Periodontal Status and A1C Change: Longitudinal results from the Study of Health in Pomerania (SHIP)

SHIP is a population-based prospective cohort in East Germany involving the cities of Greifswald, Stralsund, and Anklam and 29 surrounding villages; the 1995 population in this catchment area was 212,157. From each of these cities, German subjects with main residency in the area were randomly drawn, proportional to each community population and stratified by age and sex. A representative sample of 7,008 adults aged 20–79 years were invited to participate. This two-stage cluster sampling method was adopted from the Multinational Monitoring of Trends and Determinants in Cardiovascular Disease (MONICA) Project, Augsburg, Germany, and yielded 12 5-year age strata (20–81 years) for both sexes. After removal of 746 individuals (126 died, 615 moved away, and 5 had severe medical problems), 6,262 inhabitants were invited. The final sample included 4,310 individuals, yielding a 68.8% participation rate (3). There were 130 passive nonrespondents due to migration and 231 deceased subjects between the two examinations. Of the remaining 3,949 eligible individuals, 649 were active nonrespondents and 3,300 subjects were reexamined, resulting in an 83.6% follow-up rate (4). Participants were excluded from the current analysis if they had 1) prevalent baseline diabetes (n = 219), defined as either self-report, physician-diagnosed, or A1C ≥6.5%; 2) missing A1C (n = 74); 3) missing periodontal data (n = 136) (5); or 4) missing confounder data (n = 78). The current analysis included 2,793 participants.

The study was approved by the institutional review board of the University of Greifswald. All participants provided written informed consent.

Calibrated licensed dentists performed the oral examinations, including a full-mouth tooth count. In SHIP-0 the periodontal probe PCP 11 (Hu-Friedy, Chicago, IL) and in SHIP-1 the periodontal probe PCP 2 (Hu-Friedy) were used to assess periodontal probing depth (PPD) and clinical attachment loss for examined teeth. Measurements were taken at four sites per tooth (mesiobuccal, midbuccal, distobuccal, and midlingual), using the half-mouth method on the right or left side in alternate subjects. On the same teeth, coronal caries were scored visually and with the periodontal probe. Caries and missing teeth were registered by surface and the decayed/filled teeth (DFT) index was calculated according to World Health Organization criteria. Yearly calibration exercises (6) yielded an intraclass correlation of 0.82–0.91 per examiner and an interrater correlation of 0.84 relative to attachment loss.

Nonfasting blood samples were analyzed at one laboratory that participated in the official Germany INSTAND round-robin tests for quality assurance in analytical laboratories, at least semiannually, and internal quality controls were measured daily. At both visits, A1C was measured by cation-exchange chromatography (high-performance liquid chromatography) with spectrophotometric detection (Diamat analyzer; BioRad, Munich, Germany) and a coefficient of variation of 1.5%.

Participants were queried by computer-aided face-to-face interviews on sociodemographic characteristics, medical histories, and medication use, i.e., antidiabetes drugs (Anatomical Therapeutic Chemical [ATC] code A10) and corticoid drugs (ATC codes A01AC, A07EA, D07, D10AA, G01B, H02, M01BA, and R01AD). A self-administered questionnaire assessed region (rural versus urban) and education level (<10, 10, or >10 years of schooling). Leisure-time physical activity was reported as >2, 1–2, or <1 h/week or no activity and was then converted into METs (7). Smoking behavior was assessed with a validated questionnaire and categorized as never/occasional, former, or current smoker (7). Family history of diabetes was determined by asking whether parents or siblings had diabetes.

Height and weight were determined using calibrated scales. Waist circumference was measured at the narrowest place between the last rib and the highest part of the abdomen. Hip circumference was determined as the greatest circumference between the highest point of the iliac crest and the crotch.

Blood pressure was measured three times using a calibrated semiautomatic sphygmomanometer (HEM-705CP; Omron, Tokyo, Japan). The average of the last two measurements was used for analysis.

High-sensitivity C-reactive protein (hs-CRP) was determined in serum by particle-enhanced immunonephelometry (hs-CRP kit; Dade Behring, Deerfield, IL) with a test sensitivity of 0.2 mg/l. Triglycerides were determined enzymatically using reagents from Roche Diagnostics (Hitachi 717; Roche Diagnostics, Mannheim, Germany). Plasma fibrinogen concentrations were assayed according to Clauss using an Electra 1600 analyzer (Instrumentation Laboratory, Barcelona, Spain). White blood cell (WBC) count was measured by the impedance measurement method using the Coulter principle (Coulter MaxM; Coulter Electronics, Miami, FL).

