Life’s Essential 8 predicts the risk of periodontitis: A nomogram

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There is an interest to understand the distinct effects of individual LE8 elements on periodontal health and whether LE8 predict the risk of periodontitis. Methods: Pooled cross-sectional data from the National Health and Nutrition Examination Survey in 2009–2014 were used (n = 8,519). Periodontitis was classified into two groups (no/mild and moderate/severe). LE8 score (range 0–100), determined by eight metrics (diet, physical activity, nicotine exposure, sleep, body mass index, blood lipids, blood glucose, and blood pressure), was categorized as low (0–49), moderate (50–79), and high (80–100). The LE8–periodontitis association was investigated by multivariable logistic regression and population attributable fraction (PAF). Prediction models for periodontitis using LE8 score were developed, and the performance was tested by the area under the receiver operating characteristic curve (AUC) and calibration curve. Results: Negative associations were found between LE8 score and periodontitis. Participants with low and moderate LE8 scores had higher risks of periodontitis than those with high LE8 scores (odds ratios [OR] = 4.182 [95%CI = 3.553–4.921], and 2.274 [95%CI = 2.020–2.560], respectively). The PAF analysis showed that 37.794% of periodontitis cases can be attributed to low LE8, among which the effects of blood pressure (PAF = 24.892%), nicotine exposure (PAF = 20.557%), blood lipids (PAF = 19.627%), and diet quality (PAF = 9.252%) were found to be significant. The models constructed using the four LE8 components of blood pressure, nicotine exposure, blood lipids, and diet quality could predict the risk of periodontitis (AUC = 0.744 [0.733, 0.755]). Conclusion: Worse cardiovascular health, indicated by lower LE8 score, was related to periodontitis risk, and the LE8 score significantly predicted the periodontal health status. Periodontitis Life’s Essential 8 Risk factor NHANES Population Attributable Fraction Forecast Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 One sentence summary The cardiovascular health score constructed by Life’s essential 8 were found to be predictive for the risk of periodontitis, among which the blood pressure, nicotine exposure, blood glucose and diet being significant predictors. INTRODUCTION Periodontitis, a chronic inflammatory condition affecting the supporting structures of the teeth, poses a significant global public health challenge[ 1 ]. A complex interplay among genetic, environmental, and lifestyle factors contributes to both the development and progression of periodontitis[ 2 ]. Cardiovascular diseases, the leading cause of death in the US[ 3 ], have been reported to be associated with periodontitis[ 4 ]. Potential mechanisms include bacteraemia and the consequent systemic inflammatory response, leading to the increased level of C-reactive protein and oxidative stress[ 5 ]. In recent years, there has been a growing focus on healthy lifestyles as modifiable and cost-effective behavioural factors, given their potential benefits for overall well-being. An example can be found in the area of cardiovascular health (CVH). To quantify CVH, the American Heart Association introduced the Life’s Simple 7 (LS7) assessment system in 2010[ 6 ], which was updated to the Life’s Essential 8 (LE8) in 2022[ 7 ]. The LE8 adds sleep health as a new metric to the three health behaviours (diet, physical activity, and nicotine exposure) and four health factors (body mass index [BMI], blood lipids, blood glucose, and blood pressure). Recent studies have also demonstrated that better CVH status is associated with a reduced risk of periodontitis, indicating that CVH may be a modifiable factor of periodontal health[ 8 , 9 ]. An increasing number of researchers have emphasized the importance of lifestyle in influencing periodontal health[ 10 ], thus it might be more beneficial to concentrate on addressing one significant health concern at a time when identifying multiple metrics that require improvement. Periodontitis and CVD share numerous risk factors, most of which are components of LE8, including blood pressure[ 11 ], blood lipids[ 12 ], diet, physical activity, stress levels, sleep quality, smoking and alcohol use[ 13 ]. Determining the prioritization of LE8 metrics in periodontitis is therefore of considerable importance for developing effective periodontal public health strategies. However, which factors among the LE8 metrics contribute more to periodontal health remain uncertain. Moreover, given the fact that most periodontitis patients with CVDs tend to seek for health care in primary medical care settings, a screening tool to be utilized by CVD physicians is needed to facilitate the referral of patients at periodontitis risk for clinical examination and treatment. However, the existing prediction models for periodontitis largely rely on the well-known risk factors, and there is a lack of studies examining the predictive effect of these LE8 factors and lifestyles as a whole on periodontal health. To address the research gaps, we hypothesized that LE8 metrics were related to periodontitis and can be used as a calculator to quantify the risk of periodontitis. The objectives of the present study were twofold: first, rank the components of the LE8 metrics based on their contributions to the LE8–periodontitis association; second, construct a prediction model for periodontitis using the identified factors, thereby providing a practical tool for oral and cardiovascular healthcare providers to assess and manage periodontal health. METHODS Data Source and Participants The National Health and Nutrition Examination Survey (NHANES) is a repeated cross-sectional survey using a stratified, multistage probability design to collect a nationally representative sample of non-institutionalized US civilians. Data were collected from household interviews, mobile physical examinations, and laboratory tests. The survey was administered by the National Center for Health Statistics, and the institutional ethics review board of the centre approved the NHANES protocols. All the participants provided written informed consent during the initial data collection[ 14 , 15 ]. This study was exempt from institutional review board ethics review according to the National Institutes of Health Policy because using de-identified data with no direct participant contact. To evaluate the LE8-periodontitis association, we used the data from three waves of NHANES (2009–2010, 2011–2012, and 2013–2014), in which the participants underwent full-mouth periodontal examinations and have all eight metrics of LE8. Of the 18,504 participants from NHANES 2009–2014, we excluded participants who met the following criteria: (1) without LE8 data (n = 5,447); (2) younger than 30 years old (n = 2,403); and (3) without data on periodontal examination (n = 2,135). In total, 8,519 participants were included in the present study ( Figure S1 ). In this study, age was divided into two strata: 30–59 years and ≥ 60 years. Race/ethnicity was categorized as White, Black, and other races. The poverty ratio was calculated as the ratio of monthly family income to poverty levels and categorized into three groups: 3.5 (high income). Education levels were categorized as high school or below, some college or AA degree, and college graduate or above. Life’s Essential 8 (LE8) Score Calculation LE8 scoring comprises four health behaviours (diet, physical activity, nicotine exposure and sleep) and four health factors (BMI, blood lipids, blood glucose, and blood pressure). Each of the eight metrics was scored from 0 to 100 points. Detailed methods for calculating LE8 score in NHANES data can be found in Table S1 . The overall LE8 score was taken as the average of the scores of the eight metrics and categorized into low (0–49), moderate (50–79), and high (80–100) groups. The diet metric was evaluated by the Healthy Eating Index 2015[ 16 ]. The dietary intakes of participants collected from two 24-hour diet recalls were combined with the United States Department of Agriculture food patterns equivalents data to construct and calculate the Healthy Eating Index 2015 scores[ 17 ]. Detailed algorithms for calculating the Healthy Eating Index 2015 are given in Table S2 . Self-report questionnaires were used to collect data on physical activity, nicotine exposure, sleeping time, diabetes lifestyle, and medication history. Blood pressure and BMI were measured during the physical examination. Blood samples were collected and sent to central laboratories for the determination of blood lipids and blood glucose. Assessment of Periodontal Status Full-mouth periodontal examinations were conducted for the participants in NHANES 2009–2014, including the probing pocket depth and attachment loss for six sites around each tooth. Periodontitis was defined according to the Center for Disease Control and Prevention/American Academy of Periodontology case definition[ 18 ]. Severe periodontitis was defined as ≥ 2 interproximal sites with ≥ 6 mm of attachment loss (on different teeth) and ≥ 1 interproximal site with ≥ 5 mm of probing pocket depth; moderate periodontitis was defined as ≥ 2 interproximal sites with ≥ 4 mm of clinical attachment loss (on different teeth) or ≥ 2 interproximal sites with probing pocket depth ≥ 