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Association between high-density lipoprotein-related inflammation index and periodontitis: insights from NHANES 2009–2014

Lipids in Health and Disease volume 23, Article number: 321 (2024) Cite this article

Abstract

Background

Periodontitis, a persistent inflammatory condition, significantly impairs individuals’ overall quality of life. Lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), monocyte-to-high-density lipoprotein cholesterol ratio (MHR), neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), and platelet-to-high-density lipoprotein cholesterol ratio (PHR) are new convenient and economical biomarkers. However, whether the above high-density lipoprotein-related inflammatory biomarkers are associated with periodontitis has rarely been investigated. Therefore, the research endeavor focused on uncovering potential relationships.

Methods

The research encompassed a diverse and extensive sample, comprising 9,470 participants, selected from the National Health and Nutrition Examination Survey spanning the years 2009 to 2014. The association between high-density lipoprotein-related inflammatory biomarkers and periodontitis was explored utilizing a multivariable logistic regression model with weighted analysis. Additionally, the study employed smoothed curve fitting to explore potential nonlinear relationships. Further stratified analyses and interaction tests were performed.

Results

This study indicated no apparent association between MHR and PHR with periodontitis, whereas LHR and NHR demonstrated a statistically significant positive relationship with the prevalence of periodontitis. In the fully adjusted model, participants belonging to the highest tertile of both LHR and NHR showed a notably higher likelihood of having periodontitis compared to those in the lowest tertile (LHR: OR = 1.22, 95% CI: 1.06, 1.39; NHR: OR = 1.27, 95% CI: 1.09, 1.49). Furthermore, smooth curve fitting was employed to investigate the potential nonlinear relationship between LHR, NHR, and periodontitis. The results indicated that there was a significant increase in the occurrence of periodontitis when Log2 (LHR) exceeded 1.01 and Log2(NHR) surpassed 2.16 (Log2(LHR): OR = 1.42; 95% CI: 1.19, 1.69; Log2(NHR): OR = 1.40; 95% CI: 1.15, 1.71). The subgroup analysis revealed that the associations between periodontitis and either LHR or NHR, separately, were more pronounced among individuals under the age of 50 and those without hypertension.

Conclusions

This cross-sectional study revealed a positive relationship between LHR、NHR and periodontitis, particularly when these indicators exceeded their thresholds. LHR and NHR may serve as potential inflammatory markers for identifying periodontitis, thereby facilitating early warning for both patients and dentists, and enabling early intervention in the oral environment. Besides, extensive prospective cohort investigations are essential to confirm and solidify this observation.

Introduction

Periodontitis arises from the accumulation of plaque biofilm and is marked by the gradual deterioration of periodontal tissues [1]. Periodontitis triggers the dissemination of inflammatory mediators into the circulatory system, resulting in a pervasive inflammatory state throughout the body [2]. Therefore, it exhibits a strong link to a myriad of systemic diseases, encompassing diabetes, respiratory and cardiovascular diseases [3]. Periodontitis is often asymptomatic in its early stages, making it difficult to detect until it progresses to irreversible conditions such as tooth mobility and loss. This not only significantly impairs the patient’s chewing function, pronunciation, and appearance but also poses a significant threat to overall health. With a global prevalence of up to 50%, periodontitis has become a major healthcare, social and economic burden [4]. Hence, the identification of a simple, cost-effective, and reliable biomarker holds paramount importance for early screening, prevention of periodontitis, and prompt formulation of therapeutic strategies.

High-density lipoprotein (HDL) exhibits beneficial effects such as anti-thrombosis, anti-inflammation, anti-oxidation, and immune regulation [5,6,7,8,9,10,11], and there are complex interactions between HDL and blood cells. HDL has a bidirectional relationship with various immune cells [12]. On the one hand, HDL inhibits the production of pro-inflammatory cytokines and chemokines, thereby preventing the activation and function of immune cells [13,14,15,16], which reduces periodontal tissue damage caused by inflammatory responses. On the other hand, periodontitis can alter immune cell function and lead to decreased HDL levels through the release of pro-inflammatory cytokines [17, 18]. Clinical studies have shown that serum HDL levels in patients with periodontitis are significantly lower than those in individuals with periodontal health. Importantly, after undergoing non-surgical periodontal treatment, periodontitis patients exhibit an increase in serum HDL levels and a concurrent decrease in serum pro-inflammatory cytokine levels [19,20,21,22]. Additionally, pretreatment of platelets with HDL can inhibit the interaction between platelets and oxidized low-density lipoprotein (oxLDL), reducing platelet aggregation and reactive oxygen species production, thus mitigating inflammatory damage [23, 24]. In summary, HDL exerts immune regulatory effects through its interactions with blood cells.

