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. 2020 Nov 21;27(2):357–369. doi: 10.1111/odi.13531

Associations between osteoporosis and risk of periodontitis: A pooled analysis of observational studies

Shuai Xu 1, Gang Zhang 1, Jun‐feng Guo 1, Ying‐hui Tan 1,✉
PMCID: PMC7839743  PMID: 32615008

Abstract

Background

Periodontitis and osteoporosis are most popular among aging population and both conditions might be linked, even though, this suggestion still until now debated.

Objectives

A meta‐analysis on previous investigations has been used to evaluate the correlation between periodontitis and osteoporosis to determine whether osteoporosis is a local indicator of bone loss, or whether it is depending on or related to periodontitis causes.

Methods

The literature database, including but not excluding, The Cochrane Central Register of Controlled Trials, MEDLINE, EMBASE, CINAHL, and Science Citation Index Expanded, was searched in this work during Feb, 2020. We conducted the investigations contain cohort studies, cross‐sectional studies, as well as case–control studies with relative risk (RR) or odds ratio (OR) and 95% confidence intervals (CIs). Subgroup and Sensitivity analysis were also applied to identify heterogeneity sources.

Results

23 observational studies with 12 cohorts, 7 cross‐sectional and 4 case–control studies, were included, together with 2,157,037 participants. Osteoporosis patients were more exposed to periodontitis (OR, 1.96; 95% CI, 1.50–2.54). Subgroup analyses showed that the higher risk of osteoporosis in periodontitis patients exists in both cross‐sectional studies (OR, 2.17; 95% CI, 1.80–2.61) and case–control studies (OR 2.63; 95% CI, 1.69–4.09), and marginally in cohort studies (OR, 1.70; 95% CI, 1.16–2.49).

Conclusion

Review analyses have shown that osteoporosis is closely related to the increased risk of periodontitis in the future. Dental specialists better to understand the potential association between periodontitis and osteoporosis.

Keywords: meta‐analysis, observational studies, osteoporosis, periodontitis

1. INTRODUCTION

Periodontitis is an infectious‐inflammatory disease affecting the periodontal tissues which could cause pain, tooth mobility, gingival bleeding, alveolar bone destruction, and periodontal attachment loss that might result in tooth loss (Pihlstrom, Michalowicz, & Johnson, 2005). Periodontitis usually caused by bacterial accumulation on the teeth outer surface, which causes an imbalance between bacterial invasion and host protection (Passos et al., 2013). Periodontitis is a well‐known dental problem, and its incidence and severity of public health problems become worse with age. It is found that the periodontitis is affected by gender, genetics, smoking habits, lifestyle, inflammation, and osteoporosis (Gera, 2002).

Osteoporosis is described by bone tissues microarchitectural deterioration and low bone mineral density (BMD). Symptoms lead to bone fragility, which consequentially increase fracture risks (Penoni et al., 2016). Risk factors of periodontal diseases are found including age, lifestyle (smoking, alcohol consumption), low body mass index (BMI), and menopause (Wactawski‐Wende et al., 1996). The obvious feature of both diseases is bone loss, and it is highly conceivable that periodontal destruction could be significantly influenced by systemic bone loss. Many previous studies have revealed a positive relationship between systemic osteoporosis and periodontitis (Al Habashneh et al., 2010; Choi et al., 2017; Mongkornkarn et al., 2019; Richa, Puranik, & Shrivastava, 2017), while others disagree (Marjanovic et al., 2013; Sultan & Rao, 2011). Despite the fact many works have been done investigating the relationship between periodontitis and osteoporosis, no clear effect has yet been found, even supposing the current available evidences.

Currently, no identified meta‐analysis has been reported to clearly confirm that osteoporosis would definitely cause periodontitis. Different from the usually relatively small sample size of individual studies, a meta‐analysis can provide more reliable evidence because the method involves a systematic aggregation of existing studies (Moher, Liberati, Tetzlaff, & Altman, 2009). Herein, this study employed a meta‐analysis on numbers of previous investigations with the purpose to evaluate the correlations between periodontitis and osteoporosis. The main goal is to determine whether osteoporosis is a local indicator of bone loss, or whether it is depending on or related to periodontitis causes. Finally, we got the conclusion on that whether osteoporosis depends on its own cause, or if it is a local manifestation caused by a systemic bone loss.

