Tooth loss is associated with reduced muscle mass: the mediating roles of DII and CDAI

Tooth loss is associated with reduced muscle mass: the mediating roles of DII and CDAI | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tooth loss is associated with reduced muscle mass: the mediating roles of DII and CDAI Shifu Bao, Nai Mu, Shuxing Xing, Tao Li, Zheng Zhou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8808434/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Tooth loss may contribute to sarcopenia not only through impaired mastication but also by altering diet-related inflammation and antioxidant capacity. We investigated whether the dietary inflammatory potential index (DII) and composite dietary antioxidant capacity (CDAI) mediate the association between tooth loss and skeletal muscle mass. Methods Using NHANES 2011–2018, we included adults aged ≥ 20 years who had dentition examinations and DXA-derived appendicular skeletal muscle measurements (n = 10,080). Dentition status was categorized by the number of remaining natural teeth (NRT; 16 vs ≤ 16). ASMI was calculated as ASM/height². We conducted weighted multivariable regression, performed prespecified subgroup analyses, and applied parallel mediation with 5,000 bootstrap resamples. Results In fully adjusted models, more favourable dentition status was independently associated with higher ASMI (NRT ≥ 20: β = 0.15, 95% CI 0.04–0.26, P = 0.01; NFTL ≤ 16: β = 0.20, 95% CI 0.07–0.33, P = 0.004). Both DII and CDAI partially mediated these relationships (NRT: 10.09% and 4.61%; NFTL: 7.98% and 3.72%; all P < 0.05). The associations were generally consistent across strata and were more pronounced among men, current smokers, and individuals with dysglycaemia-related phenotypes. Conclusions Among U.S. adults, tooth loss was associated with lower skeletal muscle mass, and diet-related inflammatory potential and oxidant capacity explained a measurable proportion of this association. The results support an “oral health–dietary inflammation/oxidation status–muscle” axis and suggest that improving diet quality may represent a modifiable pathway for sarcopenia prevention, particularly for adults with poor dentition status. tooth loss skeletal muscle diet quality CDAI NHANES cross-sectional study Figures Figure 1 Figure 2 Figure 3 1 Introduction Population aging represents a critical global public health challenge. In 2021, people aged 60 years or older constituted 11.6% of the global population, and this proportion is expected to increase to 20.1% by 2050 1 . Sarcopenia is a multifactorial geriatric syndrome primarily defined by a progressive loss of skeletal muscle mass and strength. This condition markedly impairs physical function, substantially reduces quality of life, and heightens the risk of chronic disease and premature mortality. Epidemiological evidence shows that individuals with sarcopenia have an approximately 50% greater risk of falls and a 30% higher probability of developing mild cognitive impairment than those without sarcopenia 2 , 3 . The prevalence of sarcopenia has been estimated to be 10–16% 4 . Because of its high frequency in aging populations, the resulting economic burden at both societal and individual levels is considerable and should not be overlooked. To support the early identification of older adults at risk of sarcopenia, the Asian Working Group for Sarcopenia (AWGS) proposed the concept of “possible sarcopenia” in 2019, defined by either low muscle strength or impaired physical performance. Growing evidence indicates that reductions in muscle mass represent a robust marker for the diagnosis of sarcopenia. When advanced diagnostic equipment is unavailable, this criterion facilitates the early recognition of high-risk individuals and thus permits timely intervention. Cross-sectional and longitudinal investigations in older populations have consistently demonstrated that, regardless of sex, muscle strength and muscle mass start to decline gradually as early as the third or fourth decade of life 5 , 6 . Prior studies have highlighted the crucial role of skeletal muscle in preserving overall health, showing its ability to postpone the onset of multiple age-related conditions, including obesity, diabetes, cardiovascular disease (CVD), cerebrovascular events, and certain malignancies 7 . Tooth loss results from a multifaceted interaction among various risk factors. Proximal clinical determinants, including dental caries and periodontal disease, directly lead to tooth loss, whereas health-related behaviours and systemic health conditions influence it indirectly. Tooth loss not only markedly compromises masticatory function 8 , 9 but also alters dietary patterns, thereby producing far-reaching effects on oral health and overall quality of life 10 . An expanding body of evidence has demonstrated a strong link between oral health and systemic health. Tooth loss has been recognized as an independent risk factor for several non-communicable diseases, such as CVD, diabetes, hypertension, and chronic kidney disease 11 – 14 . Chronic low-grade inflammation is a hallmark of the pathophysiology underlying tooth loss. Current evidence suggests that chronic oral conditions, particularly periodontal disease, substantially increase cellular production of pro-inflammatory cytokines, including interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α) 15–18 . Persistent oral infection together with the associated inflammatory response can provoke systemic disturbances, such as impaired protein synthesis and metabolic dysregulation 19 – 21 . These systemic changes may lead to reduced synthesis of albumin and insulin-like growth factor-1 (IGF-1), both of which are essential for preserving muscle mass and strength 22 , 23 . Dietary patterns characterized by a high inflammatory potential, particularly those rich in fats and refined sugars, have been reported to promote chronic systemic inflammation 24 . In contrast, healthier dietary patterns that are abundant in anti-inflammatory components can mitigate the adverse effects of systemic inflammatory responses 25 . To quantify the inflammatory potential of diet, Shivappa et al. developed the Dietary Inflammatory Index (DII), which rates an individual’s diet according to the pro- or anti-inflammatory properties of specific dietary constituents, thereby allowing the diet–inflammation relationship to be quantified 26 . Since its development, the DII has been extensively used in epidemiological research to clarify associations between dietary exposures and disease outcomes mediated through inflammatory pathways, yielding numerous important insights 27 – 34 . Parallel to the DII, the Composite Dietary Antioxidant Index (CDAI)—which combines multiple dietary antioxidant components, including vitamins A, C, and E, zinc, selenium, and carotenoids—was used to reflect the overall antioxidant capacity of the diet. Taken together, these observations support a plausible mechanistic framework whereby oral disease and diet influence the risk of sarcopenia through inflammatory and metabolic pathways. Nevertheless, the precise nature of these interactions remains poorly characterized, and additional research is needed to elucidate their underlying biological connections. Achieving a more comprehensive understanding of the interrelationships among oral disease, dietary habits, and sarcopenia, as well as the potential mediating factors, will yield valuable clinical insights for general practitioners and other healthcare professionals. Such insights may aid in the early identification of individuals in subclinical stages of sarcopenia and guide preventive strategies designed to delay or mitigate age-related muscle loss. Against this backdrop, the present study draws on data from the National Health and Nutrition Examination Survey (NHANES) to assess the association between tooth loss and appendicular skeletal muscle mass index (ASMI), and to further determine whether this association is mediated by dietary factors. 2 Methods This cross-sectional study was based on data from the NHANES ( http://www.cdc.gov/nchs/nhanes.htm ), an ongoing, nationally representative survey designed to assess the health and nutritional status of the U.S. population. The NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics, with the most recent approval granted in August 2022, and written informed consent was obtained from all participants. For the current analysis, we included data from the 2011–2018 survey cycles. Among the 39,156 individuals initially enrolled, we excluded participants younger than 20 years (n = 16,539), individuals without appendicular skeletal muscle (ASM) measurements (n = 11,869), and those missing data on DII and CDAI (n = 668). Consequently, the final analytic sample consisted of 10,080 participants aged ≥ 20 years. The process of participant selection is illustrated in Fig. 1 . 2.1 Muscle mass ASM was measured using dual-energy X-ray absorptiometry (DXA), a technique that quantifies lean soft tissue mass in the upper and lower extremities. ASM was obtained as the sum of lean tissue mass in both arms and both legs. To account for differences in body size, the appendicular skeletal muscle mass index (ASMI) was calculated by dividing ASM by height squared (ASM/height²). According to clinical diagnostic criteria, ASMI values < 5.5 kg/m² in women or < 7.0 kg/m² in men were regarded as indicative of sarcopenia. 35 – 37 2.2 Remaining Natural Teeth (NRT) During the oral health examination, the number of remaining natural teeth (NRT) was categorized into two groups: “more natural teeth” (NRT ≥ 20) and “fewer natural teeth” (NRT < 20). This categorization aligns with the World Health Organization’s functional dentition goal, which states that retaining at least 20 natural teeth is necessary to preserve adequate oral function in older adults. 38 2.3 Functional Tooth Loss (FTL) Functional tooth loss (FTL) was defined as encompassing both missing natural teeth and non-functional teeth (e.g., residual roots, third molars). Prior studies have indicated that FTL more accurately reflects oral health–related quality of life (OHRQoL) in older adults and may serve as a potential predictor of sarcopenia. 39 Evidence from elderly dental clinic patients has shown an association between sarcopenia and OHRQoL, and the loss of 16 functional teeth has been proposed as a threshold beyond which oral health markedly impairs quality of life. 40 Consistent with this evidence, the number of functional tooth losses (NFTL) was categorized as “low FTL” (NFTL ≤ 16) or “high FTL” (NFTL > 16), permitting a more refined assessment of the impact of oral ageing. 2.4 Dietary Inflammatory Index and Composite Dietary Antioxidant Index In NHANES, the DII is derived from 27 dietary components and has shown predictive performance for inflammation-related outcomes that is comparable to estimates obtained using the full set of 45 components 41 . A DII score ≥ 0 indicates a pro-inflammatory diet, whereas a score < 0 reflects an anti-inflammatory diet. Higher DII scores correspond to less healthy, more pro-inflammatory dietary patterns, while lower scores denote healthier, anti-inflammatory dietary habits 42 , 43 . Dietary antioxidant capacity was estimated using the CDAI, which is based on an improved Wright methodology and incorporates six major dietary antioxidant components: selenium, zinc, vitamins A, C, and E, and carotenoids. For each micronutrient, intake was standardized against global reference values, and the CDAI was computed as the sum of these standardized scores 44 . 