Periodontal infection, impairedfasting glucose and impairedglucose tolerance: results fromthe Continuous National Healthand Nutrition ExaminationSurvey 2009–2010Arora N, Papapanou PN, Rosenbaum M, Jacobs DR Jr, Desvarieux M, DemmerRT. Periodontal infection, impaired fasting glucose and impaired glucose tolerance:results from The Continuous National Health and Nutrition Examination Survey2009–2010. J Clin Periodontol 2014; 41: 643–652. doi: 10.1111/jcpe.12258

AbstractAim: We investigated the relationship between periodontal disease, a clinicalmanifestation of periodontal infection, and pre-diabetes.Methods: The National Health and Nutrition Examination Survey, 2009–2010enrolled 1165 diabetes-free adults (51% female) aged 30–80 years(mean � SD=50 � 14) who received a full-mouth periodontal examination and anoral glucose tolerance test. Participants were classified as having none/mild, moder-ate or severe periodontitis and also according to mean probing depth ≥2.19 mm orattachment loss ≥1.78 mm, (respective 75th percentiles). Pre-diabetes was definedaccording to ADA criteria as either: (i) impaired fasting glucose (IFG) or impairedglucose tolerance (IGT). In multivariable logistic regression models, the odds ofIFG and IGT were regressed on levels of periodontitis category.Results: The odds ratios and 95% confidence intervals for having IGT among partici-pants with moderate or severe periodontitis, relative to participants with none/mild peri-odontitis were 1.07 [0.50, 2.25] and 1.93 [1.18, 3.17], p = 0.02. The ORs for having IFGwere 1.14 [0.74, 1.77] and 1.12 [0.58, 2.18], p = 0.84. PD ≥75th percentile was related toa 105% increase in the odds of IGT: OR [95% CI] = 2.05 [1.24, 3.39], p = 0.005.Conclusions: Periodontal infection was positively associated with prevalent impairedglucose tolerance in a cross-sectional study among a nationally representative sample.

Nidhi Arora1,†, Panos N. Papapanou2,Michael Rosenbaum3, David R.

Jacobs Jr4,5, Mo€ıse Desvarieux1,6 andRyan T. Demmer1,†

1Department of Epidemiology, Mailman

School of Public Health, Columbia University,

New York, NY, USA; 2Division of

Periodontics, Section of Oral and Diagnostic

Sciences, College of Dental Medicine,

Columbia University, New York, NY, USA;3Division of Molecular Genetics, Departments

of Pediatrics and Medicine, Columbia

University, New York, NY, USA; 4Division of

Epidemiology and Community Health, School

of Public Health, University of Minnesota,

Minneapolis, MN, USA; 5Department of

Nutrition, University of Oslo, Oslo, Norway;6Centre de recherche Epid�emiologies et

Biostatistique, INSERM U1153 Equipe:

M�ethodes en �evaluation th�erapeutique des

maladies chroniques, Paris, France

†Contributed equally to this manuscript.

Key words: glucose metabolism; infection;

periodontal disease; periodontitis

Accepted for publication 1 April 2014

Conflict of interest and source of funding statementThe authors declare that they have no conflict of interests. This research was supported by NIH grants R00 DE-018739 and R21DE-022422 to Dr. Demmer. Additional funding support was provided by a Pilot & Feasibility Award to Dr. Demmer from theDiabetes and Endocrinology Research Center, College of Physicians and Surgeons, Columbia University (DK-63608); Dr. Des-varieux also receives support from R01 DE-13094 and a Chair in Chronic Disease, �Ecole des Hautes �Etudes en Sant�e Publique,France.

© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd 643

J Clin Periodontol 2014; 41: 643–652 doi: 10.1111/jcpe.12258

There is evidence that chronic infec-tions might increase the risk for dia-betogenesis. For example, clinicalindicators of periodontal infectionwere reported to be associated witha twofold increase in the risk of dia-betes development during 20 yearsof prospective follow-up (Demmeret al. 2008) and more recently, sero-logical evidence of H. pylori infec-tion was found to be associated witha 2.7-fold increase in risk for inci-dent diabetes (Jeon et al. 2012).