2. METHODS

2.1. Research methodology

We conducted and reported this systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) statement (Liberati et al., 2009) and the Cochrane Handbook for Interventional Reviews (Higgins et al., 2019). We have tried to search the literature database, including Cochrane Library, PubMed, MEDLINE, CINAHL, EMBASE databases, and Science Citation Index Expanded, to identify relevant investigations up to February 2020. “Periodontitis,” “alveolar bone loss,” “periodontal pocket,” “periodontal disease,” “bone loss,” “bone mineral density,” “Osteoporosis,” as well as “clinical attachment loss” as shown in Table S1 were used as keywords in search engines. The selected studies were mainly limited to those conducted only on human beings and the articles should be published in English or in Chinese. Furthermore, we also manually checked selected studies to screen out other studies.

2.2. Selection and eligibility criteria

Consider analyzing eligible studies if the following criteria are met: (a) It should include case–control studies, cross‐sectional studies, as well as studies on cohort; (b) Periodontitis definition should be based on two sets of principles: clinical criteria which are based on periodontal charting, including attachment loss (CAL), clinical probing depth (PD), detecting blood, plaque index, gingival index and radiological criteria that can assess mandible or maxillary loss, (Martínez‐Maestre, González‐Cejudo, Machuca, Torrejón, & Castelo‐Branco, 2010); (c) Osteoporosis defined by the World Health Organization (WHO) that young people have a BMD score of 2.5 or a T‐score below the average value (Kanis, 1994); (d) Reporting RRs and ORs with 95% CIs; and 5. The sample size includes over 100 topics. Once the separate reports are published for the same population, consider the longest follow‐up studies and/or the most recent studies.

2.3. Data extraction

Following data classification, the 1st and 2nd co‐authors were independently tasked with selecting the studies for inclusion and data extraction. Data uncertainties were resolved independently by the 4th co‐author. The following information was extracted from the selected publications, such as the details of first author and country, type of studies and the size of the sample, gender, age, adjusted OR/RR values, follow‐up duration (in years), diagnostic criteria (osteoporosis and periodontitis), study model and quality, as well as publication year.

2.4. Quality evaluation

Based on the above results, we further evaluate the cross‐sectional study and cohort study quality using the Agency for Healthcare Research and Quality (Owens et al., 2010) combined with The Newcastle–Ottawa Quality Assessment Scale (Stang, 2010), and the Methodological Expectations for Cochrane Intervention Reviews (MECIR) (Higgins et al., 2019). Scores system has been built, for example, 0–4 is considered as low quality, 5 score to 7 score means moderate quality, high quality means the score should be 8 to 11 (Hu et al., 2015). The study quality increases with higher scores.

2.5. Statistical analysis

To evaluate statistical variances among the involved investigations, Cochran's Q test and the I2 metric have been employed for quantification. The I2 parameter was used to determine OR across studies consistent estimation (Melsen, Bootsma, Rovers, & Bonten, 2014). Statistically significant heterogeneity for the Q statistic was considered in case of p value <.10. During this study, some cut‐off points were used for the I2 statistic as following: <30% (low or no heterogeneity), 30%–75% (medium heterogeneity), and >75% (high heterogeneity) (Chen et al., 2015). With subgroup analysis based on adjusted OR, potential causes and possible associations of heterogeneity are explored. This was according to study design, geographical region, sample size, gender, diagnostic criteria, follow‐up period, and methodological quality. Furthermore, funnel plots combined with Egger's linear regression approach, as well as the Begg's rank correlation test were conducted to assess potential publication bias (Begg & Mazumdar, Mazumdar, 1994; Egger, Davey Smith, Schneider, & Minder, 1997). In cases of possible publication bias, a trim and fill algorithm strategy were used to correct the asymmetry of the funnel plot (Duval & Tweedie, 2000). Finally, we further conducted sensitivity analysis by removing a study according to the rules of the meta‐analysis with the STATA software. The software version is 12.0 and made by STATA Corporation, TX, USA. It should be noted that the condition with P lower than 0.05 is considered as statistically significant.