2.5 Covariates On the basis of prior epidemiological evidence and clinical judgment, we adjusted for a range of potential confounders that might affect the association between tooth loss and muscle mass. The covariates considered were: age (years); gender (male/female); race/ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, other); marital status (married or living with a partner vs. widowed/divorced/separated/never married); educational attainment (less than 12th grade, high school graduate or equivalent, some college or AA degree, college graduate or above); poverty-to-income ratio (PIR); smoking status (never, former, current); alcohol consumption (never, former, mild, moderate, heavy); physical activity (PA; yes/no); hypertension (yes/no); diabetes (yes/no); and CVD (yes/no).PA data were converted to metabolic equivalent (MET) minutes of moderate-to-vigorous physical activity per week. Participants were then classified as either meeting (≥ 600 MET-minutes/week) or not meeting (< 600 MET-minutes/week) the recommended physical activity guidelines for adults. 45 To more fully account for nutritional and metabolic status, we additionally adjusted for body mass index (BMI, kg/m²), total protein (g/dL), blood urea nitrogen (mg/dL), serum creatinine (mg/dL), serum uric acid (mg/dL), serum calcium (mg/dL), alkaline phosphatase (ALP, U/L), and serum phosphorus (mg/dL). Detailed definitions, measurement protocols, and coding schemes for all variables are available in the NHANES documentation provided by the National Center for Health Statistics.( https://www.cdc.gov/nchs/nhanes/ ). 2.6 Statistical Analysis In line with guidelines from the Centers for Disease Control and Prevention (CDC), all statistical analyses incorporated the appropriate NHANES sampling weights to account for the complex, multistage survey design. The survey weights were recalibrated according to the analytic recommendations issued by the National Center for Health Statistics. Continuous variables are reported as weighted means ± standard error (SE), whereas categorical variables are summarized as weighted proportions. Between-group differences were assessed using weighted Student’s t-tests for continuous variables and weighted chi-square tests for categorical variables. To estimate the association between tooth loss and ASMI, we specified three hierarchical models. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race. Model 3 additionally adjusted for age, sex, race, marital status, educational level, PIR, smoking status, alcohol consumption, PA, hypertension, diabetes, CVD, BMI, total protein, blood urea nitrogen, serum creatinine, serum uric acid, serum calcium, ALP, and serum phosphorus. Stratified analyses were carried out to investigate potential effect modification in the associations between NRT, NFTL, and ASMI. The pre-specified stratification variables were age (≤ 45 vs. >45 years), sex (female vs. male), race (non-Hispanic White vs. non-Hispanic Black vs. Mexican American vs. other), PIR (3), PA (no vs. yes), smoking status (never vs. former vs. current), BMI (< 25 vs. 25–30 vs. ≥30 kg/m²), hypertension (yes vs. no), and diabetes (yes vs. no). These variables were considered a priori as potential effect modifiers. Mediation analyses were conducted with the “mediation” package in R (version 4.1.3), using 5,000 bootstrap resamples to examine the mediating roles of DII and CDAI in the associations of NRT and NFTL with ASMI. The direct effect was defined as the effect of NRT and NFTL on ASMI that did not operate through DII or CDAI. The indirect effects represented the extent to which DII and CDAI mediated the associations between NRT, NFTL, and ASMI. The proportion mediated was obtained by dividing the indirect effect by the total effect. All data management and statistical analyses were performed in R version 4.1.3, and a two-sided P value < 0.05 was taken to indicate statistical significance. 3 Results 3.1 Baseline characteristics of participants Table 1 summarizes the baseline characteristics of the study population. In total, 10,080 participants were included; 51.68% were men and 48.32% were women, and the weighted mean age was 39.05 ± 0.26 years. Table 1 Characteristics of the study participants according to NRT and NFTL. Variable Total NFTL Pvalue NRT Pvalue NFTL>16 NFTL ≤ 16 NRT < 20 NRT ≥ 20 Age(years) 39.05 ± 0.26 49.54 ± 0.49 38.60 ± 0.26 < .0001 49.74 ± 0.40 38.40 ± 0.27 < .0001 Gender % 0.61 0.93 Female 4952( 48.32) 256(49.66) 4696(48.26) 363(48.12) 4589(48.33) Male 5128(51.68) 251(50.34) 4877(51.74) 344(51.88) 4784(51.67) Race % 0.004 < .001 Black 2077(11.07) 133(16.02) 1944(10.86) 197(16.78) 1880(10.72) Mexican American 1511(10.62) 34( 4.28) 1477(10.89) 59( 5.47) 1452(10.93) Other 3038(17.36) 127(16.60) 2911(17.39) 178(16.27) 2860(17.42) White 3454(60.96) 213(63.09) 3241(60.86) 273(61.48) 3181(60.92) Smoking status % < .0001 < .0001 former 1681(19.48) 107(18.98) 1574(19.50) 148(22.01) 1533(19.33) never 6162(59.16) 139(20.53) 6023(60.81) 218(23.48) 5944(61.33) now 2237(21.35) 261(60.49) 1976(19.69) 341(54.51) 1896(19.34) Drinking status % < .0001 < .0001 former 918( 8.07) 113(23.33) 805( 7.42) 138(21.25) 780( 7.27) heavy 2586(27.16) 119(25.83) 2467(27.22) 170(26.59) 2416(27.19) mild 3364(34.58) 134(23.97) 3230(35.03) 198(25.22) 3166(35.15) moderate 1844(20.16) 86(18.26) 1758(20.24) 111(17.34) 1733(20.33) never 1368(10.03) 55( 8.61) 1313(10.10) 90( 9.60) 1278(10.06) Diabetes % < .0001 < .0001 DM 1111( 8.47) 123(20.63) 988( 7.95) 165(19.20) 946(7.82) IFG 408( 4.21) 26(7.55) 382(4.07) 41(7.44) 367(4.02) IGT 280(2.51) 14(2.48) 266(2.51) 18(2.86) 262(2.49) no 8281(84.81) 344(69.33) 7937(85.47) 483(70.50) 7798(85.68) Hypertension % < .0001 < .0001 no 7324(73.83) 266(57.03) 7058(74.54) 366(54.09) 6958(75.02) yes 2756(26.17) 241(42.97) 2515(25.46) 341(45.91) 2415(24.98) Cardiovascular diseases % < 0.0001 < .0001 no 9732(97.12) 433(83.29) 9299(97.70) 613(85.76) 9119(97.81) yes 348( 2.88) 74(16.71) 274( 2.30) 94(14.24) 254( 2.19) Education % < .0001 < .0001 College graduate or above 2772(32.53) 40( 7.72) 2732(33.58) 59( 7.46) 2713(34.05) High School Grade or Equivalent 2190(21.46) 158(34.96) 2032(20.89) 224(37.67) 1966(20.48) Less than 12th grade 1862(13.24) 171(31.95) 1691(12.45) 228(29.20) 1634(12.28) Some college or AA degree 3256(32.77) 138(25.38) 3118(33.08) 196(25.67) 3060(33.20) Marital status % < .0001 < .0001 Divorced 886( 8.88) 100(18.41) 786( 8.47) 133(17.39) 753( 8.36) Living with partner 1125(10.72) 57(11.26) 1068(10.70) 73(10.02) 1052(10.77) Married 4923(51.24) 214(44.97) 4709(51.50) 312(48.65) 4611(51.39) Never married 2671(25.37) 86(16.55) 2585(25.75) 119(15.63) 2552(25.97) Separated 346( 2.68) 30(5.45) 316(2.56) 43(5.08) 303(2.54) Widowed 129( 1.10) 20(3.35) 109(1.01) 27(3.24) 102(0.97) Physical activity % < 0.0001 < .0001 no 3174(28.64) 221(42.77) 2953(28.06) 301(39.83) 2873(27.97) yes 6906(71.35) 286(57.22) 6620(71.96) 406(60.17) 6500(72.03) PIR 2.93 ± 0.05 1.82 ± 0.11 2.97 ± 0.05 < 0.0001 1.99 ± 0.10 2.98 ± 0.05 < 0.0001 BMI(kg/m²) 28.52 ± 0.13 28.46 ± 0.41 28.52 ± 0.14 0.88 28.77 ± 0.37 28.51 ± 0.14 0.49 Alkaline phosphatase(U/L) 66.63 ± 0.40 75.84 ± 1.87 66.23 ± 0.41 < .0001 76.58 ± 1.55 66.02 ± 0.42 < 0.0001 Serum creatinine(mg/dL) 0.85 ± 0.00 0.88 ± 0.01 0.85 ± 0.00 0.07 0.87 ± 0.01 0.85 ± 0.00 0.1 Total protein(g/dL) 7.14 ± 0.01 7.06 ± 0.03 7.15 ± 0.01 0.01 7.10 ± 0.03 7.15 ± 0.01 0.07 Serum uric acid(mg/dL) 5.33 ± 0.02 5.23 ± 0.11 5.34 ± 0.02 0.35 5.28 ± 0.09 5.33 ± 0.02 0.56 Blood urea nitrogen(mg/dL) 12.77 ± 0.08 11.86 ± 0.35 12.81 ± 0.08 0.01 11.97 ± 0.28 12.82 ± 0.08 0.003 Serum phosphorus(mg/dL) 3.72 ± 0.01 3.66 ± 0.04 3.73 ± 0.01 0.07 3.66 ± 0.03 3.73 ± 0.01 0.03 Serum calcium(mg/dL) 9.38 ± 0.01 9.36 ± 0.02 9.38 ± 0.01 0.44 9.35 ± 0.02 9.38 ± 0.01 0.12 ASMI 7.95 ± 0.03 7.49 ± 0.09 7.97 ± 0.03 < .0001 7.65 ± 0.09 7.97 ± 0.03 0.002 DII 1.31 ± 0.04 2.11 ± 0.11 1.28 ± 0.04 < .0001 2.09 ± 0.08 1.27 ± 0.04 < 0.0001 CDAI 1.02 ± 0.07 -0.28 ± 0.19 1.07 ± 0.07 < .0001 -0.18 ± 0.15 1.09 ± 0.07 < .0001 Data are expressed as weighted means ± SD or percentages (%). BMI, body mass index, PIR, poverty income ratio, DM, diabetes mellitus; IFG, impaired fasting glucose; IGT, impaired glucose tolerance, DII, dietary inflammatory potential index; CDAI, composite dietary antioxidant capacity; NRT: the number of remaining natural teeth; NFTL: the number of functional tooth loss; ASMI: appendicular skeletal muscle mass index. When participants were categorized according to dentition status, substantial differences in sociodemographic, behavioural, and clinical characteristics became apparent. Relative to individuals with NRT < 20, those with NRT ≥ 20 showed significant differences in age, race/ethnicity, PIR, educational attainment, marital status, smoking status, alcohol consumption, PA, CDAI, DII, hypertension, diabetes, blood urea nitrogen, alkaline phosphatase, and serum phosphorus (all P 16), significant between-group differences were observed in age, race/ethnicity, PIR, educational attainment, marital status, smoking status, alcohol consumption, PA, CDAI, DII, hypertension, diabetes, blood urea nitrogen, alkaline phosphatase, and total protein (all P < 0.05). Taken together, NRT and NFTL displayed consistent patterns: individuals with more favourable oral status (NRT ≥ 20 or NFTL ≤ 16) were generally younger, had higher socioeconomic status, and were more likely to engage in healthier lifestyles, accompanied by a lower burden of chronic conditions and more favourable metabolic and nutritional profiles. 3.2 Association of NRT and NFTL with ASMI Regression analyses showed a significant association between dentition status and ASMI (Table 2 ). In the fully adjusted model, retaining at least 20 natural teeth was linked to a higher ASMI (β = 0.15, 95% CI: 0.04–0.26, P = 0.01). Similarly, losing no more than 16 functional teeth was strongly related to a higher ASMI (β = 0.20, 95% CI: 0.07–0.33, P = 0.004). Taken together, these results indicate that preservation of oral function may help maintain muscle mass and physical performance, particularly in ageing populations. Table 2 Multivariate linear analysis of the association between tooth loss and ASMI. Variables Items ASMI, β(95%CI), p-value Crude model Model 1 Model 2 NRT NRT < 20 Reference NRT ≥ 20 0.32(0.13,0.52)0.002 0.39( 0.20, 0.58) 16 -0.48(-0.66,-0.29) < 0.0001 -0.5(-0.71,-0.29) < 0.0001 -0.2(-0.33,-0.07)0.004 Data are presented as β coefcient (95% CI). Model 1 was unadjusted; Model 2 adjusted for gender, age, and race; Model 3 included additional adjustments for marital status, education, poverty income ratio, smoking status, drinking status, physical activity, hypertension, diabetes, cardiovascular diseases, body mass index, and biochemical markers (total protein, blood urea nitrogen, serum creatinine, serum uric acid, serum calcium, alkaline phosphatase, and serum phosphorus).NRT: the number of remaining natural teeth; NFTL: the number of functional tooth loss; ASMI: appendicular skeletal muscle mass index. 3.3 Mediation analyses Parallel mediation analyses were undertaken to determine whether dietary factors mediated the associations of NRT and NFTL with ASMI. As shown in Fig. 2 , both DII and CDAI had significant mediating roles. For NRT, DII and CDAI explained 10.09% and 4.61% of the total effect, respectively (both P < 0.05). For NFTL, the corresponding mediated proportions were 7.98% and 3.72% (both P < 0.05). 3.4 Subgroup analyses Subgroup analyses were conducted to examine whether the associations between dentition status (NRT and NFTL) and ASMI were consistent across different population strata (Fig. 3 ). Overall, the direction of association was comparable for both dentition indicators: relative to the reference groups, having more natural teeth (NRT ≥ 20) and lower functional tooth loss (NFTL ≤ 16) was associated with higher ASMI. These associations appeared particularly prominent among men, current smokers, and individuals with dysglycaemia-related conditions. 