Dental status and cause-specific mortality in older adults

Dental status and cause-specific mortality in older adults | 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 Article Dental status and cause-specific mortality in older adults Faiz Abdurrahman, Taro Kusama, Yudai Tamada, Sakura Kiuchi, Masashige Saito, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6962054/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Nov, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Poor oral health, particularly tooth loss, is associated with increased risk of all-cause mortality. However, the impact of dental prosthesis use on these associations remains unclear. This study evaluated the relationship between the number of remaining teeth, dental prosthesis use, and mortality from specific causes using seven-year follow-up data from the Japan Gerontological Evaluation Study (JAGES). All-cause mortality and ten cause-specific mortality outcomes were assessed. The number of remaining teeth and use of dental prostheses were the main exposures. Cox proportional hazard models were used to estimate hazard ratios (HRs). Among 43,774 participants (53.2% women; mean age, 73.7 years [SD, 5.9]), 13.0% (n = 5,707) died during the follow-up period, yielding a mortality rate of 20.7 per 1,000 person-years. Participants with 0–9 or 10–19 teeth without dental prostheses exhibited higher cause-specific mortality rates than those with ≥ 20 teeth. Specifically, those with 0–9 teeth without dental prostheses had a significantly elevated risk of mortality from cancer, cardiovascular disease, respiratory disease, and injury (HRs ranging from 1.31 to 1.91; all p < 0.05). These associations were attenuated among dental prostheses users. Tooth loss was associated with increased risk of mortality from multiple specific causes; however, dental prostheses may mitigate this risk. Health sciences/Diseases/Oral diseases Health sciences/Health care/Dentistry/Dental public health Health sciences/Medical research/Epidemiology Health sciences/Health care/Geriatrics Health sciences/Health care/Dentistry/Prosthetic dentistry dentures cause of death oral health longitudinal studies periodontal disease dental caries Figures Figure 1 Introduction Oral diseases rank among the most prevalent globally. Approximately 29.4% of the global population (2.3 billion people) have untreated caries in permanent teeth, and 9.8% (0.8 billion people) have severe periodontitis 1 . Untreated caries and periodontal diseases can lead to tooth loss 2 , which is associated with various chronic diseases and subsequent mortality 3 . Therefore, tooth loss represents a critical health issue, particularly in older adults, due to its irreversible nature and cumulative impact over time 2 . Existing literature suggests a relationship between tooth loss and all-cause mortality 4 . However, few studies have examined specific causes of death. Understanding the specific causes of mortality associated with tooth loss could facilitate the identification of high-risk populations and guide targeted prevention strategies. Previous studies have examined associations between tooth loss and mortality from cardiovascular disease, respiratory disease, and lung cancer 5 – 7 . However, a comprehensive analysis of the associations between tooth loss and various cause-specific mortality outcomes has not been conducted. In addition, the use of dental prostheses is known to mitigate the adverse health effects of tooth loss 8 , yet few studies have investigated its impact on cause-specific mortality. This study evaluated the associations among the number of remaining teeth, dental prosthesis use, and cause-specific mortality from various diseases and disorders. We hypothesized that the influence of tooth loss and the use of dental prostheses on cause-specific mortality would vary by cause of death, reflecting the potential involvement of different mechanisms across diseases. Methods Study design and participants This cohort study analyzed seven-year follow-up data from the Japan Gerontological Evaluation Study (JAGES), an ongoing longitudinal study established in 2010 to investigate health-related factors among adults aged ≥ 65 years and older in Japan 9 . The 2010 wave (conducted from August 2010 to December 2011) served as the baseline. Cause-of-death data were linked through December 2017 using sex, date of birth, and date of death as key variables 10 . These data were obtained from secondary sources managed by the Ministry of Health, Labor, and Welfare of Japan. Of the 46,144 participants from nine municipalities, 6,383 died during follow-up, with cause-of-death available for 6,312 participants (combined rate: 98.9%). Participants with invalid weight or height measurements (n = 340) or those dependent in daily living activities at baseline (n = 2,030) were excluded, resulting in 43,774 participants included in the analysis (Fig. 1 ). Outcome Variables Outcome Variables All-cause and cause-specific mortality were assessed as outcomes. Cause-specific mortality categories were based on a previous study 5 and included deaths from cancer (International Classification of Diseases, Tenth Revision [ICD-10] codes C00–D48), cardiovascular disease (ICD-10 codes I00–I99), and respiratory disease (ICD-10 codes J00–J99). To comprehensively assess the effects of tooth loss across various causes of death, additional causes were included: infectious and parasitic disease (ICD-10 codes A00–B99); endocrine, nutritional, and metabolic disease (ICD-10 codes E00–E90); mental and behavioral disorder (ICD-10 codes F00–F99); nervous disease (ICD-10 codes G00–G99); digestive disease (ICD-10 code K00-K93); genitourinary disease (ICD-10 code N00–N99); and injury, poisoning, and external causes (ICD-10 code S00–T98). Explanatory variables The number of remaining teeth and dental status (a composite variable combining the number of remaining teeth and dental prosthesis use) were used as explanatory variables. The number of remaining teeth was assessed with the question: “How is the condition of your teeth?” with response options: “I have more than 20 teeth,” “I have 10–19 teeth,” “I have 1–9 teeth,” and "I have 0 teeth.” Responses were categorized into three groups: “≥20 teeth,” “10–19 teeth,” and “0–9 teeth.” 11 The self-reported number of teeth has been validated as broadly consistent with clinical examinations 12 . Dental prosthesis use was assessed with the question: “Do you wear dentures or bridges (non-removable dentures)?” with response options: “No,” “Yes, in the upper jaw,” “Yes, in the lower jaw,” and “Yes, in both jaws.” Responses were dichotomized into two groups: non-use of dental prostheses (those who answered “No”) and use of dental prostheses (all the other responses) 13 . Dental status was categorized into five groups by combining the number of remaining teeth and dental prosthesis use: “≥20 teeth,” “10–19 teeth with dental prostheses,” “10–19 teeth without dental prostheses,” “0–9 teeth with dental prostheses,” and “0–9 teeth without dental prostheses.” 14 – 17 Covariates Based on previous literature 5 , 18 , 19 , the following potential confounders were included: sex (male/female), age (65–69, 70–74, 75–79, 80–84, or ≥ 85 years), equivalent income (< 2.00, 2.00–3.99, or ≥ 4.00 million JPY), education (≤ 9, 10–12, or ≥ 13 years), comorbidities (0, 1, or ≥ 2), smoking status (never, former, or current), alcohol consumption (never, former, or current), marital status (with, or without spouse), walking time (< 30, 30–59, or ≥ 60 mins/day), and body mass index (< 18.5, 18.5–29.9, or ≥ 30.0). Equivalent income was calculated by dividing household income by the square root of the number of household members 14 . Comorbidities were defined as the number of self-reported medical conditions, including cancer, heart disease, stroke, hypertension, diabetes, obesity, hyperlipidemia, osteoporosis, joint disease, injury/fracture, respiratory disease, gastrointestinal disease, liver disease, mental disorders, swallowing difficulties, vision and hearing impairment, elimination problems, sleep problems, undiagnosed illness, and other conditions. Statistical analysis Cox proportional hazard models were used to estimate cause-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between the number of remaining teeth, dental status, and all-cause and cause-specific mortality. Multiple imputations (MI) was applied to handle missing data, reduce selection bias, and improve statistical efficiency 20 . Twenty imputed datasets were created using multivariate imputation with chained equations. Each dataset was analyzed separately, and the estimates were subsequently combined based on Rubin’s rule 21 . Additionally, complete-case analyses were conducted for sensitivity analysis. The statistical significance level was set at α = 0.05 for all the analyses. Statistical analyses were performed using Stata/MP 18.0 (StataCorp LLC, College Station, Texas, USA). The STROBE checklist was followed. Ethical issue This study was conducted under a collaborative research agreement with the associated municipalities. Ethical approval (no. 2493) was obtained from the Ethics Board of Chiba University. JAGES participants were informed that participation in the study was voluntary, and that completing and returning the questionnaire by mail constituted consent. An anonymized dataset was created. All procedures adhered to relevant guidelines and regulations, including the Declaration of Helsinki. Results The analytical sample comprised 43,774 participants (mean age: 73.7 [SD = 5.9] years, 53.2% women) ( Table 1 ). The proportions of patients with 0–9, 10–19, and ≥20 teeth were 38.3%, 26.1%, and 35.5%, respectively. Participants with 0–9 teeth were generally older, had lower income (58.2% had income <2 million JPY), and had lower educational attainment (55.5% had ≤ 9 years of education). Participants characteristics prior to imputation were similar ( Supplementary Table S1 ). Supplementary Table S2 and S3 present baseline characteristics stratified by dental status before and after multiple imputations. Distributions were generally consistent, with participants who had fewer teeth, especially those not using dental prostheses, being older and having lower income and education levels. Table 1. Baseline characteristics of participants by number of remaining teeth after multiple imputation. All p articipants (n = 43,774) Number of remaining t eeth ≥20 teeth (n = 15,555) 10–19 teeth (n = 11,441) 0–9 teeth (n = 16,778) n % n % n % n % Sex Men 20,493 46.8 7,471 48.0 5,412 47.3 7,609 45.4 Women 23,281 53.2 8,084 52.0 6,029 52.7 9,169 54.6 Age (years) 65–69 13,006 29.7 6,044 38.9 3,805 33.3 3,157 18.8 70–74 13,098 29.9 5,159 33.2 3,569 31.2 4,370 26.0 75–79 9,748 22.3 2,872 18.5 2,456 21.5 4,420 26.3 80–84 5,462 12.5 1,154 7.4 1,213 10.6 3,096 18.5 ≥85 2,460 5.6 325 2.1 398 3.5 1,737 10.4 Income ( m illion JPY) <2.00 17,910 40.9 6,528 42.0 5,687 49.7 9,772 58.2 2.00–3.99 14,391 32.9 6,950 44.7 4,539 39.7 5,568 33.2 ≥4.00 4,065 9.3 2,077 13.4 1,216 10.6 1,439 8.6 Education ( y ears) ≤9 19,401 44.3 5,618 36.1 4,994 43.7 9,308 55.5 10–12 15,338 35.0 6,221 40.0 4,284 37.4 5,173 30.8 ≥13 8,021 18.3 3,715 23.9 2,163 18.9 2,297 13.7 Comorbidities 0 109 0.2 51 0.3 36 0.3 58 0.3 1 14,111 32.2 7,059 45.4 5,009 43.8 6,766 40.3 ≥2 19,228 43.9 8,444 54.3 6,396 55.9 9,954 59.3 Smoking s tatus Never 23,390 53.4 9,801 63.0 6,778 59.2 9,661 57.6 Former 11,731 26.8 4,513 29.0 3,360 29.4 4,828 28.8 Current 4,481 10.2 1,240 8.0 1,304 11.4 2,289 13.6 Alcohol c onsumption Never 24,813 56.7 8,654 55.6 6,684 58.4 11,036 65.8 Past 1,435 3.3 464 3.0 418 3.7 637 3.8 Current 14,996 34.3 6,437 41.4 4,340 37.9 5,105 30.4 Marital s tatus With a spouse 31,131 71.1 12,203 78.4 8,462 74.0 11,077 66.0 Without a spouse 11,711 26.8 3,352 21.6 2,979 26.0 5,701 34.0 Walking t ime (min/day) ≥60 12,925 29.5 5,384 34.6 3,623 31.7 4,692 28.0 30–59 14,604 33.4 5,807 37.3 4,170 36.4 5,555 33.1 <30 13,626 31.1 4,363 28.1 3,648 31.9 6,531 38.9 Body m ass i ndex <18.5 3,047 7.0 963 6.2 734 6.4 1,483 8.8 18.5–29.9 38,490 87.9 14,314 92.0 10,498 91.8 14,905 88.8 ≥30.0 855 2.0 278 1.8 209 1.8 390 2.3 During a median follow-up of 2,485 days (interquartile