3. RESULTS

3.1. Literature screening

Figure 1 presents the process description of this study. Briefly, 893 relevant articles were identified in the preliminary literature search. After titles and abstracts were evaluated, 147 studies were selected for more detailed study. Finally, 21 articles from 23 studies (Al Habashneh et al., 2010; Chang et al., 2014; Choi et al., 2017; Gomes‐Filho et al., 2007; Huang et al., 2016; Inagaki et al., 2005; Kim et al., 2014; Lin et al., 2015; Marjanovic et al., 2013; Mau et al., 2017; Moedano, Irigoyen, Borges‐Yanez, Flores‐Sanchez, & Rotter, 2011; Mongkornkarn et al., 2019; Özçaka, Becerik, Biçakci, & Kiyak, 2014; Passos et al., 2010, 2013; Penoni et al., 2016; Renvert, Berglund, Persson, & Persson, 2011; Richa et al., 2017; Shum et al., 2010; Sperr et al., 2018; Taguchi et al., 2005) comprising data from 2,157,037 participants were studied by meta‐analysis.

FIGURE 1.

FIGURE 1

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Flowchart demonstrating the process from the identification of eligible studies to the final inclusion

3.2. Study characteristics

Table 1 shown a list of adjusted covariates and characteristics of each study. Studies analyzed were all published during 1990–2016 and data contained were all collected between 1997 and 2015. The reports included 12 cohort studies (Chang et al., 2014; Choi et al., 2017; Huang et al., 2016; Lin et al., 2015; Mau et al., 2017; Moedano et al., 2011; Özçaka et al., 2014; Renvert et al., 2011; Sperr et al., 2018; Taguchi et al., 2005), 7 cross‐sectional studies (Al Habashneh et al., 2010; Inagaki et al., 2005; Kim et al., 2014; Marjanovic et al., 2013; Mau et al., 2017; Penoni et al., 2016; Richa et al., 2017), and 4 case–control studies (Gomes‐Filho et al., 2007; Passos et al., 2010, 2013; Shum et al., 2010). Geographically, fourteen studies were conducted in Asia (Al Habashneh et al., 2010; Chang et al., 2014; Choi et al., 2017; Huang et al., 2016; Inagaki et al., 2005; Kim et al., 2014; Lin et al., 2015; Mau et al., 2017; Mongkornkarn et al., 2019; Richa et al., 2017; Shum et al., 2010; Taguchi et al., 2005) while five in the Americas (Gomes‐Filho et al., 2007; Moedano et al., 2011; Passos et al., 2010, 2013; Penoni et al., 2016) and three in Europe (Marjanovic et al., 2013; Özçaka et al., 2014; Renvert et al., 2011; Sperr et al., 2018). Among all, reported participants ages ranged from 14 to 100 years old. Except three, other studies were reported with adjusted OR, for potential confounders. Osteoporosis in all studies had been confirmed by bone mineral density. The parameters such as plaque index (PI), gingival bleeding index, clinical attachment loss (CAL), and pocket depth (PD) of periodontitis were included. The selected studies quality was quite high with no significant limitations as shown in Table S2 in the Supplemental Information.

TABLE 1.

