35 (1.07\u0026thinsp;~\u0026thinsp;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.21 (0.96\u0026thinsp;~\u0026thinsp;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emandibular teeth missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.72 (2.34\u0026thinsp;~\u0026thinsp;9.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.82 (0.92\u0026thinsp;~\u0026thinsp;3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.60 (0.79\u0026thinsp;~\u0026thinsp;3.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecomplete dentition missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.10 (3.40\u0026thinsp;~\u0026thinsp;4.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.60 (1.34\u0026thinsp;~\u0026thinsp;1.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39 (1.17\u0026thinsp;~\u0026thinsp;1.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eOR: Odds Ratio, CI: Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eModel1: Crude\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eModel2: Adjust: sex, age, race, marital status, education\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eModel3: Adjust: sex, age, race, marital status, education, hypertension, diabetes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Subgroup Analysis of the Association between Edentulism and Chronic Kidney Disease\u003c/h2\u003e \u003cp\u003eTo determine if the link between toothlessness and CKD holds steady throughout the broader populace, we carried out subgroup examinations and interaction assessments factoring in age, sex, racial background, cultural aspects, marital conditions, blood pressure issues, and diabetic status. Our results suggest that this connection isn't constant across all groups. The detailed subgroup analysis exploring the tie between tooth loss and CKD is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Once we accounted for a range of influencing factors, a notable link between toothlessness and CKD emerged (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Interestingly, the positive tie between missing teeth and CKD isn't swayed by variables such as age, sex, race, cultural nuances, marital situations, high blood pressure, or diabetes. Moreover, no significant interactions were detected, as all interaction P values were above 0.05.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The Relationship Between Edentulousness and Mortality Rate\u003c/h2\u003e \u003cp\u003eAmong the follow-up of 19,427 individuals, there were 1,579 cases (8.13%) of all-cause mortality, of which 865 individuals (54.78%) died from CKD. The results of the univariate and multivariate Cox proportional hazards models were shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicating that age, gender, marital status, hypertension, diabetes, edentulousness, and CKD are risk factors for mortality (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The mortality risk was distinctly increased in older individuals, males, those who are separated, and those with hypertension, diabetes, edentulousness, and CKD. The hazard ratio (HR) for mortality associated with edentulousness was as high as 1.85 (1.57\u0026ndash;2.17), while the HR for CKD was 2.23 (1.98\u0026ndash;2.51).\u003c/p\u003e \u003cp\u003eIn the fully adjusted model presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, participants with maxillary edentulousness had a significantly increased mortality rate due to all causes by 61% (HR\u0026thinsp;=\u0026thinsp;1.61, 95% CI\u0026thinsp;=\u0026thinsp;1.30\u0026ndash;1.98, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to those without edentulousness, and the mortality rate due to CKD also significantly increased by 45% (HR\u0026thinsp;=\u0026thinsp;1.45, 95% CI\u0026thinsp;=\u0026thinsp;1.11\u0026ndash;1.90, P\u0026thinsp;=\u0026thinsp;0.007); participants with both maxillary and mandibular edentulousness had a significantly increased all-cause mortality rate by 102% (HR\u0026thinsp;=\u0026thinsp;2.02, 95% CI\u0026thinsp;=\u0026thinsp;1.73\u0026ndash;2.35, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and the CKD mortality rate also significantly increased by 69% (HR\u0026thinsp;=\u0026thinsp;1.69, 95% CI\u0026thinsp;=\u0026thinsp;1.33\u0026ndash;2.14, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the Kaplan-Meier survival analysis, indicating that older age, male gender, complete edentulousness, and the presence of CKD were associated with the highest all-cause mortality rate (log-rank P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, Results of the univariate and multivariate Cox proportional hazards models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09 (1.09\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.07 (1.06\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-3.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.82 (0.74\u0026ndash;0.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-7.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.61 (0.54\u0026ndash;0.69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.04 (0.75\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.79 (0.60\u0026ndash;1.04)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.27 (1.71\u0026ndash;3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.20 (0.94\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.81 (1.34\u0026ndash;2.