range: 2,218–2,697 days; maximum: 2,707 days), 5,707 deaths (13.0 %) were recorded. The overall mortality rate for all-cause death was 20.7 per 1,000 person-years. Patients with 0–9 or 10–19 teeth exhibited higher all-cause and cause-specific mortality rates compared with those with ≥20 teeth ( Supplementary Table S4 ). Among participants with 0–9 or 10–19 teeth, those not using dental prostheses had higher all-cause mortality rates than those using dental prostheses. Mortality rates for all-cause and cause-specific mortality by dental status are shown in Table 2 . The highest cause-specific mortality rates for cancer, cardiovascular disease, respiratory disease, infectious and parasitic diseases, endocrine and metabolic diseases, genitourinary diseases, injury, and external causes were observed in participants with 0–9 teeth who did not use dental prostheses. Table 2. All-cause and cause-specific mortality rates by dental status ( n = 43,774). All participants (n = 43,774) Dental Status Mortality rate (per 1,000 person-years) ≥20 teeth (n = 15,555) 10–19 teeth with d ental p rostheses (n = 7,303) 10–19 teeth without d ental p rostheses (n = 4,139) 0–9 teeth with d ental p rostheses (n = 12,158) 0–9 teeth without d ental p rostheses (n = 4,620) All- c ause m ortality 20.7 13.0 17.7 18.8 29.3 31.4 Cause- s pecific m ortality Cancer 8.7 6.2 7.7 8.3 11.4 11.9 Cardiovascular diseases 4.8 2.8 4.0 4.3 7.1 7.2 Respiratory diseases 2.9 1.5 2.3 2.6 4.4 5.0 Infectious and parasitic diseases 0.5 0.3 0.3 0.3 0.6 0.9 Endocrine, nutritional, and metabolic diseases 0.2 0.1 0.2 0.2 0.2 0.5 Mental and behavioral disorders 0.1 0.0 0.1 0.2 0.1 0.1 Nervous system diseases 0.3 0.2 0.3 0.2 0.5 0.5 Digestive diseases 0.6 0.3 0.6 0.4 1.2 0.7 Genitourinary diseases 0.4 0.2 0.3 0.4 0.5 0.8 Injury, poisoning, and external causes 1.0 0.6 1.0 0.9 1.4 1.8 Cox proportional hazards models with the number of remaining teeth as the explanatory variable are shown in Table 3 . After adjusting for all the covariates, participants with 0–9 (HR = 1.39 [95% CI 1.30–1.49]) and 10–19 teeth (HR = 1.18 [95% CI 1.09–1.28]) had a higher risk of all-cause mortality compared with those who had ≥20 teeth. Additionally, participants with 0–9 teeth had a higher risk of mortality from cancer (HR = 1.31 [95% CI 1.18–1.45]), cardiovascular disease (HR = 1.37 [95% CI 1.18–1.59]), respiratory disease (HR = 1.65 [95% CI 1.35–2.02]), digestive diseases (HR = 2.01 [95% CI 1.30–3.08]), and injury and external causes (HR = 1.62 [95% CI 1.18–2.24]). Participants with 10–19 teeth had a higher risk of mortality from cancer (HR = 1.12 [95% CI 1.00–1.26]), cardiovascular disease (HR = 1.20 [95% CI 1.02–1.42]), and respiratory disease (HR = 1.35 [95% CI 1.08–1.70]). Table 3. Associations between number of remaining teeth and all-cause and cause-specific mortality ( n = 43,774). Number of remaining t eeth Ref. HR = 1: ≥20 teeth (n = 15,555) 10–19 teeth (n = 11,441) 0–9 teeth (n = 16,778) Crude m odel Adjusted m odel a Crude m odel Adjusted m odel a HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) All -c ause m ortality 1.39 (1.29, 1.51) *** 1.18 (1.09, 1.28) *** 2.30 (2.16, 2.46) *** 1.39 (1.30, 1.49) *** Cause -s pecific m ortality Cancer 1.27 (1.13, 1.43) *** 1.12 (1.00, 1.26) * 1.86 (1.69, 2.05) *** 1.31 (1.18, 1.45) *** Cardiovascular diseases 1.45 (1.23, 1.72) *** 1.20 (1.02, 1.42) * 2.51 (2.18, 2.89) *** 1.37 (1.18, 1.59) *** Respiratory diseases 1.66 (1.33, 2.08) *** 1.35 (1.08, 1.70) ** 3.16 (2.61, 3.82) *** 1.65 (1.35, 2.02) *** Infectious and parasitic diseases 1.03 (0.60, 1.77) 0.86 (0.49, 1.48) 2.19 (1.42, 3.35) *** 1.21 (0.77, 1.91) Endocrine, nutritional, and metabolic diseases 1.44 (0.67, 3.10) 1.16 (0.53, 2.50) 2.31 (1.21, 4.43) * 1.21 (0.61, 2.41) Mental and behavioral disorders 2.98 (0.92, 9.65) 2.45 (0.75, 8.03) 3.31 (1.07, 10.19) * 1.92 (0.59, 6.21) Nervous diseases 1.09 (0.59, 2.02) 0.95 (0.51, 1.78) 2.07 (1.26, 3.39) ** 1.32 (0.77, 2.24) Digestive diseases 1.66 (1.02, 2.69) * 1.38 (0.85, 2.25) 3.35 (2.23, 5.04) *** 2.01 (1.30, 3.08) ** Genitourinary diseases 1.58 (0.88, 2.84) 1.24 (0.69, 2.23) 2.67 (1.62, 4.42) *** 1.21 (0.71, 2.07) Injury, poisoning, and external causes 1.53 (1.08, 2.17) * 1.34 (0.95, 1.91) 2.43 (1.80, 3.28) *** 1.62 (1.18, 2.24) ** Abbreviations: HR, hazard ratio; 95% CI, 95% confidence interval; Ref, reference. a Adjusted for sex, age, equivalent income, education, comorbidities, smoking status, alcohol consumption, marital status, walking time, and body mass index. *p < 0.05, ** p < 0.01, *** p < 0.001. Cox proportional hazards models using dental status as the explanatory variable are presented in Table 4 . Participants with 0–9 teeth (HR = 1.42 [95% CI 1.30–1.56]) or 10–19 teeth (HR = 1.23 [95% CI 1.10–1.37]) who did not use dental prostheses had a higher risk of all-cause mortality compared to those who had ≥20 teeth. In the cause-specific mortality analysis, compared to those with ≥20 teeth, those with 0–9 teeth had a higher risk of mortality due to cancer (HR = 1.31 [95% CI = 1.14–1.52]), cardiovascular disease (HR = 1.35 [95% CI = 1.11–1.64]), respiratory disease (HR = 1.72 [95% CI = 1.34–2.21]), and injury and external cause (HR = 1.91 [95% CI = 1.27–2.88]). Similarly, participants with 10–19 teeth who did not use dental prostheses had a higher risk of mortality due to cancer (HR = 1.19 [95% CI = 1.01–1.39]) and respiratory disease (HR = 1.47 [95% CI = 1.09–1.98]) than those with ≥20 teeth. No evidence of differences in all-cause and cause-specific mortality was found between participants with ≥20 teeth and those who had 10–19 teeth who used dental prostheses. Similar results were observed in complete-case analyses ( Supplementary Tables S5 and S 6 ). Table 4. Associations between dental status and all-cause and cause-specific mortality (n = 43,774). Dental s tatus Ref. HR = 1: ≥20 teeth (n = 15,555) 10–19 teeth with d ental p rostheses (n = 7,303) 10–19 teeth without d ental p rostheses (n = 4,139) 0–9 teeth with d ental p rostheses (n = 12,158) 0–9 teeth without d ental p rostheses (n = 4,620) Crude m odel Adjusted m odel a Crude m odel Adjusted m ode l a Crude m odel Adjusted m odel a Crude m odel Adjusted m odel a HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) All -c ause m ortality 1.36 (1.25, 1.49) *** 1.16 (1.06, 1.27) ** 1.45 (1.30, 1.61) *** 1.23 (1.10, 1.37) *** 2.26 (2.11, 2.42) *** 1.38 (1.28, 1.49) *** 2.42 (2.22, 2.64) *** 1.42 (1.30, 1.56)* ** Cause -s pecific m ortality Cancer 1.23 (1.08, 1.41) ** 1.09 (0.95, 1.24) 1.34 (1.15, 1.56) *** 1.19 (1.01, 1.39) * 1.84 (1.66, 2.04) *** 1.31 (1.17, 1.46) *** 1.91 (1.67, 2.19) *** 1.31 (1.14, 1.52) *** Cardiovascular diseases 1.42 (1.17, 1.72) *** 1.17 (0.97, 1.42) 1.51 (1.20, 1.90) *** 1.24 (0.99, 1.57) 2.50 (2.16, 2.90) *** 1.38 (1.18, 1.62) *** 2.53 (2.10, 3.05) *** 1.35 (1.11, 1.64) ** Respiratory diseases 1.58 (1.21, 2.05) ** 1.29 (0.99, 1.67) 1.81 (1.34, 2.44) *** 1.47 (1.09, 1.98) * 3.05 (2.50, 3.73) *** 1.62 (1.31, 2.00) *** 3.45 (2.71, 4.38) *** 1.72 (1.34, 2.21) *** Infectious and parasitic diseases 0.99 (0.52, 1.87) 0.83 (0.44, 1.57) 1.08 (0.50, 2.36) 0.90 (0.41, 1.99) 1.89 (1.17, 3.04) ** 1.06 (0.65, 1.74) 2.95 (1.72, 5.09) *** 1.62 (0.91, 2.90) Endocrine, nutritional, and metabolic diseases 1.38 (0.56, 3.40) 1.14 (0.46, 2.83) 1.50 (0.49, 4.57) 1.16 (0.38, 3.55) 1.87 (0.91, 3.84) 1.02 (0.48, 2.16) 3.49 (1.60, 7.61) ** 1.71 (0.75, 3.87) Mental and behavioral disorders 2.60 (0.70, 9.67) 2.12 (0.57, 7.96) 3.61 (0.90, 14.43) 3.02 (0.75, 12.23) 3.57 (1.12, 11.38) * 2.07 (0.62, 6.89) 2.54 (0.51, 12.59) 1.46 (0.28, 7.59) Nervous system diseases 1.11 (0.54, 2.27) 0.96 (0.47, 1.97) 1.04 (0.42, 2.60) * 0.93 (0.37, 2.34) 1.98 (1.16, 3.38) * 1.27 (0.72, 2.23) 2.29 (1.17, 4.47) * 1.45 (0.72, 2.95) Digestive diseases 1.89 (1.12, 3.19) * 1.58 (0.93, 2.67) 1.26 (0.62, 2.58) 1.04 (0.50, 2.13) 3.82 (2.51, 5.80) *** 2.29 (1.48, 3.54) *** 2.13 (1.15, 3.95) * 1.21 (0.00, 2.30) Genitourinary diseases 1.40 (0.70, 2.81) 1.10 (0.55, 2.21) 1.89 (0.89, 4.00) 1.49 (0.70, 3.17) 2.26 (1.30, 3.91) ** 1.05 (0.59, 1.86) 3.77 (2.07, 6.87) *** 1.67 (0.88, 3.14) Injury, poisoning, and external causes 1.61 (1.08, 2.38) * 1.42 (0.96, 2.10) 1.39 (0.84, 2.30) 1.22 (0.73, 2.02) 2.23 (1.61, 3.09) *** 1.52 (1.08, 2.14) * 2.93 (2.00, 4.30) *** 1.91 (1.27, 2.88) ** Abbreviations: HR, hazard ratio; 95% CI, 95% confidence interval; Ref, reference. a Adjusted for sex, age, equivalent income, education, comorbidities, smoking status, alcohol consumption, marital status, walking time, and body mass index. *p < 0.05, ** p < 0.01, *** p < 0.001. Discussion Summary of main findings This study investigated the associations between the number of remaining teeth and ten types of cause-specific mortality outcomes, as well as the role of dental prostheses in modifying the impact of tooth loss on mortality. An inverse dose-response relationship was observed between the number of remaining teeth and mortality risk in older adults, with fewer teeth associated with increased mortality risk, varying by cause of death. Specifically, participants with 0–9 or 10–19 had a higher risk of mortality from cancer, cardiovascular disease, respiratory disease, digestive disease, injury, and external causes. However, among those with fewer teeth, individuals who used dental prostheses exhibited a lower mortality risk than those without dental prostheses, particularly for deaths due to respiratory disease, infectious and parasitic diseases, endocrine, nutritional, and metabolic diseases, nervous system disease, genitourinary disease, injury or external causes. These results indicate that although tooth loss may increase mortality risk from various causes, dental prostheses may reduce these adverse effects. Comparison with previous findings and the explanation of possible mechanism The findings align with a previous meta-analysis reporting an association between tooth loss and increased all-cause mortality risk 4 . In the cause-specific mortality analysis, after adjusting for potential confounders, participants with 0–9 teeth had a significantly higher mortality risk from cancer, cardiovascular diseases, and respiratory diseases. These results are consistent with prior studies reporting associations between the number of remaining teeth and mortality from cancer 6,22,23 , cardiovascular disease 5,18,23 , and respiratory disease 5,23 . Furthermore, this study revealed that individuals with 0–9 teeth also had a significantly higher risk of mortality from digestive system diseases, injury, poisoning, and other external causes. These findings contribute valuable insight into the specific causes of death potentially associated with tooth loss. Among individuals with 0–9 teeth, those using dental prostheses generally had lower mortality risks for several causes, including respiratory disease, infectious and parasitic diseases, endocrine and metabolic diseases, nervous system diseases, genitourinary diseases, injury, poisoning, and external causes, compared with those not using dental prostheses. These findings are consistent with a prior study suggesting that dental prosthesis use in patients with partial tooth loss is associated with reduced mortality risk from cardiovascular disease, respiratory disease, and other causes 18 , indicating a potential protective effect. However, some studies reported no statistically significant associations between tooth loss and cancer or cardiovascular mortality 24,25 , or an association limited to oro-digestive cancer mortality 26 . These discrepancies may be attributed to smaller sample sizes and limited statistical power in those studies. Several mechanisms may explain the observed association between the number of teeth and mortality. First, tooth loss impairs masticatory function. Reduced chewing ability can lead to the avoidance of hard, nutrient-dense foods such as fruits, vegetables, and meats, resulting poorer dietary quality 27 . This may lead to insufficient intake of essential micronutrients, which could impair immune function, decrease antioxidant capacity, and hinder cellular repair, thereby increasing vulnerability to cancer, cardiovascular diseases, and respiratory infections 28,29 . Second, systemic