Relationship of Bone Metabolism Biomarkers and Periodontal Disease: The Osteoporotic Fractures in Men (MrOS) Study.

ORIGINAL

ARTICLE

Relationship of Bone Metabolism Biomarkers and Periodontal Disease: The Osteoporotic Fractures in Men (MrOS) Study Ulrike Schulze-Späte1, Ryan Turner1, Ying Wang2, Raylien Chao1, P. Christian Schulze3, Kathy Phipps2, Eric Orwoll2, and Thuy-Tien Dam4 for the Osteoporotic Fractures in Men (MrOS) Research Group 1 Division of Periodontics, Section of Oral and Diagnostic Sciences, College of Dental Medicine, Columbia University, NY, NY, USA; 2 Bone and Mineral Unit, OR Health and Science University, Portland, OR, USA; 3 Division of Cardiology, Columbia University College of Physicians and Surgeons, NY, NY, USA; 4Division of Geriatric Medicine and Aging, Columbia University College of Physicians and Surgeons, NY, NY, USA

Context: Periodontitis is an inflammatory disease of tooth-supporting tissue leading to bone destruction and tooth loss. Periodontitis affects almost 50% of adults over 30 years of age. Objective: This study evaluated the association between biomarkers linked to bone formation and resorption with the occurrence and progression of periodontal disease in older men (65⫹ years of age). Design: The Osteoporotic Fractures in Men (MrOS) study is a prospective, observational study among men 65 years of age and older. Setting: This ancillary study “Oral and Skeletal Bone Loss in Older Men” was conducted at 2 of the 6 MrOS study sites (Birmingham, AL; Portland, OR). Patients: Patients underwent medical and dental evaluation. Diagnoses of periodontitis was based on clinical attachment loss (CAL), pocket depth, calculus, plaque, and bleeding on a random halfmouth. Bone metabolism biomarkers included serum levels of calcium, phosphate (Pi), alkaline phosphatase (ALP), albumin, carboxy-terminal collagen crosslinks (CTX), N-terminal propeptides of type I procollagen (P1NP), isoform 5b of tartrate-resistant acid phosphatase (TRACPb) and urine alpha- carboxy-terminal collagen crosslinks (alpha-CTX) and beta-CTX and serum levels of calciotropic hormones vitamin D (25(OH)D) and parathyroid hormone (PTH). Main Outcome Measures: The aim of this study is to correlate bone metabolism biomarkers with prevalence and progression of periodontal disease in older men. Results: Patients with more severe periodontitis had significantly higher levels of PTH (p-trend 0.0004), whereas 25 (OH) D was lower (p-trend 0.001). In a subset of men reevaluated at a second dental visit, improvement of periodontitis was associated with lower alpha-CTX, beta-CTX and CTX levels at baseline after adjusting for age, site, and BMI. Conclusion: This study suggests that a distinct set of biomarkers of bone metabolism are associated with more severe periodontal disease (PTH, 25(OH)D) and periodontal progression (alpha-CTX, beta-CTX, and CTX) over time. Keywords: Periodontal disease; bone metabolism biomarkers; vitamin D; periodontal disease progression

ISSN Print 0021-972X ISSN Online 1945-7197 Printed in U.S.A. Copyright © 2015 by the Endocrine Society Received November 21, 2014. Accepted April 2, 2015.

doi: 10.1210/jc.2014-4180

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Bone Metabolism Biomarkers and Periodontal Disease

eriodontitis is an inflammatory disease of tooth-supporting tissue that affects about 30% of the adult population in the United States (1). More recently (2012), this estimate was increased to over 47% among adults aged 30 years or more, with those over 65 years old accounting for most cases (2). Periodontitis is characterized by progressive loss of bone and periodontal attachment that ultimately leads to tooth loss if left untreated (3). Historically, periodontal disease had been considered primarily an infectious disease caused by bacteria in the dental plaque. More recently, the individual host response has been shown to be crucial in the development and progression of the disease and thus focus has been placed on identifying the determinants of the local host response to bacteria and bacterial products such as lipopolysaccarides (4, 5). In fact, various chronic inflammatory disease states, such as diabetes and obesity, may modulate the local host response to periodontal pathogens and their lipopolysaccarides, resulting in specific forms and patterns of periodontal disease (6 – 8). Periodontal disease is not a static process, but rather includes periods of stability, remission, or progression (9) and a number of patient related factors may influence these periods (10 –16). Since one of the clinical features of periodontal disease is tissue destruction including bone that ultimately results in tooth loss, interest has been focused on whether bone metabolism and specifically bone remodeling are altered during development and progression of periodontal disease. One recent small population study in elderly Japanese people revealed an association between markers of bone metabolism and the occurrence of periodontal disease (17). It showed that the percentage of sites with 6⫹ mm clinical attachment loss (CAL), a measure for the severity of periodontal disease, had a significant negative association with serum pro-osteoblastic bone remodeling marker osteocalcin that remained after adjusting for demographic variables. This was corroborated in other small population studies that showed that urinary and serum biomarkers could be used to help evaluate various aspects of periodontal disease (18, 19). Although a previous analysis of the Osteoporotic Fractures in Men Study (MrOS) did not find any significant correlation between periodontal disease and skeletal bone mineral density (BMD) in older men (20), a recent study from the Buffalo OsteoPerio Study concluded that postmenopausal women with severe periodontitis or osteoporosis may exhibit a faster rate of oral bone loss over time (20, 21). These data suggest that an individual’s overall bone metabolism might be associated with periodontal disease and be reflective of its progression (17). We hypothesize that individuals who have increased bone turnover are more likely to present with periodontitis. Therefore, the

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aim of this study is to correlate bone metabolism biomarkers with prevalence and progression of periodontal disease in older men.