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.99 (0.76\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elive together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eseparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.92 (1.69\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.65 (1.42\u0026ndash;1.91)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebelow college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62 (0.53\u0026ndash;0.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.88 (0.76\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.45 (3.87\u0026ndash;5.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.29 (1.11\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.42 (3.00\u0026ndash;3.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.26 (1.08\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEdentulous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaxillary dentition missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.59 (3.75\u0026ndash;5.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.53 (1.23\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emandibular teeth missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.49 (3.87\u0026ndash;10.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.52 (1.05\u0026ndash;2.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecomplete dentition missing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.14 (6.21\u0026ndash;8.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.85 (1.57\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.77 (6.13\u0026ndash;7.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.23 (1.98\u0026ndash;2.51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, HRs (95% CIs) for mortality according to the edentulous index\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdentulous\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003emaxillary dentition missing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003emandibular teeth missing\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecomplete dentition missing\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95%CI) \u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAll-cause mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.59 (3.75 5.62)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.49 (3.87\u0026ndash;10.88)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.14 (6.21\u0026ndash;8.22)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77 (1.44\u0026ndash;2.16)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.91 (1.23\u0026ndash;2.97)0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.33 (2.01\u0026ndash;2.70)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61 (1.30\u0026ndash;1.98)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.70 (1.13\u0026ndash;2.56)0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.02 (1.73\u0026ndash;2.35)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCKD mortality\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.59 (2.04\u0026ndash;3.30)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.18 (1.41\u0026ndash;7.15)0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.25 (2.56\u0026ndash;4.12)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.48 (1.15\u0026ndash;1.91)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.88 (0.93\u0026ndash;3.80)0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.73 (1.38\u0026ndash;2.18)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00(Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45 (1.11\u0026ndash;1.90)0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.78 (0.88\u0026ndash;3.59)0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.69 (1.33\u0026ndash;2.14)\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eHR: Hazard Ratio, CI: Confidence Interval\u003c/p\u003e \u003cp\u003eModel1: Crude\u003c/p\u003e \u003cp\u003eModel2: Adjust: Sex, Age\u003c/p\u003e \u003cp\u003eModel3: Adjust: Sex, Age, Race, Education, Marital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Interaction Test of Edentulousness and Mortality Rate\u003c/h2\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, among all patients, the HR for edentulousness and all-cause mortality was 1.34, with a 95% CI of 1.27 to 1.41, and a P\u0026lt;0.001, indicating a significant connection between edentulousness and mortality due to all-cause, with a markedly increased risk of mortality by all causes in edentulous patients. In age stratification, the P-value for the interaction test was less than 0.001, suggesting a significant interaction of age on the connection between edentulousness and all-cause mortality, with notable risk differences across different age groups. In racial stratification, the P-value for the interaction test was 0.001, indicating a significant interaction of race on the relationship between edentulousness and all-cause mortality, with significant risk differences among different racial groups. In stratifications for hypertension and diabetes, the interaction test P-values were \u0026lt;\u0026thinsp;0.001 and 0.015, respectively, indicating significant interactions of hypertension and diabetes status on the connection between edentulousness and all-cause mortality, with a more pronounced risk in the group without hypertension and diabetes. This suggested that the impact of different population characteristics on the relationship between edentulousness and mortality by all causes varies.