inflammation may play a role. Tooth loss may reflect poor oral health and a history of periodontal disease, which is associated with elevated levels of systemic inflammatory markers such as C-reactive protein (CRP), tumor necrosis factor, and interleukins. This chronic inflammation may contribute to cardiovascular mortality by promoting atherosclerosis and increasing the risk of acute cardiovascular events 30 . Inflammation may also contribute to cancer development by impairing DNA repair and increasing genomic instability 31 . Additionally, repeated bacteremia from oral pathogens, such as Porphyromonas gingivalis, may promote sustained systemic inflammation, disrupt the gut microbiota and immune homeostasis, and increase the risk of cancer and digestive diseases, including liver-related outcomes 32 . Tooth loss may also elevate the risk of injury-related mortality through its contribution to frailty and cognitive impairment, both of which are established risk factors for falls and injury 33,34 . Although this association is likely indirect, the findings suggest that tooth loss may increase vulnerability to accidental deaths through its adverse impact on physical and cognitive functions. Dental prostheses may mitigate these risks in older adults by restoring oral function, improving masticatory performance, and enhancing swallowing ability 35 . These improvements can facilitate better food selection and improved nutritional status 36 . Moreover, dental prostheses may help reduce the risk of cognitive decline associated with tooth loss among older adults 37 . Therefore, replacing missing teeth in older adults may be essential to prevent downstream health complications that contribute to increased mortality risk. Implications This study underscores the importance of oral health, particularly the number of remaining teeth, as a potential determinant of mortality from various causes. Public health strategies should prioritize population-level interventions that address the broader determinants of oral health to reduce the burden of tooth loss and associated diseases across the lifespan 38 . Additionally, the findings highlights the role of dental prostheses in restoring oral function among individuals with tooth loss, thereby improving overall health and reducing mortality risk. However, socioeconomic disparities in the utilization of dental prosthesis remain a significant concern. Expanding dental coverage under universal health coverage (UHC) may help address these disparities 39 . Policies aimed at reducing financial and access-related barriers to dental prostheses use are essential to promote equitable oral healthcare globally. Limitations and strengths of the study This study has several limitations. First, the use of self-reported data on the number of remaining teeth and dental prostheses may be subject to recall bias, potentially leading to exposure misclassification. However, previous studies have validated self-reported tooth counts 12 , suggesting limited bias from self-reporting. Second, selection bias may be present due to the baseline questionnaire response rate of 64.7%. Nonetheless, previous research has indicated that while self-selection bias may affect prevalence estimates, its impact on associations is generally minimal 40 . Third, despite adjustment for a wide range of confounders factors, residual confounding due to unmeasured variables cannot be entirely excluded. Lastly, the study population comprised older adults in Japan, which may limit the generalizability of the findings. However, consistency with studies from other countries supports cautious generalization to broader populations. Despite these limitations, the study possesses notable strengths, including the use of nationwide prospective cohort data and a large sample size ( n = 43,774), which enhance the robustness of the findings. Conclusion This study investigated the association between dental status and the mortality risk from various causes. Tooth loss was found to be significantly associated with an increased risk of mortality from cancer, cardiovascular disease, respiratory disease, digestive disease, injury, poisoning, and other external causes. Additionally, the use of dental prostheses was observed to mitigate the mortality risk associated with tooth loss, although this protective effect varied by cause of death. Addressing the burden of tooth loss and reducing socioeconomic disparities in dental prosthesis use through equitable healthcare policies and expanded dental coverage are essential for improving health outcomes, particularly among older adults. Declarations Competing interests The authors declare no competing interest. Funding: This study used data from JAGES (the Japan Gerontological Evaluation Study). It was supported by Grant-in-Aid for Scientific Research (20H00557, 20K10540, 21H03196, 21K17302, 22H00934, 22H03299, 22K04450, 22K13558, 22K17409, 23H00449, 23H03117, 23K21500) from the Japan Society for the Promotion of Science (JSPS); Health Labour Sciences Research Grants (19FA1012, 19FA2001, 21FA1012, 22FA2001, 22FA1010, 22FG2001); the Research Institute of Science and Technology for Society (JPMJOP1831) from the Japan Science and Technology (JST); a grant from the Japan Health Promotion & Fitness Foundation; the TMDU priority research areas grant; and the National Research Institute for Earth Science and Disaster Resilience. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policies or positions of the respective funding organizations. Author Contribution FA, TK, YT, and SK contributed to the conception and design, data analysis and interpretation, and drafted and critically revised the manuscript. MS, TO, JA, KK, KO, and KT contributed to the conception and design, data acquisition and interpretation, and drafted and critically revised the manuscript. All authors have approved the submitted version and agree to be accountable for all aspects of this work. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References GBD 2017 Oral Disorders Collaborators et al. Global, Regional, and National Levels and Trends in Burden of Oral Conditions from 1990 to 2017: A Systematic Analysis for the Global Burden of Disease 2017 Study. J Dent Res 99 , 362–373 (2020). Kassebaum, N. J. et al. Global Burden of Severe Tooth Loss: A Systematic Review and Meta-analysis. J Dent Res 93 , 20S-28S (2014). Hag Mohamed, S. & Sabbah, W. Is tooth loss associated with multiple chronic conditions? Acta Odontologica Scandinavica 1–6 (2023) doi:10.1080/00016357.2023.2166986. Peng, J. et al. The relationship between tooth loss and mortality from all causes, cardiovascular diseases, and coronary heart disease in the general population: systematic review and dose–response meta-analysis of prospective cohort studies. Bioscience Reports 39 , BSR20181773 (2019). Aida, J. et al. Oral Health and Cancer, Cardiovascular, and Respiratory Mortality of Japanese. J Dent Res 90 , 1129–1135 (2011). Goto, Y. et al. Number of Teeth and All-Cause and Cancer Mortality in a Japanese Community: The Takayama Study. Journal of Epidemiology 30 , 213–218 (2020). Shen, R., Chen, S., Shen, J., Lv, L. & Wei, T. Association between missing teeth number and all‐cause and cardiovascular mortality: NHANES 1999–2004 and 2009–2014. Journal of Periodontology 95 , 571–581 (2024). Kusama, T. et al. Dental prosthetic treatment reduced the risk of weight loss among older adults with tooth loss. J American Geriatrics Society 69 , 2498–2506 (2021). Kondo, K. Progress in Aging Epidemiology in Japan: The JAGES Project. Journal of Epidemiology 26 , 331–336 (2016). Saito, M. et al. Social disconnection and suicide mortality among Japanese older adults: A seven-year follow-up study. Social Science & Medicine 347 , 116778 (2024). Shiota, C., Kusama, T., Takeuchi, K., Kiuchi, S. & Osaka, K. Oral Hypofunction and Risk of Weight Change among Independent Older Adults. Nutrients 15 , 4370 (2023). Ueno, M., Shimazu, T., Sawada, N., Tsugane, S. & Kawaguchi, Y. Validity of self‐reported tooth counts and masticatory status study of a Japanese adult population. J of Oral Rehabilitation 45 , 393–398 (2018). Kusama, T. et al. The deterioration of oral function and orofacial appearance mediated the relationship between tooth loss and depression among community-dwelling older adults: A JAGES cohort study using causal mediation analysis. Journal of Affective Disorders 286 , 174–179 (2021). Kinugawa, A. et al. Association of poor dental status with eating alone: A cross-sectional Japan gerontological evaluation study among independent older adults. Appetite 168 , 105732 (2022). Tamada, Y. et al. Reduced number of teeth with and without dental prostheses and low frequency of laughter in older adults: Mediation by poor oral function. J Prosthodont Res 68 , 441–448 (2023). Kusama, T., Takeuchi, K., Kiuchi, S., Aida, J. & Osaka, K. The association between objective and subjective oral health conditions and the presence of anorexia of aging among Japanese older Adults1. Appetite 198 , 107332 (2024). Nakazawa, N. et al. Dental prosthesis use moderates association between tooth loss and risk of depressive symptoms in older adults with severe tooth loss: The JAGES cohort trial. J Prosthodont Res 68 , 578–584 (2024). Dai, M. et al. Tooth loss, denture use, and all-cause and cause-specific mortality in older adults: a community cohort study. Front. Public Health 11 , 1194054 (2023). Nakazawa, N. et al. Large Contribution of Oral Status for Death Among Modifiable Risk Factors in Older Adults: The Japan Gerontological Evaluation Study (JAGES) Prospective Cohort Study. The Journals of Gerontology: Series A 78 , 167–173 (2023). Lee, K. J. et al. Framework for the treatment and reporting of missing data in observational studies: The Treatment And Reporting of Missing data in Observational Studies framework. Journal of Clinical Epidemiology 134 , 79–88 (2021). Austin, P. C., White, I. R., Lee, D. S. & Van Buuren, S. Missing Data in Clinical Research: A Tutorial on Multiple Imputation. Canadian Journal of Cardiology 37 , 1322–1331 (2021). Ando, A. et al. Associations of number of teeth with risks for all‐cause mortality and cause‐specific mortality in middle‐aged and elderly men in the northern part of J apan: the I wate‐ KENCO study. Comm Dent Oral Epid 42 , 358–365 (2014). Kim, S. Y. et al. Is the Number of Missing Teeth Associated With Mortality? A Longitudinal Study Using a National Health Screening Cohort. Front. Med. 9 , 837743 (2022). Adolph, M. et al. Oral health in relation to all-cause mortality: the IPC cohort study. Sci Rep 7 , 44604 (2017). Ishikawa, S. et al. Association between presence of 20 or more natural teeth and all-cause, cancer-related, and cardiovascular disease-related mortality: Yamagata (Takahata) prospective observational study. BMC Oral Health 20 , 353 (2020). Ansai, T. et al. Association between tooth loss and orodigestive cancer mortality in an 80-year-old community-dwelling Japanese population: a 12-year prospective study. BMC Public Health 13 , 814 (2013). Zhu, Y. & Hollis, J. H. Tooth loss and its association with dietary intake and diet quality in American adults. Journal of Dentistry 42 , 1428–1435 (2014). Aune, D. et al. Fruit and vegetable intake and the risk of cardiovascular disease, total cancer and all-cause mortality—a systematic review and dose-response meta-analysis of prospective studies. International Journal of Epidemiology 46 , 1029–1056 (2017). Ignacio Carlotto, C., Bernardes, S., Zanella, P. & Silva, F. M. Dietary patterns and risk of Chronic Obstructive Pulmonary Disease (COPD) and clinical outcomes in diagnosed patients: A scoping review. Respiratory Medicine 233 , 107773 (2024). Carrizales-Sepúlveda, E. F., Ordaz-Farías, A., Vera-Pineda, R. & Flores-Ramírez, R. Periodontal Disease, Systemic Inflammation and the Risk of Cardiovascular Disease. Heart, Lung and Circulation 27 , 1327–1334 (2018). Kim, E. H. et al. Periodontal disease and cancer risk: A nationwide population-based cohort study. Front. Oncol. 