Materials and Methods Participants The MrOS study is a prospective, observational study to determine risk factors for osteoporosis among men 65 years and older. MrOS recruited 5994 men (March 2000 to April 2002 (Baseline)) at six clinical sites (Birmingham, AL; Minneapolis, MN; Palo Alto, CA; Pittsburgh, PA; Portland, OR; San Diego, CA). Participants without bilateral hip replacements that were able to walk without assistance were included and informed consent was obtained (22, 23). Between September 2002 to May 2003, participants at Portland and Birmingham partook in an ancillary study, “Oral and Skeletal Bone Loss in Older Men”, to evaluate dental and periodontal health. 1347 participated in the first dental visit (Dental Visit 1), of which, 1107 had nonmissing values for calcium, 25(OH)D, PTH, Pi, ALP, albumin and were included in the cross-sectional analysis (Figure 1). Of the 1107 patients analyzed above, 829 returned between March 2005 and May 2006 for a second dental visit (Dental Visit 2) and had complete periodontal measures at both dental visits. They were included in the longitudinal analysis. Furthermore, 141 participants, a subset of the 829, had nonmissing values for serum and urinary CTX, TRACP5b, P1NP and were included in a separate longitudinal analysis. This study was approved by the Institutional Review Boards at Oregon Health & Science University and the University of Alabama.

Clinical Assessment Participants underwent clinical evaluation and provided information about medical and dental history (see Supplemental Data). Six calibrated examiners recorded missing teeth and implants for the full mouth (excluding third molars) and performed periodontal examination on a randomized half-mouth based on subject ID using a UNC15 probe. CAL, distance from the cementoenamel-junction to the base of the pocket and pocket probing depth (PD), distance from the free gingival margin to the pocket base, were measured at six sites per tooth (disto-buccal, direct buccal, mesio-buccal, disto-lingual, direct lingual and mesio-lingual). For plaque, calculus and gingival bleeding, the worst score per tooth was recorded using the Silness and Loe Plaque and Gingival Index (24). Calculus was classified as none, supra-gingival, subgingival.

Periodontal Disease Definitions Periodontitis case definitions were based on the Biofilm-Gingival Interface (BGI) Level classification system by Offenbacher et al (25). Five levels of disease were defined based upon shallow (ⱕ3 mm) or deep (ⱖ4 mm) PDs in combination with bleeding on probing (BOP) scores (⬍10%, 10%–50%, or ⱖ 50%). PDs over 3 mm are classified as periodontally at risk since they could potentially provide the anaerobic conditions that are necessary for periodontal pathogens to thrive, therefore, exposing them to


doi: 10.1210/jc.2014-4180


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ods for serum analysis have been reported (28 –30) and can be found with information about urine assays in Supplemental Data.

Statistical Analyses Baseline characteristics were classified as categorical or continuous variables according to their nature and distribution. Descriptive statistics (mean and standard deviation / median and interquartile range) were obtained on all continuous variables. Number and percentages of subjects belonging to each group of categorical variables were also computed. Cochran-Armitage trend tests for categorical variables and logistic regression models for continuous variables were performed to test if these characteristics differed by baseline periodontitis status. Poisson regression models with a robust variance estimation were used to assess the relative risk of periodontitis proFigure 1. Study flow diagram of individuals included in MrOS study. gression according to levels of the baseline biomarkers. Multivariable models were manually greater risk for periodontal tissue break down (26). BOP can be constructed to assess and control for confounding factors. Durused as a predictive measure that could foretell periodontal tissue ing descriptive analyses, we determined which potential conbreakdown (27). Two shallow PD groups were defined: BGIfounding factors varied according to both periodontitis progresHealthy (BGI-H) was defined as all PDs ⱕ 3 mm and ⬍ 10% sion and levels of biomarkers. These identified confounders were BOP; BGI-Gingivitis (BGI-G) was defined as individuals with all added into the model individually and compared with the crude PDs ⱕ 3 mm and ⱖ 10% BOP. Three BGI deep lesion (DL) model that contained age, study site, and biomarkers level. Congroups were created: mild periodontal disease or BGI-deep lefounders were only included in the multivariate model if the sion/low bleeding (BGI-DL/LB) was defined as one or more sites associations were statistically significant (P ⬍ .05) or there was with PD ⱖ 4 mm and ⬍ 10% BOP; moderate periodontal disease a ⱖ 10% change in the measure of association. All adjusted or BGI-DL/moderate bleeding (BGI-DL/MB) was defined as one models included variables known to potentially influence perior more sites with PD ⱖ 4 mm and between 10% and 50%BOP; and severe periodontal disease or BGI-DL/severe bleeding (BGIodontal status and bone metabolism markers. DL/SB) included subjects with one or more sites with PD ⱖ 4 mm and ⱖ 50% BOP. This case definition was most appropriate for Analyses were performed at Oregon Health and a geriatric population since it identifies only cases with substanScience University using SAS 9.3 (SAS Institute, tial extent and severity, and offers a common clinical feature of Cary, NC). active periodontal disease to diagnoses. However, given the small number of subjects who were categorized as BGI-H or BGI-D, we combined the two groups. Furthermore, we included an edentulous subject category in the cross-sectional analysis Results (Table 1).