5 mm (on different teeth); and no/mild periodontitis was defined as the absence of any signs of moderate or severe (mod/sev) periodontitis. Consistent with the previous studies[ 19 , 20 ], the outcomes were defined as no/mild or mod/sev periodontitis. Statistical Analysis for LE8-Periodontitis association Multivariable regression models were used to assess the association between LE8 score and mod/sev periodontitis. A restricted cubic spline with knots located at the 10th, 50th, and 90th percentiles of LE8 score was used to assess their non-linear relationships. Stratified analyses were performed by sex and age. Multivariable regression models were used to assess the association between LE8 score and mod/sev periodontitis. A restricted cubic spline with knots located at the 10th, 50th, and 90th percentiles of LE8 score was used to assess their non-linear relationships. Stratified analyses were performed by sex and age. The p-value for interaction between LE8 scores was used to estimate the significance of interactions. Sensitivity analysis was performed by (1) excluding participants without any covariates (n = 654); (2) applying the 2018 EFP/AAP definition to classify periodontitis as non-Stage II and Stage III–IV; and (3) classifying the participants into three subgroups by cutting the LE8 score at the one-third and two-thirds quantiles. Complete-case analysis was used, as the missing proportions were less than 5%. Population Attributable Fraction The population attributable fraction (PAF) was calculated to assess to what extent the association could be attributed to each of the LE8 components. PAF, first proposed by Levin in 1953[ 21 ], represents the proportion of risk that would be reduced if the past were reduced to an ideal exposure scenario. In this study, the formula modified by Hanley in 2001[ 22 ] was used: $$PAF=\frac{{PF}_{1}\left\{RR-1\right\}}{{1+PF}_{1}\left\{RR-1\right\}}$$ . where PF 1 is the total population exposure rate, and RR is the risk ratio. We used the same definition as the overall LE8 score to measure and categorize the eight individual metrics: low (0–49) groups were considered high exposure, moderate (50–79) and high (80–100) groups low exposure. Development and validation of prediction model Multivariable logistic regression was used to establish a nomogram model for the prediction of periodontitis. LE8 elements were used as predictors and periodontitis as outcome. Data from the participants in NHANES 2009–2012 (n = 7,449) were used as a training set and data from the participants in NHANES 2013–2014 (n = 1,070) as a test set for the assessment of the model’s performance. The model’s discrimination performance was evaluated by sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The calibration of the models was evaluated by calibration curves. All the analyses were conducted using the R Project for Statistical Computing (version 4.3.1), with statistical significance defined as two-sided p < 0.05. This study was reported following the guideline of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)[ 23 ] ( Table S3 ) and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guideline [24] ( Table S4 ). RESULTS Participant Characteristics A total of 8,519 participants aged 30 years or older were included. The characteristics of the study population across LE8 scores are presented in Table 1 . The mean age of the study participants was 51.5 years, and 4,258 (50.0%) were female. The number of participants with low, moderate, and high LE8 scores were 1,081 (12.7%), 5,773 (67.8%) and 1,665 (19.5%), respectively. There were 3,874 (45.5%) participants diagnosed with mod/sev periodontitis. Compared to those without periodontitis, participants with periodontitis were older and more likely to be male. Table 1 Characteristics of the participants. Total (N = 8519) Low for LE8 (0–49) (N = 1081) Moderate for LE8 (50–79) (N = 5773) High for LE8 (80–100) (N = 1665) Age groups 30–59 5887 (69.1%) 668 (61.8%) 3901 (67.6%) 1318 (79.2%) ≥ 60 2632 (30.9%) 413 (38.2%) 1872 (32.4%) 347 (20.8%) Age (years) (Mean [SD]) 51.5 (14.1) 54.5 (13.0) 52.1 (14.2) 47.5 (13.7) Sex Male 4261 (50.0%) 521 (48.2%) 3059 (53.0%) 681 (40.9%) Female 4258 (50.0%) 560 (51.8%) 2714 (47.0%) 984 (59.1%) Race/ethnicity Whites 3872 (45.5%) 433 (40.1%) 2591 (44.9%) 848 (50.9%) Blacks 1670 (19.6%) 320 (29.6%) 1194 (20.7%) 156 (9.4%) Other races 2977 (34.9%) 328 (30.3%) 1988 (34.4%) 661 (39.7%) Education levels High school or below 3744 (43.9%) 677 (62.6%) 2660 (46.1%) 407 (24.4%) Some college or AA degree 2426 (28.5%) 294 (27.2%) 1743 (30.2%) 389 (23.4%) College graduate or above 2341 (27.5%) 109 (10.1%) 1363 (23.6%) 869 (52.2%) Poverty/income ratio 3.5 2792 (32.8%) 181 (16.7%) 1790 (31.0%) 821 (49.3%) Periodontitis Non/Mild 4645 (54.5%) 408 (37.7%) 3043 (52.7%) 1194 (71.7%) Mod/Sev 3874 (45.5%) 673 (62.3%) 2730 (47.3%) 471 (28.3%) Diet score (Mean [SD]) 42.4 (31.6) 22.4 (24.6) 39.4 (30.1) 65.8 (27.0) PA score (Mean [SD]) 70.8 (42.5) 31.6 (43.9) 71.3 (42.0) 94.4 (18.1) Nicotine exposure score (Mean [SD]) 72.5 (38.2) 44.0 (43.5) 72.0 (38.1) 92.4 (17.2) Sleep health score (Mean [SD]) 80.7 (25.6) 63.1 (31.0) 80.9 (25.0) 91.3 (15.9) BMI score (Mean [SD]) 59.6 (33.0) 33.9 (30.0) 57.5 (31.7) 83.7 (21.5) Blood lipids score (Mean [SD]) 61.6 (30.5) 43.7 (30.2) 59.7 (29.4) 79.8 (24.8) Blood glucose score (Mean [SD]) 79.7 (26.5) 57.9 (29.2) 79.4 (25.7) 94.9 (14.0) BP score (Mean [SD]) 65.8 (33.6) 41.1 (32.5) 63.8 (32.6) 88.8 (21.0) The data of all participants in this study were presented and divided into three groups with LE8 scores: low (00–49), medium (50–79) and high (80–100). These variables are shown as the mean and standard deviation. Missing value for total population: education levels ( n = 3, < 0.1%), poverty/income ratio ( n = 323; 7.0%). Abbreviations: LE8, Life’s Essential 8; BP, blood pressure; BMI, body mass index; PA, physical activity; mod/sev, moderate/severe; SD, standard deviation. Association of LE8 and Periodontitis and Sensitivity Analysis Figure 1 A presents the linear relationships between LE8 score and periodontitis (p for non-linear = 0.736). The results indicated that the risk of periodontitis decreased as the LE8 score increased. The reference value (null effect, odds ratio [OR] = 1.000) for LE8 score was 66.875. Figure 1 B indicates that the prevalence of periodontitis was significantly lower in participants with high LE8 scores (80–100) than in those with moderate (50–79) or low (0–49) scores. Compared with the high LE8 group, participants with moderate or low LE8 scores are at approximately two to four times higher risk of periodontitis (OR moderate−LE8 = 2.274 [95%CI: 2.020–2.560], OR low−LE8 = 4.182 [95%CI: 3.553–4.921]), suggesting that a higher LE8 score is associated with a protective effect against periodontitis. In sensitivity analysis, we excluded participants without any covariates (n = 654), suggesting that higher LE8 has an obvious protective effect against periodontitis. The findings from the sensitivity analysis were generally robust ( Table S5–7 ). The increase of LE8 score is associated with a protective effect on periodontal health. Stratification Analysis The results of the stratification analyses are presented in Fig. 2 . The LE8 score was negatively associated with periodontitis in all subgroups. We found significant interactions between LE8 score and periodontitis with different age groups and sex ( p for interaction < 0.05). Compared with the older and male participants, higher LE8 score was associated with a more significant protecting effect for the younger (aged 30–59 years) and female participants. Among the participants aged 30–59 years, ORs for moderate and low LE8 were 2.405 [95%CI: 2.078–2.783] and 4.901 [95%CI: 4.005–5.997], respectively. In contrast, among the older (aged 60 years and older) participants, the ORs for moderate and low LE8 were 1.456 [95%CI: 1.156–1.834] and 2.017 [95%CI: 1.498–2.718], respectively. Similar trends were found in female versus male participants: the ORs for female participants having periodontitis were 2.373 [95%CI: 2.000–2.815] and 4.103 [95%CI: 3.275–5.140] for moderate and low LE8 subgroups, respectively; the ORs for male were 1.927 [95% CI: 1.626–2.284] and 4.254 [95%CI: 3.324–5.444], respectively. Population Attributable Fraction of Each Metric For the PAF calculation, the LE8 score was categorized into three exposure levels: low, moderate, and high scores. The PAF of the overall LE8 score was 37.794%, suggesting that 37.79% of mod/sev periodontitis could be attributed to a lower LE8 score. As shown in Fig. 3 , the PAF of each LE8 component in descending order is blood pressure (24.892%), nicotine exposure (20.557%), blood glucose (19.627%), diet (9.252%), sleep health (5.845%), BMI (4.219%), physical activity (3.599%), and blood lipids (3.289%). Model Specification and Performance Measures According to the PAF results, four components had significant associations with periodontitis: blood pressure, nicotine exposure, blood glucose, and diet. Model 1 was constructed with age, sex, and the four significant elements. Figure 4 depicts the nomogram constructed using Model 1. Then, we added sleep health, BMI, PA, and blood lipids in turn as predictors to develop Models 2–5. The AUCs of Models 1–5 were 0.744 [0.733, 0.755], 0.745 [0.734, 0.756], 0.745 [0.734,0.756], 0.746 [0.734, 0.757], and 0.746 [0.735, 