12 , 901098 (2022). Chen, Y. et al. Association between periodontal disease, tooth loss and liver diseases risk. J Clinic Periodontology 47 , 1053–1063 (2020). Muir, S. W., Gopaul, K. & Montero Odasso, M. M. The role of cognitive impairment in fall risk among older adults: a systematic review and meta-analysis. Age and Ageing 41 , 299–308 (2012). Wang, M. et al. Frailty mediated the association between tooth loss and mortality in the oldest old individuals: a cohort study. Front. Public Health 11 , 1285226 (2024). Furuta, M. et al. Interrelationship of oral health status, swallowing function, nutritional status, and cognitive ability with activities of daily living in Japanese elderly people receiving home care services due to physical disabilities. Comm Dent Oral Epid 41 , 173–181 (2013). Tsai, A. C. & Chang, T.-L. Association of dental prosthetic condition with food consumption and the risk of malnutrition and follow-up 4- year mortality risk in elderly Taiwanese. The Journal of nutrition, health and aging 15 , 265–270 (2011). Chou, Y.-C., Weng, S.-H., Cheng, F.-S. & Hu, H.-Y. Denture Use Mitigates the Cognitive Impact of Tooth Loss in Older Adults. The Journals of Gerontology, Series A: Biological Sciences and Medical Sciences 80 , glae248 (2024). Watt, R. G. Strategies and approaches in oral disease prevention and health promotion. Bulletin of the World Health Organization (2005). Hoshi‐Harada, M. et al. Difference of income inequalities of denture use by co‐payment rates: A JAGES cross‐sectional study. Comm Dent Oral Epid 51 , 557–564 (2023). Petersen, G. L. et al. Inverse probability weighting for self-selection bias correction in the investigation of social inequality in mortality. International Journal of Epidemiology 53 , dyae097 (2024). Additional Declarations No competing interests reported. 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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-6962054","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490071428,"identity":"34f14f02-bcac-49b0-9919-eee87be97fc5","order_by":0,"name":"Faiz Abdurrahman","email":"","orcid":"","institution":"Tohoku University Graduate School of Dentistry","correspondingAuthor":false,"prefix":"","firstName":"Faiz","middleName":"","lastName":"Abdurrahman","suffix":""},{"id":490071430,"identity":"5127c8db-9f62-43c9-9a7c-93dd753089d8","order_by":1,"name":"Taro Kusama","email":"","orcid":"","institution":"Tohoku University Graduate School of 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06:08:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6962054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6962054/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-24357-1","type":"published","date":"2025-11-19T15:57:05+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87693906,"identity":"415b9e71-8368-40db-b818-67405ed81a25","added_by":"auto","created_at":"2025-07-28 05:35:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":250173,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the participants.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6962054/v1/4886c491e69e96f0061c4314.png"},{"id":96650263,"identity":"62ff7163-d5b3-4c42-be7b-b28b1d52f495","added_by":"auto","created_at":"2025-11-24 16:10:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1842991,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6962054/v1/7d70b38f-c1e0-4f6d-a442-61e92fdaf621.pdf"},{"id":87695074,"identity":"60ceb02b-0407-4ba1-a83e-a9acf82342fb","added_by":"auto","created_at":"2025-07-28 05:51:34","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":311425,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFileScientificReports.docx","url":"https://assets-eu.researchsquare.com/files/rs-6962054/v1/49800848a15bdec4e9eb1195.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dental status and cause-specific mortality in older adults","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOral diseases rank among the most prevalent globally. Approximately 29.4% of the global population (2.3\u0026nbsp;billion people) have untreated caries in permanent teeth, and 9.8% (0.8\u0026nbsp;billion people) have severe periodontitis\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Untreated caries and periodontal diseases can lead to tooth loss\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, which is associated with various chronic diseases and subsequent mortality\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Therefore, tooth loss represents a critical health issue, particularly in older adults, due to its irreversible nature and cumulative impact over time\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExisting literature suggests a relationship between tooth loss and all-cause mortality\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. However, few studies have examined specific causes of death. Understanding the specific causes of mortality associated with tooth loss could facilitate the identification of high-risk populations and guide targeted prevention strategies. Previous studies have examined associations between tooth loss and mortality from cardiovascular disease, respiratory disease, and lung cancer\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. However, a comprehensive analysis of the associations between tooth loss and various cause-specific mortality outcomes has not been conducted. In addition, the use of dental prostheses is known to mitigate the adverse health effects of tooth loss\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, yet few studies have investigated its impact on cause-specific mortality.\u003c/p\u003e\u003cp\u003eThis study evaluated the associations among the number of remaining teeth, dental prosthesis use, and cause-specific mortality from various diseases and disorders. We hypothesized that the influence of tooth loss and the use of dental prostheses on cause-specific mortality would vary by cause of death, reflecting the potential involvement of different mechanisms across diseases.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and participants\u003c/h2\u003e\u003cp\u003eThis cohort study analyzed seven-year follow-up data from the Japan Gerontological Evaluation Study (JAGES), an ongoing longitudinal study established in 2010 to investigate health-related factors among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years and older in Japan\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. The 2010 wave (conducted from August 2010 to December 2011) served as the baseline. Cause-of-death data were linked through December 2017 using sex, date of birth, and date of death as key variables\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. These data were obtained from secondary sources managed by the Ministry of Health, Labor, and Welfare of Japan. Of the 46,144 participants from nine municipalities, 6,383 died during follow-up, with cause-of-death available for 6,312 participants (combined rate: 98.9%). Participants with invalid weight or height measurements (n\u0026thinsp;=\u0026thinsp;340) or those dependent in daily living activities at baseline (n\u0026thinsp;=\u0026thinsp;2,030) were excluded, resulting in 43,774 participants included in the analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcome Variables\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eOutcome Variables\u003c/div\u003e\u003cp\u003eAll-cause and cause-specific mortality were assessed as outcomes. Cause-specific mortality categories were based on a previous study\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e and included deaths from cancer (International Classification of Diseases, Tenth Revision [ICD-10] codes C00\u0026ndash;D48), cardiovascular disease (ICD-10 codes I00\u0026ndash;I99), and respiratory disease (ICD-10 codes J00\u0026ndash;J99). To comprehensively assess the effects of tooth loss across various causes of death, additional causes were included: infectious and parasitic disease (ICD-10 codes A00\u0026ndash;B99); endocrine, nutritional, and metabolic disease (ICD-10 codes E00\u0026ndash;E90); mental and behavioral disorder (ICD-10 codes F00\u0026ndash;F99); nervous disease (ICD-10 codes G00\u0026ndash;G99); digestive disease (ICD-10 code K00-K93); genitourinary disease (ICD-10 code N00\u0026ndash;N99); and injury, poisoning, and external causes (ICD-10 code S00\u0026ndash;T98).\u003c/p\u003e\n\u003ch3\u003eExplanatory variables\u003c/h3\u003e\n\u003cp\u003eThe number of remaining teeth and dental status (a composite variable combining the number of remaining teeth and dental prosthesis use) were used as explanatory variables. The number of remaining teeth was assessed with the question: \u0026ldquo;How is the condition of your teeth?\u0026rdquo; with response options: \u0026ldquo;I have more than 20 teeth,\u0026rdquo; \u0026ldquo;I have 10\u0026ndash;19 teeth,\u0026rdquo; \u0026ldquo;I have 1\u0026ndash;9 teeth,\u0026rdquo; and \"I have 0 teeth.\u0026rdquo; Responses were categorized into three groups: \u0026ldquo;\u0026ge;20 teeth,\u0026rdquo; \u0026ldquo;10\u0026ndash;19 teeth,\u0026rdquo; and \u0026ldquo;0\u0026ndash;9 teeth.\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e The self-reported number of teeth has been validated as broadly consistent with clinical examinations\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eDental prosthesis use was assessed with the question: \u0026ldquo;Do you wear dentures or bridges (non-removable dentures)?\u0026rdquo; with response options: \u0026ldquo;No,\u0026rdquo; \u0026ldquo;Yes, in the upper jaw,\u0026rdquo; \u0026ldquo;Yes, in the lower jaw,\u0026rdquo; and \u0026ldquo;Yes, in both jaws.\u0026rdquo; Responses were dichotomized into two groups: non-use of dental prostheses (those who answered \u0026ldquo;No\u0026rdquo;) and use of dental prostheses (all the other responses)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Dental status was categorized into five groups by combining the number of remaining teeth and dental prosthesis use: \u0026ldquo;\u0026ge;20 teeth,\u0026rdquo; \u0026ldquo;10\u0026ndash;19 teeth with dental prostheses,\u0026rdquo; \u0026ldquo;10\u0026ndash;19 teeth without dental prostheses,\u0026rdquo; \u0026ldquo;0\u0026ndash;9 teeth with dental prostheses,\u0026rdquo; and \u0026ldquo;0\u0026ndash;9 teeth without dental prostheses.\u0026rdquo;\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eBased on previous literature\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, the following potential confounders were included: sex (male/female), age (65\u0026ndash;69, 70\u0026ndash;74, 75\u0026ndash;79, 80\u0026ndash;84, or \u0026ge;\u0026thinsp;85 years), equivalent income (\u0026lt;\u0026thinsp;2.00, 2.00\u0026ndash;3.99, or \u0026ge;\u0026thinsp;4.00\u0026nbsp;million JPY), education (\u0026le;\u0026thinsp;9, 10\u0026ndash;12, or \u0026ge;\u0026thinsp;13 years), comorbidities (0, 1, or \u0026ge;\u0026thinsp;2), smoking status (never, former, or current), alcohol consumption (never, former, or current), marital status (with, or without spouse), walking time (\u0026lt;\u0026thinsp;30, 30\u0026ndash;59, or \u0026ge;\u0026thinsp;60 mins/day), and body mass index (\u0026lt;\u0026thinsp;18.5, 18.5\u0026ndash;29.9, or \u0026ge;\u0026thinsp;30.0). Equivalent income was calculated by dividing household income by the square root of the number of household members\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Comorbidities were defined as the number of self-reported medical conditions, including cancer, heart disease, stroke, hypertension, diabetes, obesity, hyperlipidemia, osteoporosis, joint disease, injury/fracture, respiratory disease, gastrointestinal disease, liver disease, mental disorders, swallowing difficulties, vision and hearing impairment, elimination problems, sleep problems, undiagnosed illness, and other conditions.\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eCox proportional hazard models were used to estimate cause-specific hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations between the number of remaining teeth, dental status, and all-cause and cause-specific mortality.