Progression/Improvement of Periodontal Disease Patients were defined as having their periodontal disease status progress or improve if they changed any number of Offenbacher‘s diagnostic categories between dental visit 1 and 2 to either a more severe or less severe status. Patients were defined as having their periodontal disease status remain stable if they did not change their diagnostic periodontal disease category.

Serum/Urine Specimens Samples were collected after an overnight fast. 25(OH)D and PTH were taken at Dental Visit 1, whereas ALP, albumin, calcium, Pi, urine ␣-CTX, urine ␤-CTX, serum-CTX, P1NP, TRACPb were collected at MrOS Baseline visit. Detailed meth-

Participant Characteristics Demographics of the cross-sectional dental study population (n ⫽ 1107) by severity of periodontal disease are presented in Table 1. At Dental Visit 1, 13.7% of the study population was categorized as having healthy/gingivitis, 38.7% as having mild periodontitis, 18.3% as moderate periodontitis, 18.3% as severe periodontitis, and 10.9% were edentulous. Men with worse periodontitis were, on average, older, weighted more, and were not Caucasian (p-trend 0.002, 0.03, 0.0006, respectively). The mean age was 71.8 ⫾ 5.4 in participants with healthy/gingivitis compared to 73.7 ⫾


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Table 1.


Characteristics of the MrOS cohort by prevalence of periodontal disease groups. Periodontal Disease Categories

Characteristics Age (yr), Mean ⫾ Std BMI (kg/m2), Mean ⫾ Std Weight (kg), Mean ⫾ Std White, N(%) Smoking status, N(%) Current 20 or more pack year, N(%) Alcohol consumption/ week, N(%) None Education, N(%) College or more Physical Activity, N(%) PASE score, Median(IQR) Walk activities ⱖ 3 times/wk Light sport ⱖ 3 times/wk Moderate sport ⱖ 3 times/wk Strenuous sport ⱖ 3 times/wk Muscle exercise ⱖ 3 times/wk Comorbid Disease, N(%) CVD Arthritis or gout Osteoarthritis Diabetes Fracture history Metabolic Syndrome, N(%) Obesity Insulin resistance Hypertension Annual Testosterone Injections, N(%) Medication, N(%) Benzodiazepine Calcium chanel blocker HMG CoA reductase inhibitor(statin) NSAID Dental History Annual Dental Visit, N(%) Personal hygiene, N(%) Daily Brushing Daily Flossing Permanent tooth pulled due to gum disease, N(%)1 Number of pulled teeth, Median(IQR) Self-reported Gingivitis, N(%)2 Self-report Periodontitis, N(%)3 Ever referred to a periodontist, N(%)4 Surgery due to gum disease, N(%)5 Deep cleaning/root planning for treatment of gum disease, N(%)6 Missing any teeth, N(%) Number of missing teeth ⫹ implants, both side, Median(IQR) Plaque index, Mean ⫾ Std Biomarkers Serum Albumin g/dl, Mean ⫾ Std Serum Calcium (mg/dL), Mean ⫾ Std 25(OH)D (ng/mL), Mean ⫾ Std Total intact PTH (pg/mL), Mean ⫾ Std Pi (mg/dL), Mean ⫾ Std ALP (U/liter), Mean ⫾ Std Urine ␣ CTX (␮g/liter), Median(IQR)7 Urine ␤ CTX(g/mL), Median(IQR)7 Serum CTX (ng/mL), Median (IQR)7 Serum P1NP (ng/mL), Median (IQR)7 Serum TRACPb (U/liter), Median (IQR)7

Overall n ⫽ 1107 72.6 ⫾ 5.5 27.3 ⫾ 3.7 89.5 ⫾ 13.8 993 (89.7)

BGI-H BGI-G n ⫽ 152 71.8 ⫾ 5.4 27.1 ⫾ 3.9 88.8 ⫾ 13.7 139 (91.5)

BGI-DL/LB n ⫽ 428 72.1 ⫾ 5.2 27.2 ⫾ 3.7 89.4 ⫾ 13.3 404 (94.4)

BGI-DL/MB n ⫽ 203 73.1 ⫾ 5.9 27.1 ⫾ 3.2 88.0 ⫾ 11.9 175 (86.2)

BGI-DL/SB n ⫽ 203 73.7 ⫾ 5.4 27.2 ⫾ 3.8 89.2 ⫾ 14.2 173 (85.2)

Edentulous* n ⫽ 121 73.0 ⫾ 6.1 28.1 ⫾ 4.0 94.1 ⫾ 16.8 102 (84.3)

p-trend 0.002 0.09 0.03 0.0006

41 (3.7) 388 (35.1)

3 (2.0) 40 (26.3)

12 (2.8) 136 (31.8)

8 (3.9) 60 (29.6)

7 (3.5) 79 (38.9)

11 (9.1) 73 (60.3)

0.008 ⬍0.0001

455 (41.1)

76 (50.0)

172 (40.2)

67 (33.0)

63 (31.0)

77 (63.6)

.92 ⬍0.0001

387 (35.0)

58 (38.2)