Studies have also examined therelationship between infection andearly markers of impaired glucosemetabolism to advance our under-standing of the natural history ofassociations. Most research on thistopic arises from designs using peri-odontal infection models to studythe general hypothesis of microbial-induced diabetes risk. Periodontalinfection models are useful becauseminimally invasive clinical measuresare manifestations of adverse subgin-gival microbial exposures (Demmeret al. 2010b). Accordingly, clinicalperiodontal measures have beenreported to be associated withincreased risk for accelerated 5-yearprogression of haemoglobin A1c(A1c; Demmer et al. 2010a) as wellas elevated levels of insulin and insu-lin resistance (Demmer et al. 2012b).

Periodontal infections have alsobeen linked to increased risk for pre-diabetes defined as either impairedfasting glucose (Zadik et al. 2010,Choi et al. 2011) or impaired glucosetolerance (Saito et al. 2004). The ini-tial reports linking periodontal infec-tions to pre-diabetes have providedhelpful insights but some importantlimitations exist such as: (i) lack offull-mouth clinical periodontalexams (Saito et al. 2004, Zadik et al.2010, Choi et al. 2011) that canmore accurately reflect the extentand severity of infection; (ii) exclu-sion of women (Saito et al. 2004,Zadik et al. 2010); and/or (iii) theuse of old criteria for defining pre-diabetes (Saito et al. 2004). More-over, no previous study has providedresults comparing the relativestrength of association betweeninfection and both impaired fastingglucose (IFG) and impaired glucosetolerance (IGT) in separate analysesfrom the same study population.Comparative studies of these out-comes would be meaningful as IFG

and IGT are believed to each por-tend different levels of risk for futurediabetes and cardiovascular disease.IFG and IGT might also representa different underlying pathophysi-ology and diabetes risk phenotype(Blake et al. 2004, Nathan et al.2007).

We studied the association betweenclinical measures of periodontal infec-tion and pre-diabetes. Periodontalinfections were assessed using full-mouth periodontal examinations andpre-diabetes was defined using bothfasting glucose and 2-h post-challengeglucose levels. Participants were adultmen and women enrolled in theContinuous NHANES 2009–2010, arandomly sampled, population-basedstudy of non-institutionalized US resi-dents.

Methods

The Continuous National Health andNutrition Examination Survey(NHANES) 2009–2010 is a nationallyrepresentative, stratified, multistageprobability sample of the civiliannon-institutionalized US population.The current analysis includes menand women aged 30–80 years of agewho received a periodontal examina-tion and an oral glucose tolerance test(OGTT). Individuals were excluded ifthey had diabetes as determined via:(i) Self-reported, diabetes diagnosis;or (ii) HbA1c levels ≥6.5% or (iii)fasting glucose ≥126 mg/dl. Individu-als were also excluded if they: (i) werenot fasting for ≥9 h at the time of thefirst OGTT blood collection; or (ii)were missing important covariatedata collection. The final sample sizefor the current analysis is n = 1165.

Periodontal examination

Periodontal probing depth (PD) andclinical attachment loss (AL) mea-surements were made by trained,registered hygienists in the full-mouth(excluding third molars) at six sitesper tooth (Eke et al. 2012). Peri-odontal examiners received intensetraining followed by periodic moni-toring and recalibration against areference examiner. The referenceexaminer made three visits to eachdental examination team per yearto observe field operations and toreplicate 20–25 oral health examina-tions.

Oral glucose tolerance test

Plasma was collected after a mini-mum 9-h fast. Immediately after theinitial venipuncture, participantswere then asked to drink a cali-brated 75 gram dose of glucose solu-tion (TrutolTM). A second venousplasma collection was performed 2 hafter the glucose challenge (http://www.cdc.gov/nchs/data/nhanes/nhanes_09_10/OGTT.pdf). Plasma specimenswere processed, stored and shipped toFairview Medical Center Laboratoryat the University of Minnesota, Min-neapolis, Minnesota for analysis. Glu-cose concentration was determined bya hexokinase method (Demmer et al.2012b).