Considering the multiple positive effects of HDL on the host, scholars have explored the potential clinical application value of HDL-related inflammatory biomarkers. Lymphocyte-to-high-density lipoprotein cholesterol ratio (LHR), monocyte-to-high-density lipoprotein cholesterol ratio (MHR), neutrophil-to-high-density lipoprotein cholesterol ratio (NHR), and platelet-to-high-density lipoprotein cholesterol ratio (PHR) have recently been considered as novel biomarkers. These biomarkers are respectively the ratios of each blood cell count (lymphocyte, monocyte, neutrophil, or platelet) to the high-density lipoprotein cholesterol (HDL-C) level. They can be easily acquired and calculated from routine blood tests. It has been reported to be an early predictor of a range of diseases such as cardiovascular disease, metabolic syndrome, non-alcoholic fatty liver disease, and chronic obstructive pulmonary disease [25,26,27,28,29]. However, whether the high-density lipoprotein-related inflammation index is associated with periodontitis is not clear.

In summary, due to the complex interactions between inflammatory cells, platelets, and HDL, and the fact that studies have confirmed that MHR, LHR, NHR, and PHR are closely related to various systemic diseases, we speculated that the above-mentioned HDL-related inflammatory biomarkers were potential novel inflammatory markers for periodontitis, and they were likely to have reflected the body’s inflammatory and immune status better than single blood cell or blood lipid indicators.

The present study exploited the National Health and Nutrition Examination Survey (NHANES) data to investigate the possible associations between LHR, MHR, NHR, or PHR and the likelihood of periodontitis onset. During medical check-up, clinicians can monitor LHR and NHR through blood routine examination and lipid profiles, which may detect people with a heightened predisposition to periodontal disease early and then recommend targeted interventions. This finding might provide a simple and cost-effective biomarker to predict the occurrence of periodontitis, which is of great significance for preventing periodontitis and enhancing the quality of life.

Methods

Data sources and study population

The NHANES database is a large and wide-ranging clinical database that collects demographic, health, and nutritional information on participants by sampling a representative group of individuals. The present study included adult residents of the United States aged 30 and above, utilizing data from three consecutive cycles of the NHANES database spanning the years 2009 to 2014 (Fig. 1). To ensure the validity and comprehensiveness of the study, the exclusion criteria are as follows: (1) individuals with incomplete periodontal data; (2) participants with incomplete data on platelet, neutrophil, lymphocyte, and monocyte counts, as well as HDL levels; (3) individuals with incomplete covariables data (including sex, age, race, education background, marital status, poverty income ratio (PIR), smoking habits, alcohol consumption, diabetes, hypertension, and body mass index (BMI)).

Fig. 1
figure 1

Study population selection (N = 9,470)

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Exposure variable

MHR, LHR, NHR, and PHR were calculated using data from the NHANES database of complete blood counts, including monocyte count (MON), lymphocyte count (LYM), neutrophil count (NEU), platelet count (PLT), and HDL-C. The calculation formulas were outlined as follows: LHR = LYM / HDL-C, MHR = MON / HDL-C, NHR = NEU / HDL-C, PHR = PLT / HDL-C. Furthermore, due to the skewed data distribution of these inflammatory biomarkers, when the HDL-related inflammation index was analyzed as a continuous variable, a log2 transformation was applied to meet the normal distribution [30].

Outcome variable

The study assessed periodontal status using attachment loss (AL) and pocket depth (PD) at a maximum of 168 sites on 28 teeth (excluding the third molar) from the periodontal examination of the NHANES database. The categorization of periodontitis adhered to the standards set forth by the Centers for Disease Control and Prevention (CDC) in conjunction with the American Academy of Periodontology (AAP) [31]. A diagnosis of periodontitis can be made when the following conditions are met: two or more adjacent sites with AL ≥ 3 mm, and either two or more adjacent sites with PD ≥ 4 mm (not on the same tooth) or one site with PD ≥ 5 mm. All other cases are classified as non-periodontitis.

Covariables

The following variables have been selected as probable covariables in the study. Demographic parameters included age, gender, race, PIR, education background and marital condition. Race encompassed four primary categories: Mexican American, non-Hispanic White, non-Hispanic Black, and other races. The PIR was categorized into low (≤ 1), middle (1–3), and high (> 3). Education background can be grouped into below high school, high school diploma, or above high school education. As for marital condition, it can be divided into two categories: married/living, never married/separated/divorced/widowed.

In the analysis, other changes were also accounted for, including BMI, drinking status, smoking habit, diabetes mellitus and hypertension. Individuals who have consumed more than 12 glasses of alcohol per year are considered drinkers. And participants with a minimum of 100 cigarettes throughout their lifespan are classified as smokers. The BMI was categorized into three distinct groups: under 25 kg/m², ranging from 25 to 30 kg/m², and exceeding 30 kg/m². Diabetes diagnosis was made when one of the following is met: glycohemoglobin ≥ 6.5%; fasting glucose level ≥ 126 mg/dL; two-hour postprandial glucose level (OGTT) exceeding 200 mg/dL; self-disclosed history of diabetes diagnosis by a physician; the ongoing administration of insulin or any other glucose-regulating medication. A diagnosis of hypertension can be made by meeting any of the following criteria: a diastolic blood pressure measurement exceeding 90 mmHg; elevated systolic blood pressure at or above 140 mmHg; a previous physician-confirmed diagnosis of hypertension reported by the individual; the current administration of medication specifically prescribed for lowering blood pressure.