Analyses were performed using SAS for Windows (version 9.2; SAS Institute, Cary, NC). PROC SURVEYREG was used to generate variance estimates appropriate for the clustered design.

Periodontal categorizations were created to provide a meaningful contrast in exposure while maintaining reasonable sample size balance across categories. The primary exposure variable was the percentage of periodontal sites with attachment loss (AL) ≥5 mm at baseline (%AL ≥5 mm), which was determined a priori (5). Dentate participants were categorized into four groups based on %AL ≥5 mm as follows: 1) 1,122 participants without any 5-mm attachment loss who were considered periodontally “healthy” (ALI); 2) 488 participants with %AL ≥5 mm, ranging from 1 to 8% (ALII); 3) 463 participants with %AL ≥5 mm, ranging from 9 to 33% (ALIII); 4) 479 participants with %AL ≥5 mm, ranging from 34 to 100% (ALIV); and 5) 241 edentulous participants formed a fifth category (7,8).

Three alternate periodontal exposure definitions were also considered. First, baseline mean PPD was categorized into four groups as follows: 1) 1.04 ≤ mean PPD ≤ 2.00 mm (PPDI); 2) 2.01 ≤ mean PPD ≤2.34 mm (PPDII); 3) 2.35 ≤ mean PPD ≤ 2.75 mm (PPDIII); and 4) 2.76 ≤ mean PPD ≤ 7.25 mm (PPDIV). Second, dentate participants were categorized into tooth count tertiles (26–28, 21–25, and 1–20 teeth); edentulous participants formed a fourth category. Finally, dentate participants with longitudinal periodontal data were categorized into four groups according to 5-year change (follow-up − baseline) in the percentage of sites with attachment loss ≥5 mm (Δ%AL ≥5 mm) as follows: 1) ΔALI: 565 participants with improving periodontal health (Δ%AL ≥5 mm < 0); 2) ΔALII: 887 participants without any change in periodontal status (Δ%AL ≥5 mm = 0); 3) ΔALIII: 423 participants with Δ%AL ≥5 mm, ranging from 1 to 8%; and 4) ΔALIV: 437 participants with Δ%AL ≥5 mm, ranging from 9 to 100%. To address the specificity of any observed associations to periodontal infection, we also assessed the association between percentage of decayed/filled teeth (%DFT) and A1C change.

Multivariable linear regression models were used to examine the association between periodontal status (baseline and/or change) and absolute 5-year A1C change (ΔA1C) (calculated as year 5 %A1C − baseline %A1C). Subgroup analyses were performed, removing extreme A1C changes to reduce the potential for biased statistical inferences due to possible ΔA1C normality violations. To reduce the potential for age-related confounding, we also performed stratified analyses in three age strata defined by 20-year age ranges (20–39, 40–59, and ≥60 years). Interaction models assessed the statistical evidence of effect modification between periodontal status and systemic inflammatory variables.

Participants were Caucasian, aged 48 ± 15 years (mean ± SD), and 53% female. Baseline periodontal disease (%AL ≥5 mm) was highly correlated with age (r = 0.50, P < 0.0001) and was associated with several sociodemographic, behavior/lifestyle, and medical variables (Table 1). Among dentate participants, the mean number of teeth (excluding third molars) was 21 ± 6 after age adjustment. On average, 17% of sites per mouth had attachment loss ≥5 mm, which was higher among men (18%) than among women (13%) (P < 0.0001).

Among participants with longitudinal periodontal measures (n = 2,312), mean absolute 5-year change in %AL ≥5 mm was 2.3 ± 14% (P < 0.0001) with a range of −75 to 70%. Five-year change in mean PPD was −0.08 ± 0.50 mm (P < 0.0001). Baseline %AL ≥ 5 mm was weakly and inversely correlated with change in the same variable (r = −0.05, P = 0.008), whereas baseline mean PPD was positively correlated with 5-year change in %AL ≥ 5 mm (r = 0.08, P = 0.0001). Men, current smokers, and younger participants were more likely to experience a decline in periodontal health during follow-up. ΔA1C was 0.05 ± 0.59% (range −4.2 to 6.5%); 98% of A1C changes were ≥−1.0% and ≤1.0%. After multivariable adjustment, the following nonperiodontal variables were associated with ΔA1C (P < 0.05): waist-to-hip ratio (WHR), fibrinogen, hs-CRP, and former smoking (Table 2).