168 (39.3)

83 (40.9)

65 (32.0)

13 (10.7)

143 (106 192) 785 (70.9) 293 (26.5) 83 (7.5) 195 (17.6) 344 (31.1)

150 (104, 194) 107 (70.4) 33 (21.7) 8 (5.3) 31 (20.4) 56 (36.8)

145 (109, 190) 308 (72.0) 129 (30.1) 28 (6.5) 84 (19.6) 137 (32.0)

147 (98, 198) 147 (72.4) 52 (25.6) 27 (13.3) 45 (22.2) 76 (37.4)

139 (105, 180) 143 (70.4) 57 (28.1) 17 (8.4) 31 (15.3) 51 (25.1)

147 (100, 192) 80 (66.1) 22 (18.2) 3 (2.5) 4 (3.3) 24 (19.8)

.69 .50 .61 .76 0.0009 0.002

171 (15.5) 561 (50.7) 40 (3.6) 123 (11.1) 643 (58.1)

22 (14.5) 76 (50.0) 6 (4.0) 15 (9.9) 80 (52.6)

69 (16.1) 216 (50.5) 20 (4.7) 41 (9.6) 251 (58.6)

35 (17.2) 95 (46.8) 4 (2.0) 23 (11.3) 112 (55.2)

24 (11.8) 102 (50.3) 9 (4.4) 24 (11.8) 128 (63.1)

21 (17.4) 72 (59.5) 1 (0.8) 20 (16.5) 72 (59.5)

.95 .31 .21 0.08 .13

222 (20.1) 504 (45.5) 461 (41.6) 10 (0.9)

27 (17.8) 69 (45.4) 60 (39.5) 1 (0.7)

85 (19.9) 186 (43.5) 183 (42.8) 5 (1.2)

33 (16.3) 86 (42.4) 76 (37.4) 1 (0.5)

43 (21.2) 98 (48.3) 82 (40.4) 1 (0.5)

34 (28.1) 65 (53.7) 60 (49.6) 2 (1.7)

0.08 .13 036 .83

48 (4.3) 163 (14.7) 275 (24.8) 171 (15.5)

8 (5.3) 19 (12.5) 41 (27.0) 32 (21.1)

18 (4.2) 65 (15.2) 116 (27.1) 77 (18.0)

6 (3.0) 26 (12.8) 49 (24.1) 25 (12.3)

8 (3.9) 34 (16.8) 41 (20.2) 22 (10.8)

8 (6.6) 19 (15.7) 28 (23.1) 15 (12.4)

.93 .40 .11 0.002

817 (73.8)

120 (79.0)

366 (85.5)

165 (81.3)

157 (77.3)

9 (7.4)

⬍0.0001

965 (87.2) 302 (27.3) 266 (24.0) 3 (1, 8) 162 (14.6) 193 (17.4) 188 (17.0) 138 (12.5) 169 (15.3) 957 (86.5) 4 (1, 11) 1.1 ⫾ 0.5

147 (96.7) 45 (29.6) 30 (19.7) 3.5 (2, 20) 19 (12.5) 23 (15.1) 24 (15.8) 18 (11.8) 22 (14.5) 131 (86.2) 5 (2, 14) 0.9 ⫾ 0.5

417 (97.4) 147 (34.4) 89 (20.8) 2.5 (1, 4) 71 (16.6) 87 (20.3) 91 (21.3) 66 (15.4) 83 (19.4) 346 (80.8) 2 (1,6) 0.8 ⫾ 0.4

199 (98.0) 71 (35.0) 53 (26.1) 3 (1, 5) 25 (12.3) 25 (12.3) 29 (14.3) 22 (10.8) 29 (14.3) 172 (84.7) 3 (1, 7) 1.2 ⫾ 0.4

193 (95.1) 38 (18.7) 46 (22.7) 3 (1, 6) 29 (14.3) 34 (16.8) 34 (16.8) 24 (11.8) 29 (14.3) 187 (92.1) 5 (2, 11) 1.5 ⫾ 0.4

9 (7.4) 1 (0.8) 48 (39.7) 28 (12, 28) 18 (14.9) 24 (19.8) 10 (8.3) 8 (6.6) 6 (5.0) 121 (100.0) 28 (28, 28) 1.5 ⫾ 0.9

.48 0.03 ⬍0.0001 ⬍0.0001 .81 .67 0.06 .11 0.02 ⬍0.0001 ⬍0.0001 ⬍0.0001

4.27 ⫾ 0.24 9.3 ⫾ 0.4 23.0 ⫾ 8.0 31.2 ⫾ 16.3 3.2 ⫾ 0.4 75.5 ⫾ 22.6 4.2 (2.4 – 6.9) 12.7 (7.6 –19.7) 0.4 (0.3– 0.5) 33.2 (26.6 – 44.0) 3.2 ⫾ 1.0

4.32 ⫾ 0.24 9.4 ⫾ 0.4 23.5 ⫾ 7.4 28.2 ⫾ 14.4 3.2 ⫾ 0.4 74.1 ⫾ 20.9 4.5 (2.1– 6.8) 11.7 (6.5–19.7) 0.4 (0.3– 0.5) 35.2 (28.6 –39.9) 3.5 ⫾ 1.0