Statistical analysis

To ensure that the sample is nationally representative, the NHANES database adopted sophisticated multi-phased sampling strategy. Sample weights were considered in the study, which were calculated as 1/3 of the weights of the 2-year mobile examination centers (WTMEC2YR*1/3). Non-normal continuous variables were presented in terms of median [interquartile range (IQR)], while categorical variables are expressed as percentages. The Wilcoxon test was applied for non-normal continuous variables, and the chi-square test is employed in categorical variables.

Logistic regression models with weighted observations were employed to explore the association between HDL-related inflammation index and the prevalence of periodontitis across three different models. In addition to being considered as a continuous variable, the HDL-related inflammation index was also converted into a categorical variable. Univariable logistic regression analysis can be used to initially explore the relationship. Furthermore, multivariable logistic regression can further eliminate the effects of other confounding factors, thereby determining the association between HDL-related inflammation index and periodontitis. In Model 1, no account was taken of any confounding factors. In contrast, Model 2 incorporated adjustments specifically for the key demographic features of sex, age, and race. In Model 3, the scope of adjustments was broadened to encompass a comprehensive list of confounding factors, including sex, age, race, education background, marital condition, PIR, smoking, alcohol consumption, diabetes, hypertension, and BMI.

The nonlinear relationships between HDL-related inflammation index and the prevalence of periodontitis were explored using smooth curve fitting, and dose-response associations were assessed using threshold effects analysis. In addition, the study used stratified multivariable logistic regression models with subgroup analyses of age, sex, hypertension, diabetes, and smoking to explore the relationship between HDL-related inflammation index and periodontitis. The statistical analyses of all data were carried out utilizing Empower Stats (version 4.2) and the R software package (version 4.3.1).

Results

Baseline characteristics of study population

The study population’s baseline features were presented according to whether or not they had periodontitis (Table 1). In total, 9,470 participants, a female proportion of 49.5%, were selected for the investigation. The study revealed no statistically noteworthy disparity in platelet levels between periodontitis patients and those non-periodontitis individuals (P = 0.4829). Nevertheless, periodontitis patients exhibited notably elevated counts of neutrophils, lymphocytes, and monocytes, and significantly decreased levels of HDL-C compared to the non-periodontitis participants, along with increased values for MHR, LHR, NHR, and PHR.

In addition, participants from the non-Hispanic white population (63.38%), marital condition “married/living with partner” (64.29%), higher than high school education background (52.14%), PIR > 3 (41.75%), obesity (BMI > 30 kg/m2) (39.95%), and smoking (55.43%) exhibited a significantly higher prevalence of periodontitis. The prevalence of periodontitis was 48.96% and 20.37% in hypertensive and diabetic patients respectively.

Table 1 Weighted characteristics of the study population (N = 9,470)
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Association between HDL-related inflammation index and periodontitis

When using the HDL-related inflammation index as a continuous variable, the data exhibited a significant skewed distribution, prompting the application of a log2 transformation. Taking into full consideration all covariables in Model 2, excluding Log2(PHR), it was observed that Log2(LHR), Log2(MHR), and Log2(NHR) showed a positive association with periodontitis (Log2(LHR): OR = 1.15, 95% CI: 1.05, 1.27; Log2(MHR): OR = 1.13, 95% CI: 1.03, 1.24; Log2(NHR): OR = 1.16, 95% CI: 1.05, 1.29) (Table 2).

Subsequently, the HDL-related inflammation index was stratified into three groups across various models (Table 2). In the fully adjusted Model 2, notably, individuals in the highest tertile of both LHR and NHR demonstrated a significantly elevated prevalence of periodontitis compared to those in the lowest tertile (LHR: OR = 1.22, 95% CI: 1.06, 1.39; NHR: OR = 1.27, 95% CI: 1.09, 1.49). However, there was no significant difference in the occurrence of periodontitis between the second tertiles of LHR and NHR compared to the lowest tertile, suggesting that the relationship between LHR and NHR with periodontitis may be nonlinear (LHR: OR = 1.05, 95% CI: 0.93, 1.18; NHR: OR = 1.15, 95% CI: 0.98, 1.36). As for MHR and PHR, no significant association was found between them and the odds of having periodontitis (P > 0.05) (Table 2).