Pre-diabetes definitions

Impaired fasting glucose (IFG) andImpaired glucose tolerance (IGT) weredefined in accordance with the Ameri-can Diabetes Association (ADA) crite-ria (2012) as follows: IFG = fastingplasma glucose ≥100 mg/dl and<126 mg/dl; IGT = 2-h post-challengeglucose values ≥140 mg/dl and<200 mg/dl.

Risk factor assessment

Comprehensive questionnaires toassess risk factors relevant to bothperiodontal disease and pre-diabeteswere administered as previouslydescribed (Demmer et al. 2012b).The demographic variables age, race/ethnicity, sex, education (<highschool, completed high school, somecollege, college graduate) and pov-erty-income-ratio (calculated bydividing family income by the pov-erty guidelines, specific to family size,as well as the appropriate year andstate according to Department ofHealth and Human Services guide-lines) were collected. Behavioural riskfactor assessments included physicalactivity level (none, moderate andvigorous based on occupational andrecreational related physical activityperformed in a typical week), ciga-rette smoking, alcohol consumptionand caloric intake. Height, weightand blood pressure measures weremade by trained research assistantsaccording to standardized protocols.BMI was calculated as weight (kilo-grams)/height (meters)2 and partici-pants were categorized asunderweight/normal weight (<25

© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

644 Arora et al.

kg/m2), overweight (25–29.9 kg/m2)or obese (≥30 kg/m2). Triglycerides,total cholesterol and HDL-choles-terol, C-reactive protein (CRP) andwhite blood cell count (WBC) weremeasured from fasting blood sam-ples.

Statistical analysis

SAS Survey procedures (version 9.3)were used for data analysis. Analysisof variance and categorical analysismethods were used to obtaindescriptive statistics according toboth periodontal and pre-diabetesstatus for important demographic,behavioural, dental and cardiometa-bolic variables. p-Values presentedarise from F-statistics or chi-squarestatistics. The odds of pre-diabetesdefined as any-IFG (irrespective ofIGT) or any-IGT (irrespective ofIFG) were regressed across catego-ries of periodontal disease in multi-variable logistic regression models.Periodontal infection was definedusing three separate approachesbased on measures of PD and AL.First, participants were categorizedas having non/mild, moderate orsevere periodontitis according to theCenters for Disease Control and Pre-vention/American Academy of Peri-odontology (CDC/AAP) definition(Page & Eke 2007). A second defini-tion was created by categorizing par-ticipants as being either ≥75thpercentile or <75th percentile formean PD at inter-proximal sites(2.19 mm). The third periodontaldefinition was based on being either≥75th percentile or <75th percentilefor mean AL at inter-proximal sites(1.78 mm). For the latter two defini-tions, supplemental analyses wereconducted using cut-points derivedfrom all periodotnal sites (i.e. includ-ing mid-facial and mid-buccal sites).For regressions modelling, the CDC/AAP definition of periodontitis asthe primary exposure, we addition-ally report the p-value for lineartrend derived from the ordinal three-level periodontitis variable (none/mild, moderate or severe).

In addition, multivariable general-ized logistic regression models exam-ined the association between CDC/AAP defined periodontitis and apolytomous pre-diabetes outcomewhich categorized participants ashaving either: (i) no pre-diabetes; (ii)

isolated-IFG; (iii) isolated-IGT or (iv)combined IFG & IGT. NHANESsurvey weights, cluster and stratavariables were included in the analysisto account for the complex surveydesign as previously described (Dem-mer et al. 2012b). In addition to oddsratios and 95% confidence intervals,we report the p-value for any differ-ence in the odds of the polytomouspre-diabetes outcome according tolevel of periodontitis derived fromWald chi-square tests.