4.28 ⫾ 0.22 9.3 ⫾ 0.4 23.7 ⫾ 7.4 30.6 ⫾ 17.0 3.2 ⫾ 0.5 74.8 ⫾ 21.1 4.5 (2.1– 6.7) 13.4 (7.6 –19.6) 0.4 (0.3– 0.5) 32.7 (26.5– 44.5) 3.4 ⫾ 0.9

4.24 ⫾ 0.25 9.2 ⫾ 0.4 22.8 ⫾ 7.7 31.7 ⫾ 14.3 3.1 ⫾ 0.4 73.1 ⫾ 24.2 3.4 (2.1–5.8) 10.1 (5.9 –17.7) 0.4 (0.3– 0.5) 33.6 (23.0 –37.6) 3.0 ⫾ 0.9

4.23 ⫾ 0.25 9.3 ⫾ 0.4 22.4 ⫾ 8.9 32.5 ⫾ 16.6 3.1 ⫾ 0.5 76.3 ⫾ 21.1 4.4 (2.8 –7.9) 12.4 (8.1–22.6) 0.4 (0.2– 0.5) 34.8 (28.4 – 44.1) 2.7 ⫾ 0.7

4.30 ⫾ 0.26 9.5 ⫾ 0.4 21.2 ⫾ 8.6 34.4 ⫾ 17.9 3.2 ⫾ 0.4 82.0 ⫾ 28.2 4.6 (2.9 –9.1) 13.1 (7.9 –18.8) 0.4 (0.2– 0.5) 31.5 (25.2– 47.2) 3.8 ⫾ 1.2

0.02 .14 0.001 0.0004 .24 0.02 .21 .39 .91 .65 .15

* Edentulous: Periodontal assessment was completed to a random selected half-mouth. Measurement was not available if the subjects had no teeth, implants only, or canines only on that half-mouth; 1

126 men with missing value of extraction of permanent tooth due to gum disease.

2

88 men with missing value of self-reported gingivitis.

3

94 men with missing value of self-reported periodontitis.

4

21 men with missing value of self-reported referral to periodontist.

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26 men with missing value of self-reported surgery experience due to gum disease.

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85 men with missing value of self-reported experience of deep cleaning or root planning for treatment of gum disease.

915 men with missing value of urine ␣ CTX, 917 men with missing value of urine ␤ CTX, 915 men with missing value of serum CTX, 915 men with missing value of serum P1NP; 917 men with missing TRACP.

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5.4 in those with severe periodontitis. The percentage of minorities ranged from 5.6% in the mild periodontitis group to 14.8% in the severe periodontitis group. Smokers

were more likely to have more severe periodontitis or were often edentulous; the percentage of subjects with 20 or more pack years of smoking was highest in the severe peri-


doi: 10.1210/jc.2014-4180


odontitis and edentulous group (p-trend 0.008, ⬍0.0001). Subjects who did not attend college and those who do not exercise were also more likely to have periodontitis (ptrend ⬍ 0.0001, 0.009, 0.002 respectively). Between periodontitis categories, individuals who visited the dentist with the least frequency had more severe disease (77.3% with annual dental visits) (p-trend ⬍ 0.001). This was also observed in relation to individuals’ oral hygiene habits at home (ie, flossing). For instance, 18.7% of individuals with severe periodontitis reported daily flossing compared to 29.6% of individuals with healthy/gingivitis (p-trend 0.03). Overall plaque accumulation was highest in patients with severe periodontal disease (p-trend ⬍ 0.0001). In addition, individuals who previously had teeth extracted for periodontal reasons had a higher prevalence of more severe disease (p-trend ⬍ 0.0001). Levels of 25(OH)D were slightly lower (p-trend 0.001), and levels of PTH were slightly higher (p-trend 0.0004), in those with more severe periodontitis (Table 1). There were no differences in serum levels of bone resorption markers CTX and TRACPb, urinary CTX levels nor bone formation marker P1NP by periodontal severity (Table 1). Relationship Between Transitions of Periodontal Disease and Markers of Mineral Metabolism Approximately 10.3% (n ⫽ 85) who attended both dental visits had improvement in their periodontal disease, while 42.6% (n ⫽ 353) progressed and 47.2% (n ⫽ 391) remained in the same. In those who progressed, 256 worsened by 1 severity level, 84 worsened by 2 severity levels, and 13 worsened by 3⫹ levels. At baseline, there were no significant differences in calcium, 25(OH)D, PTH, Pi, ALP, nor albumin levels between categories of periodontal transition groups (Table 2). Furthermore, there were no significant differences in risk ratios for disease transition Table 2.