Based on this application of smooth curve fitting, it was revealed that a nonlinear relationship existed between Log2(LHR) and Log2(NHR) with periodontitis (P for nonlinearity < 0.001), suggesting that their influence on periodontitis altered significantly once Log2(LHR) and Log2(NHR) exceeded specific inflection points (Fig. 2). Concurrently, comparing the linear regression model with the two-piecewise linear regression model, the Logarithmic likelihood ratio test P-values were 0.013 and 0.036, respectively. This suggested that the two-piecewise linear regression model should be adopted for fitting. The inflection points for Log2(LHR) and Log2(NHR) were calculated to be 1.01 and 2.16 (Table 3). When Log2 (LHR) > 1.01 and Log2 (NHR) > 2.16, there was an increased prevalence of periodontitis (Log2(LHR): OR = 1.42; 95% CI: 1.19, 1.69; Log2(NHR): OR = 1.40; 95% CI: 1.15, 1.71).

Table 2 Weighted multivariable regression analysis of association between HDL- related inflammation index and periodontitis
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Fig. 2
figure 2

Smooth curve fitting analysis of the association between Log2(LHR), Log2(NHR) and the prevalence of periodontitis (Adjusted for all covariables, including sex, age, race, education background, marital condition, PIR, smoking, alcohol consumption, diabetes, hypertension, and BMI). (A) Smooth curve fitting for Log2(LHR) and periodontitis; (B) Smooth curve fitting for Log2(NHR) and periodontitis

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Table 3 Threshold effects analysis of the association between Log2(LHR), Log2(NHR) and the prevalence of periodontitis
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Subgroup analyses

To evaluate the relationships between LHR, NHR, and periodontitis in various population segments, stratified analyses were conducted (Fig. 3). The findings from the subgroup analysis indicated that in people aged 50 or younger and those without hypertension, there was a notable association between LHR and NHR levels, and an elevated prevalence of periodontitis, while this relationship was not significant in people over 50 years old and those with hypertension. Other variables, including gender and diabetes mellitus, did not significantly alter the relationships between LHR, NHR, and periodontitis (P for interaction > 0.05).

Fig. 3
figure 3

Subgroup analysis of the association between Log2(LHR), Log2(NHR) and the prevalence of periodontitis

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Discussion

This research found a novel association between LHR, NHR and the prevalence of periodontitis. Notably, an increase in the prevalence of periodontitis was observed when the log2(LHR) surpassed 1.01 and the log2(NHR) exceeded 2.16. In addition, further subgroup analysis and interaction tests showed an enhanced association between LHR or NHR and periodontitis among people under the age of 50 and those without hypertension. It was speculated that the adoption of healthier lifestyles, diets, and corresponding treatment measures by individuals over 50 years old and those with hypertension may have introduced a bias in the results. Further investigations may be required to elucidate the underlying causes.

Currently, regarding the relationship between periodontitis and HDL-related inflammation index, there existed solely one preliminary clinical study conducted by Lalitha et al., which enrolled 90 individuals from India and demonstrated elevated MHR levels in patients with periodontal disease [32]. However, our research, utilizing a larger sample size of American adults, did not yield the same conclusion, which could potentially be attributed to differences in the study populations and variations in sample sizes. In addition, several prior studies have established that LHR or NHR can serve as early predictors for a multitude of diseases, including atherosclerosis, cardiovascular diseases, and metabolic syndrome (MetS), all of which are intricately linked to periodontitis [26, 27, 33, 34].

In this study, it was observed that patients with periodontitis had significantly lower HDL levels and significantly elevated neutrophil and lymphocytes counts compared to individuals with periodontal health, which is consistent with previous research findings [19, 35, 36]. Zhou et al.‘s research revealed a positive relationship between NHR and atherosclerosis, with a possible underlying mechanism involving HDL-C in preventing the progression of atherosclerotic plaques by inhibiting inflammation-induced cell aggregation and infiltration mediated by neutrophils, safeguarding vascular endothelial cells, and reversing cholesterol transport [33]. However, the precise mechanism connecting LHR or NHR and periodontitis remains elusive. The intricate interplay between lymphocytes, neutrophils, and HDL-C in the physiological and pathological processes of periodontal tissues may offer insights into this phenomenon.

Neutrophils play both defensive and destructive roles in periodontitis, thus an equilibrated neutrophil reaction is paramount for preserving periodontal health [37]. Excessively activated neutrophils can produce matrix metalloproteinases and reactive oxygen species (ROS), causing destruction to periodontal tissues [38,39,40,41]. Recent investigations have demonstrated that neutrophil extracellular traps (NETs) stimulate an increase in IL-17/Th17 activity and contribute to bone degradation, especially when periodontitis occurs [42].

