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Association of lipid profile and reported edentulism in the elder population: data from the China Health and Retirement Longitudinal Study



Relationship between lipid profile and periodontitis has been reported. However, the association between lipid parameters and edentulism is unclear. This study aimed to investigate the association between lipid profile and reported edentulism in the elder population using a national cohort.


A total of 3 100 participants aged 65 or above were enrolled in 2011 from China Health and Retirement Longitudinal Study, which was a national population-based survey. We used adjusted logistics models to investigate the relationship between lipid profile and reported edentulism before and after propensity score matching.


The mean (SD) age was 71.96 (5.63) years, and 1 581 (51.0%) were men. There were 254 (8.2%) individuals reporting edentulism, and the low-density lipoprotein cholesterol (LDL-C) was significantly higher in the reported edentulism group, compared with the non-edentulism (122.48 vs. 116.91 mg/dl, P = 0.015). In the multivariable model, LDL-C was significantly associated with a higher odds of reported edentulism (adjusted OR [95% CI], 1.004 [1.001–1.008]). In the matched population, LDL-C, non high-density lipoprotein cholesterol, remnant cholesterol, total cholesterol and triglycerides were positively associated with reported edentulism, while HDL-C was negatively associated.


Lipid profiles are probably associated with edentulism, indicating the interaction between oral health and metabolic status in the elder population.

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Oral diseases are among the most prevalent diseases worldwide and have serious health, mental and economic burdens [1]. Dental caries and periodontal disease are two major oral diseases impacting the quality of life globally [2]. Edentulism and tooth loss are severe consequences of these diseases and occur throughout life, especially in the elder population. Although the epidemiology studies show a gradual decline in the prevalence and incidence of edentulism and tooth loss at the global, regional, and country levels [3], the prevalence still increases with aging showing an incidence peak at 65 years [4].

Edentulism and tooth loss are closely related with obesity [5], diabetes [6], dementia [7, 8], chronic obstructive pulmonary disease [9], nonalcoholic fatty liver disease [10] and cancer [11, 12]. Dyslipidaemias are alterations in the lipid profiles which are independently associated with cardiovascular diseases, particularly elevated plasma low-density lipoprotein cholesterol (LDL-C) [13]. The global burden of dyslipidaemias is gradually increasing, and LDL-C level has ranked the 8th leading risk factor of mortality in 2019, which ranked 15th in 1990 [14]. There is emerging evidence regarding the interaction between lipid metabolism and systemic, local inflammation and inflammation related diseases [15,16,17]. Edentulism and tooth loss, as polymicrobial chronic inflammatory diseases, reflect sustained local inflammatory milieu [18], which play a potential role in the lipid metabolism and lipid level. A recent study reported the association between oral health conditions and changes in lipid profile among people aged 40 years or above [19]. However, the association of edentulism and lipid levels remains unclear in the elder population with the highest risk of tooth loss and edentulism.

Triglycerides and cholesterol perform distinct functions in the body, and are important markers for health. Triglycerides are a most common type of fat or lipid in the blood storing excess energy. Cholesterol is a waxy substance embedded in the lipoprotein, generally classified into high-density lipoprotein cholesterol (HDL-C), LDL-C and remnant cholesterol according to the density. In this study, we aimed to investigate the association of reported edentulism with lipid parameters in the elder population using a national cohort.

Materials and methods

Data source

Participants of the China Health and Retirement Longitudinal Study (CHARLS) at baseline visit between were included in this study. CHARLS is a public open research cohort database collecting a wide range of social-economic data and personal health information ( CHARLS covers a nationally representative sample of Chinese residents ages 45 and older to promote research on the elderly. The baseline national wave of was initiated in 2011 and included about 10 000 households and 17 500 individuals in 150 counties/districts and 450 villages/resident committees using multi-stage stratified probability proportionate to size (PPS) sampling.