4 Discussion This study investigated the association between tooth loss as an exposure and skeletal muscle mass in a nationally representative sample of U.S. adults, and additionally explored the potential mediating roles of the DII and CDAI. Our main findings can be summarized as follows: (1) greater severity of tooth loss was linked to lower muscle mass, indicating a significant positive association between dentition status and sarcopenia risk—succinctly captured as “more missing teeth, less muscle”; and (2) both DII and CDAI partially mediated the relationship between tooth loss and muscle mass. These findings suggest that tooth loss may influence muscle mass not only directly, via impaired masticatory function, but also indirectly, by shaping the inflammatory and antioxidant profiles of dietary patterns and thereby affecting the onset and progression of sarcopenia. Taken together, these observations support the presence of a continuous “oral health–oxidation/inflammation–muscle” pathway, offer more nuanced mechanistic evidence linking oral health to sarcopenia, and highlight the importance of anti-inflammatory and antioxidant dietary patterns in mitigating muscle loss. Accumulating evidence indicates that a reduced number of teeth and impaired oral function are closely linked to an elevated risk of sarcopenia. In a Korean population, Han et al. showed that tooth loss was significantly associated with sarcopenia and suggested that diminished chewing ability may reduce food intake and lead to malnutrition, ultimately contributing to muscle loss 46 . Consistent with these findings, Wang et al. reported in older adults from a Chinese community that tooth loss was strongly related to sarcopenia and identified it as an important risk factor for this condition 47 . In addition, another investigation conducted in China demonstrated a significant correlation between tooth number and sarcopenia and found that nutritional status partially mediated this association, further supporting the biological plausibility of a “teeth–nutrition–muscle” axis 48 . Taken together, these studies align with our observation that tooth loss is associated with lower muscle mass, and our work builds on this body of evidence by emphasizing the potential roles of dietary inflammatory potential and antioxidant capacity in the “teeth–sarcopenia” relationship. In recent years, the DII has been increasingly applied to quantify the inflammatory potential of overall dietary patterns. A growing body of evidence indicates that more pro-inflammatory diets are linked to a higher likelihood of sarcopenia as well as declines in muscle mass and strength. In the study by Linton et al., lower dietary inflammatory potential was associated with greater muscle mass and stronger muscle function, whereas higher DII scores were related to more pronounced sarcopenic characteristics 49 . Similar results have been reported in different populations; for example, Bian et al. observed that higher DII scores were associated with an elevated risk of sarcopenia among older adults in Chinese cohorts 50 . At the same time, CDAI has drawn increasing attention, and emerging data suggest that higher CDAI values are associated with a reduced risk of muscle loss 51 , 52 . Building on this body of evidence, our study shows that both DII and CDAI act as significant mediators in the relationship between tooth loss and muscle mass. In particular, a greater extent of tooth loss was related to higher DII scores and lower CDAI values, which were, in turn, associated with reduced muscle mass. Tooth loss and impaired chewing function are well recognized to markedly affect food selection and overall dietary behaviours. Prior research has indicated that individuals with fewer teeth tend to limit their consumption of hard-to-chew items, such as meat, nuts, and certain fruits and vegetables, and instead prefer softer, more processed, and energy-dense foods. These dietary shifts can lead to insufficient intake of protein, dietary fibre, and essential micronutrients, thereby heightening the risk of malnutrition and subsequent sarcopenia 53 . Our results are consistent with, and extend, this literature by demonstrating that poorer dentition status is linked to higher dietary inflammatory potential and reduced antioxidant capacity. Pro-inflammatory dietary patterns are believed to activate inflammatory pathways—for instance, by increasing gut-derived endotoxin production and triggering NF-κB signaling—which, in turn, raise circulating levels of inflammatory biomarkers, including C-reactive protein (CRP), IL-6, and TNF-α. These inflammatory responses can enhance muscle protein degradation, suppress muscle protein synthesis, and ultimately facilitate the onset and progression of sarcopenia 54 . Oxidative stress is likewise a key contributor to the development and advancement of sarcopenia. Higher CDAI values generally indicate diets rich in antioxidant nutrients, which are capable of scavenging reactive oxygen species, limiting mitochondrial damage, and maintaining myocyte function, thereby attenuating the decline in muscle mass and strength 55 . When tooth loss limits the intake of antioxidant-rich foods, such as fresh fruits, vegetables, and nuts, overall dietary antioxidant capacity is reduced, CDAI levels fall, and oxidative stress is amplified, which further hastens muscle loss. This study offers several important strengths. First, it draws on a large, nationally representative sample, which enhances the generalizability of our findings to the broader U.S. adult population. Second, we not only demonstrate a positive association between tooth count and ASMI, but also systematically investigate potential pathways linking “oral health–dietary patterns–muscle mass,” thereby providing a more integrated perspective on the interaction between oral and musculoskeletal health. Third, we carefully adjusted for a broad spectrum of potential confounders and performed extensive subgroup analyses, which reinforces the robustness and interpretability of our findings across diverse population groups. Even so, several limitations should be recognized. First, the cross-sectional nature of the analysis precludes causal inference; we are unable to definitively establish the temporal sequence of tooth loss, dietary changes, inflammation, antioxidant status, and muscle loss. Prospective cohort and interventional studies are required to clarify causal relationships and directional effects. Second, both DII and CDAI were calculated from self-reported dietary recall data, which are inherently prone to recall bias and measurement error. Such misclassification may either attenuate or overestimate the observed mediating effects. Third, tooth count was used as the primary marker of oral health status, and we could not incorporate more comprehensive indicators of oral health, such as periodontal disease, oral pain, salivary flow, and masticatory efficiency. Future work employing more detailed and objective assessments of oral health may more fully elucidate the “oral health–muscle” relationship and its underlying mechanisms. 5 Conclusions By integrating both DII and CDAI into our analytical framework, this study offers additional insight into the relationship between tooth loss and declines in muscle mass. Our results indicate that tooth loss may not only directly compromise muscle mass through impaired chewing function, but also indirectly contribute to sarcopenia by adversely altering the inflammatory and oxidative profiles of dietary patterns. Taken together, these findings add to the evidence supporting an “oral–oxidation/inflammation–muscle” axis and underscore the potential importance of anti-inflammatory, antioxidant-rich dietary patterns in mitigating muscle loss, particularly among individuals with poor dentition status. Abbreviations BMI Body mass index PIR Poverty income ratio DM Diabetes mellitus IFG Impaired fasting glucose IGT Impaired glucose tolerance DII Dietary inflammatory potential index CDAI Composite dietary antioxidant capacity NRT The number of remaining natural teeth NFTL The number of functional tooth loss ASMI Appendicular skeletal muscle mass index AWGS The Asian Working Group for Sarcopenia CVD Cardiovascular disease IL-6 Interleukin-6 TNF-α Tumour necrosis factor-α IGF-1 Insulin-like growth factor-1 NHANES The National Health and Nutrition Examination Survey ASM Appendicular skeletal muscle DXA Dual-energy X-ray absorptiometry OHRQoL Oral health–related quality of life PA Physical activity SE Standard error Declarations Ethics approval and consent to participate Ethical approval of NHANES was obtained from the National Center for Health Statistics and the Centers for Disease Control. Written informed consent has been obtained from the patient(s) to publish this paper. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author Contributions SFB: analysis and interpretation of data, and drafted the work. NM,SXX: substantively revised the manuscript. TL,ZZ substantial contributions to the conception, design of the work, the acquisition of data. All authors read and approved the final version. Funding This study was supported by the Major technology application demonstration project of Chengdu Science and Technology Bureau(2022-YF09-00014-SN), the Sichuan Provincial Department of Science and Technology (2023ZYFS0235) and the Chengdu Science and Technology Bureau Key Research and Development Project(2022-YF05-01676-SN). Acknowledgments The authors appreciate the efforts given by participants in the NHANES project. Data Availability Statement The datasets used in this study are available in online repositories. Details including the repository names and accession numbers can be accessed here: https://www.cdc.gov/nchs/nhanes/index.htm. References GBD 2023 Causes of Death Collaborators. Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. 2025;406:1811–72. Hu Y, Peng W, Ren R, Wang Y, Wang G. Sarcopenia and mild cognitive impairment among elderly adults: The first longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle. 2022;13:2944–52. Zhang X, et al. Falls among older adults with sarcopenia dwelling in nursing home or community: A meta-analysis. Clin Nutr. 2020;39:33–9. Yuan S, Larsson SC. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism. 2023;144:155533. Bao S, et al. Inflammation mediates the association between muscle mass and accelerated phenotypic aging: results from the NHANES 2011–2018. 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Mediating role of systemic inflammation on the association between periodontitis and gestational diabetes mellitus: a cross-sectional study. BMC Oral Health 25, (2025). Yang H et al. Effect of age and systemic inflammation on the association between severity of periodontitis and blood pressure in periodontitis patients. BMC Oral Health 25, (2025). Isola G, Polizzi A, Serra S, Boato M, Sculean A. Relationship between periodontitis and systemic diseases: A bibliometric and visual study. Periodontology 2000 https://doi.org/10.1111/prd.12621 (2025) doi:10.1111/prd.12621. Hatta K, Ikebe K. Association between oral health and sarcopenia: A literature review. J Prosthodontic Res. 2021;65:131–6. Helenius-Hietala J, et al. Periodontitis is associated with incident chronic liver disease—A population‐based cohort study. Liver Int. 2018;39:583–91. Joo SK, Kim W. Interaction between sarcopenia and nonalcoholic fatty liver disease. Clin Mol Hepatol. 2023;29:S68–78. Zhao W, et al. The association between systemic inflammatory markers and sarcopenia: Results from the West China Health and Aging Trend Study (WCHAT). Arch Gerontol Geriatr. 2021;92:104262. Casarin M, da Silveira TM, Bezerra B, Pirih FQ. & Pola, N. M. Association between different dietary patterns and eating disorders and periodontal diseases. Front Oral Health 4, (2023). Li A, Chen Y, Schuller AA, van der Sluis LWM, Tjakkes GE. Dietary inflammatory potential is associated with poor periodontal health: A population-based study. J Clin Periodontol. 