The interaction between HDL and its components with neutrophils may affect the occurrence and development of periodontitis. HDL interacts with neutrophils to produce inflammation-suppressing responses, and HDL has been demonstrated to decrease the adhesion, spreading, and migration of neutrophils by inhibiting the synthesis of IL-8 and limiting the expression of CD11b [43, 44]. Apolipoprotein A-I (ApoA-I), the primary structural protein and functional component of HDL, has the ability to decrease neutrophil formation through inhibiting the production of granulocyte colony-stimulating factor (G-CSF) [45]. However, inflammation may cause activated neutrophils to employ the myeloperoxidase/hydrogen peroxide/chloride system to oxidatively damage ApoA-I, which severely impairs the ability of HDL to inhibit inflammatory factors [46].

Furthermore, the interaction between lymphocytes and HDL also influences the occurrence and progression of periodontitis. It has been shown that lipopolysaccharide (LPS) from Porphyromonas gingivalis can directly induce Th17 cells specialization through TLR2 signaling in vitro. The persistent presence of Th17 population has been proven to mediate the destruction of periodontal tissues [47, 48]. Another investigation has revealed a pronounced inverse relationship between the levels of regulatory T lymphocytes (Tregs) and plasma HDL-C [49]. CD4 + T cells play a central role in adaptive immune responses, and their activation may lead to the occurrence of inflammatory diseases. HDL and its components affect CD4+ T cell homeostasis by regulating cholesterol efflux, immune synapses, proliferation, differentiation, oxidative stress, and apoptosis [50]. Moreover, Tang et al. have evidenced that HDL2b particles potentially exhibit anti-inflammatory capability through suppressing lymphocyte activation [15]. A deficiency of ApoA-I in plasma has been implicated in enhancing the migration of neutrophils towards tissues, as well as the proliferation of CD45RA+, CD16+, and CD56 + lymphocytes [51].

The vast majority of periodontitis cases can be significantly alleviated or actively controlled through non-surgical periodontal treatment, whether it’s quadrant-wise subgingival instrumentation or one-stage full-mouth subgingival instrumentation, leading to a substantial improvement in the quality of life for periodontitis patients [21, 22]. Even more importantly, early warning of periodontitis would be obtained through our newly discovered HDL-related inflammation index, which will undoubtedly benefit both periodontitis patients and periodontist, and irreversible destruction of periodontal tissue could be avoided.

Strengths and limitations

As a novel discovery in the cross-sectional research, it was established that elevated levels of LHR and NHR were notably associated with a heightened vulnerability to periodontitis among adult residents of the United States. The strengths of this research stemmed from its capacity to scrutinize the link between the HDL-related inflammation index and the prevalence of periodontitis within a vast, nationally representative population, which increased the study’s credibility and applicability. In addition, the investigation further enhanced the reliability of the results by adjusting for confounding covariables.

Certainly, it is essential to acknowledge the constraints present in this study as well. First, in the context of a cross-sectional study, it cannot determine causality. Second, despite adjustments for various correlates, the effects of all potential confounding variables could not be fully excluded. Furthermore, given that this survey was conducted solely in the U.S. population over the age of 30, it is imperative to investigate both its potential applicability across diverse populations and the requirement for prospective trials.

Conclusion

To summarize, the research highlighted a significant association between LHR and NHR levels and the odds of having periodontitis among U.S. adults, particularly those under the age of 50 and non-hypertensive individuals. This finding suggested LHR and NHR might be independent indicators of periodontitis. Dental doctors should pay close attention to the oral hygiene of people with high levels of LHR and NHR and take corresponding intervention measures as early as possible.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

IQR:

interquartile range

BMI:

body mass index

PIR:

Income to poverty ratio

HDL-C:

High-density lipoprotein cholesterol

LHR:

lymphocyte count/ high-density lipoprotein cholesterol

MHR:

monocyte count/ high-density lipoprotein cholesterol

NHR:

neutrophil count/ high-density lipoprotein cholesterol

PHR:

platelet count/ high-density lipoprotein cholesterol

References

  1. Slots J. Periodontitis: facts, fallacies and the future. Periodontol 2000. 2017;75(1):7–23.

    Article  PubMed  Google Scholar 

  2. Tonetti MS, Van Dyke TE. Working group 1 of the joint EFP/AAP workshop. Periodontitis and atherosclerotic cardiovascular disease: consensus report of the Joint EFP/AAP Workshop on Periodontitis and systemic diseases. J Periodontol. 2013;84(4 Suppl):S24–29.

    PubMed  Google Scholar 

  3. Herrera D, Sanz M, Shapira L, et al. Association between periodontal diseases and cardiovascular diseases, diabetes and respiratory diseases: Consensus report of the Joint Workshop by the European Federation of Periodontology (EFP) and the European arm of the World Organization of Family doctors (WONCA Europe). J Clin Periodontol. 2023;50(6):819–41.

    Article  CAS  PubMed  Google Scholar 

  4. Chen MX, Zhong YJ, Dong QQ, Wong HM, Wen YF. Global, regional, and national burden of severe periodontitis, 1990–2019: an analysis of the global burden of Disease Study 2019. J Clin Periodontol. 2021;48(9):1165–88.