This study was in accordance with the principles of the Declaration of Helsinki and was approved by the Ethics Committee of Capital Medical University (grant number: 2020SY031). All participants provided their written informed consents before participating in the current study. The following criteria were required: (1) taking blood lipid examinations; (2) reporting oral health and tooth status; (3) without history of any malignancy; (4) aged 65 years or above. Figure 1 shows the design and flowchart of this current study. Finally, there were 3 100 individuals enrolled in the final analysis.

Fig. 1
figure 1

Flowchart of this current study

Measurements and definitions

The demographic characteristics, lifestyles, and health status were acquired using the standard questionnaire, including age, sex, education level, marital status, smoking status, drinking status and self-reported health conditions. Educational level was categorized as ‘primary school or below’, ‘middle school’, and ‘high school or above’. Marital status included ‘married’, ‘unmarried’ and ‘others’. Smoking status was defined as ‘never smoking’, ‘current smoker’, and ‘former smoker’. Drinking status was defined as ‘current drinking more than once per month’, ‘current drinking once or less than once per month’, and ‘no current drinking’. Self-reported health conditions included tooth loss status and the diagnosis of hypertension, diabetes and dyslipidemia. Body mass index (BMI) was calculated as weight (kg)/[height (m)*height (m)]. The definition of obesity was BMI ≥ 28.0 kg/m2 for the Asian population [20]. Blood laboratory tests were carried out using fasting venous blood samples, and the lipid profiles included triglycerides, total cholesterol, HDL-C and LDL-C. The nonHDL-C was calculated as total cholesterol minus HDL-C. The remnant cholesterol was calculated using the Martin-Hopkins equation using the ( The reported edentulism was assessed by the following question: “Have you lost all of your teeth? (Yes / no)”.

Statistical analysis

The characteristics of the population were stratified by reported edentulism or not. The basic characteristics were presented as the mean (standard deviation, SD) for continuous variables and number (proportion) for categorical variables. The differences were compared by student’s t-test test for continuous variables and chi-square test for categorical variables. The distributions of lipid profiles were presented using box-plots.

The unadjusted and adjusted logistics regression models were used to analyze the association of the lipid parameters with reported edentulism. To control the potential confounding factors, age, sex, education level, marriage status, smoking status, drinking, BMI level, hypertension or not, dyslipidemia or not, diabetes or not were adjusted in multivariable model. In addition, we used propensity score method to match controls for individuals of reported edentulism. The matching ratio was 1:2, and all the potential confounding factors mentioned above were considered in the matching process. The association between lipid parameters and reported edentulism was further explored in the matched population.

All the analyses above were analyzed using R software (version 4.1.0). The differences were considered statistically significant at two-side P < 0.05.


Population characteristics

The mean (SD) age of the whole population was 71.96 (5.63) years, and 1 581 (51.0%) were men. There were 254 (8.2%) individuals with reported edentulism. The individuals of reported edentulism were older than people of non-edentulism (72.63 vs. 71.91, P = 0.049). There were no significant differences for sex, education, marriage, BMI, smoking, drinking and self-reported diagnosis of hypertension, diabetes and dyslipidemia as shown in Table 1. LDL-C was significantly higher in people with reported edentulism (122.48 vs. 116.91 mg/dl, P = 0.015), and no significant differences were observed for HDL-C, nonHDL-C, remnant cholesterol, total cholesterol and triglycerides (P > 0.05). In the matched population, 208 individuals with reported edentulism and 416 controls were selected, and the covariates were balanced as shown in Table 2. Of note, LDL-C (122.53 vs. 115.00 mg/dl, P = 0.009), nonHDL-C (145.14 vs. 134.02 mg/dl, P < 0.001), remnant cholesterol (22.40 vs. 20.16 mg/dl, P = 0.004), total cholesterol (197.76 vs. 190.47 mg/dl, P = 0.022) and triglycerides (116.30 vs. 101.64 mg/dl, P = 0.017) were significantly higher in the reported edentulism group, while HDL-C (52.62 vs. 56.45 mg/dl, P = 0.005) was significantly lower. The distributions of lipid profiles between the reported edentulism and non-edentulism groups in the whole population and matched population were shown in Fig. 2.