\u003c/p\u003e\u003cp\u003eMultiple imputations (MI) was applied to handle missing data, reduce selection bias, and improve statistical efficiency\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Twenty imputed datasets were created using multivariate imputation with chained equations. Each dataset was analyzed separately, and the estimates were subsequently combined based on Rubin\u0026rsquo;s rule\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Additionally, complete-case analyses were conducted for sensitivity analysis. The statistical significance level was set at α\u0026thinsp;=\u0026thinsp;0.05 for all the analyses. Statistical analyses were performed using Stata/MP 18.0 (StataCorp LLC, College Station, Texas, USA). The STROBE checklist was followed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEthical issue\u003c/h2\u003e\u003cp\u003e This study was conducted under a collaborative research agreement with the associated municipalities. Ethical approval (no. 2493) was obtained from the Ethics Board of Chiba University. JAGES participants were informed that participation in the study was voluntary, and that completing and returning the questionnaire by mail constituted consent. An anonymized dataset was created. All procedures adhered to relevant guidelines and regulations, including the Declaration of Helsinki.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThe analytical sample comprised 43,774 participants (mean age: 73.7 [SD = 5.9] years, 53.2% women) (\u003cstrong\u003eTable 1\u003c/strong\u003e). The proportions of patients with 0\u0026ndash;9, 10\u0026ndash;19, and \u0026ge;20 teeth were 38.3%, 26.1%, and 35.5%, respectively. Participants with 0\u0026ndash;9 teeth were generally older, had lower income (58.2% had income \u0026lt;2 million JPY), and had lower educational attainment (55.5% had\u0026nbsp;\u0026le; 9 years of education).\u0026nbsp;Participants characteristics prior to imputation were similar\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e(\u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e). \u003cstrong\u003eSupplementary Table S2\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;S3\u003c/strong\u003e present baseline characteristics stratified by dental status before and after multiple imputations. Distributions were generally consistent, with participants who had fewer teeth, especially those not using dental prostheses, being older and having lower income and education levels.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1. Baseline characteristics of participants by number of remaining teeth after multiple imputation.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"672\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003earticipants\u003cbr\u003e\u0026nbsp;(n = 43,774)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 367px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eremaining t\u003c/strong\u003e\u003cstrong\u003eeeth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;20\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eteeth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 15,555)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u0026ndash;19 teeth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 11,441)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u0026ndash;9 teeth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 16,778)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003en\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Men\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e20,493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e7,471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e48.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e47.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e7,609\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e45.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e23,281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8,084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e52.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e52.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9,169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 65\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13,006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e29.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e33.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 70\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13,098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e31.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 75\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e9,748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2,872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2,456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 80\u0026ndash;84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5,462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e7.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2,460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e5.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome (\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eillion JPY)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e17,910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e49.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9,772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e58.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 2.00\u0026ndash;3.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14,391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e32.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e44.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e39.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e33.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4,065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2,077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e13.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (\u003c/strong\u003e\u003cstrong\u003ey\u003c/strong\u003e\u003cstrong\u003eears)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026le;9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e19,401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e44.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e36.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e43.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9,308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e55.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 10\u0026ndash;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e15,338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e35.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e37.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e8,021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e23.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2,163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2,297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eComorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14,111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e7,059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e45.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e43.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e40.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e19,228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e43.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8,444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e54.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e55.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9,954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e59.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e23,390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e53.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9,801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e63.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e59.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e9,661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e57.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Former\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e11,731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e29.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e28.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e4,481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2,289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eonsumption\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Never\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e24,813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8,654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e55.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e11,036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e65.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Past\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1,435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14,996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e37.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; With a spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e31,131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e71.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e12,203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e78.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8,462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e74.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e11,077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e66.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; Without a spouse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e11,711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2,979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e26.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e34.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWalking\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003et\u003c/strong\u003e\u003cstrong\u003eime (min/day)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e12,925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e34.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e31.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 30\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e14,604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e33.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e5,555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e13,626\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e31.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e4,363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e28.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e3,648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e31.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6,531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e38.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBody\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eass\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003endex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026lt;18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e3,047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e7.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1,483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; 18.5\u0026ndash;29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e38,490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e87.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14,314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e92.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e10,498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e14,905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e88.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 183px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026ge;30.