2021;48:907–18. Wu R, Gong H. The association between non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio and chronic obstructive pulmonary disease: the mediating role of dietary inflammatory index. Front Nutr 11, (2024). Zhang X et al. Association between dietary inflammatory index and metabolic syndrome: Analysis of the NHANES 2005–2016. Front Nutr 9, (2022). Guo M et al. Association between dietary inflammatory index and chronic kidney disease in middle-aged and elderly populations. Front Nutr 11, (2024). Charisis S et al. Diet Inflammatory Index and Dementia Incidence. Neurology 97, (2021). Shi Y, Zhang X, Feng Y. Association between the dietary inflammatory index and pain in US adults from NHANES. Nutr Neurosci. 2023;27:460–9. Liu H, Wang D, Wu F, Dong Z, Yu S. Association between inflammatory potential of diet and self-reported severe headache or migraine: A cross-sectional study of the National Health and Nutrition Examination Survey. Nutrition. 2023;113:112098. Farazi M, Jayedi A, Shab-Bidar S. Dietary inflammatory index and the risk of non-communicable chronic disease and mortality: an umbrella review of meta-analyses of observational studies. Crit Rev Food Sci Nutr. 2021;63:57–66. Luo Z, Zhu X, Hu Y, Yan S, Chen L. Association between dietary inflammatory index and oral cancer risk: A systematic review and dose–response meta-analysis. Front Oncol 12, (2022). Choi SW, et al. Association between inflammatory potential of diet and periodontitis disease risks: Results from a Korean population-based cohort study. J Clin Periodontol. 2023;50:952–63. Chen L, et al. Association between appendicular skeletal muscle index and leukocyte telomere length in adults: A study from National Health and Nutrition Examination Survey (NHANES) 1999–2002. Clin Nutr. 2021;40:3470–8. Kim KM, Jang HC, Lim S. Differences among skeletal muscle mass indices derived from height-, weight-, and body mass index-adjusted models in assessing sarcopenia. Korean J Intern Med. 2016;31:643–50. Cruz-Jentoft AJ, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48:601–601. Recent advances in oral health. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser. 1992;826:1–37. Cheng Y et al. New measure of functional tooth loss for successful Oral ageing: a cross-sectional study. BMC Geriatr 23, (2023). Takahashi M, Maeda K, Wakabayashi H. Prevalence of sarcopenia and association with oral health-related quality of life and oral health status in older dental clinic outpatients. Geriatr Gerontol Int. 2018;18:915–21. Ryu S, et al. Secular trends in Dietary Inflammatory Index among adults in the United States, 1999–2014. Eur J Clin Nutr. 2018;73:1343–51. Zuercher MD, et al. Energy-Adjusted Dietary Inflammatory Index and Diabetes Risk in Postmenopausal Hispanic Women. J Acad Nutr Dietetics. 2024;124:1431–9. Qing L, Zhu Y, Yu C, Zhang Y, Ni J. Exploring the association between dietary Inflammatory Index and chronic pain in US adults using NHANES 1999–2004. Sci Rep 14, (2024). Liu Z et al. Association between dietary antioxidant levels and chronic obstructive pulmonary disease: a mediation analysis of inflammatory factors. Front Immunol 14, (2024). Liu C, Hua L, Xin Z. Synergistic impact of 25-hydroxyvitamin D concentrations and physical activity on delaying aging. Redox Biol. 2024;73:103188. Han CH, Chung JH. Association Between Sarcopenia and Tooth Loss. Annals Geriatric Med Res. 2018;22:145–50. Wang F et al. Relationship between tooth loss and sarcopenia in suburban community-dwelling older adults in Shanghai and Tianjin of China. Sci Rep 12, (2022). Xia X et al. Nutrition mediates the relationship between number of teeth and sarcopenia: a pathway analysis. BMC Geriatr 22, (2022). Linton C, Wright HH, Wadsworth DP, Schaumberg MA. Dietary Inflammatory Index and Associations with Sarcopenia Symptomology in Community-Dwelling Older Adults. Nutrients. 2022;14:5319. Bian D, et al. The association of dietary inflammatory potential with sarcopenia in Chinese community-dwelling older adults. BMC Geriatr. 2023;23:281. Wang M, Shi H. Composite dietary antioxidant index is nonlinearly associated with low muscle mass in the general US population: findings from NHANES 2001–2018. Nutr J 24, (2025). Chen H, et al. Association between the composite dietary antioxidant index and sarcopenia among United States adults: A cross-sectional study. J Parenter Enter Nutr. 2024;49:103–11. Yücel M, Ünlüer NÖ, Sari YA. A comparison of oral health, nutrition, and swallowing function in older adults with and without sarcopenia: A cross-sectional study. Nutr Clin Pract. 2025;40:596–604. Xie H et al. The association of dietary inflammatory potential with skeletal muscle strength, mass, and sarcopenia: a meta-analysis. Front Nutr 10, (2023). Li B, et al. Supplement-driven iron overload accelerates phenotypic aging via inflammatory biomarkers: Potential counteraction through anti-inflammatory or antioxidant diets. Redox Biol. 2025;85:103733. 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-8808434","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594173836,"identity":"4a1355f2-0660-4e27-89db-ffb3ccbba3fa","order_by":0,"name":"Shifu Bao","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shifu","middleName":"","lastName":"Bao","suffix":""},{"id":594173837,"identity":"9fd80f14-f756-4aa1-a36f-fb7f27c00e5b","order_by":1,"name":"Nai Mu","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nai","middleName":"","lastName":"Mu","suffix":""},{"id":594173838,"identity":"dbd22139-0029-4852-9ff9-0661b00ca5a5","order_by":2,"name":"Shuxing Xing","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuxing","middleName":"","lastName":"Xing","suffix":""},{"id":594173839,"identity":"477ecbb8-6ae2-49c3-b97e-cfae73c2d135","order_by":3,"name":"Tao Li","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Li","suffix":""},{"id":594173840,"identity":"ad50e42c-a161-4e99-81a4-8d6d8646b65f","order_by":4,"name":"Zheng Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACCSBmbAAS7A1QkQNEa+GBKSVei0QCkVrkZzcfk/y5wyZPPvLt4U83ahjk+G4kMH4uwKOFcc6xNAnJM2nFhrfz0qRzjjEYS95IYJaegUcLs0SOmYRh2+HEjbNzzJhzGxgSN9xIYGPmwaOFDaQlse1/4saZZ4w/A7XUE9TCA9JysO1A4nwJHgNpoJYEA0JaJCTSki0b25ITN/DkmAH9ImE488zDZml8WuRnJB+8+bPNLnF+O9BhOTU28nzHkw9+xqcFDgwOQGxlgEYTEUCeSHWjYBSMglEwAgEAXXlKhsZDNNoAAAAASUVORK5CYII=","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine Affiliated Fifth People's hospital, Chengdu Fifth People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Zhou","suffix":""}],"badges":[],"createdAt":"2026-02-06 14:54:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8808434/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8808434/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103252775,"identity":"665e79bf-12b2-4b17-a8c5-11f046541128","added_by":"auto","created_at":"2026-02-23 16:16:05","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":61132,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the sample selection from NHANES 2011–2018.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8808434/v1/9dd564645a7c6c931b3c7823.jpeg"},{"id":103252816,"identity":"bc98ed5f-ab24-40d3-b7f5-68a3fa35fbd5","added_by":"auto","created_at":"2026-02-23 16:16:18","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63111,"visible":true,"origin":"","legend":"\u003cp\u003eIndirect effects of dietary factors in the association between tooth loss and ASMI. NRT: the number of remaining natural teeth; NFTL: the number of functional tooth loss; ASMI: appendicular skeletal muscle mass index; DII: dietary inflammatory potential index; CDAI: composite dietary antioxidant capacity; IE: indirect effects; DE: direct effects.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8808434/v1/789072a0f78449fce7b807d6.jpeg"},{"id":103252719,"identity":"82943bb5-2bfe-4f95-8dc3-79a2bab87576","added_by":"auto","created_at":"2026-02-23 16:15:57","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":145199,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between tooth loss and ASMI. ASMI:appendicular skeletal muscle mass index; PIR: poverty income ratio; DM:diabetes mellitus; IFG:impaired fasting glucose; IGT: impaired glucose tolerance; BMI: body mass index.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8808434/v1/91bed840e1cfa39152954cf4.jpeg"},{"id":105035312,"identity":"fabc9bde-17c6-4603-a69e-d7838f274d15","added_by":"auto","created_at":"2026-03-20 07:25:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1138964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8808434/v1/1eddc33a-5ffa-493a-8f18-908a7eea13cb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tooth loss is associated with reduced muscle mass: the mediating roles of DII and CDAI","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePopulation aging represents a critical global public health challenge. In 2021, people aged 60 years or older constituted 11.6% of the global population, and this proportion is expected to increase to 20.1% by 2050\u003csup\u003e1\u003c/sup\u003e. Sarcopenia is a multifactorial geriatric syndrome primarily defined by a progressive loss of skeletal muscle mass and strength. This condition markedly impairs physical function, substantially reduces quality of life, and heightens the risk of chronic disease and premature mortality. Epidemiological evidence shows that individuals with sarcopenia have an approximately 50% greater risk of falls and a 30% higher probability of developing mild cognitive impairment than those without sarcopenia\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The prevalence of sarcopenia has been estimated to be 10\u0026ndash;16%\u003csup\u003e4\u003c/sup\u003e. Because of its high frequency in aging populations, the resulting economic burden at both societal and individual levels is considerable and should not be overlooked.\u003c/p\u003e \u003cp\u003eTo support the early identification of older adults at risk of sarcopenia, the Asian Working Group for Sarcopenia (AWGS) proposed the concept of \u0026ldquo;possible sarcopenia\u0026rdquo; in 2019, defined by either low muscle strength or impaired physical performance. Growing evidence indicates that reductions in muscle mass represent a robust marker for the diagnosis of sarcopenia. When advanced diagnostic equipment is unavailable, this criterion facilitates the early recognition of high-risk individuals and thus permits timely intervention. Cross-sectional and longitudinal investigations in older populations have consistently demonstrated that, regardless of sex, muscle strength and muscle mass start to decline gradually as early as the third or fourth decade of life\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Prior studies have highlighted the crucial role of skeletal muscle in preserving overall health, showing its ability to postpone the onset of multiple age-related conditions, including obesity, diabetes, cardiovascular disease (CVD), cerebrovascular events, and certain malignancies\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTooth loss results from a multifaceted interaction among various risk factors. Proximal clinical determinants, including dental caries and periodontal disease, directly lead to tooth loss, whereas health-related behaviours and systemic health conditions influence it indirectly. Tooth loss not only markedly compromises masticatory function\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e but also alters dietary patterns, thereby producing far-reaching effects on oral health and overall quality of life\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. An expanding body of evidence has demonstrated a strong link between oral health and systemic health. Tooth loss has been recognized as an independent risk factor for several non-communicable