\u003c/p\u003e \u003cp\u003eIn Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, among all patients, the HR for edentulousness and CKD mortality was 1.25, with a 95% CI of 1.15 to 1.35, and a P\u0026lt;0.001, indicating a significant connection between edentulousness and CKD mortality, with a markedly increased risk of CKD mortality in edentulous patients. The above results remained consistent across different subgroups, demonstrating a positive correlation between edentulousness and both types of mortality.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, Stratified analyses of the connections between edentulous and Mortality due to all causes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19427 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.27\u0026thinsp;~\u0026thinsp;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9564 (49.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.18\u0026thinsp;~\u0026thinsp;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9863 (50.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.41 (1.31\u0026thinsp;~\u0026thinsp;1.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13335 (68.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67 (1.43\u0026thinsp;~\u0026thinsp;1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6092 (31.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.31 (1.24\u0026thinsp;~\u0026thinsp;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003erace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2908 (14.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.01\u0026thinsp;~\u0026thinsp;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4601 (23.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.67 (1.45\u0026thinsp;~\u0026thinsp;1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7811 (40.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.26\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4107 (21.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19 (1.07\u0026thinsp;~\u0026thinsp;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebelow college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9438 (48.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.32 (1.25\u0026thinsp;~\u0026thinsp;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9989 (51.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.28\u0026thinsp;~\u0026thinsp;1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elive together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12108 (62.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.42 (1.29\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eseparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7319 (37.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (1.19\u0026thinsp;~\u0026thinsp;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11526 (59.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.58 (1.38\u0026thinsp;~\u0026thinsp;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7882 (40.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.21\u0026thinsp;~\u0026thinsp;1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15570 (81.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.39 (1.30\u0026thinsp;~\u0026thinsp;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3564 (18.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.28 (1.17\u0026thinsp;~\u0026thinsp;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHR: Hazard Ratio,\u003c/p\u003e \u003cp\u003eCI: Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e, Stratified analyses of the connections between edentulous and CKD mortality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP for interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3096 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.15\u0026thinsp;~\u0026thinsp;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003esex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1321 (42.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27 (1.12\u0026thinsp;~\u0026thinsp;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1775 (57.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.11\u0026thinsp;~\u0026thinsp;1.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eage\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1157 (37.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.43 (0.98\u0026thinsp;~\u0026thinsp;2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1939 (62.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.23 (1.13\u0026thinsp;~\u0026thinsp;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003erace\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400 (12.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (0.96\u0026thinsp;~\u0026thinsp;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1285 (41.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.25 (1.13\u0026thinsp;~\u0026thinsp;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e835 (26.