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDuring a median follow-up of 2,485 days (interquartile range: 2,218\u0026ndash;2,697 days; maximum: 2,707 days), 5,707 deaths (13.0 %) were recorded. The overall mortality rate for all-cause death was 20.7 per 1,000 person-years. Patients with 0\u0026ndash;9 or 10\u0026ndash;19 teeth exhibited higher all-cause and cause-specific mortality rates compared with those with \u0026ge;20 teeth (\u003cstrong\u003eSupplementary Table S4\u003c/strong\u003e). Among participants with 0\u0026ndash;9 or 10\u0026ndash;19 teeth, those not using dental prostheses had higher all-cause mortality rates than those using dental prostheses. Mortality rates for all-cause and cause-specific mortality by dental status are shown in \u003cstrong\u003eTable 2\u003c/strong\u003e. The highest cause-specific mortality rates for cancer, cardiovascular disease, respiratory disease, infectious and parasitic diseases, endocrine and metabolic diseases, genitourinary diseases, injury, and external causes were observed in participants with 0\u0026ndash;9 teeth who did not use dental prostheses.\u003c/p\u003e\n\u003cp\u003eTable 2. All-cause and cause-specific mortality rates by dental status (\u003cem\u003en\u003c/em\u003e = 43,774).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"668\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll participants (n = 43,774)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 422px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDental Status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality rate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(per 1,000 person-years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;20\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eteeth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 15,555)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u0026ndash;19 teeth with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 7,303)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u0026ndash;19 teeth without\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 4,139)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u0026ndash;9 teeth with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 12,158)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u0026ndash;9 teeth without\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 4,620)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll-\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eause\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e20.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e13.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e17.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e18.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCause-\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003epecific\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eCancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e7.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e11.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eCardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eRespiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eInfectious and parasitic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eEndocrine, nutritional, and metabolic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eMental and behavioral disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eNervous system diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eDigestive diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eGenitourinary diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 162px;\"\u003e\n \u003cp\u003eInjury, poisoning, and external causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCox proportional hazards models with the number of remaining teeth as the explanatory variable are shown in \u003cstrong\u003eTable 3\u003c/strong\u003e. After adjusting for all the covariates, participants with 0\u0026ndash;9 (HR = 1.39 [95% CI 1.30\u0026ndash;1.49]) and 10\u0026ndash;19 teeth (HR = 1.18 [95% CI 1.09\u0026ndash;1.28]) had a higher risk of all-cause mortality compared with those who had \u0026ge;20 teeth.\u0026nbsp;Additionally, participants with 0\u0026ndash;9 teeth had a higher risk of mortality from cancer (HR = 1.31 [95% CI 1.18\u0026ndash;1.45]), cardiovascular disease (HR = 1.37 [95% CI 1.18\u0026ndash;1.59]), respiratory disease (HR = 1.65 [95% CI 1.35\u0026ndash;2.02]), digestive diseases (HR = 2.01 [95% CI 1.30\u0026ndash;3.08]), and injury and external causes (HR = 1.62 [95% CI 1.18\u0026ndash;2.24]). Participants with 10\u0026ndash;19 teeth had a higher risk of mortality from cancer (HR = 1.12 [95% CI 1.00\u0026ndash;1.26]), cardiovascular disease (HR = 1.20 [95% CI 1.02\u0026ndash;1.42]), and respiratory disease (HR = 1.35 [95% CI 1.08\u0026ndash;1.70]).\u003c/p\u003e\n\u003cp\u003eTable 3. Associations between number of remaining teeth and all-cause and cause-specific mortality (\u003cem\u003en\u003c/em\u003e = 43,774).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 520px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eremaining t\u003c/strong\u003e\u003cstrong\u003eeeth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRef. HR = 1:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026ge;20\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eteeth\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(n = 15,555)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u0026ndash;19 teeth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 11,441)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 260px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u0026ndash;9 teeth\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 16,778)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003cstrong\u003e-c\u003c/strong\u003e\u003cstrong\u003eause\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.39 (1.29, 1.51)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.18 (1.09, 1.28)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.30 (2.16, 2.46)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.39 (1.30, 1.49)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCause\u003c/strong\u003e\u003cstrong\u003e-s\u003c/strong\u003e\u003cstrong\u003epecific\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.27 (1.13, 1.43)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.12 (1.00, 1.26)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.86 (1.69, 2.05)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.31 (1.18, 1.45)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eCardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.45 (1.23, 1.72)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.20 (1.02, 1.42)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.51 (2.18, 2.89)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.37 (1.18, 1.59)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eRespiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.66 (1.33, 2.08)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.35 (1.08, 1.70)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e3.16 (2.61, 3.82)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.65 (1.35, 2.02)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eInfectious and parasitic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.03 (0.60, 1.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.86 (0.49, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.19 (1.42, 3.35)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.21 (0.77, 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eEndocrine, nutritional, and metabolic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.44 (0.67, 3.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.16 (0.53, 2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.31 (1.21, 4.43)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.21 (0.61, 2.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eMental and behavioral disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.98 (0.92, 9.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.45 (0.75, 8.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e3.31 (1.07, 10.19)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.92 (0.59, 6.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eNervous diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.09 (0.59, 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e0.95 (0.51, 1.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.07 (1.26, 3.39)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.32 (0.77, 2.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eDigestive diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.66 (1.02, 2.69)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.38 (0.85, 2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e3.35 (2.23, 5.04)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.01 (1.30, 3.08)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eGenitourinary diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.58 (0.88, 2.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.24 (0.69, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.67 (1.62, 4.42)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.21 (0.71, 2.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003eInjury, poisoning, and external causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.53 (1.08, 2.17)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.34 (0.95, 1.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e2.43 (1.80, 3.28)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 130px;\"\u003e\n \u003cp\u003e1.62 (1.18, 2.24)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; 95% CI, 95% confidence interval; Ref, reference.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for sex, age, equivalent income, education, comorbidities, smoking status, alcohol consumption, marital status, walking time, and body mass index.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u003c/p\u003e\n\u003cp\u003eCox proportional hazards models using dental status as the explanatory variable are presented in \u003cstrong\u003eTable 4\u003c/strong\u003e. Participants with 0\u0026ndash;9 teeth (HR = 1.42 [95% CI 1.30\u0026ndash;1.56]) or 10\u0026ndash;19 teeth (HR = 1.23 [95% CI 1.10\u0026ndash;1.37]) who did not use dental prostheses had a higher risk of all-cause mortality compared to those who had \u0026ge;20 teeth. In the cause-specific mortality analysis, compared to those with \u0026ge;20 teeth, those with 0\u0026ndash;9 teeth had a higher risk of mortality due to cancer (HR = 1.31 [95% CI = 1.14\u0026ndash;1.52]), cardiovascular disease (HR = 1.35 [95% CI = 1.11\u0026ndash;1.64]), respiratory disease (HR = 1.72 [95% CI = 1.34\u0026ndash;2.21]), and injury and external cause (HR = 1.91 [95% CI = 1.27\u0026ndash;2.88]). Similarly, participants with 10\u0026ndash;19 teeth who did not use dental prostheses had a higher risk of mortality due to cancer (HR = 1.19 [95% CI = 1.01\u0026ndash;1.39]) and respiratory disease (HR = 1.47 [95% CI = 1.09\u0026ndash;1.98]) than those with \u0026ge;20 teeth. No evidence of differences in all-cause and cause-specific mortality was found between participants with \u0026ge;20 teeth and those who had 10\u0026ndash;19 teeth who used dental prostheses. Similar results were observed in complete-case analyses (\u003cstrong\u003eSupplementary Tables S5\u0026nbsp;\u003c/strong\u003eand S\u003cstrong\u003e6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eTable 4. Associations between dental status and all-cause and cause-specific mortality (n = 43,774).