    Article  PubMed  Google Scholar 

  5. Haas MJ, Mooradian AD. Regulation of high-density lipoprotein by inflammatory cytokines: establishing links between immune dysfunction and cardiovascular disease. Diabetes Metab Res Rev. 2010;26(2):90–9.

    Article  CAS  PubMed  Google Scholar 

  6. Mineo C, Deguchi H, Griffin JH, Shaul PW. Endothelial and antithrombotic actions of HDL. Circ Res. 2006;98(11):1352–64.

    Article  CAS  PubMed  Google Scholar 

  7. Säemann MD, Poglitsch M, Kopecky C, Haidinger M, Hörl WH, Weichhart T. The versatility of HDL: a crucial anti-inflammatory regulator. Eur J Clin Invest. 2010;40(12):1131–43.

    Article  PubMed  Google Scholar 

  8. Zhang Q, Jiang Z, Xu Y. HDL and oxidation. Adv Exp Med Biol. 2022;1377:63–77.

    Article  CAS  PubMed  Google Scholar 

  9. Xepapadaki E, Zvintzou E, Kalogeropoulou C, Filou S, Kypreos KE. Τhe antioxidant function of HDL in atherosclerosis. Angiology. 2020;71(2):112–21.

    Article  CAS  PubMed  Google Scholar 

  10. Taborda NA, Blanquiceth Y, Urcuqui-Inchima S, Latz E, Hernandez JC. High-density lipoproteins decrease proinflammatory activity and modulate the Innate Immune Response. J Interferon Cytokine Res. 2019;39(12):760–70.

    Article  CAS  PubMed  Google Scholar 

  11. Fernandes das Neves M, Batuca JR, Delgado Alves J. The role of high-density lipoprotein in the regulation of the immune response: implications for atherosclerosis and autoimmunity. Immunology. 2021;164(2):231–41.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ehteshami A, Shirban F, Bagherniya M, Sathyapalan T, Jamialahmadi T, Sahebkar A. The association between high-density lipoproteins and periodontitis. Curr Med Chem. Published online July 26, 2023.

  13. Murphy AJ, Woollard KJ, Hoang A, et al. High-density lipoprotein reduces the human monocyte inflammatory response. Arterioscler Thromb Vasc Biol. 2008;28(11):2071–7.

    Article  CAS  PubMed  Google Scholar 

  14. Harlan LM, Chan MA, Benedict SH. Two different modes of costimulation predispose human T lymphocytes to differential responses in the presence of HDL or oxidized LDL. Atherosclerosis. 2007;193(2):309–20.

    Article  CAS  PubMed  Google Scholar 

  15. Tang H, Xiang Z, Li L, et al. Potential role of anti-inflammatory HDL subclasses in metabolic unhealth/obesity. Artif Cells Nanomed Biotechnol. 2021;49(1):565–75.

    Article  PubMed  Google Scholar 

  16. Gao J, Lu J, Sha W, et al. Relationship between the neutrophil to high-density lipoprotein cholesterol ratio and severity of coronary artery disease in patients with stable coronary artery disease. Front Cardiovasc Med. 2022;9:1015398.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Priyamvara A, Dey AK, Bandyopadhyay D, et al. Periodontal inflammation and the risk of Cardiovascular Disease. Curr Atheroscler Rep. 2020;22(7):28.

    Article  PubMed  Google Scholar 

  18. Zhu H, Ye G, Xie Y, Zhu K, Zhu F, Chen Q. Association of high-density lipoprotein cholesterol and periodontitis severity in Chinese elderly: a cross-sectional study. Clin Oral Investig. 2022;26(7):4753–9.

    Article  PubMed  Google Scholar 

  19. 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  PubMed  PubMed Central  Google Scholar 

  20. Fu YW, Li XX, Xu HZ, Gong YQ, Yang Y. Effects of periodontal therapy on serum lipid profile and proinflammatory cytokines in patients with hyperlipidemia: a randomized controlled trial. Clin Oral Investig. 2016;20(6):1263–9.

    Article  PubMed  Google Scholar 

  21. Isola G, Tartaglia GM, Santonocito S, Chaurasia A, Marya A, Lo Giudice A. Growth differentiation factor-15 and circulating biomarkers as predictors of periodontal treatment effects in patients with periodontitis: a randomized-controlled clinical trial. BMC Oral Health. 2023;23(1):582.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Isola G, Polizzi A, Santonocito S, Alibrandi A, Pesce P, Kocher T. Effect of quadrantwise versus full-mouth subgingival instrumentation on clinical and microbiological parameters in periodontitis patients: a randomized clinical trial. J Periodontal Res. 2024;59(4):647–56.