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.18 (1.04\u0026thinsp;~\u0026thinsp;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e576 (18.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44 (1.15\u0026thinsp;~\u0026thinsp;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ebelow college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1746 (56.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20 (1.08\u0026thinsp;~\u0026thinsp;1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecollege Graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1350 (43.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.34 (1.21\u0026thinsp;~\u0026thinsp;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elive together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1668 (53.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.19\u0026thinsp;~\u0026thinsp;1.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eseparation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1428 (46.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.05\u0026thinsp;~\u0026thinsp;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypertension\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e908 (29.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.36 (1.15\u0026thinsp;~\u0026thinsp;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2186 (70.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.22 (1.11\u0026thinsp;~\u0026thinsp;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDiabetes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1794 (59.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (1.11\u0026thinsp;~\u0026thinsp;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1244 (40.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.24 (1.11\u0026thinsp;~\u0026thinsp;1.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eHR: Hazard Ratio, CI: Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4, Discussion","content":"\u003cp\u003eThis study utilized a large population sample from the NHANES conducted between 2009 and 2020 to explore the relationship between edentulism, CKD, and mortality. It revealed that edentulism was not only a risk factor for increased CKD prevalence but was also closely connected with a significant rise in all-cause mortality and CKD mortality. Compared to previous studies, this research had made beneficial expansions and supplements in several key aspects, further highlighting its significance.\u003c/p\u003e\n\u003ch3\u003e1. Supplementation and Expansion of Research Results\u003c/h3\u003e\n\u003cp\u003ePrevious research had primarily focused on the qualitative connection between edentulism and CKD [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In contrast, this study employed multivariable-adjusted logistic regression and Cox regression models to accurately quantify the increased risks of CKD prevalence, all-cause mortality, and CKD mortality among different types of edentulous patients. For instance, the CKD risk among participants with complete edentulism rose by 39% (OR 1.39, 95% CI = 1.17 ~ 1.66, P\u0026lt;0.001), while the all-cause mortality of participants with both upper and lower jaw edentulism significantly rose by 102% (HR 2.02, 95% CI = 1.73–2.35, P\u0026lt;0.001), and CKD mortality also significantly rose by 69% (HR 1.69, 95% CI = 1.33–2.14, P\u0026lt;0.001). This quantitative analysis provides a more intuitive and precise basis for clinical risk assessment, aiding physicians in formulating personalized prevention and treatment plans based on patients' specific dental loss conditions. Compared to previous studies, this research has made solid strides in the quantification of risk.\u003c/p\u003e \u003cp\u003eThis study further conducted multidimensional subgroup analyses according to age, gender, race, education condition, marital condition, diabetes, and hypertension history, finding that the positive correlation between edentulism and CKD is not influenced by these confounding factors and remains consistent across different subgroups. This is more comprehensive than some previous researchs that only focused on the correction of a single or a few confounding factors, revealing the universality and stability of the association between edentulism and CKD, thereby enhancing the reliability and generalizability of the research conclusions [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For example, in age stratification, the interaction test P-value was less than 0.001, indicating a significant interaction effect of age on the relationship between edentulism and mortality from all causes, with significant risk differences across different age groups, providing a scientific basis for differentiated oral health and kidney disease prevention strategies targeting different age populations, thus addressing the shortcomings of previous studies in the depth of subgroup analysis.