The characteristics of the studies in included in the analysis

Author Year Country Study design Sample size (case/control) Male/Female Age/median age(range) Follow‐up (years) Diagnostic criteria OR (95% CI) Adjustment for covariates Quality
Osteoporosis Periodontitis
Sperr et al. (2018) 2018 Austria a nationwide population‐based cohort study 1,199 patients with Periodontitis 558 males; 641 females, 49 (14 to 83) January 2006 to April 2009 bone densitometry CAL 0.44 (0.28–0.70) sex, age, education, smoking, alcohol consumption, and BMI high
Choi et al. (2017) 2017 Korea a nationwide population‐based cohort study 13,464 participants 8,884males;4,580 females ≥30 2002 to 2013 bone densitometry NS Male 1.39 (0.85–2.29) NS low
Choi et al. (2017) 2017 Korea a nationwide population‐based cohort study 13,464 participants 8,884males;4,580 females ≥30 2002 to 2013 diagnostic code M80–M82 Diagnostic code ICD‐10, K05.3‐K05.6 Female 1.22 (1.01–1.48) NS low
Mau et al. (2017) 2017 China (Taiwan) population‐based cohort study 29,463 patients with Periodontitis 16,114male 13,349 Female ≥40 2002 to 2008 diagnostic codeICD‐9CM, 733.0 by DXA) diagnosis codes ICD‐9‐CM, 5,234 2.08 (1.08–4.03) age, sex, diabetes mellitus, hypertension, coronary artery disease, stroke, hyperlipidemia, chronic kidney disease low
Huang et al. (2016) 2016 China (Taiwan) population‐based cohort study 85,583 (35,127 Osteoporosis patients and 50,498 controls 21,994male 63,588 female 61.7 2000 and 2010 diagnosis code (ICD‐9‐CM 733.0, V13.51, and V82.81) NS 6.02 (4.65–7.81) age, sex, and comorbidities low
Lin et al. (2015) 2015 China (Taiwan) population‐based cohort study 2,000,000 927,189male 951,212 female 2005 and 2010 diagnostic code ICD‐9‐CM ,733.0. CD PD male 2.37 (0.88–6.39) age, income, and geographical region high
Lin et al. (2015) 2015 China (Taiwan) population‐based cohort study 2,000,000 927,189male 951,212 female 2005 and 2010 diagnostic code ICD‐9‐CM ,733.0. CD PD Female 1.96 (1.17–3.26) age, income, and geographical region. high
Chang et al. (2014) 2014 China (Taiwan) population‐based cohort study 10,102(2,527Osteoporosis 7,575 Non‐Osteoporosis) 2,626 males 7,476 females, 70.16 (50–100) 2003–2005 diagnostic code (ICD‐9‐CM: 733.0 to 733.90) probing sulcus, and radiographs 1.14 (1.05–1.24) Urbanization level, monthly income, geographic region, hypertension, hyperlipidemia high
Özçaka et al. (2014) 2014 Turkey population‐based cohort study 201 older subjects 130male 71female M:62.65 ± 5.31F:62.23 ± 4.86 March 2008 and May 2009 bone densitometry PI 2.05 (1.11–3.78) age, sex, smoking high
Renvert et al. (2011) 2011 Sweden population‐based cohort study 778 subjects 365male 412female 73.9 ± 9.4 (59– 96) September 2001 and April 2004 bone density assessment by PIXI panoramic radiograph 1.80 (1.10–3.30) NS low
Moedano et al. (2011) 2011 Mexico population‐based cohort study 166 19male
147female 69.1 ± 6.7(60–80) NS BMD at lumbar spine by DXA CAL 1.82(1.04–3.18) sex, socio‐economic status and dental plaque high
Taguchi et al. (2005) 2005 Japan population‐based cohort study 253 postmenopausal women 56.6 ± 7.7 1997 and 2003 BMD at the lumbar spine and the femoral neck by DXA periodontal symptoms 2.01 (1.15–3.50) age and height high