Table 1 Characteristics of this study
Table 2 Characteristics of the matched population
Fig. 2
figure 2

Distribution of lipid parameters in the whole and matched population

Association of lipid profiles and reported edentulism

In the whole population, LDL-C was significantly associated with a higher odds of reported edentulism, and the adjusted OR (95% CI) was 1.004 (1.001–1.008). We did not observe independent association between reported edentulism and other lipid parameters as shown in Table 3. In the matched population, LDL-C, HDL-C, nonHDL-C, remnant cholesterol, total cholesterol and triglycerides were all significantly associated with reported edentulism as shown in Fig. 3, and the adjusted OR (95% CI) values were 1.007 (1.002–1.012), 0.985 (0.974–0.995), 1.008 (1.004–1.013), 1.027 (1.008–1.046), 1.005 (1.001–1.010) and 1.003 (1.001–1.005) respectively.

Table 3 Association between lipid profiles and tooth loss in the whole population
Fig. 3
figure 3

Association of lipid parameters and tooth loss risk in the matched population


In this study, we found that reported edentulism was independently associated with lipid metabolism in the elder population, particularly LDL-C. In the matched population, six lipid parameters were significantly associated with reported edentulism including LDL-C, HDL-C, nonHDL-C, remnant cholesterol, total cholesterol and triglycerides.

The relationships between periodontitis and blood lipid parameters have been reported in different populations and regions. Previous studies showed that periodontitis was associated with low HDL-C, high triglycerides and total cholesterol [21,22,23,24]. On the contrary, some studies found no significant associations between periodontitis and lipid profiles [25, 26]. On the other hand, Song et al. found that periodontitis was both associated with baseline lipid profile and the longitudinal changes of blood lipid parameters [19]. The age levels of different populations could partially contribute to the inconsistent results. Kim et al. reported that the number of tooth loss was positively correlated with triglycerides level [27]. Our study provided further information on the association between lipid parameters and reported edentulism in the elder population.

The local and systematic inflammation may partially explain the underlying mechanism between edentulism and blood lipid parameters. Tooth loss causes the surrounding tissues damages and leads to chronic local inflammation [28,29,30]. Through the local tissue injury and chronic inflammation, oral bacteria could harm blood vessels or cause systemic inflammatory response [31,32,33]. In addition, control of the oral local infection is associated with a reduction in serum inflammatory markers including C-reactive protein (CRP) and interleukin-6 (IL-6) [34, 35]. These inflammatory reactions play an important role in the lipid metabolism [16], especially the atherogenic lipid components, such as LDL-C and remnant cholesterol [36, 37]. Similarly, the oral-derived systematic inflammatory is associated with glycemic control, complications, and incidence of diabetes, which have been reported in previous studies [38,39,40].

In our study, reported edentulism was positively associated with LDL-C, nonHDL-C, remnant cholesterol, total cholesterol and triglycerides, and negatively associated with HDL-C. Similarly, Meisel et al. [41] showed that statins use and reduction in LDL-C were associated with diminished tooth loss as a long-term response, which also supported the interaction effect between tooth loss or edentulism and lipid metabolism.

There were some limitations in this study. First, our study failed to collect the dietary information, as the dietary fat and cholesterol intake have an effect on plasma lipid profiles. Second, oral hygiene indicators, such as tooth brushing frequency were not considered in this current study, which could affect the local inflammatory response and lipid metabolism. Third, our study was a cross-sectional observational study, and we could not claim the causal association between edentulism and lipid parameters. Whether oral hygiene improvement could improve lipid level or lipid control could prevent edentulism remain to be investigated in further studies. Fourth, although there was a statistically significant association, the effect size was weak from the clinical perspective, indicating oral health as a systemic indicator of many health indicators. In the matched population, there were significant associations of LDL-C, HDL-C, nonHDL-C, remnant cholesterol, total cholesterol and triglycerides with reported edentulism. In the whole population, only LDL-C was significantly associated with reported edentulism probably due to the confounding bias. The effect size of lipid profile on edentulism needs further investigation in other studies.