\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"681\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"8\" style=\"width: 566px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003etatus\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRef. HR = 1:\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026ge;20\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eteeth\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(n = 15,555)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u0026ndash;19 teeth with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 7,303)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u0026ndash;19 teeth without\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 4,139)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u0026ndash;9 teeth with\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 12,158)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0\u0026ndash;9 teeth without\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ed\u003c/strong\u003e\u003cstrong\u003eental\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erostheses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n = 4,620)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eode\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003el\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrude\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodel\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003c/strong\u003e\u003cstrong\u003e-c\u003c/strong\u003e\u003cstrong\u003eause\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.36\u003c/p\u003e\n \u003cp\u003e(1.25, 1.49)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(1.06, 1.27)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003cp\u003e(1.30, 1.61)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(1.10, 1.37)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003cp\u003e(2.11, 2.42)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(1.28, 1.49)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003cp\u003e(2.22, 2.64)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003cp\u003e(1.30, 1.56)*\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCause\u003c/strong\u003e\u003cstrong\u003e-s\u003c/strong\u003e\u003cstrong\u003epecific\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eortality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003cp\u003e(1.08, 1.41)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003cp\u003e(0.95, 1.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003cp\u003e(1.15, 1.56)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003cp\u003e(1.01, 1.39)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003cp\u003e(1.66, 2.04)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003cp\u003e(1.17, 1.46)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003cp\u003e(1.67, 2.19)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.31\u003c/p\u003e\n \u003cp\u003e(1.14, 1.52)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eCardiovascular diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003cp\u003e(1.17, 1.72)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003cp\u003e(0.97, 1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.51\u003c/p\u003e\n \u003cp\u003e(1.20, 1.90)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003cp\u003e(0.99, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.50\u003c/p\u003e\n \u003cp\u003e(2.16, 2.90)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(1.18, 1.62)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003cp\u003e(2.10, 3.05)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003cp\u003e(1.11, 1.64)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eRespiratory diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e(1.21, 2.05)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003cp\u003e(0.99, 1.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.81\u003c/p\u003e\n \u003cp\u003e(1.34, 2.44)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003cp\u003e(1.09, 1.98)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003cp\u003e(2.50, 3.73)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003cp\u003e(1.31, 2.00)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.45\u003c/p\u003e\n \u003cp\u003e(2.71, 4.38)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.72\u003c/p\u003e\n \u003cp\u003e(1.34, 2.21)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eInfectious and parasitic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003cp\u003e(0.52, 1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.44, 1.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003cp\u003e(0.50, 2.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003cp\u003e(0.41, 1.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003cp\u003e(1.17, 3.04)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003cp\u003e(0.65, 1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003cp\u003e(1.72, 5.09)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003cp\u003e(0.91, 2.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eEndocrine, nutritional, and metabolic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.38\u003c/p\u003e\n \u003cp\u003e(0.56, 3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003cp\u003e(0.46, 2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003cp\u003e(0.49, 4.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003cp\u003e(0.38, 3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003cp\u003e(0.91, 3.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003cp\u003e(0.48, 2.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003cp\u003e(1.60, 7.61)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003cp\u003e(0.75, 3.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eMental and behavioral disorders\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003cp\u003e(0.70, 9.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003cp\u003e(0.57, 7.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.61\u003c/p\u003e\n \u003cp\u003e(0.90, 14.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.02\u003c/p\u003e\n \u003cp\u003e(0.75, 12.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.57\u003c/p\u003e\n \u003cp\u003e(1.12, 11.38)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.07\u003c/p\u003e\n \u003cp\u003e(0.62, 6.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003cp\u003e(0.51, 12.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003cp\u003e(0.28, 7.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eNervous system diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003cp\u003e(0.54, 2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003cp\u003e(0.47, 1.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(0.42, 2.60)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003cp\u003e(0.37, 2.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003cp\u003e(1.16, 3.38)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003cp\u003e(0.72, 2.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003cp\u003e(1.17, 4.47)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003cp\u003e(0.72, 2.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eDigestive diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003cp\u003e(1.12, 3.19)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.58\u003c/p\u003e\n \u003cp\u003e(0.93, 2.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003cp\u003e(0.62, 2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003cp\u003e(0.50, 2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.82\u003c/p\u003e\n \u003cp\u003e(2.51, 5.80)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003cp\u003e(1.48, 3.54)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003cp\u003e(1.15, 3.95)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.21\u003c/p\u003e\n \u003cp\u003e(0.00, 2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eGenitourinary diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003cp\u003e(0.70, 2.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003cp\u003e(0.55, 2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003cp\u003e(0.89, 4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003cp\u003e(0.70, 3.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003cp\u003e(1.30, 3.91)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003cp\u003e(0.59, 1.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e3.77\u003c/p\u003e\n \u003cp\u003e(2.07, 6.87)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003cp\u003e(0.88, 3.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003eInjury, poisoning, and external causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.61\u003c/p\u003e\n \u003cp\u003e(1.08, 2.38)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003cp\u003e(0.96, 2.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003cp\u003e(0.84, 2.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003cp\u003e(0.73, 2.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.23\u003c/p\u003e\n \u003cp\u003e(1.61, 3.09)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003cp\u003e(1.08, 2.14)\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.93\u003c/p\u003e\n \u003cp\u003e(2.00, 4.30)\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.91\u003c/p\u003e\n \u003cp\u003e(1.27, 2.88)\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HR, hazard ratio; 95% CI, 95% confidence interval; Ref, reference.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for sex, age, equivalent income, education, comorbidities, smoking status, alcohol consumption, marital status, walking time, and body mass index.\u003c/p\u003e\n\u003cp\u003e*p \u0026lt; 0.05, ** p \u0026lt; 0.01, *** p \u0026lt; 0.001.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003ch2\u003e\u003cem\u003eSummary of main findings\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study investigated the associations between the number of remaining teeth and ten types of cause-specific mortality outcomes, as well as the role of dental prostheses in modifying the impact of tooth loss on mortality. An inverse dose-response relationship was observed between the number of remaining teeth and mortality risk in older adults, with fewer teeth associated with increased mortality risk, varying by cause of death. Specifically, participants with 0\u0026ndash;9 or 10\u0026ndash;19 had a higher risk of mortality from cancer, cardiovascular disease, respiratory disease, digestive disease, injury, and external causes. However, among those with fewer teeth, individuals who used dental prostheses exhibited a lower mortality risk than those without dental prostheses, particularly for deaths due to respiratory disease, infectious and parasitic diseases, endocrine, nutritional, and metabolic diseases, nervous system disease, genitourinary disease, injury or external causes. These results indicate that although tooth loss may increase mortality risk from various causes, dental prostheses may reduce these adverse effects.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eComparison with previous findings and the explanation of possible mechanism\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThe findings align with a previous meta-analysis reporting an association between tooth loss and increased all-cause mortality risk\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;In the cause-specific mortality analysis, after adjusting for potential confounders, participants with 0\u0026ndash;9 teeth had a significantly higher mortality risk from cancer, cardiovascular diseases, and respiratory diseases. These results are consistent with prior studies reporting associations between the number of remaining teeth and mortality from cancer\u003csup\u003e6,22,23\u003c/sup\u003e, cardiovascular disease\u003csup\u003e5,18,23\u003c/sup\u003e, and respiratory disease\u003csup\u003e5,23\u003c/sup\u003e. Furthermore, this study revealed that individuals with 0\u0026ndash;9 teeth also had a significantly higher risk of mortality from digestive system diseases, injury, poisoning, and other external causes. These findings contribute valuable insight into the specific causes of death potentially associated with tooth loss.\u003c/p\u003e\n\u003cp\u003eAmong individuals with 0\u0026ndash;9 teeth, those using dental prostheses generally had lower mortality risks for several causes, including respiratory disease, infectious and parasitic diseases, endocrine and metabolic diseases, nervous system diseases, genitourinary diseases, injury, poisoning, and external causes, compared with those not using dental prostheses. These findings are consistent with a prior study suggesting that dental prosthesis use in patients with partial tooth loss is associated with reduced mortality risk from cardiovascular disease, respiratory disease, and other causes\u003csup\u003e18\u003c/sup\u003e, indicating a potential protective effect.\u0026nbsp;However, some studies reported no statistically significant associations between tooth loss and cancer or cardiovascular mortality\u003csup\u003e24,25\u003c/sup\u003e, or an association limited to oro-digestive cancer mortality\u003csup\u003e26\u003c/sup\u003e. These discrepancies may be attributed to smaller sample sizes and limited statistical power in those studies.