diseases, such as CVD, diabetes, hypertension, and chronic kidney disease\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eChronic low-grade inflammation is a hallmark of the pathophysiology underlying tooth loss. Current evidence suggests that chronic oral conditions, particularly periodontal disease, substantially increase cellular production of pro-inflammatory cytokines, including interleukin-6 (IL-6) and tumour necrosis factor-α (TNF-α)\u003csup\u003e15\u0026ndash;18\u003c/sup\u003e. Persistent oral infection together with the associated inflammatory response can provoke systemic disturbances, such as impaired protein synthesis and metabolic dysregulation\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. These systemic changes may lead to reduced synthesis of albumin and insulin-like growth factor-1 (IGF-1), both of which are essential for preserving muscle mass and strength\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDietary patterns characterized by a high inflammatory potential, particularly those rich in fats and refined sugars, have been reported to promote chronic systemic inflammation\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. In contrast, healthier dietary patterns that are abundant in anti-inflammatory components can mitigate the adverse effects of systemic inflammatory responses\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To quantify the inflammatory potential of diet, Shivappa et al. developed the Dietary Inflammatory Index (DII), which rates an individual\u0026rsquo;s diet according to the pro- or anti-inflammatory properties of specific dietary constituents, thereby allowing the diet\u0026ndash;inflammation relationship to be quantified\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Since its development, the DII has been extensively used in epidemiological research to clarify associations between dietary exposures and disease outcomes mediated through inflammatory pathways, yielding numerous important insights\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30 CR31 CR32 CR33\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Parallel to the DII, the Composite Dietary Antioxidant Index (CDAI)\u0026mdash;which combines multiple dietary antioxidant components, including vitamins A, C, and E, zinc, selenium, and carotenoids\u0026mdash;was used to reflect the overall antioxidant capacity of the diet.\u003c/p\u003e \u003cp\u003eTaken together, these observations support a plausible mechanistic framework whereby oral disease and diet influence the risk of sarcopenia through inflammatory and metabolic pathways. Nevertheless, the precise nature of these interactions remains poorly characterized, and additional research is needed to elucidate their underlying biological connections. Achieving a more comprehensive understanding of the interrelationships among oral disease, dietary habits, and sarcopenia, as well as the potential mediating factors, will yield valuable clinical insights for general practitioners and other healthcare professionals. Such insights may aid in the early identification of individuals in subclinical stages of sarcopenia and guide preventive strategies designed to delay or mitigate age-related muscle loss.\u003c/p\u003e \u003cp\u003eAgainst this backdrop, the present study draws on data from the National Health and Nutrition Examination Survey (NHANES) to assess the association between tooth loss and appendicular skeletal muscle mass index (ASMI), and to further determine whether this association is mediated by dietary factors.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003eThis cross-sectional study was based on data from the NHANES (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cdc.gov/nchs/nhanes.htm\u003c/span\u003e\u003cspan address=\"http://www.cdc.gov/nchs/nhanes.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), an ongoing, nationally representative survey designed to assess the health and nutritional status of the U.S. population. The NHANES protocol was approved by the Ethics Review Board of the National Center for Health Statistics, with the most recent approval granted in August 2022, and written informed consent was obtained from all participants. For the current analysis, we included data from the 2011\u0026ndash;2018 survey cycles. Among the 39,156 individuals initially enrolled, we excluded participants younger than 20 years (n\u0026thinsp;=\u0026thinsp;16,539), individuals without appendicular skeletal muscle (ASM) measurements (n\u0026thinsp;=\u0026thinsp;11,869), and those missing data on DII and CDAI (n\u0026thinsp;=\u0026thinsp;668). Consequently, the final analytic sample consisted of 10,080 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years. The process of participant selection is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Muscle mass\u003c/h2\u003e \u003cp\u003eASM was measured using dual-energy X-ray absorptiometry (DXA), a technique that quantifies lean soft tissue mass in the upper and lower extremities. ASM was obtained as the sum of lean tissue mass in both arms and both legs. To account for differences in body size, the appendicular skeletal muscle mass index (ASMI) was calculated by dividing ASM by height squared (ASM/height\u0026sup2;). According to clinical diagnostic criteria, ASMI values\u0026thinsp;\u0026lt;\u0026thinsp;5.5 kg/m\u0026sup2; in women or \u0026lt;\u0026thinsp;7.0 kg/m\u0026sup2; in men were regarded as indicative of sarcopenia.\u003csup\u003e\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Remaining Natural Teeth (NRT)\u003c/h2\u003e \u003cp\u003eDuring the oral health examination, the number of remaining natural teeth (NRT) was categorized into two groups: \u0026ldquo;more natural teeth\u0026rdquo; (NRT\u0026thinsp;\u0026ge;\u0026thinsp;20) and \u0026ldquo;fewer natural teeth\u0026rdquo; (NRT\u0026thinsp;\u0026lt;\u0026thinsp;20). This categorization aligns with the World Health Organization\u0026rsquo;s functional dentition goal, which states that retaining at least 20 natural teeth is necessary to preserve adequate oral function in older adults.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional Tooth Loss (FTL)\u003c/h2\u003e \u003cp\u003eFunctional tooth loss (FTL) was defined as encompassing both missing natural teeth and non-functional teeth (e.g., residual roots, third molars). Prior studies have indicated that FTL more accurately reflects oral health\u0026ndash;related quality of life (OHRQoL) in older adults and may serve as a potential predictor of sarcopenia. \u003csup\u003e39\u003c/sup\u003eEvidence from elderly dental clinic patients has shown an association between sarcopenia and OHRQoL, and the loss of 16 functional teeth has been proposed as a threshold beyond which oral health markedly impairs quality of life.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e Consistent with this evidence, the number of functional tooth losses (NFTL) was categorized as \u0026ldquo;low FTL\u0026rdquo; (NFTL\u0026thinsp;\u0026le;\u0026thinsp;16) or \u0026ldquo;high FTL\u0026rdquo; (NFTL\u0026thinsp;\u0026gt;\u0026thinsp;16), permitting a more refined assessment of the impact of oral ageing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Dietary Inflammatory Index and Composite Dietary Antioxidant Index\u003c/h2\u003e \u003cp\u003eIn NHANES, the DII is derived from 27 dietary components and has shown predictive performance for inflammation-related outcomes that is comparable to estimates obtained using the full set of 45 components\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. A DII score\u0026thinsp;\u0026ge;\u0026thinsp;0 indicates a pro-inflammatory diet, whereas a score\u0026thinsp;\u0026lt;\u0026thinsp;0 reflects an anti-inflammatory diet. Higher DII scores correspond to less healthy, more pro-inflammatory dietary patterns, while lower scores denote healthier, anti-inflammatory dietary habits\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDietary antioxidant capacity was estimated using the CDAI, which is based on an improved Wright methodology and incorporates six major dietary antioxidant components: selenium, zinc, vitamins A, C, and E, and carotenoids. For each micronutrient, intake was standardized against global reference values, and the CDAI was computed as the sum of these standardized scores\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Covariates\u003c/h2\u003e \u003cp\u003eOn the basis of prior epidemiological evidence and clinical judgment, we adjusted for a range of potential confounders that might affect the association between tooth loss and muscle mass. The covariates considered were: age (years); gender (male/female); race/ethnicity (Mexican American, non-Hispanic White, non-Hispanic Black, other); marital status (married or living with a partner vs. widowed/divorced/separated/never married); educational attainment (less than 12th grade, high school graduate or equivalent, some college or AA degree, college graduate or above); poverty-to-income ratio (PIR); smoking status (never, former, current); alcohol consumption (never, former, mild, moderate, heavy); physical activity (PA; yes/no); hypertension (yes/no); diabetes (yes/no); and CVD (yes/no).PA data were converted to metabolic equivalent (MET) minutes of moderate-to-vigorous physical activity per week. Participants were then classified as either meeting (\u0026ge;\u0026thinsp;600 MET-minutes/week) or not meeting (\u0026lt;\u0026thinsp;600 MET-minutes/week) the recommended physical activity guidelines for adults.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003eTo more fully account for nutritional and metabolic status, we additionally adjusted for body mass index (BMI, kg/m\u0026sup2;), total protein (g/dL), blood urea nitrogen (mg/dL), serum creatinine (mg/dL), serum uric acid (mg/dL), serum calcium (mg/dL), alkaline phosphatase (ALP, U/L), and serum phosphorus (mg/dL). Detailed definitions, measurement protocols, and coding schemes for all variables are available in the NHANES documentation provided by the National Center for Health Statistics.(\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e \u003cp\u003e In line with guidelines from the Centers for Disease Control and Prevention (CDC), all statistical analyses incorporated the appropriate NHANES sampling weights to account for the complex, multistage survey design. The survey weights were recalibrated according to the analytic recommendations issued by the National Center for Health Statistics.\u003c/p\u003e \u003cp\u003eContinuous variables are reported as weighted means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE), whereas categorical variables are summarized as weighted proportions. Between-group differences were assessed using weighted Student\u0026rsquo;s t-tests for continuous variables and weighted chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003eTo estimate the association between tooth loss and ASMI, we specified three hierarchical models. Model 1 was unadjusted. Model 2 was adjusted for age, sex, and race. Model 3 additionally adjusted for age, sex, race, marital status, educational level, PIR, smoking status, alcohol consumption, PA, hypertension, diabetes, CVD, BMI, total protein, blood urea nitrogen, serum creatinine, serum uric acid, serum calcium, ALP, and serum phosphorus.\u003c/p\u003e \u003cp\u003eStratified analyses were carried out to investigate potential effect modification in the associations between NRT, NFTL, and ASMI. The pre-specified stratification variables were age (\u0026le;\u0026thinsp;45 vs. \u0026gt;45 years), sex (female vs. male), race (non-Hispanic White vs. non-Hispanic Black vs. Mexican American vs. other), PIR (\u0026lt;\u0026thinsp;1 vs. 1\u0026ndash;3 vs. \u0026gt;3), PA (no vs. yes), smoking status (never vs. former vs. current), BMI (\u0026lt;\u0026thinsp;25 vs. 25\u0026ndash;30 vs. \u0026ge;30 kg/m\u0026sup2;), hypertension (yes vs. no), and diabetes (yes vs. no). These variables were considered a priori as potential effect modifiers.