Mongkornkarn et al. (2019) 2019 Thailand cross‐sectional study 3,282 2,393male 889female 30–82 2012–2014 BMD at femoral neck, total hip and lumbar spine by DXA CAL 3.97 (1.20–13.19) sex, age, plaque score, diabetes, BMI, smoking, alcohol consumption, income, education and menopause. high
Richa et al. (2017) 2017 India cross‐sectional comparative study 600(300 osteoporotic and 300 non‐osteoporotic) postmenopausal women 45–65 October 2012 and March 2013. BMD at heel by an ultrasonometer based on QUS CAL 2.52 (1.43–4.44) the other variables presented. low
Penoni et al. (2016) 2016 Brazil cross‐sectional study 134(48 normal BMD and 86 Osteoporosis) postmenopausal women 69.84 ± 3.90 December 2013 and January 2015 BMD at lumbar spine, femoral neck, and total femur by DXA PD and CAL 2.49 (1.14–5.43) NS low
Kim et al. (2014) 2014 Korean cross‐sectional study 9,977 subjects 4,446male 5,531female ≥40 years 2008, 2009, and 2010, BMD at the lumbar vertebrae and the left femur by DXA PD 2.26 (1.83–2.78) age, sex, sociodemographic variables (household income, educational level, smoking, and high‐risk drinking), and medical status variables (diabetes mellitus, BMI, and limitation of physical activity high
Marjanovic et al. (2013) 2013 UK cross‐sectional study 380 women 45–65 years March 2008 and June 2010 BMD at lumbar spine and proximal femur by DXA PD 1.17 (0.67–2.05) age, smoking, hormone replacement therapy, alcohol high
Al Habashneh et al. (2010) 2010 Jordan cross‐sectional study 400 postmenopausal women 62.5 ± 6.4(50–75) June 2008 and February 2009 BMD at lumbar spine,femoral neck and trochanter by DXA PD and CAL 2.45 (1.38–4.34) age, years of education, income, high
Inagaki et al. (2005) 2005 Japan cross‐sectional study 356(171 premenopausal 185 postmenopausal) women premenopausal (37.9 ± 8.0) postmenopausal (63.3 ± 7.7) May 1998 and 1999 BMD at right second metacarpal by CXD PD 2.00 (1.10–3.70) age and menopausal status high
Passos et al. (2013) 2013 Brazil case–control study 521(cases :94 postmenopausal women control: 427 comparisons) women 60.8 ± 7.4 (50–80) June 2008 and September 2011 bone mineral density CAL 2.24 (1.24–4.06) age, smoking habit, last visit to dentist, and family income high
Passos et al. (2010) 2010 Brazil case–control study 139(case: 48 postmenopausal women and control:91 women ＞50 June and November 2006 bone mineral density PD and CAL 3.75 (1.22–11.49) smoking habit and age high
Shum et al. (2010) 2010 China case–control study 200 male 71.9 ± 3.3(69–78) May 2005 and July 2006 BMD at the hip, spine (lumbar vertebrae 1 through 4) by DXA CAL 5.30 (0.10–10.05) smoking and BM high
Gomes‐Filho et al. (2007) 2007 Brazil case–control study 139(case:48 Periodontitis control:91 without Periodontitis) postmenopausal women ＞50 June and November 2006 BMD PD and CAL 2.71 (1.12–6.55) smoking habit and age high
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Abbreviations: BMI, body mass index; CAL, clinical attachment loss; CXD, computerized X‐ray densitometry; DXA, dual‐energy X‐ray absorptiometry; NS, not specified; OR, odd rat; PD, pocket depth; PI, plaque index; PIXI, ultrasonography calcaneus T‐scores; QUS, quantitative ultrasound technique.