In conclusion, lipid profiles are probably associated with edentulism in the elder population. The interaction between oral condition and metabolic health needs further attention in clinical practice.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.



China Health and Retirement Longitudinal Study


Low-density lipoprotein cholesterol


High-density lipoprotein cholesterol


Body mass index


Standard deviation


C-reactive protein




  1. Peres MA, Macpherson LMD, Weyant RJ, Daly B, Venturelli R, Mathur MR, Listl S, Celeste RK, Guarnizo-Herreño CC, Kearns C, et al. Oral diseases: a global public health challenge. Lancet. 2019;394(10194):249–60.

    Article  Google Scholar 

  2. Peres MA, Macpherson LMD, Weyant RJ, et al. Oral diseases: a global public health challenge. Lancet. 2019;394(10194):249–60.

    Article  Google Scholar 

  3. Kassebaum NJ, Bernabé E, Dahiya M, Bhandari B, Murray CJ, Marcenes W. Global burden of severe tooth loss: a systematic review and meta-analysis. J Dent Res. 2014;93(7 Suppl):20s–8s.

    Article  Google Scholar 

  4. Müller F, Naharro M, Carlsson GE. What are the prevalence and incidence of tooth loss in the adult and elderly population in Europe? Clin Oral Implants Res. 2007;18(Suppl 3):2–14.

    Article  Google Scholar 

  5. Vallim AC, Gaio EJ, Oppermann RV, Rösing CK, Albandar JM, Susin C, Haas AN. Obesity as a risk factor for tooth loss over 5 years: a population-based cohort study. J Clin Periodontol. 2021;48(1):14–23.

    Article  Google Scholar 

  6. Genco RJ, Borgnakke WS. Diabetes as a potential risk for periodontitis: association studies. Periodontol 2000. 2020;83(1):40–5.

    Article  Google Scholar 

  7. Fang WL, Jiang MJ, Gu BB, Wei YM, Fan SN, Liao W, Zheng YQ, Liao SW, Xiong Y, Li Y, et al. Tooth loss as a risk factor for dementia: systematic review and meta-analysis of 21 observational studies. BMC Psychiatry. 2018;18(1):345.

    Article  Google Scholar 

  8. Thomson WM, Barak Y. Tooth loss and dementia: a critical examination. J Dent Res. 2021;100(3):226–31.

    Article  Google Scholar 

  9. Dwibedi N, Wiener RC, Findley PA, Shen C, Sambamoorthi U. Asthma, chronic obstructive pulmonary disease, tooth loss, and edentulism among adults in the United States: 2016 Behavioral Risk Factor Surveillance System survey. J Am Dent Assoc. 2020;151(10):735-744.e731.

    Article  Google Scholar 

  10. Chen Y, Yang YC, Zhu BL, Wu CC, Lin RF, Zhang X. Association between periodontal disease, tooth loss and liver diseases risk. J Clin Periodontol. 2020;47(9):1053–63.

    Article  Google Scholar 

  11. Chen Y, Zhu BL, Wu CC, Lin RF, Zhang X. Periodontal disease and tooth loss are associated with lung cancer risk. Biomed Res Int. 2020;2020:5107696.

    PubMed  PubMed Central  Google Scholar 

  12. Michaud DS, Fu Z, Shi J, Chung M. Periodontal disease, tooth loss, and cancer risk. Epidemiol Rev. 2017;39(1):49–58.

    Article  Google Scholar 

  13. Chen P, Zhang M, Zhang Y, Su X, Chen J, Xu B, Tao J, Wang Z, Ma A, Li H. Economic burden of myocardial infarction combined with Dyslipidemia. Front Public Health. 2021;9: 648172.