\u003c/p\u003e \u003cp\u003eMediation analyses were conducted with the \u0026ldquo;mediation\u0026rdquo; package in R (version 4.1.3), using 5,000 bootstrap resamples to examine the mediating roles of DII and CDAI in the associations of NRT and NFTL with ASMI. The direct effect was defined as the effect of NRT and NFTL on ASMI that did not operate through DII or CDAI. The indirect effects represented the extent to which DII and CDAI mediated the associations between NRT, NFTL, and ASMI. The proportion mediated was obtained by dividing the indirect effect by the total effect.\u003c/p\u003e \u003cp\u003eAll data management and statistical analyses were performed in R version 4.1.3, and a two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was taken to indicate statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of participants\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the baseline characteristics of the study population. In total, 10,080 participants were included; 51.68% were men and 48.32% were women, and the weighted mean age was 39.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26 years.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study participants according to NRT and NFTL.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNFTL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eNRT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNFTL\u0026gt;16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNFTL\u0026thinsp;\u0026le;\u0026thinsp;16\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNRT\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNRT\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e49.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49.74\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e38.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4952(\u003c/p\u003e \u003cp\u003e48.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e256(49.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4696(48.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e363(48.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4589(48.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5128(51.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e251(50.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4877(51.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e344(51.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4784(51.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2077(11.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133(16.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1944(10.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e197(16.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1880(10.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1511(10.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34( 4.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1477(10.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59( 5.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1452(10.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3038(17.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(16.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2911(17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e178(16.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2860(17.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3454(60.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e213(63.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3241(60.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e273(61.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3181(60.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1681(19.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107(18.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1574(19.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e148(22.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1533(19.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6162(59.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139(20.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6023(60.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e218(23.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5944(61.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2237(21.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e261(60.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1976(19.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e341(54.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1896(19.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking status %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eformer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e918( 8.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113(23.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e805( 7.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e138(21.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e780( 7.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2586(27.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119(25.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2467(27.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e170(26.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2416(27.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3364(34.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134(23.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3230(35.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e198(25.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3166(35.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emoderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1844(20.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(18.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1758(20.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e111(17.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1733(20.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1368(10.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55( 8.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1313(10.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e90( 9.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1278(10.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1111( 8.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123(20.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e988( 7.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e165(19.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e946(7.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIFG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e408( 4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(7.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e382(4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41(7.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e367(4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e280(2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(2.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e266(2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18(2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e262(2.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8281(84.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e344(69.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7937(85.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e483(70.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7798(85.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7324(73.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266(57.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7058(74.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e366(54.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6958(75.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2756(26.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e241(42.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2515(25.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e341(45.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2415(24.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular diseases %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9732(97.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e433(83.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9299(97.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e613(85.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9119(97.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348( 2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(16.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e274( 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e94(14.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e254( 2.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollege graduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2772(32.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40( 7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2732(33.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59( 7.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2713(34.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh School Grade or Equivalent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2190(21.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158(34.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2032(20.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e224(37.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1966(20.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than 12th grade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1862(13.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(31.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1691(12.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e228(29.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1634(12.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSome college or AA degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3256(32.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138(25.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3118(33.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e196(25.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3060(33.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e886( 8.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100(18.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e786( 8.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e133(17.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e753( 8.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving with partner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1125(10.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(11.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1068(10.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e73(10.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1052(10.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4923(51.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214(44.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4709(51.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e312(48.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4611(51.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2671(25.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86(16.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2585(25.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119(15.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2552(25.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeparated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e346( 2.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(5.