A series of multivariable modelswere developed to better assess theinfluence of confounding. On thebasis of data availability, we focus onconfounding by sociodemographicindicators (age, sex, race/ethnicityand educational level), health behav-iours (smoking status, caloric intake,alcohol consumption and physicalactivity) and adiposity (body massindex). We also consider variablesthat might mediate the associationbetween periodontal infection andpre-diabetes (blood pressure, choles-terol profile, WBC and CRP). Modelswere additionally informed by twoDirected Acyclic Graphs (DAGs)constructed using, Dagitty (Textoret al. 2011). Figure 1A assumes morecomplex causal structures in whichpotential sociodemographic con-founders relate to periodontal infec-tion and pre-diabetes throughmultiple mechanisms (e.g. confound-ing can act through health behavioursand adiposity but also through othermechanisms not represented in ourdata); this causal structure necessi-tates adjustment for all sociodemo-graphic variables. Alternatively, theDAG in Figure 1B assumes that allconfounding effects act througheither health behaviours or adiposity;this causal structure does not requireadjustment for sociodemographicvariables if health behaviours andadiposity adjustments are made.

The following multivariable mod-els were considered. Model 2 adjustedfor only health behaviours (smokingstatus, caloric intake, alcohol con-sumption and physical activity) and amarker of adiposity (body massindex) based on assumptions inherentin Figure 1B. Model 3 adjusted foronly the sociodemographic variablesage, sex, race/ethnicity and educa-tional level. Model 4 adjusted forhealth behaviours and sociodemo-graphic variables. Model 5 adjusted

for sociodemographics, health behav-iours and adiposity. Finally, model 6expanded model 5 by additionallyadjusting for variables that could beconsidered as either a confounder ormediator of associations betweenperiodontal infection and pre-diabe-tes depending on the causal structurehypothesized; this model includedadjustment for systolic blood pres-sure, total cholesterol-to-HDL ratio,WBC and CRP as previous reportssuggest periodontal infection as apossible risk factor for these out-comes (D’Aiuto et al. 2006, Des-varieux et al. 2010, Demmer et al.2013). Unless otherwise stated, ORsreported in the main text were derivedfrom model 5 as we believe this pro-vides the best combination of parsi-mony and validity.

To provide additional informa-tion regarding the magnitude anddirection of confounding by theaforementioned variables consideredindividually, we also constructedtables summarizing the differencebetween parameter estimates fromlogistic models with more versusless covariable adjustment using a“change-in-estimate” approach(Mickey & Greenland 1989); change-in-estimate was defined asfollows: [(LN (more adjusted OR) �LN (less adjusted OR))/LN (adjustedOR)] 9 100, yielding the percentchange in the OR resulting fromlack of adjustment. The approachuses 13 model selection iterations(the number of possible covariables).Iteration 1 started with the unad-justed parameter estimate for peri-odontal infection and then ran 13separate regressions considering theinfluence of all potential confound-ers on the unadjusted parameterestimate. The confounder thatproduced the greatest change-in-esti-mate was added to the regression toform an “intermediate” model andanother modelling iteration wasrepeated; each modelling iterationincreases the number of indepen-dent variables in the intermediatemodel by 1 and decreases the numberof remaining confounders by 1. Fourvariables hypothesized as possiblemediators were assessed in thelast four interactions. Results fromour change-in-estimate analysis didnot suggest that the aforemen-tioned models 1–6 would be inappro-priate.

© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

Periodontal infection and pre-diabetes 645

Results

General characteristics

Participants had a mean � SD age of50 � 14 years and 51% were women.

Hispanics, Whites and Blacks repre-sented 31%, 49% and 15% of thesample (5% reported other race/eth-nicity). Mean PD was (mean � SD)1.63 � 0.58 mm and mean AL was1.59 � 1.05 mm. The prevalence of

moderate and severe periodontitiswas 33% and 10%, respectively, andthe remaining participants had no/mild periodontitis. Periodontitis wasassociated with adverse levels of sev-eral risk factors for cardiometabolicdisease such as age, education smok-ing status, HDL-cholesterol and sys-tolic blood pressure (Table S1). Bodymass index and obesity prevalencedid not vary according to levels ofperiodontitis (Table S1). Table S1also summarizes periodontal clinicalcharacteristics across periodontitisstatus to better inform the severity ofdisease in this population-based sam-ple. For example, mean PD and ALamong individuals with severe peri-odontitis were 2.5 mm and 3.4 mmrespectively. In comparison, meanPD and AL values among individualswith PD or AL ≥75th percentile were2.4 mm and 2.9 mm respectively.