3.3. Meta‐analysis

Figure 2 exhibited the adjusted ORs from 23 reports. Generally, after excluding smoking, age, and sex factors, osteoporosis revealed an obvious relationship with periodontitis (OR, 1.96; 95% CI, 1.50–2.54). The results indicate that patients who suffer from osteoporosis are significantly exposed to increased risk of periodontitis.

FIGURE 2.

FIGURE 2

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Forest plot of osteoporosis and periodontitis in a random effects model meta‐analyses by study design. The horizontal lines correspond to the study‐specific OR and 95% CI. The areas of the squares reflect the study‐specific weights. The diamond represents the pooled results of the OR and 95% CI. OR, odds ratio; CI, confidence interval

A random effects model combining OR values was used in some statistical heterogeneities studies (Q = 222.69, p < .001, I2 = 90.1%). Figure 3 shows some revealed minor publication bias were funnel plot, Begg's test with P equal 0.139 and Egger's test with P < lower than .05. Therefore, the pruning and filling methods were used to re‐evaluate the aggregated risk estimates, that is, "no pruning; data unchanged" results, by eliminating each study once and performing a sensitivity analysis to reveal the influence of previous investigations on the combined OR. Through deleting a single report in turn, the combined OR and 95% CI did not show any significant changes (Figure 4). This shows that the proposed results are reproducible and reasonable.

FIGURE 3.

FIGURE 3

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Begg's funnel plot of publication bias in the included studies. Each point represents a separate study for the indicated association

FIGURE 4.

FIGURE 4

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Sensitivity analysis of the meta‐analysis of the correlation between osteoporosis and periodontitis. The OR and 95% CI were determined by omitting each study from the pool of eligible studies concerning the link between osteoporosis and periodontitis. OR, odds ratio; CI, confidence interval

3.4. Subgroup analysis

To inspect the studies reproducibility based on several factors, we further conduct subgroup analyses deeply. Patients with periodontitis exists in both cross‐sectional studies with OR of 2.17 and 95% CI ranging from 1.80 to 2.61, and case–control studies with OR of 2.63 and 95% CI ranging from 1.69 to 4.09, and have higher risk, and slightly in cohort studies (OR, 1.70; 95% CI, 1.16–2.49). When we stratified subjects by sex, we found that female with osteoporosis (OR, 2.24; 95% CI, 1.52–3.30) are exposed to a higher risk of developing periodontitis, compared to male (OR, 1.61; 95% CI, 1.04–2.50) as shown in Table 2. All in all, the results from subgroup analysis showed that patients with osteoporosis are at high risk for periodontitis.

TABLE 2.

Results of subgroup analyses included in this meta‐analysis

Group Number of studies Pooled OR 95% CI P(heterogeneity) I2(%)
All studies 23 1.96 1.50–2.54 0.000 90.1
Diagnostic criteria
CAL 6 1.83 0.85–3.96 0.000 86.1
PD 7 2.09 1.77–2.45 0.542 0.0
Both 5 2.00 1.21–3.32 0.001 77.6
Study design
Cohort study 12 1.70 1.16–2.49 0.000 93.8
Cross‐sectional study 7 2.17 1.80–2.61 0.376 6.7
Case–control study 4 2.63 1.69–4.09 0.797 0.0
Location
Asian 14 2.17 1.55–3.05 0.000 93.0
America 5 2.22 1.59–3.09 0.861 0.0
Europe 4 1.16 0.55–2.42 0.000 86.5
Gender
Male 3 1.61 1.04–2.50 0.381 0.0
Female 13 2.24 1.52–3.30 0.000 88.2
Number participants
<1,000 13 2.05 1.72–2.45 0.815 0.0
>1,000 10 1.77 1.16–2.69 0.000 95.5
Length of follow‐up
>5 years 7 2.13 1.15–3.51 0.000 93.8
<5 years 15 1.85 1.37–2.48 0.000 84.5
Study quality
High 16 1.81 1.37–2.39 0.000 83.0
Low and moderate 7 2.19 1.18–4.05 0.000 93.9
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Abbreviations: CI, confidence interval, CAL, Clinical attachment loss, PD, Pocket depthOR, odds ratio.