0.757], respectively (Fig. 5A; Figure S2 ), indicating the strong discriminative ability of the five models. The AUC in the test dataset of Model 1 was 0.718 [0.687, 0.749] (Fig. 5B), and the AUCs in the test datasets of Models 2–5 can be found in Figure S3 . The standard curves drawn in the calibration plots were close to the standard 45-degree diagonal line, suggesting perfect consistency between the predicted value and the actual result (Fig. 5C; Fig. 5D ). DISCUSSION In this study, we found that people whose lifestyles aligned with poor CVH (lower LE8 score) were more likely to experience periodontitis. Among the eight metrics in the LE8 score, two health behaviours (nicotine exposure and diet) and two health factors (blood glucose and blood pressure) were found to be more significantly associated with periodontitis. Based on the findings, we constructed models to predict periodontitis using LE8, yielding strong predictive performances in both the training dataset (AUC = 0.744 [0.733, 0.755]) and the test dataset (AUC = 0.718 [0.687, 0.749]). Our findings not only suggest that the LE8 score is a determinant of CVH, but also suggest that the management of the LE8 score can potentially reduce the risk of periodontitis at the population level. Our findings are consistent with the previous research showing that nicotine, diet, blood pressure, and blood glucose are associated with periodontitis 7,8,[25],[26] . Various explanations have been given for the biological processes underlying this association. For example, nicotine was found to inhibit the proliferation and proteogenesis of periodontal ligament cells, influence the activity of alkaline phosphatase in the cells[ 27 ], and activate a range of inflammatory factors such as c-fos, cox-2, and NF-κB[ 28 – 30 ]. Although the effect of daily nutrition on periodontitis has seldom been investigated, possible biological explanations include an inflammatory response[ 31 ] and the influence on the oral microbiome[ 32 ]. The health factors blood pressure and blood glucose have been shown to be significantly associated with periodontitis. Large amounts of high sugar or refined carbohydrates promote dysbiosis of the oral microbiome, induce an inflammatory response[ 33 ], promote apoptosis, and inhibit proliferation of periodontal ligament cells[ 34 ]. In addition, the systemic inflammatory nature and immune memory in hypertension link high blood pressure with periodontitis[ 35 ]. The PAF value of the overall LE8 score was significantly higher than those of the individual elements, suggesting that, compared with the change of a single factor, the change of LE8 as a proxy for healthy lifestyle has a more significant effect on periodontitis. Despite the clear association identified for four components of LE8, significant contributions were not observed for the other four components (i.e., sleep health, physical activity, BMI, and blood lipids), leading to non-distinguishable performance between the five prediction models. One possible reason may be the multidimensionality and interrelated nature of these factors. For example, poor sleep has been found to be associated with obesity, hypertension, and hyperlipidemia[ 36 ]. Another interesting finding in our study was that the strength of the association between LE8 score and periodontitis varied by population. The subgroup analysis indicated that the negative association between LE8 score and periodontitis was stronger among younger and female participants. This can be attributed to lifestyle differences, hormonal influences, socioeconomic factors, and genetic and biological variations[ 1 , 10 , 37 , 38 ]. These findings have significant implications for periodontal health promotion at the individual and population levels. For example, behavioural and lifestyle interventions targeted at specific populations (e.g., women, young people) could improve periodontal health at the population-level. This is the first study to predict periodontitis using the CVH score constructed by LE8 metrics. The predictive tool developed in this study can be used by cardiovascular specialists for the screening of patients with periodontitis, and identify their needs for periodontal treatment at earlier stages. The use of a large nationally representative sample of US participants makes this finding generalizable to a broader population. In terms of variable selection, we applied PAF to rank the ‘importance’ of the eight elements in LE8 and constructed a prediction model using those with significant PAF values, improving the clinical applicability and interpretability of the predictive tool. Despite the strengths, there were some limitations in the study. Firstly, data of the health behaviour metrics of LE8 assessments were collected by self-report questionnaires, which are subject to measurement errors. Secondly, although our findings were consistent after multiple adjustments and were generally robust in sensitivity analyses, we can include more confounders in the analysis. Thirdly, because of the nature of the cross-sectional study design, we cannot draw conclusions about causality and temporality between LE8 and periodontitis. Further research is needed to validate the predictive ability of the LE8 score for periodontitis across diverse populations and settings. Longitudinal studies tracking individuals’ scores and subsequent development of periodontal disease could provide robust evidence supporting its utility as a predictive tool. CONCLUSION Participants with poor CVH, indicated by lower LE8 score, were at higher risk of periodontitis. Prediction models for periodontitis were constructed using LE8 metrics, among which blood pressure, nicotine exposure, blood glucose, and diet were found to have significant effects on the condition. This finding indicates a potentially effective method of using LE8 score to screen for periodontitis and promote periodontal health. Declarations CONFLICT OF INTEREST STATEMENT The authors declare that they have no competing interests and gave their final approval and agreed to be accountable for all aspects of the work. Author Contribution L.G. acquired original data, conducted data analysis, plotted figures, interpreted the results and drafted the manuscript; Z.L. conducted data analysis, interpreted the results, and drafted the manuscript; K.D. conceptualized the study, critically reviewed and revised the manuscript. A.L. conceptualized the study, conducted data analysis, interpreted the results, critically reviewed, and revised the manuscript. M.D. conceptualized the study, interpreted the results, drafted, critically reviewed, and revised the manuscript. ACKNOWLEDGEMENT The authors acknowledge the support from the Major Innovation Projects in Shandong Province (No. 2021SFGC0502), the Natural Science Foundation of Shandong Province (No. ZR2022QH278), the Construction Engineering Special Fund of “Taishan Scholars” of Shandong Province (No. tsqn202306369), the Science Research Cultivation Program of Stomatological Hospital of Southern Medical University, and Guangdong Basic and Applied Basic Research Foundation. DATA AVAILABILITY STATEMENT The present study used the data from the National Health and Nutrition Examination Survey (NHANES). The datasets generated and analyzed during this study are publicly available in the NHANES repository. References Tonetti MS, Jepsen S, Jin L, Otomo-Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action. 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Chang YC, Huang FM, Tai KW, Yang LC, Chou MY. Mechanisms of cytotoxicity of nicotine in human periodontal ligament fibroblast cultures in vitro. J Periodontal Res. 2002;37(4):279–85. Iho S, Tanaka Y, Takauji R, Kobayashi C, Muramatsu I, Iwasaki H, Nakamura K, Sasaki Y, Nakao K, Takahashi T. Nicotine induces human neutrophils to produce IL-8 through the generation of peroxynitrite and subsequent activation of NF-kappaB. J Leukoc Biol. 2003;74(5):942–51. Chang YC, Hsieh YS, Lii CK, Huang FM, Tai KW, Chou MY. Induction of c-fos expression by nicotine in human periodontal ligament fibroblasts is related to cellular thiol levels. J Periodontal Res. 2003;38(1):44–50. Shoji M, Tanabe N, Mitsui N, Suzuki N, Takeichi O, Katono T, Morozumi A, Maeno M. Lipopolysaccharide enhances the production of nicotine-induced prostaglandin E2 by an increase in cyclooxygenase-2 expression in osteoblasts. Acta Biochim Biophys Sin. 2007;39(3):163–72. Di Giosia P, Stamerra CA, Giorgini P, Jamialahamdi T, Butler AE, Sahebkar A. The role of nutrition in inflammaging. Ageing Res Rev. 2022;77:101596. Kato I, Vasquez A, Moyerbrailean G, Land S, Djuric Z, Sun J, Lin HS, Ram JL. Nutritional Correlates of Human Oral Microbiome. J Am Coll Nutr. 2017;36(2):88–98. Li L, Bao J, Wang M, Chen B, Luo B, Yan F. High-fat diet exacerbates periodontitis: is it because of dysbacteriosis or stem cell dysfunction? J Biol Regul Homeost Agents. 2021;35(2):641–55. Skoczek-Rubińska A, Bajerska J, Menclewicz K. Effects of fruit and vegetables intake in periodontal diseases: A systematic review. Dent Med Probl. 2018;55(4):431–9. Guzik TJ, Nosalski R, Maffia P, Drummond GR. Immune and inflammatory mechanisms in hypertension. Nat reviews Cardiol 2024. Schmid SM, Hallschmid M, Schultes B. The metabolic burden of sleep loss. lancet Diabetes Endocrinol. 2015;3(1):52–62. Persson GR. Periodontal complications with age. Periodontol 2000. 