    Article  Google Scholar 

  14. Pirillo A, Casula M, Olmastroni E, Norata GD, Catapano AL. Global epidemiology of dyslipidaemias. Nat Rev Cardiol. 2021;18(10):689–700.

    Article  Google Scholar 

  15. Khovidhunkit W, Kim MS, Memon RA, Shigenaga JK, Moser AH, Feingold KR, Grunfeld C. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host. J Lipid Res. 2004;45(7):1169–96.

    Article  Google Scholar 

  16. He J, Zhang P, Shen L, Niu L, Tan Y, Chen L, Zhao Y, Bai L, Hao X, Li X, et al. Short-chain fatty acids and their association with signalling pathways in inflammation, glucose and lipid metabolism. Int J Mol Sci. 2020.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Zhang C, Wang K, Yang L, Liu R, Chu Y, Qin X, Yang P, Yu H. Lipid metabolism in inflammation-related diseases. Analyst. 2018;143(19):4526–36.

    Article  Google Scholar 

  18. Singhrao SK, Harding A, Simmons T, Robinson S, Kesavalu L, Crean S. Oral inflammation, tooth loss, risk factors, and association with progression of Alzheimer’s disease. J Alzheimers Dis. 2014;42(3):723–37.

    Article  Google Scholar 

  19. Song TJ, Kim JW, Kim J. Oral health and changes in lipid profile: a nationwide cohort study. J Clin Periodontol. 2020;47(12):1437–45.

    Article  Google Scholar 

  20. Zhou BF. Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults–study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Biomed Environ Sci. 2002;15(1):83–96.

    PubMed  Google Scholar 

  21. Jaramillo A, Lafaurie GI, Millán LV, Ardila CM, Duque A, Novoa C, López D, Contreras A. Association between periodontal disease and plasma levels of cholesterol and triglycerides. Colomb Med (Cali). 2013;44(2):80–6.

    Article  Google Scholar 

  22. Lösche W, Karapetow F, Pohl A, Pohl C, Kocher T. Plasma lipid and blood glucose levels in patients with destructive periodontal disease. J Clin Periodontol. 2000;27(8):537–41.

    Article  Google Scholar 

  23. Thapa S, Wei F. Association between high serum total cholesterol and periodontitis: National Health and Nutrition Examination Survey 2011 to 2012 Study of American Adults. J Periodontol. 2016;87(11):1286–94.

    Article  Google Scholar 

  24. Penumarthy S, Penmetsa GS, Mannem S. Assessment of serum levels of triglycerides, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol in periodontitis patients. J Indian Soc Periodontol. 2013;17(1):30–5.

    Article  Google Scholar 

  25. Taleghani F, Shamaei M, Shamaei M. Association between chronic periodontitis and serum lipid levels. Acta Med Iran. 2010;48(1):47–50.

    PubMed  Google Scholar 

  26. Machado AC, Quirino MR, Nascimento LF. Relation between chronic periodontal disease and plasmatic levels of triglycerides, total cholesterol and fractions. Braz Oral Res. 2005;19(4):284–9.

    Article  Google Scholar 

  27. Kim SW, Cho KH, Han KD, Roh YK, Song IS, Kim YH. Tooth loss and metabolic syndrome in South Korea: The 2012 Korean National Health and Nutrition Examination Survey. Medicine (Baltimore). 2016;95(16): e3331.

    Article  Google Scholar 

  28. Kinane DF, Marshall GJ. Periodontal manifestations of systemic disease. Aust Dent J. 2001;46(1):2–12.

    Article  Google Scholar 

  29. 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.

    Article  Google Scholar 

  30. Nazir MA. Prevalence of periodontal disease, its association with systemic diseases and prevention. Int J Health Sci (Qassim). 2017;11(2):72–80.