4. DISCUSSION

Our results indicate an obvious association between osteoporosis and periodontitis, which is consistent with previous investigations. Since Groen et al. (Groen, Menczel, & Shapiro, 1968) first reported the relationship between chronic destructive periodontal disease and presenile osteoporosis, several investigators have referred to a correlation between periodontal disease progression and low BMD in postmenopausal women. Previously, Wowern, Klausen, and Kollerup (1994) have found that the disease stages are different between osteoporosis patients and a control group without osteoporosis. Furthermore, relationship between periodontitis, lumbar vertebral BMD, and loss of periodontal adhesion has been reported by Mohammad, Hooper, Vermilyea, Mariotti, and Preshaw (2003). On the other hand, 347 postmenopausal women have been selected by the research group of Takahashi, Yoshihara, Nakamura, and Miyazaki (2012) and it is found that periodontal diseases and truncal BMD have a significant negative correlation. Penoni et al. (2017) have reported a systematic review, in which the postmenopausal women with osteoporosis or osteopenia are shown to exhibit larger possibility to have CAL compared with the control group of women with normal BMD. Finally, it is found that the risk of periodontitis has increased by over twofold in patients with osteoporosis by Al Habashneh et al. (2010), which is consistent with our research. Therefore, osteoporosis is found to increase the possibility of being periodontitis, but the underlying mechanism is still not fully understood.

Based on the above analysis, knowing the relationship between periodontitis and osteoporosis and investigating the underlying causes would be beneficial for health professionals prevent, detection, and earlier treatment (Al Habashneh et al., 2010). The link between osteoporosis and periodontitis is still controversial, and many hypotheses exist. One possible link between osteoporosis and periodontal destruction is systemic and simultaneous alveolar bone resorption. First, systemic bone mineral density decline, taking osteoporosis as an example, also occurs in the alveolar bones of the upper and lower jaws (Takaishi et al., 2005). And the reduction of BMD is associated with the effect of oral bacterial flora, which can lead to faster alveolar bone resorption, which can lead to rapid development of periodontal destruction (Estrugo‐Devesa, Gómez‐Vaquero, & López‐López, 2013). On the other hand, the change of local tissue responses can be caused by systemic inflammatory mediators that influence bone remodeling. Cytokines (such as Kappa‐B ligand (RANKL), tumor necrosis factor (TNF‐α), and interleukin (IL‐1β and IL)‐known to patients with systemic bone loss and patients with periodontitis‐ 6) (Lerner, 2006). On the one hand, these cytokines promote the continuous production of osteoclasts by osteoclast progenitor cells, which in turn causes bone loss. On the other hand, it can also impair tissue response to periodontal disease. This increase can stimulate the activity of local osteoclasts, promote clinical loss of adhesion and alveolar bone, and accelerate the development of periodontal disease (Lerner et al., 2006). Generally, one of the possible mechanisms for these two diseases is through the inflammatory pathway. Second, people with genetic factors that are prone to systemic reduction of BMD may be at risk of damaging alveolar bone through the same pathophysiological mechanisms. Finally, daily lifestyles such as smoking and low calcium intake may also increase BMD reduction and the risk of periodontal disease (Hildebolt et al., 1997). Therefore, in summary, we believe that osteoporosis can be listed as one of the risk indicators of periodontitis.

In addition to the above, risk factors that are generally considered to affect BMD due to smoking, diabetes, and hormone levels are also the effects of infection. Some researchers think that periodontitis is an early sign of osteoporosis (Tezal et al., 2000). Considering the biological feasibility of a factor in the occurrence and development of osteoporosis may be periodontitis. It is well known that periodontitis is a chronic inflammatory disease caused by the colonization of bacterial plaque biofilms. Therefore, the host often has an immune response. Locally increased production of cytokines associated with periodontal disease may accelerate systemic bone resorption by regulating host response (Xiao, Li, Pacios, Wang, & Graves, 2016). In addition, these cytokines mentioned above may also be induced by osteoclasts from osteoclast progenitor cells, and therefore are prone to cause bone loss. Therefore, the proinflammatory cytokine IL‐6 produced by immune cells may play a key role in this potential mechanism (Aspalli et al., 2014). Generally, in normal bone homeostasis, the production of IL‐6 stimulates the activity of osteoclasts, leading to changes in bone resorption. At the same time, some of its effects on BMD can also be adjusted by IL‐6. On the other hand, genetic factors that make individuals prone to systemic bone loss may also make them more prone to periodontal damage. Generally, the factors that down‐regulate IL‐6 gene expression is mainly estrogen and testosterone. Therefore, postmenopausal women have elevated IL‐6 levels, even without infection, trauma, or stress. In addition, IL‐6 gene expression also changes with individual age. Therefore, both osteoporosis and chronic periodontal disease may be related to age (Ershler & Keller, 2000).