2018;78(1):185–94. Petersen PE, Ogawa H. The global burden of periodontal disease: towards integration with chronic disease prevention and control. Periodontol 2000. 2012;60(1):15–39. Additional Declarations No competing interests reported. Supplementary Files Supplementalfiles.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4594866","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322259078,"identity":"71336110-2b6e-483a-afc0-de28f92b0046","order_by":0,"name":"Linxu Guo","email":"","orcid":"","institution":"Shandong University \u0026 Shandong Provincial Clinical Research Center for Oral Diseases","correspondingAuthor":false,"prefix":"","firstName":"Linxu","middleName":"","lastName":"Guo","suffix":""},{"id":322259080,"identity":"7e22c5b3-da2a-4de0-9095-febdb91b5be5","order_by":1,"name":"Zhixin Luo","email":"","orcid":"","institution":"Shandong University \u0026 Shandong Provincial Clinical Research Center for Oral Diseases","correspondingAuthor":false,"prefix":"","firstName":"Zhixin","middleName":"","lastName":"Luo","suffix":""},{"id":322259081,"identity":"0e25ed67-4e22-497d-88f7-d1721bec6141","order_by":2,"name":"Ke Deng","email":"","orcid":"","institution":"The University of Hong Kong","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Deng","suffix":""},{"id":322259082,"identity":"558beb74-ac5f-4599-840d-8031420b5a52","order_by":3,"name":"An Li","email":"","orcid":"","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"An","middleName":"","lastName":"Li","suffix":""},{"id":322259083,"identity":"49d90942-5b25-4873-91a7-10ab783eb695","order_by":4,"name":"Mi Du","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACZgYGCYYKBsYGBhCDeC1nSNICUsnYRooW3XYGxtu88+pkNxxgPnibh8Euj6AWs8MMzNa82w4bbzjAlmzNw5BcTIwWNmnebQcSNxzgMZPmYTiQ2ECcljl1QC3830jR0sAMsoWNWC2MzZZzjh02nnmYzdhyjkEyEVrOHz54401NnWzf8eaHN95U2BHWwgCJEQZwnDIwGBBWPwpGwSgYBaOACAAATu83LIjG9BAAAAAASUVORK5CYII=","orcid":"","institution":"Shandong University \u0026 Shandong Provincial Clinical Research Center for Oral Diseases","correspondingAuthor":true,"prefix":"","firstName":"Mi","middleName":"","lastName":"Du","suffix":""}],"badges":[],"createdAt":"2024-06-17 14:45:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4594866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4594866/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60025028,"identity":"d6e79050-438a-4f03-9911-34aad5ccf466","added_by":"auto","created_at":"2024-07-10 17:03:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":233705,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between Life’s Essential 8 (LE8) score and periodontitis. \u003cstrong\u003eA. \u003c/strong\u003eRestricted cubic splines showing the linear association between LE8 score and periodontitis among participants in the National Health and Nutrition Examination Survey 2009–2014. Knots located at the 10th, 50th, and 90th percentiles for LE8 score. All models were adjusted for age, sex, race/ethnicity, education levels, and poverty/income ratio. The red solid line represents the odds ratio (OR) for moderate/severe periodontitis, and the dashed lines represent the 95% confidence interval.\u003cstrong\u003eB.\u003c/strong\u003e OR of moderate and low LE8 score taking high score as the reference value. Abbreviations: LE8, Life’s Essential 8; OR, odd ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4594866/v1/c882da69f6601754a024e920.png"},{"id":60025026,"identity":"9594a4aa-f5a8-4e43-89f6-aebd22d97a42","added_by":"auto","created_at":"2024-07-10 17:03:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":94687,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot for the associations between Life’s Essential 8 (LE8) score and periodontitis risk overall and stratified by age and sex. LE8 score was characterized as low (0–49), moderate (50–79), and high (80–100) according to American Heart Association. Abbreviations: OR, odd ratio; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4594866/v1/bc6cb8e8f6bf44847a39caca.png"},{"id":60025031,"identity":"0c36495e-94c3-4b55-ba9e-5d9d93a3aaf2","added_by":"auto","created_at":"2024-07-10 17:03:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25489,"visible":true,"origin":"","legend":"\u003cp\u003ePopulation attributable fraction (PAF) of Life’s Essential 8 (LE8) and its eight terms. The y-axis represents the PAF value, and the x-axis represents the eight terms of LE8. Abbreviations: LE8, Life’s Essential 8; BP, blood pressure; BMI, body mass index; PA, physical activity.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4594866/v1/9261051ef46bf69b9af63836.png"},{"id":60025027,"identity":"cb9d99e6-884c-45b3-a29b-56b55995bd91","added_by":"auto","created_at":"2024-07-10 17:03:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":20538,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive nomogram for periodontitis risk using Life’s Essential 8.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4594866/v1/eff07fb6498e2625f367e8d0.png"},{"id":60025679,"identity":"03e4d368-d85d-460e-abe2-83fe88d1414b","added_by":"auto","created_at":"2024-07-10 17:11:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":573631,"visible":true,"origin":"","legend":"\u003cp\u003ePerformance of the nomogram for predicting periodontitis using Life’s Essential 8. \u003cstrong\u003eA.\u003c/strong\u003e AUC of training set.\u003cstrong\u003e B.\u003c/strong\u003e AUC of test set. The y-axis represents the sensitivity, and the x-axis represents the specificity. \u003cstrong\u003eC. \u003c/strong\u003eCalibration plots of training set. \u003cstrong\u003eD.\u003c/strong\u003e Calibration plots of validation set. The y-axis represents the observed probability, and the x-axis represents the predicted probability. Abbreviations: AUC, area under the receiver operating characteristic curve.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4594866/v1/c31621b5cda584e98fd3acfd.png"},{"id":61103486,"identity":"c5ef603e-1225-4089-aecc-30c0a433f21f","added_by":"auto","created_at":"2024-07-25 15:35:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1567443,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4594866/v1/99c86ac9-2df7-441f-987a-fcb2e214a18d.pdf"},{"id":60025030,"identity":"2b1bcdc0-40f0-4708-b3b4-645aa043a335","added_by":"auto","created_at":"2024-07-10 17:03:01","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":463995,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-4594866/v1/cc836010dc1092df89e63b25.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Life’s Essential 8 predicts the risk of periodontitis: A nomogram","fulltext":[{"header":"One sentence summary","content":"\u003cp\u003eThe cardiovascular health score constructed by Life\u0026rsquo;s essential 8 were found to be predictive for the risk of periodontitis, among which the blood pressure, nicotine exposure, blood glucose and diet being significant predictors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"INTRODUCTION","content":"\u003cp\u003ePeriodontitis, a chronic inflammatory condition affecting the supporting structures of the teeth, poses a significant global public health challenge[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. A complex interplay among genetic, environmental, and lifestyle factors contributes to both the development and progression of periodontitis[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Cardiovascular diseases, the leading cause of death in the US[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], have been reported to be associated with periodontitis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Potential mechanisms include bacteraemia and the consequent systemic inflammatory response, leading to the increased level of C-reactive protein and oxidative stress[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, there has been a growing focus on healthy lifestyles as modifiable and cost-effective behavioural factors, given their potential benefits for overall well-being. An example can be found in the area of cardiovascular health (CVH). To quantify CVH, the American Heart Association introduced the Life\u0026rsquo;s Simple 7 (LS7) assessment system in 2010[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which was updated to the Life\u0026rsquo;s Essential 8 (LE8) in 2022[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The LE8 adds sleep health as a new metric to the three health behaviours (diet, physical activity, and nicotine exposure) and four health factors (body mass index [BMI], blood lipids, blood glucose, and blood pressure). Recent studies have also demonstrated that better CVH status is associated with a reduced risk of periodontitis, indicating that CVH may be a modifiable factor of periodontal health[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn increasing number of researchers have emphasized the importance of lifestyle in influencing periodontal health[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], thus it might be more beneficial to concentrate on addressing one significant health concern at a time when identifying multiple metrics that require improvement. Periodontitis and CVD share numerous risk factors, most of which are components of LE8, including blood pressure[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], blood lipids[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], diet, physical activity, stress levels, sleep quality, smoking and alcohol use[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Determining the prioritization of LE8 metrics in periodontitis is therefore of considerable importance for developing effective periodontal public health strategies. However, which factors among the LE8 metrics contribute more to periodontal health remain uncertain. Moreover, given the fact that most periodontitis patients with CVDs tend to seek for health care in primary medical care settings, a screening tool to be utilized by CVD physicians is needed to facilitate the referral of patients at periodontitis risk for clinical examination and treatment. However, the existing prediction models for periodontitis largely rely on the well-known risk factors, and there is a lack of studies examining the predictive effect of these LE8 factors and lifestyles as a whole on periodontal health.