Variation in levels of cardiometa-bolic risk factors across levels ofmean AL or PD were similar to whatwas observed for periodontitis. How-ever, participants with mean AL≥75th versus <75th percentile were7 years older on average (p < 0.0001)while those with PD ≥75th versus<75th percentile were only 1 yearolder (p = 0.18) which is consistentwith previous report from NHANES(Demmer et al. 2012b).

The mean � SD values for 2-hpost-challenge glucose and fastingplasma glucose were 109 � 32 mg/dland 98 � 9 mg/dl (Table S1). Therespective prevalence estimates forisolated-IFG, isolated-IGT and com-bined IFG+IGT were 33%, 6% and10%; the prevalence estimates of anyIFG and any IGT were 43% and16%. Among participants with ver-sus without IGT, 65% and 39% alsohad IFG (26% risk difference,p < 0.0001). In comparison, amongparticipants with versus withoutIFG, 25% versus 10% also had IGT(15% risk difference, p < 0.0001).

As compared to participants with-out pre-diabetes or with isolated-IFG,participants with isolated-IGT tendedto be older, female, have higher CRPand WBC levels but intermediate fast-ing insulin and HOMA-IR values;they were also less likely to smokeor participate in vigorous activity(Table 1). Fasting glucose levels weresimilar among participants withoutany pre-diabetes and those with iso-lated-IGT (1.5 mg/dl difference,

I f i P di b tInfection Prediabetes

Mediators

Diet PhysicalActivity

Socio demographic

Diet, Physical Activity,

Socio demographic

Hypertension,Hypercholesteremia,

Inflammation

Adiposity

Health Behaviors

Smoking, Alcohol

VariablesAge, Sex, Race, Education

Infection Prediabetes

MediatorsHypertension,

Hypercholesteremia,Inflammation

Adiposity

Health Behaviors

Socio demographic

Diet, Physical Activity,Smoking, Alcohol

Socio demographicVariables

Age, Sex, Race, Education

(A)

(B)

Fig. 1. (A) Directed Acyclic Graph (DAG) representing one possible underlying causalstructure of inter-relationships among periodontal infection, pre-diabetes and severalmediators or confounders of the association. This causal structure assumes potentialsociodemographic confounders relate to periodontal infection and pre-diabetesthrough multiple mechanisms (e.g. confounding by sociodemographic variables can actthrough health behaviours and adiposity but also through other mechanisms not rep-resented in our data); this causal structure would necessitate adjustment for allsociodemographic variables. A similar argument can be made for more proximal vari-ables in the causal chain such as health behaviours and adiposity. Only statisticaladjustment for all potential confounders will provide the least confounded estimate.(B) Directed Acyclic Graph (DAG) representing one possible underlying causal struc-ture of inter-relationships among periodontal infection, pre-diabetes and several medi-ators or confounders of the association. This causal structure assumes that allconfounding effects of sociodemographic variables act entirely through effects oneither health behaviours or adiposity (two constructs which are measured in our data);this causal structure does not require adjustment for sociodemographic variables solong as health behaviours and adiposity adjustments are made.

© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

646 Arora et al.

p = 0.11). In contrast, 2-h post-chal-lenge glucose levels were higher in iso-lated-IFG versus no pre-diabetes:

105 � 1.3 versus 93 � 0.9, p < 0.0001(Table 1). Relative to participantswithout any IGT/IFG, or those with

isolated-IFG, those with isolated-IGTwere older, had lower indicators ofsocioeconomic status and higher lev-

Table 1. Characteristics of participants according to pre-diabetes status. One thousand one hundred and sixty five men and women 30–80 years old, enrolled in The Continuous National Health and Nutrition Examination Survey (NHANES) 2009–2010

No Pre-diabetes,n = 598 (51%)

Isolated IFG,n = 379 (33%)

Isolated IGT,n = 66 (6%)