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in calcium, 25(OH)D, PTH, Pi, ALP, or albumin levels between men who improved vs. stayed the same or progressed vs. stayed the same (Table 3). Relationship Between Transitions of Periodontal Disease and Markers of Bone Metabolism A subset of subjects who attended both dental visits (n ⫽ 141) had serum and urinary ␣-CTX and ␤-CTX available for analyses. The nonisomerized (alpha) forms are indicative of new bone resorption and the ␤-isomerized (beta) forms are standing for old bone degradation (31). Further, serum P1NP, TRACP5b levels were available for analyses. In this subset, 7.8% (n ⫽ 11) had improvement in their periodontal disease, while 43.3% (n ⫽ 61) progressed and 49.0% (n ⫽ 69) remained in the same category of periodontal disease at both dental visit 1 and 2. Participants who had improved periodontitis had lower ␣-CTX (ug/L), ␤-CTX (ug/L), and total CTX (ng/ mL) levels at baseline compared to those who remained the same (Table 2). For example, ␣-CTX baseline levels, on average, were 2.9 ⫾ 1.2 ug/L in the improved group compared to 5.7 ⫾ 4.8 ug/L in participants who stayed in the same category of periodontal disease (P ⬍ .05). Similar associations were observed for ␤-CTX (8.9 ⫾ 3.6 ug/L vs. 17.4 ⫾ 14.4 ug/L; P ⬍ .05) and total CTX (0.27 ⫾ 0.11 ng/mL vs. 0.38 ⫾ 0.17 ng/mL; P ⬍ .05). Furthermore, bone formation marker P1NP was slightly lower at baseline in subjects that improved in their periodontal status as compared to subjects that remained stable. There were no statistically significant differences in CTX, P1NP, or TRACP5b baseline levels between men who progressed or stayed in the same periodontal group (Table 2). Urine ␣-CTX and ␤-CTX exhibited significant associations between decreased values at baseline and an increased likelihood of improving vs being stable when ad-

Relationship between Biomarkers and Periodontal Disease Progression Periodontal Progression

Biomarkers available in participants who attended both Dental Visit 1 and 2, n ⴝ 829 Calcium (mg/dL), Mean ⫾ Std 25(OH)D (ng/ml), Mean ⫾ Std Total intact PTH (pg/ml), Mean ⫾ Std Whole PTH (pg/ml), Mean ⫾ Std Pi (mg/dL), Mean ⫾ Std ALP (IU/liter), Mean ⫾ Std Albumin (g/dL), Mean ⫾ Std Biomarkers available in a subset of participants who attended both Dental Visit 1 and 2, n ⫽ 141 ␣-CTX (ug/liter), Mean ⫾ SD ␤-CTX (ug/liter), Mean ⫾ SD CTX (ng/mL), Mean ⫾ SD P1NP (ng/mL), Mean ⫾ Std TRACP5b(U/liter), Mean ⫾ Std

P-value

Improved n ⴝ 85

Same n ⴝ 391

Progression n ⴝ 353

Improved VS Same

Progression VS Same

9.31 ⫾ 0.37 24.00 ⫾ 8.03 30.25 ⫾ 16.26 19.50 ⫾ 9.94 3.21 ⫾ 0.41 74.31 ⫾ 21.83 4.24 ⫾ 0.23 Improved n ⫽ 11 2.9 ⫾ 1.2 8.9 ⫾ 3.6 0.27 ⫾ 0.11 27.8 ⫾ 13.2 3.0 ⫾ 0.9

9.31 ⫾ 0.41 23.08 ⫾ 7.81 30.39 ⫾ 17.22 20.10 ⫾ 12.02 3.14 ⫾ 0.45 73.46 ⫾ 19.71 4.28 ⫾ 0.24 Same n ⫽ 69 5.7 ⫾ 4.8 17.4 ⫾ 14.4 0.38 ⫾ 0.17 37.4 ⫾ 15.7 3.1 ⫾ 1.0

9.29 ⫾ 0.38 23.68 ⫾ 7.64 29.33 ⫾ 12.88 19.58 ⫾ 8.32 3.15 ⫾ 0.41 74.41 ⫾ 22.75 4.27 ⫾ 0.22 Progression n ⫽ 61 5.9 ⫾ 5.5 17.4 ⫾ 14.9 0.41 ⫾ 0.14 40.2 ⫾ 30.9 3.3 ⫾ 0.9

0.99 0.34 0.94 0.59 0.14 0.74 0.17 Improved VS Same 0.005 0.003 0.02 0.04 0.71

0.66 0.28 0.34 0.47 0.76 0.53 0.86 Progression VS Same 0.85 0.98 0.35 0.28 0.16

1 Because the evaluated biomarkers are highly correlated and the tests shown are not independent, statistical tests were performed separately and are presented as unadjusted p-values.

If Bonferroni corrections were performed for the number of biomarkers evaluated, P value ⬍ 0.0028 in the whole study cohort (n ⫽ 829), and P value ⬍ 0.005 in the subset cohort (n ⫽ 141), were considered to be significant, in order to achieve an overall Type 1 error rate of 0.05.


6



Table 3. Risk ratio (RR), 95% confidence intervals (95% CI) for periodontal transition in dentate men per standard deviation difference in metabolism variables Risk ratio in participants who attended both Dental Visit 1 and 2, N total ⴝ 829

Progression VS Same Risk of progression n ⴝ 353/391

Calcium (mg/dL), per SD ⫽ 0.4 increase 25(OH)D (ng/ml), per SD ⫽ 7.8 increase Total Intact PTH (pg/ml), per SD ⫽ 15.4 increase Whole PTH (pg/ml), per SD ⫽ 10.4 increase Pi (mg/dL), per SD ⫽ 0.4 increase ALP (IU/liter), per SD ⫽ 21.3 increase Albumin (g/dL), per SD ⫽ 0.2 increase Risk ratio in a subset of participants who attended both Dental Visit 1 and 2, N total ⫽ 141 ␣-CTX (ug/liter), per SD ⫽ 5.0 increase ␤-CTX (ug/liter), per SD ⫽ 14.2 increase CTX (ng/mL), per SD ⫽ 0.2 increase P1NP, per SD ⫽ 23.5 increase TRACPb, per SD ⫽ 0.9 increase