\u003c/p\u003e \u003cp\u003eTo address the research gaps, we hypothesized that LE8 metrics were related to periodontitis and can be used as a calculator to quantify the risk of periodontitis. The objectives of the present study were twofold: first, rank the components of the LE8 metrics based on their contributions to the LE8\u0026ndash;periodontitis association; second, construct a prediction model for periodontitis using the identified factors, thereby providing a practical tool for oral and cardiovascular healthcare providers to assess and manage periodontal health.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Source and Participants\u003c/h2\u003e \u003cp\u003eThe National Health and Nutrition Examination Survey (NHANES) is a repeated cross-sectional survey using a stratified, multistage probability design to collect a nationally representative sample of non-institutionalized US civilians. Data were collected from household interviews, mobile physical examinations, and laboratory tests. The survey was administered by the National Center for Health Statistics, and the institutional ethics review board of the centre approved the NHANES protocols. All the participants provided written informed consent during the initial data collection[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. This study was exempt from institutional review board ethics review according to the National Institutes of Health Policy because using de-identified data with no direct participant contact. To evaluate the LE8-periodontitis association, we used the data from three waves of NHANES (2009\u0026ndash;2010, 2011\u0026ndash;2012, and 2013\u0026ndash;2014), in which the participants underwent full-mouth periodontal examinations and have all eight metrics of LE8.\u003c/p\u003e \u003cp\u003eOf the 18,504 participants from NHANES 2009\u0026ndash;2014, we excluded participants who met the following criteria: (1) without LE8 data (n\u0026thinsp;=\u0026thinsp;5,447); (2) younger than 30 years old (n\u0026thinsp;=\u0026thinsp;2,403); and (3) without data on periodontal examination (n\u0026thinsp;=\u0026thinsp;2,135). In total, 8,519 participants were included in the present study (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). In this study, age was divided into two strata: 30\u0026ndash;59 years and \u0026ge;\u0026thinsp;60 years. Race/ethnicity was categorized as White, Black, and other races. The poverty ratio was calculated as the ratio of monthly family income to poverty levels and categorized into three groups: \u0026lt;1.3 (low income), 1.3\u0026ndash;3.5 (middle income), and \u0026gt;\u0026thinsp;3.5 (high income). Education levels were categorized as high school or below, some college or AA degree, and college graduate or above.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eLife\u0026rsquo;s Essential 8 (LE8) Score Calculation\u003c/h2\u003e \u003cp\u003eLE8 scoring comprises four health behaviours (diet, physical activity, nicotine exposure and sleep) and four health factors (BMI, blood lipids, blood glucose, and blood pressure). Each of the eight metrics was scored from 0 to 100 points. Detailed methods for calculating LE8 score in NHANES data can be found in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e. The overall LE8 score was taken as the average of the scores of the eight metrics and categorized into low (0\u0026ndash;49), moderate (50\u0026ndash;79), and high (80\u0026ndash;100) groups. The diet metric was evaluated by the Healthy Eating Index 2015[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The dietary intakes of participants collected from two 24-hour diet recalls were combined with the United States Department of Agriculture food patterns equivalents data to construct and calculate the Healthy Eating Index 2015 scores[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Detailed algorithms for calculating the Healthy Eating Index 2015 are given in \u003cb\u003eTable S2\u003c/b\u003e. Self-report questionnaires were used to collect data on physical activity, nicotine exposure, sleeping time, diabetes lifestyle, and medication history. Blood pressure and BMI were measured during the physical examination. Blood samples were collected and sent to central laboratories for the determination of blood lipids and blood glucose.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAssessment of Periodontal Status\u003c/h2\u003e \u003cp\u003eFull-mouth periodontal examinations were conducted for the participants in NHANES 2009\u0026ndash;2014, including the probing pocket depth and attachment loss for six sites around each tooth. Periodontitis was defined according to the Center for Disease Control and Prevention/American Academy of Periodontology case definition[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Severe periodontitis was defined as \u0026ge;\u0026thinsp;2 interproximal sites with \u0026ge;\u0026thinsp;6 mm of attachment loss (on different teeth) and \u0026ge;\u0026thinsp;1 interproximal site with \u0026ge;\u0026thinsp;5 mm of probing pocket depth; moderate periodontitis was defined as \u0026ge;\u0026thinsp;2 interproximal sites with \u0026ge;\u0026thinsp;4 mm of clinical attachment loss (on different teeth) or \u0026ge;\u0026thinsp;2 interproximal sites with probing pocket depth\u0026thinsp;\u0026ge;\u0026thinsp;5 mm (on different teeth); and no/mild periodontitis was defined as the absence of any signs of moderate or severe (mod/sev) periodontitis. Consistent with the previous studies[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the outcomes were defined as no/mild or mod/sev periodontitis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis for LE8-Periodontitis association\u003c/h2\u003e \u003cp\u003eMultivariable regression models were used to assess the association between LE8 score and mod/sev periodontitis. A restricted cubic spline with knots located at the 10th, 50th, and 90th percentiles of LE8 score was used to assess their non-linear relationships. Stratified analyses were performed by sex and age. Multivariable regression models were used to assess the association between LE8 score and mod/sev periodontitis. A restricted cubic spline with knots located at the 10th, 50th, and 90th percentiles of LE8 score was used to assess their non-linear relationships. Stratified analyses were performed by sex and age. The p-value for interaction between LE8 scores was used to estimate the significance of interactions. Sensitivity analysis was performed by (1) excluding participants without any covariates (n\u0026thinsp;=\u0026thinsp;654); (2) applying the 2018 EFP/AAP definition to classify periodontitis as non-Stage II and Stage III\u0026ndash;IV; and (3) classifying the participants into three subgroups by cutting the LE8 score at the one-third and two-thirds quantiles. Complete-case analysis was used, as the missing proportions were less than 5%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Attributable Fraction\u003c/h2\u003e \u003cp\u003eThe population attributable fraction (PAF) was calculated to assess to what extent the association could be attributed to each of the LE8 components. PAF, first proposed by Levin in 1953[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], represents the proportion of risk that would be reduced if the past were reduced to an ideal exposure scenario. In this study, the formula modified by Hanley in 2001[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] was used:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$PAF=\\frac{{PF}_{1}\\left\\{RR-1\\right\\}}{{1+PF}_{1}\\left\\{RR-1\\right\\}}$$\u003c/div\u003e\u003c/div\u003e .\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ePF\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e is the total population exposure rate, and \u003cem\u003eRR\u003c/em\u003e is the risk ratio. We used the same definition as the overall LE8 score to measure and categorize the eight individual metrics: low (0\u0026ndash;49) groups were considered high exposure, moderate (50\u0026ndash;79) and high (80\u0026ndash;100) groups low exposure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment and validation of prediction model\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression was used to establish a nomogram model for the prediction of periodontitis. LE8 elements were used as predictors and periodontitis as outcome. Data from the participants in NHANES 2009\u0026ndash;2012 (n\u0026thinsp;=\u0026thinsp;7,449) were used as a training set and data from the participants in NHANES 2013\u0026ndash;2014 (n\u0026thinsp;=\u0026thinsp;1,070) as a test set for the assessment of the model\u0026rsquo;s performance. The model\u0026rsquo;s discrimination performance was evaluated by sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). The calibration of the models was evaluated by calibration curves.