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e316(2.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e43(5.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e303(2.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129( 1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(3.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109(1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27(3.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e102(0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3174(28.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e221(42.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2953(28.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e301(39.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2873(27.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6906(71.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e286(57.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6620(71.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e406(60.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6500(72.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.46\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase(U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.58\u0026thinsp;\u0026plusmn;\u0026thinsp;1.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e66.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.85\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein(g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum uric acid(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.81\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum phosphorus(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum calcium(mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eData are expressed as weighted means\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or percentages (%). BMI, body mass index, PIR, poverty income ratio, DM, diabetes mellitus; IFG, impaired fasting glucose; IGT, impaired glucose tolerance, DII, dietary inflammatory potential index; CDAI, composite dietary antioxidant capacity; NRT: the number of remaining natural teeth; NFTL: the number of functional tooth loss; ASMI: appendicular skeletal muscle mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhen participants were categorized according to dentition status, substantial differences in sociodemographic, behavioural, and clinical characteristics became apparent. Relative to individuals with NRT\u0026thinsp;\u0026lt;\u0026thinsp;20, those with NRT\u0026thinsp;\u0026ge;\u0026thinsp;20 showed significant differences in age, race/ethnicity, PIR, educational attainment, marital status, smoking status, alcohol consumption, PA, CDAI, DII, hypertension, diabetes, blood urea nitrogen, alkaline phosphatase, and serum phosphorus (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eLikewise, when participants were classified by NFTL (\u0026le;\u0026thinsp;16 vs. \u0026gt;16), significant between-group differences were observed in age, race/ethnicity, PIR, educational attainment, marital status, smoking status, alcohol consumption, PA, CDAI, DII, hypertension, diabetes, blood urea nitrogen, alkaline phosphatase, and total protein (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eTaken together, NRT and NFTL displayed consistent patterns: individuals with more favourable oral status (NRT\u0026thinsp;\u0026ge;\u0026thinsp;20 or NFTL\u0026thinsp;\u0026le;\u0026thinsp;16) were generally younger, had higher socioeconomic status, and were more likely to engage in healthier lifestyles, accompanied by a lower burden of chronic conditions and more favourable metabolic and nutritional profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association of NRT and NFTL with ASMI\u003c/h2\u003e \u003cp\u003eRegression analyses showed a significant association between dentition status and ASMI (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the fully adjusted model, retaining at least 20 natural teeth was linked to a higher ASMI (β\u0026thinsp;=\u0026thinsp;0.15, 95% CI: 0.04\u0026ndash;0.26, P\u0026thinsp;=\u0026thinsp;0.01). Similarly, losing no more than 16 functional teeth was strongly related to a higher ASMI (β\u0026thinsp;=\u0026thinsp;0.20, 95% CI: 0.07\u0026ndash;0.33, P\u0026thinsp;=\u0026thinsp;0.004). Taken together, these results indicate that preservation of oral function may help maintain muscle mass and physical performance, particularly in ageing populations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate linear analysis of the association between tooth loss and ASMI.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eASMI, β(95%CI), p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCrude model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNRT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNRT\u0026thinsp;\u0026lt;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNRT\u0026thinsp;\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.32(0.13,0.52)0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.39( 0.20, 0.58)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15( 0.04, 0.26)0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNFTL\u0026thinsp;\u0026le;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNFTL\u0026thinsp;\u0026gt;\u0026thinsp;16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.48(-0.66,-0.29)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.5(-0.71,-0.29)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.2(-0.33,-0.07)0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as β coefcient (95% CI). Model 1 was unadjusted; Model 2 adjusted for gender, age, and race; Model 3 included additional adjustments for marital status, education, poverty income ratio, smoking status, drinking status, physical activity, hypertension, diabetes, cardiovascular diseases, body mass index, and biochemical markers (total protein, blood urea nitrogen, serum creatinine, serum uric acid, serum calcium, alkaline phosphatase, and serum phosphorus).NRT: the number of remaining natural teeth; NFTL: the number of functional tooth loss; ASMI: appendicular skeletal muscle mass index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Mediation analyses\u003c/h2\u003e \u003cp\u003eParallel mediation analyses were undertaken to determine whether dietary factors mediated the associations of NRT and NFTL with ASMI. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, both DII and CDAI had significant mediating roles. For NRT, DII and CDAI explained 10.09% and 4.61% of the total effect, respectively (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For NFTL, the corresponding mediated proportions were 7.98% and 3.72% (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Subgroup analyses\u003c/h2\u003e \u003cp\u003eSubgroup analyses were conducted to examine whether the associations between dentition status (NRT and NFTL) and ASMI were consistent across different population strata (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, the direction of association was comparable for both dentition indicators: relative to the reference groups, having more natural teeth (NRT\u0026thinsp;\u0026ge;\u0026thinsp;20) and lower functional tooth loss (NFTL\u0026thinsp;\u0026le;\u0026thinsp;16) was associated with higher ASMI. These associations appeared particularly prominent among men, current smokers, and individuals with dysglycaemia-related conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study investigated the association between tooth loss as an exposure and skeletal muscle mass in a nationally representative sample of U.S. adults, and additionally explored the potential mediating roles of the DII and CDAI. Our main findings can be summarized as follows: (1) greater severity of tooth loss was linked to lower muscle mass, indicating a significant positive association between dentition status and sarcopenia risk\u0026mdash;succinctly captured as \u0026ldquo;more missing teeth, less muscle\u0026rdquo;; and (2) both DII and CDAI partially mediated the relationship between tooth loss and muscle mass. These findings suggest that tooth loss may influence muscle mass not only directly, via impaired masticatory function, but also indirectly, by shaping the inflammatory and antioxidant profiles of dietary patterns and thereby affecting the onset and progression of sarcopenia. Taken together, these observations support the presence of a continuous \u0026ldquo;oral health\u0026ndash;oxidation/inflammation\u0026ndash;muscle\u0026rdquo; pathway, offer more nuanced mechanistic evidence linking oral health to sarcopenia, and highlight the importance of anti-inflammatory and antioxidant dietary patterns in mitigating muscle loss.\u003c/p\u003e \u003cp\u003eAccumulating evidence indicates that a reduced number of teeth and impaired oral function are closely linked to an elevated risk of sarcopenia. In a Korean population, Han et al. showed that tooth loss was significantly associated with sarcopenia and suggested that diminished chewing ability may reduce food intake and lead to malnutrition, ultimately contributing to muscle loss\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Consistent with these findings, Wang et al. reported in older adults from a Chinese community that tooth loss was strongly related to sarcopenia and identified it as an important risk factor for this condition\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. In addition, another investigation conducted in China demonstrated a significant correlation between tooth number and sarcopenia and found that nutritional status partially mediated this association, further supporting the biological plausibility of a \u0026ldquo;teeth\u0026ndash;nutrition\u0026ndash;muscle\u0026rdquo; axis\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Taken together, these studies align with our observation that tooth loss is associated with lower muscle mass, and our work builds on this body of evidence by emphasizing the potential roles of dietary inflammatory potential and antioxidant capacity in the \u0026ldquo;teeth\u0026ndash;sarcopenia\u0026rdquo; relationship.\u003c/p\u003e \u003cp\u003eIn recent years, the DII has been increasingly applied to quantify the inflammatory potential of overall dietary patterns. A growing body of evidence indicates that more pro-inflammatory diets are linked to a higher likelihood of sarcopenia as well as declines in muscle mass and strength. In the study by Linton et al., lower dietary inflammatory potential was associated with greater muscle mass and stronger muscle function, whereas higher DII scores were related to more pronounced sarcopenic characteristics\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Similar results have been reported in different populations; for example, Bian et al. observed that higher DII scores were associated with an elevated risk of sarcopenia among older adults in Chinese cohorts\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. At the same time, CDAI has drawn increasing attention, and emerging data suggest that higher CDAI values are associated with a reduced risk of muscle loss\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBuilding on this body of evidence, our study shows that both DII and CDAI act as significant mediators in the relationship between tooth loss and muscle mass. In particular, a greater extent of tooth loss was related to higher DII scores and lower CDAI values, which were, in turn, associated with reduced muscle mass. Tooth loss and impaired chewing function are well recognized to markedly affect food selection and overall dietary behaviours. Prior research has indicated that individuals with fewer teeth tend to limit their consumption of hard-to-chew items, such as meat, nuts, and certain fruits and vegetables, and instead prefer softer, more processed, and energy-dense foods. These dietary shifts can lead to insufficient intake of protein, dietary fibre, and essential micronutrients, thereby heightening the risk of malnutrition and subsequent sarcopenia\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Our results are consistent with, and extend, this literature by demonstrating that poorer dentition status is linked to higher dietary inflammatory potential and reduced antioxidant capacity.