\u003c/p\u003e \u003cp\u003eUtilizing Kaplan-Meier survival analysis, this study visually demonstrated the dynamic association between the degree of tooth loss and CKD and mortality, making the research results easier to understand and disseminate. The survival curves clearly showed that older age, male gender, complete edentulism, and the presence of CKD were associated with the highest all-cause mortality (log-rank p \u0026lt; 0.001). This visualization method is relatively rare in previous studies and helps clinicians, public health workers, and patients themselves to more intuitively recognize the impact of edentulism on survival prognosis[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], thereby increasing the emphasis on oral health and kidney disease prevention and treatment. Compared to previous studies, this research presents results in a more innovative and practical manner.\u003c/p\u003e\n\u003ch3\u003e2. Highlighting the Significance of the Research\u003c/h3\u003e\n\u003cp\u003ePrecision of Public Health Strategies: The results of this study offered strong support for the formulation of more precise and effective oral health and kidney disease prevention and control strategies in the public health field. Based on the strong association between edentulism, CKD, and mortality, public health departments can combine oral health examinations with kidney disease screenings, conducting joint screening programs for high-risk populations (such as the elderly and edentulous patients) to achieve early detection and early intervention[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. At the same time, by strengthening oral health education and raising public awareness of dental care, the incidence of edentulism can be reduced from the source, thereby lowering the prevalence and mortality of CKD, optimizing the allocation of public health resources, and perfecting the overall health level and life expectancy of the population. Compared to previous studies, this research provides more operational strategy recommendations for public health practice.\u003c/p\u003e \u003cp\u003eSynergy in Clinical Diagnosis and Treatment Models: In clinical practice, this study encourages physicians to shift their diagnostic and treatment thinking, incorporating oral health as an important component of physical health assessment for patients. During patient consultations, physicians should not only focus on oral symptoms but also be vigilant about the potential kidney disease risks that may be hidden behind edentulism, achieving collaborative diagnosis and treatment among multiple disciplines such as dentistry and nephrology[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. For example, when providing oral restoration or treatment for edentulous patients, simultaneous kidney function monitoring can be conducted to timely identify potential kidney issues and intervene; in the management of kidney disease patients, attention should be paid to oral health status, and targeted oral care measures should be implemented to improve patients' quality of life and prognosis. Compared to previous studies, this research provides stronger theoretical support for the transformation of clinical diagnosis and treatment models.\u003c/p\u003e \u003cp\u003eDiversification of Scientific Research Directions: This study offers new ideas and directions for further exploring the potential mechanisms linking edentulism with CKD and mortality. Factors such as chewing dysfunction, inadequate nutritional intake, and oral microbiome dysbiosis caused by edentulism may affect kidney health through various pathways, such as triggering chronic inflammatory responses, promoting oxidative stress, and interfering with endocrine metabolism[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Future research can build on this foundation to further explore these potential mechanisms from multiple perspectives, including molecular biology, cell biology, and immunology, laying the foundation for the developing new diagnostic markers, therapeutic targets, and intervention methods, and promoting the interdisciplinary integration and development of related fields. Compared to previous studies, this research expands the scope for scientific inquiry.\u003c/p\u003e \u003cp\u003eTransformation of Social Cognition and Health Concepts: At the societal level, the benefits of this study help to break the traditional cognitive limitations of the public regarding oral health, allowing people to fully recognize the close connection and mutual influence between oral health and overall health[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This will stimulate various sectors of society to pay more attention to oral health, encouraging individuals to develop good oral hygiene habits, such as regular brushing, using dental floss, and undergoing regular oral check-ups. At the same time, it will also promote social forces to actively participate in oral health promotion activities, such as corporate sponsorship of oral health education programs and communities offering free oral screening services, creating a positive atmosphere for society to focus on oral health and maintain overall health, thereby enhancing the health literacy of the entire population. Compared to previous studies, this research has more far-reaching significance in terms of social impact.