Participants in the fourth versus first %AL ≥5 mm category experienced an ∼0.08% greater 5-year increase in A1C (P = 0.02) (Table 3, model 4). ΔA1C was accelerated by ∼0.10% among edentulous participants, relative to periodontally healthy participants (P = 0.05). When ΔA1C was compared between participants in the fourth versus first attachment loss category among those aged 20–39 years, the absolute difference was 0.08% (P = 0.04). Findings were consistent among participants aged 40–59 years (0.10%, P = 0.005) and those aged ≥60 years (0.07%, P = 0.27). There was evidence for an interaction between %AL ≥5 mm and hs-CRP (P_interaction = 0.01), in which the increase in ΔA1C with higher %AL ≥5 mm was stronger among participants with hs-CRP ≥1.0 mg/l than in those with hs-CRP <1.0 mg/l (Fig. 1). Trends were consistent when fibrinogen was considered as the effect modifier (P_interaction = 0.24) but less clear for WBC count (P_interaction = 0.66) as shown in Fig. 1.

Respective ΔA1C changes in the first to fourth quartiles of mean PPD were −0.001, 0.050, 0.033, and 0.107% (P_trend < 0.01 after multivariable adjustment as in Table 3, model 4). Mean A1C changes across four categories of baseline tooth count were 0.09% (26–28 teeth), 0.04% (21–25 teeth), 0.02% (1–20 teeth), and 0.07% (edentulous) (P_trend = 0.84).

Five-year change in %AL ≥5 mm (ΔAL) was associated with ΔA1C (supplementary Figure A3, available in an online appendix at http://care.diabetesjournals.org/cgi/content/full/dc09-1778/DC1). When individuals who were periodontally healthy at both baseline and follow-up were compared with individuals who had poor baseline periodontal health combined with periodontal deterioration during follow-up (ALIV and ΔALIV), the respective mean A1C changes in the two groups were 0.005 vs. 0.143% (P = 0.003). Five-year change in mean PPD was not associated with ΔA1C; ΔA1C values across categories of mean PPD change were 0.03, 0.06, 0.04, and 0.01%, respectively (P_trend = 0.46).

The %DFT was not correlated with %AL ≥ 5mm (r = −0.02, P = 0.36). After multivariable adjustment, A1C progression across quartiles of %DFT was 0.06, 0.02, 0.05, and 0.03% (P_trend = 0.70).

This is the first study to report that a chronic infection predicts progression of A1C among diabetes-free individuals. We report a positive association between clinical periodontal status and 5-year A1C change among German participants in SHIP. When compared with participants considered periodontally healthy, those participants with elevated levels of baseline periodontal disease experienced an approximate 0.08% greater increase in ΔA1C during 5 years of follow-up. Deteriorating periodontal health during follow-up was also associated with greater increases in ΔA1C. There was evidence of an additive effect of baseline and follow-up periodontal status such that participants with severe baseline periodontal disease coupled with large declines in longitudinal periodontal health experienced an ∼0.13% greater increase in 5-year ΔA1C relative to participants who were periodontal healthy at both baseline and follow-up. In addition, the association between periodontal status and ΔA1C was strongest among participants with elevated levels of hs-CRP.

These findings remained after comprehensive adjustment for confounders, which minimized the potential for spurious associations arising from behavioral/lifestyle variations across periodontal disease levels. The results from multivariable models suggest that age was a strong confounder, whereas other covariates had only minimal impact on the results. Additional analyses within age strata were consistent with results from regression models in the full sample and further minimized the potential for age-related confounding.

These data advance previous findings from NHANES I, in which elevated levels of baseline periodontal disease were associated with an approximate twofold increase in incident diabetes risk (1). Specifically, our data demonstrate a temporal trend in which periodontal disease predicts an accelerated longitudinal change in A1C after control for baseline A1C levels and among participants deemed to be diabetes free at baseline using a standardized diabetes definition. This result greatly minimizes the possibility that elevated blood glucose levels were present before and therefore contributed to periodontal disease observed at baseline.

The hypothesis that periodontal infections can influence diabetogenesis is biologically plausible, and the concept of inflammation-mediated insulin resistance is intriguing (9). Animal models have demonstrated that virulence factors secreted by periodontal pathogens can stimulate production of inflammatory cytokines such as tumor necrosis factor-α (10). Human studies from this population (11) and others (12) support the notion that periodontal infections can induce a state of low-grade chronic inflammation, and periodontal therapy has been shown to decrease systemic inflammation (13). Accordingly, tumor necrosis factor-α can induce a state of insulin resistance (14), and systemic inflammation has also emerged as a novel predictor of incident diabetes (15,16).