Two major non‐modifiable factors causing periodontitis and osteoporosis have been found to be age and gender ( Kuo, Polson, & Kang, 2008). Many scientific studies have shown a similar conclusion when women are the object of study, especially postmenopausal women (Lin et al., 2015; Passos et al., 2013; Penoni et al., 2016). Our study also has found that female with osteoporosis (OR, 2.24; 95% CI, 1.52–3.30) faced a higher risk of developing periodontitis, compared to male (OR, 1.61; 95% CI, 1.04–2.50). However, Shum et al. (2010) report that osteoporosis is associated with severe CAL in elderly Chinese men. Based on the assumption that osteoporosis affects only a specific population, this study included only this group of patients. We found that when participants reached the age of 40, the distinction between men and women widened. The reason is that women experience estrogen deficiency after menopause. Therefore, the main risk factor for osteoporosis in women is menopause, which is related to reducing estrogen (Lin et al., 2015).

In addition, studies show that estrogen tends to reduce the protective effect of bone absorption and inhibit calcium absorption (Recker, Lappe, Davies, & Heaney, 2004). Therefore, one of the mechanisms is that bone remodeling and inflammation‐related estrogen deficiency may link osteoporosis to periodontitis (Wang & McCauley, 2016). Due to changes in hormonal secretion, in general, women begin to experience bone loss early in life and then stabilize. On the other hand, due to hormonal changes and their effects on bone remodeling, bone loss will occur later in life (Inagaki et al., 2005). Although research shows that BMD decreases with age. And because sex hormone levels usually drop sharply in postmenopausal women. Therefore, prevention of osteoporosis and the fractures caused by it in women is usually achieved by increasing the total bone mass. In this study, the male participants may be attributed to the small number of studies included in the sub‐meta‐analysis. Therefore, control of smoking, excessive drinking, insufficient nutrition, low body weight, insufficient exercise, and various drugs and diseases should be taken to prevent bone density loss at all ages, as these factors can lead to osteoporosis (Kim et al., 2014).

Preventing osteoporosis is the most reasonable way to defeat the disease, and early diagnosis is one of the foundations of modern medicine (Richa et al., 2017). Therefore, it is recommended that routine oral and BMD screening be mandatory for postmenopausal women to detect early bone changes and disease conditions, prevent disease early, and hinder disease progression (Wang & McCauley, 2016). Early detection of these conditions could allow patients to be treated more fully before osteoporosis causes debilitating fractures.

5. CONCLUSION

In summary, we used a meta‐analysis to analyze observational studies and the results showed that osteoporosis is an independent risk factor for periodontitis. This finding of this study may affect the clinical understanding of the etiology of periodontitis in this field and may further enrich the knowledge and means of preventing and controlling osteoporosis. Therefore, this study has great research value in the context of clinical practice focusing on oral health, stratifying patients according to osteoporosis risk, and formulating policies to promote oral health. Finally, although this study considered possible mechanisms, further research is needed to explain the specific association between periodontitis and osteoporosis.

AUTHOR CONTRIBUTIONS

Shuai Xu: Conceptualization; Data curation; Writing‐original draft. Gang Zhang: Data curation; Methodology. Jun feng Guo: Data curation; Supervision. Yin hui Tan: Supervision; Writing‐review & editing.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1111/odi.13531.

Supporting information

Table S1‐S2

Click here for additional data file. (21.1KB, docx)

ACKNOWLEDGEMENTS

The authors declare no conflict of interests related to this study. The study was self‐funded by the authors and their institution.

Xu S, Zhang G, Guo J‐F, Tan Y‐H. Associations between osteoporosis and risk of periodontitis: A pooled analysis of observational studies. Oral Dis.2021;27:357–369. 10.1111/odi.13531

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Table S1‐S2

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