1.02 (0.94, 1.10) 1.03 (0.96, 1.11) 0.99 (0.91, 1.08) 0.99 (0.91, 1.07) 1.04 (0.96, 1.12) 1.04 (0.97, 1.11) 1.01 (0.93, 1.09) Progression VS Same Risk of progression n ⫽ 61/69 1.01 (0.87, 1.18) 1.00 (0.84, 1.20) 1.06 (0.89, 1.27) 1.04 (0.96, 1.14) 1.17 (0.96, 1.43)

Improved VS Same Risk of improvement n ⴝ 85/391 0.92 (0.77, 1.11) 1.11 (0.91, 1.33) 1.04 (0.89, 1.21) 1.01 (0.86, 1.19) 1.14 (0.96, 1.35) 1.01 (0.82, 1.26) 0.87 (0.74, 1.02) Improved VS Same Risk of improvement n ⫽ 11/69 0.29 (0.14, 0.62) 0.29 (0.14, 0.62) 0.48 (0.27, 0.86) 0.35 (0.11, 1.18) 0.78 (0.44, 1.40)

Fully adjusted Log-binomial regression models: 1. Calcium was adjusted for age, site, arthritis or gout, fracture history, obesity and plaque index; 2. 25(OH)D was adjusted for age, site, most weighed, arthritis or gout, fracture history, obesity, annual dental visit, daily flossing and plaque index; 3. Total Intact PTH, whole PTH were adjusted for age, site, most weighed, smoked 20⫹ pack per year, CVD, Arthritis or gout, Fracture history, Obesity, Annual Dental visit and plaque index; 4. Pi was adjusted for age, site, most weighed, arthritis or gout, fracture history and obesity; 5. ALP was adjusted for age, site and obesity; 6. Albumin was adjusted for age, site, smoked 20⫹ pack per year, walk activity ⬎ ⫽ 3 times/wk, HMG CoA reductase inhibitor(stain) and plaque index; 7. ␣-ctx, ␤-ctx and P1NP were adjusted for age and site; 8. CTX and TRACPb were adjusted for age, site and BMI.

justed for age and site (Table 3). In addition, serum CTX demonstrated a significant correlation between decreased baseline values and an increased likelihood of improving vs being stable when adjusted for age, site and BMI.

Discussion In this study we examined the association between measures of mineral metabolism and markers of bone metabolism and the prevalence and progression of periodontal disease in a cohort of older American men. At the initial dental visit, men with more severe periodontitis had lower levels of vitamin D and higher levels of PTH; but these measures were not associated with progression. On the other hand, markers of bone metabolism were not related to the severity of periodontitis cross-sectionally, but men who had improvement in periodontitis had lower levels of bone remodeling markers at baseline than those who remained stable or progressed. The prevalence of periodontitis in this population was similar to that previously described in other large-scale epidemiologic studies. In general we detected that men with worse periodontitis were, on average, older, weighted more, were not Caucasian and included a higher percentage of subjects with 20 or more pack years of smoking. This corresponds to what has been documented in numerous studies (32). Though tests for trend showed significance between

periodontal disease categories, some of the measures analyzed as part of patient characteristics did not increase or decrease in relation to worsening of the periodontal condition linearly. For instance, history of smoking is slightly higher in patients with mild vs moderate periodontitis, nevertheless, it does not reach the level of the severe and edentulous patient group. This suggests that there may not be a direct relationship between each patient characteristic and disease severity, even if a specific characteristic showed a significant trend for association. Instead, it may be that other influences play a roll in these trends, as periodontitis is a complex, multifactorial disease. Still, in general, demographic information that is typically associated with negative health outcomes (ie, smoking status, low education, poor oral hygiene, etc.,) were highest in participants with severe periodontitis or edentulism. After adjusting for potential confounders and covariates, decreased alpha and ␤-CTX and total CTX measurements demonstrated significant risk ratios for improvement of disease compared to men who remained stable. Serum CTX (C-telopeptide) is a measure of fragments derived from degradation of type I collagen and levels are indicative of osteoclastic activity. CTX can be found in nonisomerized (alpha) or ␤-isomerized (beta) forms depending on the age of bone. Typically, new bone is correlated to the alpha form, and old bone is related to the beta form. Despite our entire patient population being over the age of 65 (average ⫽ 72.6 ⫾ 5.5 years), both