    Article  PubMed  Google Scholar 

  23. Maaninka K, Neuvonen M, Kerkelä E, et al. OxLDL sensitizes platelets for increased formation of extracellular vesicles capable of finetuning macrophage gene expression. Eur J Cell Biol. 2023;102(2):151311.

    Article  CAS  PubMed  Google Scholar 

  24. Szymańska P, Luzak B, Siarkiewicz P, Golański J. Platelets as potential non-traditional Cardiovascular Risk factor-analysis performed in healthy donors. Int J Mol Sci. 2023;24(19):14914.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Duran Karaduman B, Ayhan H, Keleş T, Bozkurt E. Association between monocyte to high-density lipoprotein cholesterol ratio and bicuspid aortic valve degeneration. Turk J Med Sci. 2020;50(5):1307–13.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Chen H, Xiong C, Shao X, et al. Lymphocyte to high-density lipoprotein ratio as a New Indicator of inflammation and metabolic syndrome. Diabetes Metab Syndr Obes. 2019;12:2117–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yu S, Guo X, Li G, Yang H, Zheng L, Sun Y. Lymphocyte to high-density lipoprotein ratio but not platelet to lymphocyte ratio effectively predicts metabolic syndrome among subjects from Rural China. Front Cardiovasc Med. 2021;8:583320.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Lu CF, Cang XM, Liu WS, et al. Association between the platelet/high-density lipoprotein cholesterol ratio and nonalcoholic fatty liver disease: results from NHANES 2017–2020. Lipids Health Dis. 2023;22(1):130.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Huang Y, Jiang B, Miao X, et al. The relationship of lymphocyte to high-density lipoprotein ratio with pulmonary function in COPD. Int J Chron Obstruct Pulmon Dis. 2020;15:3159–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Liu Z, Luo S, Jiao R, et al. Red blood cell folate level is associated with periodontitis in American adults: results from the NHANES 2009–2014. BMC Oral Health. 2024;24(1):825.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Eke PI, Page RC, Wei L, Thornton-Evans G, Genco RJ. Update of the case definitions for population-based surveillance of periodontitis. J Periodontol. 2012;83(12):1449–54.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Lalitha TA, Balakrishnan A, Parthiban S, Kadhiresan R, Mani E, Sivasankari T. Monocyte-to-High-Density Lipoprotein Cholesterol Ratio as a novel inflammatory marker in Periodontal Disease: a pilot study. J Contemp Dent Pract. 2022;23(7):709–12.

    Article  CAS  PubMed  Google Scholar 

  33. Zhou Y, Dan H, Bai L, Jia L, Lu B, Cui W. Nonlinear relationship with saturation effect observed between neutrophil to high-density lipoprotein cholesterol ratio and atherosclerosis in a health examination population: a cross-sectional study. BMC Cardiovasc Disord. 2022;22(1):424.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Cândido FG, da Silva A, Zanirate GA, Oliveira NMCE, Hermsdorff HHM. Lymphocyte to High-density Lipoprotein Cholesterol Ratio is Positively Associated with Pre-diabetes, Metabolic Syndrome, and Non-traditional Cardiometabolic Risk Markers: A Cross-sectional Study at Secondary Health Care. Inflammation. Published online June 6, 2024.

  35. Bhattacharya HS, Srivastava R, Gummaluri SS, Agarwal MC, Bhattacharya P, Astekar MS. Comparison of blood parameters between periodontitis patients and healthy participants: a cross-sectional hematological study. J Oral Maxillofac Pathol. 2022;26(1):77–81.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Khoury W, Glogauer J, Tenenbaum HC, Glogauer M. Oral inflammatory load: neutrophils as oral health biomarkers. J Periodontal Res. 2020;55(5):594–601.

    Article  CAS  PubMed  Google Scholar 

  37. Uriarte SM, Hajishengallis G. Neutrophils in the periodontium: interactions with pathogens and roles in tissue homeostasis and inflammation. Immunol Rev. 2023;314(1):93–110.

    Article  CAS  PubMed  Google Scholar 

  38. Franco C, Patricia HR, Timo S, Claudia B, Marcela H. Matrix metalloproteinases as regulators of Periodontal inflammation. Int J Mol Sci. 2017;18(2):440.

    Article  PubMed  Google Scholar 

  39. Lee W, Aitken S, Sodek J, McCulloch CA. Evidence of a direct relationship between neutrophil collagenase activity and periodontal tissue destruction in vivo: role of active enzyme in human periodontitis. J Periodontal Res. 1995;30(1):23–33.

    Article  CAS  PubMed  Google Scholar 

  40. Chapple ILC, Matthews JB. The role of reactive oxygen and antioxidant species in periodontal tissue destruction. Periodontol 2000. 2007;43:160–232.