\u003c/p\u003e\n\u003ch3\u003e3. Limitations and Directions for Improvement of the Research\u003c/h3\u003e\n\u003cp\u003eLimitations of Causal Relationships: Although this study has attempted to control confounding factors as much as possible through multivariable adjustments and other statistical methods, the analysis design based on cross-sectional data still cannot fully establish causal relationships between edentulism, CKD, and mortality. Future research could use more rigorous designs such as prospective studies or randomized controlled trials to conduct long-range follow-up observations of the occurrence and development of CKD and mortality events in edentulous patients, to more accurately reveal causal links and provide a more solid basis for clinical interventions.\u003c/p\u003e \u003cp\u003eOmission of Potential Confounding Factors: Among the covariates included, although common variables for example age, gender, race/ethnicity, cultural difference, marital condition, diabetes, and hypertension history were considered, there may still be some unaccounted potential confounding variables, for example patients' dietary habits, smoking and drinking history, and oral hygiene behaviors, which may have some impact on the research results. Subsequent studies should further refine the selection of covariates and comprehensively collect relevant information to reduce confounding bias and improve the accuracy of research conclusions.\u003c/p\u003e \u003cp\u003eRepresentativeness and Generalizability of Data: Although NHANES data is nationally representative, it is primarily based on the U.S. population, which may somewhat limit the extrapolation of the research results to other countries or regions. Differences in racial composition, lifestyle, and healthcare systems across different countries and regions may all influence the relationship between edentulism, CKD, and mortality. Future research could consider conducting multicenter studies in different countries or regions, incorporating more diverse populations to validate the generalizability of the findings of this study and provide broader references for public health policy formulation and clinical practice on a global scale.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eEdentlessness may be a risk factor for increased prevalence of CKD and may contribute to all-cause and CKD-specific mortality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are publicly available from the National Health and Nutrition Examination Survey (NHANES) through the Centers for Disease Control and Prevention (CDC) website. The data files and related documentation can be accessed at the NHANES website (https://www.cdc.gov/nchs/nhanes/index.html) .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone of the authors have any relevant financial relationship(s) with a commercial interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFund project:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2023 Jiangxi Province Traditional Chinese Medicine Science and technology project- Mechanism of astragaloside regulating osteogenic differentiation of bone marrow mesenchymal stem cells via SP1/microRNA-107/DKK1 axis(2023Z030).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHuaxiang Jiang:Conceptualization and original draft；Liping Yin:Writing-review＆editing and Data curation; Zihao Chen : Methodology and Investigation,; Zishun Qin：Project administration and Funding acquisition.Le Gan:Regulation Mapping and Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the National Health and Nutrition Examination Survey (NHANES) for providing the data used in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthors:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuaxiang Jiang\u003csup\u003ea,1\u003c/sup\u003e, M.M., Resident, [email protected]\u003c/p\u003e\n\u003cp\u003eLiping Yin\u003csup\u003ea,1\u003c/sup\u003e, M.M., Resident, [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZihao Chen\u003csup\u003ea\u003c/sup\u003e, M.M., Resident, [email protected] \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZishun Qin\u003csup\u003ea,b,\u003c/sup\u003e*,DDS, Ph.D.Professor, [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLe Gan\u003csup\u003ec,\u003c/sup\u003e*,[email protected]\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eDepartment of Stomatology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Jiangxi,330000, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eSchool/Hospital of Stomatology, Lanzhou University, Lanzhou 730000, China\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Jinxi County Hospital of Traditional Chinese Medicine\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eThis authors contributed to this work equally.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*Correspondence Author:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLe Gan\u003csup\u003ec,\u003c/sup\u003e*,[email protected]\u003c/p\u003e\n\u003cp\u003eZishun Qin\u003csup\u003ea,*\u003c/sup\u003e,DDS, Ph.D.Professor, [email protected]\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhao WM, Li XL, Shi R, et al. Global, regional and national burden of CKD in children and adolescents from 1990 to 2019. Nephrol Dial Transplant. 2024;39(8):1268-1278. doi:10.1093/ndt/gfad269\u003c/li\u003e\n\u003cli\u003eWang L, Xu X, Zhang M, et al. Prevalence of Chronic Kidney Disease in China: Results From the Sixth China Chronic Disease and Risk Factor Surveillance. JAMA Intern Med. 2023;183(4):298-310. doi:10.1001/jamainternmed.2022.6817\u003c/li\u003e\n\u003cli\u003eGBD Chronic Kidney Disease Collaboration. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395(10225):709-733. doi:10.1016/S0140-6736(20)30045-3\u003c/li\u003e\n\u003cli\u003eYing M, Shao X, Qin H, et al. Disease Burden and Epidemiological Trends of Chronic Kidney Disease at the Global, Regional, National Levels from 1990 to 2019. Nephron. 2024;148(2):113-123. doi:10.1159/000534071\u003c/li\u003e\n\u003cli\u003eEvenepoel P, Stenvinkel P, Shanahan C, Pacifici R. Inflammation and gut dysbiosis as drivers of CKD-MBD. Nat Rev Nephrol. 2023;19(10):646-657. doi:10.1038/s41581-023-00736-7\u003c/li\u003e\n\u003cli\u003eLevin A, Stevens PE. Early detection of CKD: the benefits, limitations and effects on prognosis. Nat Rev Nephrol. 2011;7(8):446-457. Published 2011 Jun 28. doi:10.1038/nrneph.2011.86\u003c/li\u003e\n\u003cli\u003eLevey AS, Coresh J. Chronic kidney disease. Lancet. 2012;379(9811):165-180. doi:10.1016/S0140-6736(11)60178-5\u003c/li\u003e\n\u003cli\u003eSumayin Ngamdu K, Mallawaarachchi I, Dunipace EA, et al. Association Between Periodontal Disease and Cardiovascular Disease (from the NHANES). Am J Cardiol. 2022;178:163-168. doi:10.1016/j.amjcard.2022.05.028\u003c/li\u003e\n\u003cli\u003eLiccardo D, Cannavo A, Spagnuolo G, et al. Periodontal Disease: A Risk Factor for Diabetes and Cardiovascular Disease. Int J Mol Sci. 2019;20(6):1414. Published 2019 Mar 20. doi:10.3390/ijms20061414\u003c/li\u003e\n\u003cli\u003eChatzopoulos GS, Jiang Z, Marka N, Wolff LF. Periodontal Disease, Tooth Loss, and Systemic Conditions: An Exploratory Study. Int Dent J. 2024;74(2):207-215. doi:10.1016/j.identj.2023.08.002\u003c/li\u003e\n\u003cli\u003eYeung AWK, Leung WK. Functional Neuroplasticity of Adults with Partial or Complete Denture Rehabilitation with or without Implants: Evidence from fMRI Studies. Nutrients. 2023;15(7):1577. Published 2023 Mar 24. doi:10.3390/nu15071577\u003c/li\u003e\n\u003cli\u003eLu EM. The role of vitamin D in periodontal health and disease. J Periodontal Res. 2023;58(2):213-224. doi:10.1111/jre.13083\u003c/li\u003e\n\u003cli\u003eKostunov J, Menzel R, Bermejo JL, Rammelsberg P, Giannakopoulos NN, Kappel S. Immediate loading of dental implants in edentulous mandibles using Locator attachments or Dolder bars: A 9-year prospective randomized clinical study. J Clin Periodontol. 2023;50(11):1530-1538. doi:10.1111/jcpe.13857\u003c/li\u003e\n\u003cli\u003eTran F, Schirmer JH, Ratjen I, et al. Patient Reported Outcomes in Chronic Inflammatory Diseases: Current State, Limitations and Perspectives. Front Immunol. 2021;12:614653. Published 2021 Mar 18. doi:10.3389/fimmu.2021.614653\u003c/li\u003e\n\u003cli\u003eD\u0026apos;Aiuto F, Parkar M, Andreou G, et al. Periodontitis and systemic inflammation: control of the local infection is associated with a reduction in serum inflammatory markers. J Dent Res. 2004;83(2):156-160. doi:10.1177/154405910408300214\u003c/li\u003e\n\u003cli\u003eLevey AS, Stevens LA, Schmid CH, et al. A new equation to estimate glomerular filtration rate [published correction appears in Ann Intern Med. 2011 Sep 20;155(6):408]. Ann Intern Med. 2009;150(9):604-612. doi:10.7326/0003-4819-150-9-200905050-00006\u003c/li\u003e\n\u003cli\u003eSun DQ, Jin Y, Wang TY, et al. MAFLD and risk of CKD. Metabolism. 2021;115:154433. doi:10.1016/j.metabol.2020.154433\u003c/li\u003e\n\u003cli\u003eDelbove T, Gueyffier F, Juillard L, et al. Effect of periodontal treatment on the glomerular filtration rate, reduction of inflammatory markers and mortality in patients with chronic kidney disease: A systematic review. PLoS One. 2021;16(1):e0245619. Published 2021 Jan 22. doi:10.1371/journal.pone.0245619\u003c/li\u003e\n\u003cli\u003eIoannidou E, Hall Y, Swede H, Himmelfarb J. Periodontitis associated with chronic kidney disease among Mexican Americans. J Public Health Dent. 2013;73(2):112-119. doi:10.1111/j.1752-7325.2012.00350.x\u003c/li\u003e\n\u003cli\u003eSharma P, Dietrich T, Ferro CJ, Cockwell P, Chapple IL. Association between periodontitis and mortality in stages 3-5 chronic kidney disease: NHANES III and linked mortality study. J Clin Periodontol. 2016;43(2):104-113. doi:10.1111/jcpe.12502\u003c/li\u003e\n\u003cli\u003eFujimoto P, Wong KA, Kataoka-Yahiro M. Behind the Smile: Detecting Chronic Kidney Disease Through Oral Health Screenings. Hawaii J Health Soc Welf. 2024;83(9):260-262. doi:10.62547/VYCO2960\u003c/li\u003e\n\u003cli\u003eSezer B, Kaya R, Kodaman Dokumacıgil N, et al. Assessment of the oral health status of children with chronic kidney disease. Pediatr Nephrol. 2023;38(1):269-277. doi:10.1007/s00467-022-05590-6\u003c/li\u003e\n\u003cli\u003eKinane DF, Stathopoulou PG, Papapanou PN. Periodontal diseases. Nat Rev Dis Primers. 2017;3:17038. Published 2017 Jun 22. doi:10.1038/nrdp.2017.38\u003c/li\u003e\n\u003cli\u003eŽmavc JB, Verdenik M, Skomina Z, Ihan Hren N. Tooth Loss and Systemic Diseases in the Slovenian Elderly Population: A Cross-Sectional Study of the Associaton Between Oral and Systemic Health. Zdr Varst. 2024;63(3):142-151. Published 2024 Jun 14. doi:10.2478/sjph-2024-0019\u003c/li\u003e\n\u003cli\u003eIssrani R, Reddy J, Dabah THE, et al. 