\u003c/p\u003e \u003cp\u003eAll the analyses were conducted using the R Project for Statistical Computing (version 4.3.1), with statistical significance defined as two-sided \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. This study was reported following the guideline of Strengthening the Reporting of Observational Studies in Epidemiology (STROBE)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] (\u003cb\u003eTable S3\u003c/b\u003e) and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guideline\u003csup\u003e[24]\u003c/sup\u003e (\u003cb\u003eTable S4\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 8,519 participants aged 30 years or older were included. The characteristics of the study population across LE8 scores are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The mean age of the study participants was 51.5 years, and 4,258 (50.0%) were female. The number of participants with low, moderate, and high LE8 scores were 1,081 (12.7%), 5,773 (67.8%) and 1,665 (19.5%), respectively. There were 3,874 (45.5%) participants diagnosed with mod/sev periodontitis. Compared to those without periodontitis, participants with periodontitis were older and more likely to be male.\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 participants.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;8519)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow for LE8 (0\u0026ndash;49)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1081)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate for LE8 (50\u0026ndash;79)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;5773)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh for LE8 (80\u0026ndash;100)\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;1665)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge groups\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5887 (69.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e668 (61.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3901 (67.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1318 (79.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2632 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e413 (38.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1872 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e347 (20.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years) (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51.5 (14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.5 (13.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.1 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47.5 (13.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4261 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e521 (48.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3059 (53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e681 (40.9%)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4258 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e560 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2714 (47.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e984 (59.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace/ethnicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3872 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433 (40.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2591 (44.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e848 (50.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlacks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1670 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e320 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1194 (20.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e156 (9.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2977 (34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e328 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1988 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e661 (39.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation levels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3744 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e677 (62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2660 (46.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e407 (24.4%)\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2426 (28.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e294 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1743 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e389 (23.4%)\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2341 (27.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109 (10.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1363 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e869 (52.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoverty/income ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2251 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e452 (41.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1556 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e243 (14.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2822 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e373 (34.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1980 (34.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e469 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2792 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1790 (31.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e821 (49.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeriodontitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon/Mild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4645 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e408 (37.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3043 (52.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1194 (71.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMod/Sev\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3874 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e673 (62.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2730 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e471 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiet score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e42.4 (31.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.4 (24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.4 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65.8 (27.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70.8 (42.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.6 (43.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71.3 (42.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.4 (18.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNicotine exposure score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.5 (38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.0 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e72.0 (38.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e92.4 (17.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep health score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.7 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.1 (31.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80.9 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e91.3 (15.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59.6 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.9 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.5 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e83.7 (21.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood lipids score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e61.6 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.7 (30.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.7 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79.8 (24.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood glucose score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e79.7 (26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.9 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e79.4 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.9 (14.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBP score (Mean [SD])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.8 (33.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.1 (32.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.8 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e88.8 (21.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eThe data of all participants in this study were presented and divided into three groups with LE8 scores: low (00\u0026ndash;49), medium (50\u0026ndash;79) and high (80\u0026ndash;100). These variables are shown as the mean and standard deviation. Missing value for total population: education levels (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3, \u0026lt; 0.1%), poverty/income ratio (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;323; 7.0%). Abbreviations: LE8, Life\u0026rsquo;s Essential 8; BP, blood pressure; BMI, body mass index; PA, physical activity; mod/sev, moderate/severe; SD, standard deviation.