    Article  PubMed  Google Scholar 

  41. Matthews JB, Wright HJ, Roberts A, Cooper PR, Chapple ILC. Hyperactivity and reactivity of peripheral blood neutrophils in chronic periodontitis. Clin Exp Immunol. 2007;147(2):255–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kim TS, Silva LM, Theofilou VI, et al. Neutrophil extracellular traps and extracellular histones potentiate IL-17 inflammation in periodontitis. J Exp Med. 2023;220(9):e20221751.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Murphy AJ, Woollard KJ, Suhartoyo A, et al. Neutrophil activation is attenuated by high-density lipoprotein and apolipoprotein A-I in in vitro and in vivo models of inflammation. Arterioscler Thromb Vasc Biol. 2011;31(6):1333–41.

    Article  CAS  PubMed  Google Scholar 

  44. Cockerill GW, McDonald MC, Mota-Filipe H, Cuzzocrea S, Miller NE, Thiemermann C. High density lipoproteins reduce organ injury and organ dysfunction in a rat model of hemorrhagic shock. FASEB J. 2001;15(11):1941–52.

    Article  CAS  PubMed  Google Scholar 

  45. Dai C, Yao X, Keeran KJ, et al. Apolipoprotein A-I attenuates ovalbumin-induced neutrophilic airway inflammation via a granulocyte colony-stimulating factor-dependent mechanism. Am J Respir Cell Mol Biol. 2012;47(2):186–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Bergt C, Marsche G, Panzenboeck U, Heinecke JW, Malle E, Sattler W. Human neutrophils employ the myeloperoxidase/hydrogen peroxide/chloride system to oxidatively damage apolipoprotein A-I. Eur J Biochem. 2001;268(12):3523–31.

    Article  CAS  PubMed  Google Scholar 

  47. Kini V, Mohanty I, Telang G, Vyas N. Immunopathogenesis and distinct role of Th17 in periodontitis: a review. J Oral Biosci. 2022;64(2):193–201.

    Article  CAS  PubMed  Google Scholar 

  48. Zhang L, Gao L, Xu C, et al. Porphyromonas gingivalis lipopolysaccharide promotes T- helper 17 cell differentiation from human CD4 + naïve T cells via toll-like receptor-2 in vitro. Arch Oral Biol. 2019;107:104483.

    Article  CAS  PubMed  Google Scholar 

  49. Ammirati E, Cianflone D, Banfi M, et al. Circulating CD4 + CD25hiCD127lo regulatory T-Cell levels do not reflect the extent or severity of carotid and coronary atherosclerosis. Arterioscler Thromb Vasc Biol. 2010;30(9):1832–41.

    Article  CAS  PubMed  Google Scholar 

  50. Atehortua L, Davidson WS, Chougnet CA. Interactions between HDL and CD4 + T cells: a Novel understanding of HDL Anti-inflammatory properties. Arterioscler Thromb Vasc Biol. 2024;44(6):1191–201.

    Article  CAS  PubMed  Google Scholar 

  51. Pashinskaya KO, Samodova AV, Dobrodeeva LK. The effect of the content of ApoA-I in peripheral blood on the state of immune homeostasis in people living in extreme climatic conditions of the Arctic. Klin Lab Diagn. 2021;66(9):539–45.

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors express their gratitude for the availability of the NHANES database.

Funding

This study was supported by the National Natural Science Foundation of China, project approval number: 81970943.

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Authors and Affiliations

  1. Department of Periodontics, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Disease, China Medical University, Heping District, Nanjing North Street No.117, Shenyang, 110002, China

    Jiahui Zhao, Qifan Zheng, Yue Ying, Shiyin Luo, Nan Liu, Liu Wang, Tong Xu, Yaping Pan & Dongmei Zhang

  2. Department of Oral mucosiology, School and Hospital of Stomatology, Liaoning Provincial Key Laboratory of Oral Disease, China Medical University, Heping District, Nanjing North Street No.117, Shenyang, 110002, China

    Aijia Jiang

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  1. Jiahui Zhao
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Contributions

JHZ and DMZ conceived the idea and were responsible for designing the research. JHZ conducted the data analysis and drafted the paper. QFZ, YY, SYL, NL were involved in the preparation of figures and tables. TX, LW, AJJ, YPP, DMZ directed the study and refined the manuscript. All authors took part in the discussion of the findings and made valuable contributions to the content of the article.

Corresponding author

Correspondence to Dongmei Zhang.

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Ethics approval and consent to participate

The NHANES study protocol was approved by the National Center for Health Statistics Research Ethics Review Committee. All participants provided their written consent after being fully informed about the study.

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Not applicable.

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The authors declare no competing interests.

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Zhao, J., Zheng, Q., Ying, Y. et al. Association between high-density lipoprotein-related inflammation index and periodontitis: insights from NHANES 2009–2014. Lipids Health Dis 23, 321 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02312-9

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02312-9

Keywords

Lipids in Health and Disease

ISSN: 1476-511X