doi: 10.1210/jc.2014-4180

markers were significantly different between periodontal transition groups. Nevertheless, further investigation must be performed to determine whether these values could be used in a younger population, and to what their role of all of the bone metabolism biomarkers might be in predicting progression of periodontitis in the general population. It may be that ␣-CTX is a better indicator in risk of progression in a younger population, while ␤-CTX is superior for an older population (31). Our data from the cross-sectional analysis show that men with more severe periodontal disease have lower 25(OH)D and higher PTH serum levels. An association between periodontal disease occurrence and lower 25(OH)D levels has been reported previously in the literature (33–35). Men and women over the age of 50 exhibited 0.39 mm and 0.26 mm more periodontal attachment loss, respectively, when serum 25(OH)D concentrations were in the lowest compared to the highest quintile of the population (34). Further, gingival inflammation as assessed by bleeding on probing was increased as well in the subjects in the lowest quintile (36). PTH (an indirect modulator of osteoclastic activity) typically varies inversely with 25(OH)D (37). Our data from dental visit 1 confirm this opposite relationship. Patients with more severe periodontal disease have lower levels of 25(OH)D and higher levels of PTH. Bone formation marker P1NP gets secreted mainly from bone into circulation. However, since it reflects the amount of newly synthesized collagen, these propeptides can also come from skin, tendons, ligaments, cornea, blood vessels, fibrocartilage, and many other tissues (38). This might explain higher levels in participants who had progression in periodontitis. Since P1NP levels were lowest in the improvement group, it also might indicate that disease improvement is associated with a decrease in bone resorption rather than an increase in bone formation. It has been shown in other studies that inflammatory disease progression results in an uncoupling between bone resorption and formation and that it favors excessive bone resorption (39). There exists a lack of uniformity within the literature with respect to the criteria used to define a periodontitis ‘case’, and in turn, a number of definitions are available for use, each with different diagnostic criteria and thresholds. In this study, Offenbacher et al’s Biofilm-Gingival Interface categories were used to define severity of periodontal disease. With the age of the population, this seemed to provide the most accurate representation of active periodontal disease, as it included a clinical indicator of inflammation. Although this is not the most commonly used definition of periodontitis, we felt that it provided a better model for our older population, who may have had inci-


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dental bone loss throughout life unrelated to periodontal disease, or may have had a previous case of periodontal disease that was treated and arrested. Further investigation should be performed to help validate our results using other case definitions, including the European Workshop in Periodontology, the Centers for Disease Control and Prevention (CDC), and American Academy of Periodontology (AAP), in a more diverse and larger patient population. The study had several strengths and limitations. We were able to study a large number of community-dwelling men who were extensively characterized for overall health status and potential confounders, nevertheless, there might have been additional factors that were unidentified and not accounted for in our study cohort. Seventy five percent of the participants returned for longitudinal assessments of periodontal status. Though, not all were available for follow-up exams because many were deceased or had missing data (22, 23) (20). Cross-sectional analysis revealed significantly lower 25(OH)D levels and higher levels of PTH in those with more severe periodontitis at the time of their first dental visit. However, 25(OH)D levels across all periodontitis groups are considered to be low. Studies that evaluated thresholds for serum 25(OH)D concentrations in relation to BMD, lower-extremity function, dental health, fractures, and colorectal cancer describe that advantageous serum concentrations of 25(OH)D begin at 75 nmol/L (30 ng/mL) (40). To examine periodontal progression, we analyzed calcium, vitamin D, PTH, alkaline phosphatase separately from alpha, beta and total CTX, which were only available in a smaller subset of participants. Moreover, CTX and some of the other biomarkers were collected at the baseline MrOS visit before the first dental examination. To what extent those levels were different during the period of the dental examinations is unknown. Ideally, additional blood/urine samples should have been taken at the dental follow-up visit, to help correlate mineral and bone metabolism biomarkers to progression of periodontal disease. Further, no information was available about whether subjects received periodontal treatment after dental visit 1. However, we assume that disease progression was not due to an intervention since most the subjects either remained within their disease category or progressed to a more severe one. All study subjects underwent periodontal evaluation performed by a total of six examiners that were calibrated to ensure evaluation consistency, however, there might have been still slight differences between the examiners. Despite all these limitations, significant differences were still detectable and future studies should determine further whether they are clinically significant as well.


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Another potential drawback was that random halfmouth evaluations were used for periodontal examinations. This study design typically under-reports disease prevalence (41, 42). However, 41% of our participants had either moderate or severe forms of periodontitis using our disease definition, which is similar to 47% of subjects over 30 years old had periodontitis in the most recent NHANES study that utilized a full mouth periodontal examination. This supports that our choice of periodontal disease definition and examination method corresponded to an accurate representation of periodontal disease prevalence on a national scale (2). Due to sample size, we combined participants who had periodontal disease transition into either the improved or progressed group, regardless of how many periodontal disease categories were changed. This might have under or overestimated associations since groups included subjects that transitioned between 1 and 3 categories. Improvement in periodontitis, associated with lower levels of bone remodeling markers at baseline, could indicate a less pronounced inflammatory response. However, our study design only allows for identifying associations and we, therefore, can only speculate about underlying biological mechanism. Additional studies should be performed to expand on our findings and to help correlate bone metabolism biomarkers to periodontal disease occurrence and determine when periodontal disease might progress further. This information could be used to establish better clinical guidelines for the ideal treatment(s) among patients diagnosed with various forms of periodontal disease, and to help determine the ideal time to provide this treatment.


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Acknowledgments

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Address all correspondence and requests for reprints to: Ulrike Schulze-Späte, DDS, PhD, Columbia University, College of Dental Medicine, 630 W 168th St, PH7C-200B, NY, NY 10 032, Tel: 212–305-3787, Fax: 212–305-9313, [email protected]. This work was supported by Funding: MrOS: U01 AR45580, U01 AR45614, U01 AR45632, U01 AR45657, U01 AR45654 U01 AR 45 583, U01 AG18197, U01 AG027810 and UL1 TR000128, R01 DE14386, R01 AR52862; US-S: K08DE018968; T-TD: KL2 TR000081, K23AG040168; PCS: P30 HL101272, R01 HL114813, UL1 RR024156. Conflict of interest: The authors have no conflict of interest to declare.

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