The fact that inflammatory adjustments did not remove the association between periodontal status and ΔA1C suggests that these variables might be sufficient but not necessary mediators in the hypothesized causal path between infection and diabetogenesis (17). Alternatively, the observation of a potential interaction between periodontal status and hs-CRP supports the notion that a synergistic combination of both oral infection and systemic immune response, as opposed to a localized oral infection alone, might be necessary for relevant metabolic abnormalities to develop. These subgroup findings require confirmation in other populations.

The observed ∼0.08% difference in 5-year A1C progression between high and low levels of periodontal disease is potentially clinically relevant. This difference was similar to that observed for a 2-SD increase in either WHR or age in this population. Moreover, the difference in ΔA1C is on the same order of magnitude as that reported previously for intervention studies from the Diabetes Prevention Program Research Group (DPPRG) (18). The DPPRG observed an ∼0.15% reduction in ΔA1C over 4 years among diabetes-free participants receiving either metformin or lifestyle modification relative to participants receiving placebo; these A1C findings translated into either a 31% (metformin) or 58% (lifestyle modification) reduction in incident diabetes.

We have used clinical attachment loss as an indirect surrogate of cumulative exposure to a specific array of chronic infections. Future studies with periodontal bacterial measures and/or a broader range of chronic infection assessment can clarify the nature of associations between infection and diabetogenesis; these approaches have been illuminating for cardiovascular disease (19,20). Data on the association between periodontal status and fasting blood glucose levels can further inform our current findings, which were limited to A1C.

The finding that caries were not associated with ΔA1C enhances the specificity of the oral infection hypothesis. Although caries also have a bacterial etiology, they are not typically known to influence systemic inflammation (21).

The observation that edentulous participants demonstrated mean A1C increases similar to those of dentate participants with periodontal disease might appear counterintuitive to the concept of systemic risk induced by bacterial biofilms on tooth surfaces. However, these findings are consistent with those of others who have shown edentulous participants to be at elevated risk for atherosclerosis (7,8), hypertension (22), and diabetes (1). Desvarieux et al. (8) have suggested that once infection-induced systemic damage has occurred, it is not entirely reversible. Alternatively, systemic inflammation related to oral infections might not abate after resolution of clinical disease. It is also possible that repeated bacteremic events occurring before extraction of periodontally affected teeth (23) might allow oral pathogens to colonize systemically. Indeed, viable oral pathogens have been identified in atheromatous plaques (24). Therefore, once bacterial species have colonized the host, tooth extraction, by itself, should not affect their future viability and/or pathogenicity. Although these concepts are speculative and cannot be tested in the current study, they are biologically plausible and consistent with the published literature.

We have found elevated levels of periodontal disease, as well as progression of periodontal disease, to be predictors of A1C progression in a randomly sampled, population-based study of Germans over 5 years of follow-up. These data support the hypothesis that chronic infections might contribute to diabetogenesis. We await longitudinal results from year 10 in SHIP, which will allow us to determine whether these subclinical A1C changes translate into increased risk for incident diabetes. Focused intervention studies will be necessary to make definitive causal inference regarding the potential role of anti-infective therapy in reducing the risk of diabetes. If confirmed, this relationship would be of substantial public health importance, given the population prevalence of periodontal infections (25) and the availability of effective periodontal disease therapies.

This research was supported by SHIP, which is part of the Community Medicine Net (http://www.medizin.uni-greifswald.de/cm) of the University of Greifswald and is funded by the German Federal Ministry of Education and Research (Grants 01ZZ96030 and 01ZZ0701); the Ministry for Education, Research, and Cultural Affairs; and the Ministry for Social Affairs of the Federal State of Mecklenburg–West Pomerania. GABA, Switzerland, provided unlimited education funds to support B.H. This work was also supported by the National Institutes of Health (Grants K99-DE-018739 to R.T.D. and R01-DE-13094 to M.D. and R.T.D.). R.T.D. was formerly a fellow on T32-HL-07779. A Chair of Excellence award from the French Agency for Research and the Institut National de la Santé et de la Recherche Médicale to M.D. (R05115DD) and a Mayo Chair Endowment, School of Public Health, University of Minnesota to D.R.J. also provided support.

No potential conflicts of interest relevant to this article were reported.