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociation of LE8 and Periodontitis and Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA presents the linear relationships between LE8 score and periodontitis (p for non-linear\u0026thinsp;=\u0026thinsp;0.736). The results indicated that the risk of periodontitis decreased as the LE8 score increased. The reference value (null effect, odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.000) for LE8 score was 66.875. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB indicates that the prevalence of periodontitis was significantly lower in participants with high LE8 scores (80\u0026ndash;100) than in those with moderate (50\u0026ndash;79) or low (0\u0026ndash;49) scores. Compared with the high LE8 group, participants with moderate or low LE8 scores are at approximately two to four times higher risk of periodontitis (OR \u003csub\u003emoderate\u0026minus;LE8\u003c/sub\u003e = 2.274 [95%CI: 2.020\u0026ndash;2.560], OR \u003csub\u003elow\u0026minus;LE8\u003c/sub\u003e = 4.182 [95%CI: 3.553\u0026ndash;4.921]), suggesting that a higher LE8 score is associated with a protective effect against periodontitis. In sensitivity analysis, we excluded participants without any covariates (n\u0026thinsp;=\u0026thinsp;654), suggesting that higher LE8 has an obvious protective effect against periodontitis. The findings from the sensitivity analysis were generally robust (\u003cb\u003eTable S5\u0026ndash;7\u003c/b\u003e). The increase of LE8 score is associated with a protective effect on periodontal health.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStratification Analysis\u003c/h2\u003e \u003cp\u003eThe results of the stratification analyses are presented in \u003cb\u003eFig.\u0026nbsp;2\u003c/b\u003e. The LE8 score was negatively associated with periodontitis in all subgroups. We found significant interactions between LE8 score and periodontitis with different age groups and sex (\u003cem\u003ep\u003c/em\u003e for interaction\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared with the older and male participants, higher LE8 score was associated with a more significant protecting effect for the younger (aged 30\u0026ndash;59 years) and female participants. Among the participants aged 30\u0026ndash;59 years, ORs for moderate and low LE8 were 2.405 [95%CI: 2.078\u0026ndash;2.783] and 4.901 [95%CI: 4.005\u0026ndash;5.997], respectively. In contrast, among the older (aged 60 years and older) participants, the ORs for moderate and low LE8 were 1.456 [95%CI: 1.156\u0026ndash;1.834] and 2.017 [95%CI: 1.498\u0026ndash;2.718], respectively. Similar trends were found in female versus male participants: the ORs for female participants having periodontitis were 2.373 [95%CI: 2.000\u0026ndash;2.815] and 4.103 [95%CI: 3.275\u0026ndash;5.140] for moderate and low LE8 subgroups, respectively; the ORs for male were 1.927 [95% CI: 1.626\u0026ndash;2.284] and 4.254 [95%CI: 3.324\u0026ndash;5.444], respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePopulation Attributable Fraction of Each Metric\u003c/h2\u003e \u003cp\u003eFor the PAF calculation, the LE8 score was categorized into three exposure levels: low, moderate, and high scores. The PAF of the overall LE8 score was 37.794%, suggesting that 37.79% of mod/sev periodontitis could be attributed to a lower LE8 score. As shown in \u003cb\u003eFig.\u0026nbsp;3\u003c/b\u003e, the PAF of each LE8 component in descending order is blood pressure (24.892%), nicotine exposure (20.557%), blood glucose (19.627%), diet (9.252%), sleep health (5.845%), BMI (4.219%), physical activity (3.599%), and blood lipids (3.289%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eModel Specification and Performance Measures\u003c/h2\u003e \u003cp\u003eAccording to the PAF results, four components had significant associations with periodontitis: blood pressure, nicotine exposure, blood glucose, and diet. Model 1 was constructed with age, sex, and the four significant elements. Figure\u0026nbsp;4 depicts the nomogram constructed using Model 1. Then, we added sleep health, BMI, PA, and blood lipids in turn as predictors to develop Models 2\u0026ndash;5. The AUCs of Models 1\u0026ndash;5 were 0.744 [0.733, 0.755], 0.745 [0.734, 0.756], 0.745 [0.734,0.756], 0.746 [0.734, 0.757], and 0.746 [0.735, 0.757], respectively (Fig.\u0026nbsp;5A; \u003cb\u003eFigure S2\u003c/b\u003e), indicating the strong discriminative ability of the five models. The AUC in the test dataset of Model 1 was 0.718 [0.687, 0.749] (Fig.\u0026nbsp;5B), and the AUCs in the test datasets of Models 2\u0026ndash;5 can be found in \u003cb\u003eFigure S3\u003c/b\u003e. The standard curves drawn in the calibration plots were close to the standard 45-degree diagonal line, suggesting perfect consistency between the predicted value and the actual result (Fig.\u0026nbsp;5C; \u003cb\u003eFig.\u0026nbsp;5D\u003c/b\u003e). \u003cb\u003eDISCUSSION\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn this study, we found that people whose lifestyles aligned with poor CVH (lower LE8 score) were more likely to experience periodontitis. Among the eight metrics in the LE8 score, two health behaviours (nicotine exposure and diet) and two health factors (blood glucose and blood pressure) were found to be more significantly associated with periodontitis. Based on the findings, we constructed models to predict periodontitis using LE8, yielding strong predictive performances in both the training dataset (AUC\u0026thinsp;=\u0026thinsp;0.744 [0.733, 0.755]) and the test dataset (AUC\u0026thinsp;=\u0026thinsp;0.718 [0.687, 0.749]). Our findings not only suggest that the LE8 score is a determinant of CVH, but also suggest that the management of the LE8 score can potentially reduce the risk of periodontitis at the population level.\u003c/p\u003e \u003cp\u003eOur findings are consistent with the previous research showing that nicotine, diet, blood pressure, and blood glucose are associated with periodontitis\u003csup\u003e7,8,[25],[26]\u003c/sup\u003e. Various explanations have been given for the biological processes underlying this association. For example, nicotine was found to inhibit the proliferation and proteogenesis of periodontal ligament cells, influence the activity of alkaline phosphatase in the cells[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and activate a range of inflammatory factors such as c-fos, cox-2, and NF-κB[\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although the effect of daily nutrition on periodontitis has seldom been investigated, possible biological explanations include an inflammatory response[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the influence on the oral microbiome[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The health factors blood pressure and blood glucose have been shown to be significantly associated with periodontitis. Large amounts of high sugar or refined carbohydrates promote dysbiosis of the oral microbiome, induce an inflammatory response[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], promote apoptosis, and inhibit proliferation of periodontal ligament cells[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In addition, the systemic inflammatory nature and immune memory in hypertension link high blood pressure with periodontitis[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe PAF value of the overall LE8 score was significantly higher than those of the individual elements, suggesting that, compared with the change of a single factor, the change of LE8 as a proxy for healthy lifestyle has a more significant effect on periodontitis. Despite the clear association identified for four components of LE8, significant contributions were not observed for the other four components (i.e., sleep health, physical activity, BMI, and blood lipids), leading to non-distinguishable performance between the five prediction models. One possible reason may be the multidimensionality and interrelated nature of these factors. For example, poor sleep has been found to be associated with obesity, hypertension, and hyperlipidemia[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Another interesting finding in our study was that the strength of the association between LE8 score and periodontitis varied by population. The subgroup analysis indicated that the negative association between LE8 score and periodontitis was stronger among younger and female participants. This can be attributed to lifestyle differences, hormonal influences, socioeconomic factors, and genetic and biological variations[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese findings have significant implications for periodontal health promotion at the individual and population levels. For example, behavioural and lifestyle interventions targeted at specific populations (e.g., women, young people) could improve periodontal health at the population-level. This is the first study to predict periodontitis using the CVH score constructed by LE8 metrics. The predictive tool developed in this study can be used by cardiovascular specialists for the screening of patients with periodontitis, and identify their needs for periodontal treatment at earlier stages. The use of a large nationally representative sample of US participants makes this finding generalizable to a broader population. In terms of variable selection, we applied PAF to rank the \u0026lsquo;importance\u0026rsquo; of the eight elements in LE8 and constructed a prediction model using those with significant PAF values, improving the clinical applicability and interpretability of the predictive tool. Despite the strengths, there were some limitations in the study. Firstly, data of the health behaviour metrics of LE8 assessments were collected by self-report questionnaires, which are subject to measurement errors. Secondly, although our findings were consistent after multiple adjustments and were generally robust in sensitivity analyses, we can include more confounders in the analysis. Thirdly, because of the nature of the cross-sectional study design, we cannot draw conclusions about causality and temporality between LE8 and periodontitis. Further research is needed to validate the predictive ability of the LE8 score for periodontitis across diverse populations and settings. Longitudinal studies tracking individuals\u0026rsquo; scores and subsequent development of periodontal disease could provide robust evidence supporting its utility as a predictive tool.