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Published 2022 Nov 15. doi:10.3390/diagnostics12112800\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"NHANES, edentulism, CKD, relevance, mortality","lastPublishedDoi":"10.21203/rs.3.rs-6548683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6548683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Limited research has examined the connection between edentulous jaws and chronic kidney disease (CKD), as well as their implications for mortality rates. This study aims to elucidate the correlation between edentulous jaws and CKD, in addition to exploring the connections between all-cause mortality and CKD mortality in this patient population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u0026nbsp;\u003c/strong\u003eThe study analyzed 19,427 patients with varying degrees of tooth loss from the National Health and Nutrition Examination Survey (NHANES) from 2009 to 2020. The endpoints were the mortality by all causes and CKD, determined through the National Death Index (NDI). CKD was calculated based on the eGFR and urinary albumin-to-creatinine ratio. A Logistic regression classification model and interaction test were used to determine the connection between edentulousness and CKD. Kaplan-Meier survival analysis, multivariable Cox regression survival models, and stratified analyses were used to explore the correlation between edentulousness and mortality risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u0026nbsp;\u003c/strong\u003eDuring a follow-up period encompassing 19,427 persons, a total of 1,579 cases of all-cause mortality were recorded, representing an incidence rate of 8.13%. Among these, 865 cases, accounting for 54.78% of the total mortality, were attributed to chronic kidney disease (CKD). After multivariable-adjusted logistic regression analysis, it was found that the risk of CKD increased by 39% among participants with complete edentulism (OR 1.39, 95% CI 1.17 ~ 1.66, P\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003eFollowing multivariable-adjusted Cox regression models, a significant connection was identified between edentulism and the mortality by CKD or all causes. Compared to participants without tooth loss, those with maxillary tooth loss had a significantly increased all-cause mortality rate of 61% (HR 1.61, 95% CI 1.30 ~ 1.98, p \u0026lt; 0.001), and CKD mortality rate was also significantly elevated by 45% (HR 1.45, 95% CI 1.11~1.90, p=0.007); participants with both maxillary and mandibular tooth loss exhibited a significant increase in all-cause mortality rate of 102% (HR 2.02, 95% CI 1.73 ~ 2.35, p\u0026lt;0.001), and CKD mortality rate was significantly increased by 69% (HR 1.69, 95% CI 1.33 ~ 2.14, p\u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe complete loss of both maxillary and mandibular dentition not only increases the prevalence of CKD but also elevates the all-cause mortality rate and the mortality rate associated with CKD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number: \u003c/strong\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"The relationship among edentulism, chronic kidney disease and mortality: Results from the NHANES study(2009-2020)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-09 11:22:25","doi":"10.21203/rs.3.rs-6548683/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-27T07:14:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T02:44:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-22T16:36:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36443598676452914085182585160266027254","date":"2025-06-16T14:49:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106714259899990641294369616268079112722","date":"2025-06-16T12:48:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78812786002781073853265254074066003408","date":"2025-06-16T08:13:18+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-03T17:12:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-13T10:13:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-13T10:12:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Oral Health","date":"2025-04-28T14:02:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-oral-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ohea","sideBox":"Learn more about [BMC Oral Health](http://bmcoralhealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/ohea/default.aspx","title":"BMC Oral Health","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b28c73cd-eca0-4120-a805-90e98402f053","owner":[],"postedDate":"June 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-08T16:01:41+00:00","versionOfRecord":{"articleIdentity":"rs-6548683","link":"https://doi.org/10.1186/s12903-025-07166-w","journal":{"identity":"bmc-oral-health","isVorOnly":false,"title":"BMC Oral Health"},"publishedOn":"2025-12-01 15:57:43","publishedOnDateReadable":"December 1st, 2025"},"versionCreatedAt":"2025-06-09 11:22:25","video":"","vorDoi":"10.1186/s12903-025-07166-w","vorDoiUrl":"https://doi.org/10.1186/s12903-025-07166-w","workflowStages":[]},"version":"v1","identity":"rs-6548683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6548683","identity":"rs-6548683","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}