\u003c/p\u003e \u003cp\u003ePro-inflammatory dietary patterns are believed to activate inflammatory pathways\u0026mdash;for instance, by increasing gut-derived endotoxin production and triggering NF-κB signaling\u0026mdash;which, in turn, raise circulating levels of inflammatory biomarkers, including C-reactive protein (CRP), IL-6, and TNF-α. These inflammatory responses can enhance muscle protein degradation, suppress muscle protein synthesis, and ultimately facilitate the onset and progression of sarcopenia\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOxidative stress is likewise a key contributor to the development and advancement of sarcopenia. Higher CDAI values generally indicate diets rich in antioxidant nutrients, which are capable of scavenging reactive oxygen species, limiting mitochondrial damage, and maintaining myocyte function, thereby attenuating the decline in muscle mass and strength\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. When tooth loss limits the intake of antioxidant-rich foods, such as fresh fruits, vegetables, and nuts, overall dietary antioxidant capacity is reduced, CDAI levels fall, and oxidative stress is amplified, which further hastens muscle loss.\u003c/p\u003e \u003cp\u003eThis study offers several important strengths. First, it draws on a large, nationally representative sample, which enhances the generalizability of our findings to the broader U.S. adult population. Second, we not only demonstrate a positive association between tooth count and ASMI, but also systematically investigate potential pathways linking \u0026ldquo;oral health\u0026ndash;dietary patterns\u0026ndash;muscle mass,\u0026rdquo; thereby providing a more integrated perspective on the interaction between oral and musculoskeletal health. Third, we carefully adjusted for a broad spectrum of potential confounders and performed extensive subgroup analyses, which reinforces the robustness and interpretability of our findings across diverse population groups.\u003c/p\u003e \u003cp\u003eEven so, several limitations should be recognized. First, the cross-sectional nature of the analysis precludes causal inference; we are unable to definitively establish the temporal sequence of tooth loss, dietary changes, inflammation, antioxidant status, and muscle loss. Prospective cohort and interventional studies are required to clarify causal relationships and directional effects. Second, both DII and CDAI were calculated from self-reported dietary recall data, which are inherently prone to recall bias and measurement error. Such misclassification may either attenuate or overestimate the observed mediating effects. Third, tooth count was used as the primary marker of oral health status, and we could not incorporate more comprehensive indicators of oral health, such as periodontal disease, oral pain, salivary flow, and masticatory efficiency. Future work employing more detailed and objective assessments of oral health may more fully elucidate the \u0026ldquo;oral health\u0026ndash;muscle\u0026rdquo; relationship and its underlying mechanisms.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eBy integrating both DII and CDAI into our analytical framework, this study offers additional insight into the relationship between tooth loss and declines in muscle mass. Our results indicate that tooth loss may not only directly compromise muscle mass through impaired chewing function, but also indirectly contribute to sarcopenia by adversely altering the inflammatory and oxidative profiles of dietary patterns. Taken together, these findings add to the evidence supporting an \u0026ldquo;oral\u0026ndash;oxidation/inflammation\u0026ndash;muscle\u0026rdquo; axis and underscore the potential importance of anti-inflammatory, antioxidant-rich dietary patterns in mitigating muscle loss, particularly among individuals with poor dentition status.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI Body mass index\u003c/p\u003e\u003cp\u003ePIR Poverty income ratio\u003c/p\u003e\u003cp\u003eDM Diabetes mellitus\u003c/p\u003e\u003cp\u003eIFG Impaired fasting glucose\u003c/p\u003e\u003cp\u003eIGT Impaired glucose tolerance\u003c/p\u003e\u003cp\u003eDII Dietary inflammatory potential index\u003c/p\u003e\u003cp\u003eCDAI Composite dietary antioxidant capacity\u003c/p\u003e\u003cp\u003eNRT The number of remaining natural teeth\u003c/p\u003e\u003cp\u003eNFTL The number of functional tooth loss\u003c/p\u003e\u003cp\u003eASMI Appendicular skeletal muscle mass index\u003c/p\u003e\u003cp\u003eAWGS The Asian Working Group for Sarcopenia\u003c/p\u003e\u003cp\u003eCVD Cardiovascular disease\u003c/p\u003e\u003cp\u003eIL-6 Interleukin-6\u003c/p\u003e\u003cp\u003eTNF-α Tumour necrosis factor-α\u003c/p\u003e\u003cp\u003eIGF-1 Insulin-like growth factor-1\u003c/p\u003e\u003cp\u003eNHANES The National Health and Nutrition Examination Survey\u003c/p\u003e\u003cp\u003eASM Appendicular skeletal muscle\u003c/p\u003e\u003cp\u003eDXA Dual-energy X-ray absorptiometry\u003c/p\u003e\u003cp\u003eOHRQoL Oral health\u0026ndash;related quality of life\u003c/p\u003e\u003cp\u003ePA Physical activity\u003c/p\u003e\u003cp\u003eSE Standard error\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eEthical approval of NHANES was obtained from the National Center for Health Statistics and the Centers for Disease Control. Written informed consent has been obtained from the patient(s) to publish this paper.\u003c/p\u003e\n\u003cp\u003eConflict of Interest\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions\u003c/p\u003e\n\u003cp\u003eSFB: analysis and interpretation of data, and drafted the work. NM,SXX: substantively revised the manuscript. TL,ZZ substantial contributions to the conception, design of the work, the acquisition of data. All authors read and approved the final version.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Major technology application demonstration project of Chengdu Science and Technology Bureau(2022-YF09-00014-SN), \u0026nbsp; the Sichuan Provincial Department of Science and Technology (2023ZYFS0235) and the Chengdu Science and Technology Bureau Key Research and Development Project(2022-YF05-01676-SN).\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the efforts given by participants in the NHANES project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData Availability Statement\u003c/p\u003e\n\u003cp\u003eThe datasets used in this study are available in online repositories. Details including the repository names and accession numbers can be accessed here: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGBD 2023 Causes of Death Collaborators. Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990\u0026ndash;2023: a systematic analysis for the Global Burden of Disease Study 2023. Lancet. 2025;406:1811\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Peng W, Ren R, Wang Y, Wang G. Sarcopenia and mild cognitive impairment among elderly adults: The first longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle. 2022;13:2944\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, et al. Falls among older adults with sarcopenia dwelling in nursing home or community: A meta-analysis. Clin Nutr. 2020;39:33\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Larsson SC. Epidemiology of sarcopenia: Prevalence, risk factors, and consequences. Metabolism. 2023;144:155533.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBao S, et al. Inflammation mediates the association between muscle mass and accelerated phenotypic aging: results from the NHANES 2011\u0026ndash;2018. Front Nutr. 2025;11:1503702.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGregor RA, Cameron-Smith D, Poppitt SD. It is not just muscle mass: a review of muscle quality, composition and metabolism during ageing as determinants of muscle function and mobility in later life. Longev Healthspan 3, (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou Y, et al. Accelerometer-measured physical activity patterns are associated with phenotypic age: Isotemporal substitution effects. 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Redox Biol. 2025;85:103733.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"tooth loss, skeletal muscle, diet quality, CDAI, NHANES, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-8808434/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8808434/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTooth loss may contribute to sarcopenia not only through impaired mastication but also by altering diet-related inflammation and antioxidant capacity. We investigated whether the dietary inflammatory potential index (DII) and composite dietary antioxidant capacity (CDAI) mediate the association between tooth loss and skeletal muscle mass.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing NHANES 2011\u0026ndash;2018, we included adults aged\u0026thinsp;\u0026ge;\u0026thinsp;20 years who had dentition examinations and DXA-derived appendicular skeletal muscle measurements (n\u0026thinsp;=\u0026thinsp;10,080). Dentition status was categorized by the number of remaining natural teeth (NRT; \u0026lt;20 vs\u0026thinsp;\u0026ge;\u0026thinsp;20) and functional tooth loss (NFTL; \u0026gt;16 vs\u0026thinsp;\u0026le;\u0026thinsp;16). ASMI was calculated as ASM/height\u0026sup2;. We conducted weighted multivariable regression, performed prespecified subgroup analyses, and applied parallel mediation with 5,000 bootstrap resamples.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn fully adjusted models, more favourable dentition status was independently associated with higher ASMI (NRT\u0026thinsp;\u0026ge;\u0026thinsp;20: β\u0026thinsp;=\u0026thinsp;0.15, 95% CI 0.04\u0026ndash;0.26, P\u0026thinsp;=\u0026thinsp;0.01; NFTL\u0026thinsp;\u0026le;\u0026thinsp;16: β\u0026thinsp;=\u0026thinsp;0.20, 95% CI 0.07\u0026ndash;0.33, P\u0026thinsp;=\u0026thinsp;0.004). Both DII and CDAI partially mediated these relationships (NRT: 10.09% and 4.61%; NFTL: 7.98% and 3.72%; all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The associations were generally consistent across strata and were more pronounced among men, current smokers, and individuals with dysglycaemia-related phenotypes.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAmong U.S. adults, tooth loss was associated with lower skeletal muscle mass, and diet-related inflammatory potential and oxidant capacity explained a measurable proportion of this association. The results support an \u0026ldquo;oral health\u0026ndash;dietary inflammation/oxidation status\u0026ndash;muscle\u0026rdquo; axis and suggest that improving diet quality may represent a modifiable pathway for sarcopenia prevention, particularly for adults with poor dentition status.\u003c/p\u003e","manuscriptTitle":"Tooth loss is associated with reduced muscle mass: the mediating roles of DII and CDAI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 16:12:27","doi":"10.21203/rs.3.rs-8808434/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d89dd57a-d22d-4c91-b94a-f0bb5094dcdc","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T11:10:57+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 16:12:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8808434","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8808434","identity":"rs-8808434","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}