    Google Scholar 

  31. Loos BG. Systemic markers of inflammation in periodontitis. J Periodontol. 2005;76(11 Suppl):2106–15.

    Article  Google Scholar 

  32. Pink C, Kocher T, Meisel P, Dörr M, Markus MR, Jablonowski L, Grotevendt A, Nauck M, Holtfreter B. Longitudinal effects of systemic inflammation markers on periodontitis. J Clin Periodontol. 2015;42(11):988–97.

    Article  Google Scholar 

  33. Shi D, Meng H, Xu L, Zhang L, Chen Z, Feng X, Lu R, Sun X, Ren X. Systemic inflammation markers in patients with aggressive periodontitis: a pilot study. J Periodontol. 2008;79(12):2340–6.

    Article  Google Scholar 

  34. Cairo F, Nieri M, Gori AM, Tonelli P, Branchi R, Castellani S, Abbate R, Pini-Prato GP. Markers of systemic inflammation in periodontal patients: chronic versus aggressive periodontitis. An explorative cross-sectional study. Eur J Oral Implantol. 2010;3(2):147–53.

    PubMed  Google Scholar 

  35. D’Aiuto F, Parkar M, Andreou G, Suvan J, Brett PM, Ready D, Tonetti MS. Periodontitis and systemic inflammation: control of the local infection is associated with a reduction in serum inflammatory markers. J Dent Res. 2004;83(2):156–60.

    Article  Google Scholar 

  36. D’Aiuto F, Ready D, Tonetti MS. Periodontal disease and C-reactive protein-associated cardiovascular risk. J Periodontal Res. 2004;39(4):236–41.

    Article  Google Scholar 

  37. Mustapha IZ, Debrey S, Oladubu M, Ugarte R. Markers of systemic bacterial exposure in periodontal disease and cardiovascular disease risk: a systematic review and meta-analysis. J Periodontol. 2007;78(12):2289–302.

    Article  Google Scholar 

  38. Genco RJ, Graziani F, Hasturk H. Effects of periodontal disease on glycemic control, complications, and incidence of diabetes mellitus. Periodontol 2000. 2020;83(1):59–65.

    Article  Google Scholar 

  39. López-Gómez SA, González-López BS, Scougall-Vilchis RJ, Pontigo-Loyola AP, Márquez-Corona ML, Villalobos-Rodelo JJ, Rueda-Ibarra V, Medina-Solís CE. Tooth loss in patients with and without diabetes: a large-scale, cross-sectional study of Mexican adults. J Am Dent Assoc. 2020;151(4):276–86.

    Article  Google Scholar 

  40. Helal O, Göstemeyer G, Krois J, Fawzy El Sayed K, Graetz C, Schwendicke F. Predictors for tooth loss in periodontitis patients: Systematic review and meta-analysis. J Clin Periodontol. 2019;46(7):699–712.

    PubMed  Google Scholar 

  41. Meisel P, Kroemer HK, Nauck M, Holtfreter B, Kocher T. Tooth loss, periodontitis, and statins in a population-based follow-up study. J Periodontol. 2014;85(6):e160-168.

    Article  Google Scholar 

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Authors and Affiliations



YTW, XW and MS conceptualized and developed the study, reviewing and interpreting the results, and editing of the manuscript. SPW and RYY were responsible for data analysis, development of tables, and writing first draft of the manuscript. DXY, YFN and LXW helped with statistical analyses, reviewed and interpreted the results. All authors read and approved the final manuscript.

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Correspondence to Xin Wang.

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The CHARLS study was approved by the institutional review board of Peking University (IRB00001052-11015). All procedures were performed in accordance with the 1964 Declaration of Helsinki and its later amendments or with comparable ethical standards. All participants have provided the informed consents.

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Wang, S., Wang, Y., Yu, R. et al. Association of lipid profile and reported edentulism in the elder population: data from the China Health and Retirement Longitudinal Study. BMC Oral Health 22, 445 (2022).

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