\u003c/p\u003e \u003c/div\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eParticipants with poor CVH, indicated by lower LE8 score, were at higher risk of periodontitis. Prediction models for periodontitis were constructed using LE8 metrics, among which blood pressure, nicotine exposure, blood glucose, and diet were found to have significant effects on the condition. This finding indicates a potentially effective method of using LE8 score to screen for periodontitis and promote periodontal health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCONFLICT OF INTEREST STATEMENT\u003c/h2\u003e \u003cp\u003e The authors declare that they have no competing interests and gave their final approval and agreed to be accountable for all aspects of the work.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.G. acquired original data, conducted data analysis, plotted figures, interpreted the results and drafted the manuscript; Z.L. conducted data analysis, interpreted the results, and drafted the manuscript; K.D. conceptualized the study, critically reviewed and revised the manuscript. A.L. conceptualized the study, conducted data analysis, interpreted the results, critically reviewed, and revised the manuscript. M.D. conceptualized the study, interpreted the results, drafted, critically reviewed, and revised the manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENT\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the support from the Major Innovation Projects in Shandong Province (No. 2021SFGC0502), the Natural Science Foundation of Shandong Province (No. ZR2022QH278), the Construction Engineering Special Fund of \u0026ldquo;Taishan Scholars\u0026rdquo; of Shandong Province (No. tsqn202306369), the Science Research Cultivation Program of Stomatological Hospital of Southern Medical University, and Guangdong Basic and Applied Basic Research Foundation.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY STATEMENT\u003c/h2\u003e \u003cp\u003eThe present study used the data from the National Health and Nutrition Examination Survey (NHANES). The datasets generated and analyzed during this study are publicly available in the NHANES repository.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTonetti MS, Jepsen S, Jin L, Otomo-Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: A call for global action. J Clin Periodontol. 2017;44(5):456\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLoos BG, Van Dyke TE. The role of inflammation and genetics in periodontal disease. Periodontol 2000. 2020;83(1):26\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad FB, Anderson RN. The Leading Causes of Death in the US for 2020. JAMA. 2021;325(18):1829\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBotelho J, Mascarenhas P, Viana J, Proen\u0026ccedil;a L, Orlandi M, Leira Y, Chambrone L, Mendes JJ, Machado V. 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Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeite FRM, Nascimento GG, Scheutz F, L\u0026oacute;pez R. Effect of Smoking on Periodontitis: A Systematic Review and Meta-regression. Am J Prev Med. 2018;54(6):831\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaffee BW, Couch ET, Vora MV, Holliday RS. Oral and periodontal implications of tobacco and nicotine products. Periodontol 2000. 2021;87(1):241\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang YC, Huang FM, Tai KW, Yang LC, Chou MY. Mechanisms of cytotoxicity of nicotine in human periodontal ligament fibroblast cultures in vitro. 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Acta Biochim Biophys Sin. 2007;39(3):163\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDi Giosia P, Stamerra CA, Giorgini P, Jamialahamdi T, Butler AE, Sahebkar A. The role of nutrition in inflammaging. Ageing Res Rev. 2022;77:101596.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato I, Vasquez A, Moyerbrailean G, Land S, Djuric Z, Sun J, Lin HS, Ram JL. Nutritional Correlates of Human Oral Microbiome. J Am Coll Nutr. 2017;36(2):88\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Bao J, Wang M, Chen B, Luo B, Yan F. High-fat diet exacerbates periodontitis: is it because of dysbacteriosis or stem cell dysfunction? J Biol Regul Homeost Agents. 2021;35(2):641\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkoczek-Rubińska A, Bajerska J, Menclewicz K. Effects of fruit and vegetables intake in periodontal diseases: A systematic review. Dent Med Probl. 2018;55(4):431\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzik TJ, Nosalski R, Maffia P, Drummond GR. Immune and inflammatory mechanisms in hypertension. Nat reviews Cardiol 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmid SM, Hallschmid M, Schultes B. The metabolic burden of sleep loss. lancet Diabetes Endocrinol. 2015;3(1):52\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePersson GR. Periodontal complications with age. Periodontol 2000. 2018;78(1):185\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen PE, Ogawa H. The global burden of periodontal disease: towards integration with chronic disease prevention and control. Periodontol 2000. 2012;60(1):15\u0026ndash;39.\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":"Periodontitis, Life’s Essential 8, Risk factor, NHANES, Population Attributable Fraction, Forecast","lastPublishedDoi":"10.21203/rs.3.rs-4594866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4594866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eLife’s Essential 8 (LE8), a metric to quantify cardiovascular health, comprises eight elements of health behaviours and lifestyles. There is an interest to understand the distinct effects of individual LE8 elements on periodontal health and whether LE8 predict the risk of periodontitis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003ePooled cross-sectional data from the National Health and Nutrition Examination Survey in 2009–2014 were used (n = 8,519). Periodontitis was classified into two groups (no/mild and moderate/severe). LE8 score (range 0–100), determined by eight metrics (diet, physical activity, nicotine exposure, sleep, body mass index, blood lipids, blood glucose, and blood pressure), was categorized as low (0–49), moderate (50–79), and high (80–100). The LE8–periodontitis association was investigated by multivariable logistic regression and population attributable fraction (PAF). Prediction models for periodontitis using LE8 score were developed, and the performance was tested by the area under the receiver operating characteristic curve (AUC) and calibration curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eNegative associations were found between LE8 score and periodontitis. Participants with low and moderate LE8 scores had higher risks of periodontitis than those with high LE8 scores (odds ratios [OR] = 4.182 [95%CI = 3.553–4.921], and 2.274 [95%CI = 2.020–2.560], respectively). The PAF analysis showed that 37.794% of periodontitis cases can be attributed to low LE8, among which the effects of blood pressure (PAF = 24.892%), nicotine exposure (PAF = 20.557%), blood lipids (PAF = 19.627%), and diet quality (PAF = 9.252%) were found to be significant. The models constructed using the four LE8 components of blood pressure, nicotine exposure, blood lipids, and diet quality could predict the risk of periodontitis (AUC = 0.744 [0.733, 0.755]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eWorse cardiovascular health, indicated by lower LE8 score, was related to periodontitis risk, and the LE8 score significantly predicted the periodontal health status.\u003c/p\u003e","manuscriptTitle":"Life’s Essential 8 predicts the risk of periodontitis: A nomogram","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-10 17:02:56","doi":"10.21203/rs.3.rs-4594866/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":"2cf76561-2288-43c1-98f7-59da5e77a3a7","owner":[],"postedDate":"July 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-25T15:27:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-10 17:02:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4594866","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4594866","identity":"rs-4594866","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}