\u003c/p\u003e\n\u003cp\u003eSeveral mechanisms may explain the observed association between the number of teeth and mortality. First, tooth loss impairs masticatory function. Reduced chewing ability can lead to the avoidance of hard, nutrient-dense foods such as fruits, vegetables, and meats, resulting poorer dietary quality\u003csup\u003e27\u003c/sup\u003e. This may lead to\u0026nbsp;insufficient intake of essential micronutrients, which could impair immune function, decrease antioxidant capacity, and hinder cellular repair, thereby increasing vulnerability to cancer, cardiovascular diseases,\u0026nbsp;and respiratory infections\u003csup\u003e28,29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSecond, systemic inflammation may play a role. Tooth loss may reflect poor oral health and a history of periodontal disease, which is associated with elevated levels of systemic inflammatory markers such as C-reactive protein (CRP), tumor necrosis factor, and interleukins. This chronic inflammation may contribute to cardiovascular mortality by promoting atherosclerosis and increasing the risk of acute cardiovascular events\u003csup\u003e30\u003c/sup\u003e. Inflammation may also contribute to cancer development by impairing DNA repair and increasing genomic instability\u003csup\u003e31\u003c/sup\u003e. Additionally, repeated bacteremia from oral pathogens, such as \u003cem\u003ePorphyromonas gingivalis,\u0026nbsp;\u003c/em\u003emay promote sustained systemic inflammation, disrupt\u0026nbsp;the gut microbiota\u0026nbsp;and immune homeostasis, and increase the risk of cancer and digestive diseases, including liver-related outcomes\u003csup\u003e32\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTooth loss may also elevate the risk of injury-related mortality through its contribution to frailty and cognitive impairment, both of which are established risk factors for falls and injury\u003csup\u003e33,34\u003c/sup\u003e. Although this association is likely indirect, the findings suggest that tooth loss may increase vulnerability to accidental deaths through its adverse impact on physical and cognitive functions.\u003c/p\u003e\n\u003cp\u003eDental prostheses may mitigate these risks in older adults by restoring oral function, improving masticatory performance, and enhancing swallowing ability\u003csup\u003e35\u003c/sup\u003e. These improvements can facilitate better food selection and improved nutritional status\u003csup\u003e36\u003c/sup\u003e. Moreover, dental prostheses may help reduce the risk of cognitive decline associated with tooth loss among older adults\u003csup\u003e37\u003c/sup\u003e. Therefore, replacing missing teeth in older adults may be essential to prevent downstream health complications that contribute to increased mortality risk.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eImplications\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study underscores the importance of oral health, particularly the number of remaining teeth,\u0026nbsp;as a potential determinant of mortality from various causes. Public health strategies should prioritize population-level interventions that address the broader determinants of oral health to reduce the burden of tooth loss and associated diseases across the lifespan\u003csup\u003e38\u003c/sup\u003e. Additionally, the findings highlights the role of dental prostheses in restoring oral function among individuals with tooth loss, thereby improving overall health and reducing mortality risk. However, socioeconomic disparities in the utilization of dental prosthesis remain a significant concern. Expanding dental coverage under universal health coverage (UHC) may help address these disparities\u003csup\u003e39\u003c/sup\u003e. Policies aimed at reducing financial and access-related barriers to dental prostheses use are essential to promote equitable oral healthcare globally.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003e\u003cem\u003eLimitations and\u0026nbsp;\u003c/em\u003e\u003cem\u003estrengths\u003c/em\u003e\u003cem\u003e\u0026nbsp;of the\u0026nbsp;\u003c/em\u003e\u003cem\u003estudy\u003c/em\u003e\u003c/h2\u003e\n\u003cp\u003eThis study has several limitations. First, the use of self-reported data on the number of remaining teeth and dental prostheses may be subject to recall bias, potentially leading to exposure misclassification. However, previous studies have validated self-reported tooth counts\u003csup\u003e12\u003c/sup\u003e, suggesting limited bias from self-reporting. Second, selection bias may be present due to the baseline questionnaire response rate of\u0026nbsp;64.7%. Nonetheless, previous research has indicated that while self-selection bias may affect\u0026nbsp;prevalence estimates, its impact on associations is generally minimal\u003csup\u003e40\u003c/sup\u003e. Third, despite adjustment for a wide range of confounders factors, residual confounding due to unmeasured variables cannot be entirely excluded. Lastly, the study population comprised older adults in Japan, which may limit the generalizability of the findings. However, consistency with\u0026nbsp;studies from other countries\u0026nbsp;supports cautious generalization to broader populations. Despite these limitations, the study possesses notable strengths, including the use of nationwide prospective cohort data and a large sample size (\u003cem\u003en\u003c/em\u003e = 43,774), which enhance the robustness of the findings.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study investigated the association between dental status and the mortality risk from various causes. Tooth loss was found to be significantly associated with an increased risk of mortality from cancer, cardiovascular disease, respiratory disease, digestive disease, injury, poisoning, and other external causes. Additionally, the use of dental prostheses was observed to mitigate the mortality risk associated with tooth loss, although this protective effect varied by cause of death. Addressing the burden of tooth loss and reducing socioeconomic disparities in dental prosthesis use through equitable healthcare policies and expanded dental coverage are essential for improving health outcomes, particularly among older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis study used data from JAGES (the Japan Gerontological Evaluation Study). It was supported by Grant-in-Aid for Scientific Research (20H00557, 20K10540, 21H03196, 21K17302, 22H00934, 22H03299, 22K04450, 22K13558, 22K17409, 23H00449, 23H03117, 23K21500) from the Japan Society for the Promotion of Science (JSPS); Health Labour Sciences Research Grants (19FA1012, 19FA2001, 21FA1012, 22FA2001, 22FA1010, 22FG2001); the Research Institute of Science and Technology for Society (JPMJOP1831) from the Japan Science and Technology (JST); a grant from the Japan Health Promotion \u0026amp; Fitness Foundation; the TMDU priority research areas grant; and the National Research Institute for Earth Science and Disaster Resilience. The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policies or positions of the respective funding organizations.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eFA, TK, YT, and SK contributed to the conception and design, data analysis and interpretation, and drafted and critically revised the manuscript. MS, TO, JA, KK, KO, and KT contributed to the conception and design, data acquisition and interpretation, and drafted and critically revised the manuscript. All authors have approved the submitted version and agree to be accountable for all aspects of this work.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGBD 2017 Oral Disorders Collaborators \u003cem\u003eet al.\u003c/em\u003e Global, Regional, and National Levels and Trends in Burden of Oral Conditions from 1990 to 2017: A Systematic Analysis for the Global Burden of Disease 2017 Study. \u003cem\u003eJ Dent Res\u003c/em\u003e \u003cstrong\u003e99\u003c/strong\u003e, 362\u0026ndash;373 (2020).\u003c/li\u003e\n\u003cli\u003eKassebaum, N. 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L. \u003cem\u003eet al.\u003c/em\u003e Inverse probability weighting for self-selection bias correction in the investigation of social inequality in mortality. \u003cem\u003eInternational Journal of Epidemiology\u003c/em\u003e \u003cstrong\u003e53\u003c/strong\u003e, dyae097 (2024).\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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"dentures, cause of death, oral health, longitudinal studies, periodontal disease, dental caries","lastPublishedDoi":"10.21203/rs.3.rs-6962054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6962054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePoor oral health, particularly tooth loss, is associated with increased risk of all-cause mortality. However, the impact of dental prosthesis use on these associations remains unclear. This study evaluated the relationship between the number of remaining teeth, dental prosthesis use, and mortality from specific causes using seven-year follow-up data from the Japan Gerontological Evaluation Study (JAGES). All-cause mortality and ten cause-specific mortality outcomes were assessed. The number of remaining teeth and use of dental prostheses were the main exposures. Cox proportional hazard models were used to estimate hazard ratios (HRs). Among 43,774 participants (53.2% women; mean age, 73.7 years [SD, 5.9]), 13.0% (n\u0026thinsp;=\u0026thinsp;5,707) died during the follow-up period, yielding a mortality rate of 20.7 per 1,000 person-years. Participants with 0\u0026ndash;9 or 10\u0026ndash;19 teeth without dental prostheses exhibited higher cause-specific mortality rates than those with \u0026ge;\u0026thinsp;20 teeth. Specifically, those with 0\u0026ndash;9 teeth without dental prostheses had a significantly elevated risk of mortality from cancer, cardiovascular disease, respiratory disease, and injury (HRs ranging from 1.31 to 1.91; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These associations were attenuated among dental prostheses users. Tooth loss was associated with increased risk of mortality from multiple specific causes; however, dental prostheses may mitigate this risk.\u003c/p\u003e","manuscriptTitle":"Dental status and cause-specific mortality in older adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 05:35:30","doi":"10.21203/rs.3.rs-6962054/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-19T11:47:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-18T16:37:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189992835663315676351378536418968594862","date":"2025-08-06T07:50:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-28T00:09:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209665194269078816960334675706990200716","date":"2025-07-24T04:41:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-23T06:04:58+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-11T05:36:40+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-26T07:22:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-26T04:32:03+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-06-24T06:06:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa9d14ed-1f33-49b3-8116-b8806940cb93","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52031970,"name":"Health sciences/Diseases/Oral diseases"},{"id":52031971,"name":"Health sciences/Health care/Dentistry/Dental public health"},{"id":52031972,"name":"Health sciences/Medical research/Epidemiology"},{"id":52031973,"name":"Health sciences/Health care/Geriatrics"},{"id":52031974,"name":"Health sciences/Health care/Dentistry/Prosthetic dentistry"}],"tags":[],"updatedAt":"2025-11-24T16:05:13+00:00","versionOfRecord":{"articleIdentity":"rs-6962054","link":"https://doi.org/10.1038/s41598-025-24357-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-11-19 15:57:05","publishedOnDateReadable":"November 19th, 2025"},"versionCreatedAt":"2025-07-28 05:35:30","video":"","vorDoi":"10.1038/s41598-025-24357-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-24357-1","workflowStages":[]},"version":"v1","identity":"rs-6962054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6962054","identity":"rs-6962054","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}