CombinedIFG & IGT,n = 122 (10%)

p-value

Age (years) 46.9 � 0.6 48.9 � 0.8a 55.2 � 2.7a,b 57.3 � 1.5 <0.0001Male (%) 38 66a 26b 57 <0.0001Race/ethnicity% Hispanic 30 31 32 31 0.99% Non-hispanic white 49 49 47 50% Non-hispanic black 16 14 15 13% Other 5 6 6 6

Education% Less than high school 22 26a 36a 31 0.004% Completed high school 21 25 20 25% Some college or AA degree 29 26 27 26% College graduate and above 29 24 17 17

Family poverty income ratio 3.3 � 0.1 3.3 � 0.1 2.9 � 0.2a,b 3.1 � 0.2 0.11Smoking status% Never 60 53 68b 54 0.09% Former 23 26 18 29% Current 17 21 14 17

CDC BMI category, %<25 kg/m2 36 18a 21a 20 <0.000125–29.9 kg/m2 37 41 38 32≥30 kg/m2 27 42 41 48

BMI (kg/m2) 27.3 � 0.5 29.9 � 0.3a 29.8 � 1.1a 30.9 � 0.8 0.0004Alcohol use (grams/day) 10.9 � 1.4 10.0 � 1.33 4.9 � 2.8 9.9 � 2.4 0.28Physical activity in a typical week, %None 28 26 44a,b 41 0.004Moderate activity 35 33 35 34Vigorous activity 37 41 21 25

Kilocalories consumedin previous 24 h

2118.9 � 45 2419.8 � 81a 1957.3 � 132b 2120.7 � 94 0.04

Periodontal statusMean probing depth (mm) 1.47 � 0.03 1.60 � 0.04a 1.56 � 0.09 1.68 � 0.09 0.004

Mean attachment loss (mm) 1.31 � 0.04 1.51 � 0.08a 1.46 � 0.12 1.90 � 0.14 0.003Periodontal disease (CDC AAP definition), %Healthy 64 53a 52 40 <0.0001Moderate 29 36 38 37Severe 7 11 11 23

Blood pressureSystolic BP (mmHg) 116 � 0.9 121 � 0.8a 125 � 1.8a 124 � 1.9 <0.0001Diastolic BP (mmHg) 68 � 1.0 71 � 1.0a 70 � 1.8 70 � 1.6 0.1HDL-cholesterol 58 � 0.6 50 � 1.0a 56 � 3.2 52 � 2.0 <0.0001LDL-cholesterol 122 � 0.8 124 � 2.8 127 � 3.9 117 � 3.1 0.25

Total cholesterol-to-HDL-cholesterol ratio

3.7 � 0.04 4.3 � 0.08a 4.1 � 0.2 4.1 � 0.2 <0.0001

WBC count (cells 9 109/l) 6.2 � 0.09 6.5 � 0.13 6.7 � 0.26 6.9 � 0.2 0.01hs-C-reactive Protein (mg/l) 2.6 � 0.2 4.4 � 0.6a 5.4 � 0.7a 5.0 � 0.8 <0.0001American Heart Association hs-CRP Categories, %<1.0 mg/l 36 30a 27a,b 25 <0.00011.0–3.0 mg/l 36 36 23 27>3.0 mg/l 28 34 50 48

Plasma fasting glucose (mg/dl) 91.7 � 0.3 105.8 � 0.4a 93.2 � 1.0b 109.1 � 0.8 <0.0001Two hour glucose

(OGTT) (mg/dl)93 � 0.9 105 � 1.3a 158 � 3.2a,b 166 � 1.7 <0.0001

Insulin levels (lU/ml) 9.8 � 0.4 15.3 � 0.6a 12.7 � 1.1a 17.4 � 1.4 <0.0001HOMA-IR 2.2 � 0.1 4.0 � 0.17a 2.9 � 0.3a,b 4.7 � 0.4 <0.0001HbA1c% (mmol/mol) 5.3 � 0.01 (34 � 0.1) 5.5 � 0.02 (37 � 0.2)a 5.6 � 0.07 (38 � 0.8)a 5.7 � 0.05 (39 � 0.5) <0.0001

ap < 0.05 for any comparison with the no pre-diabetes group.bp < 0.05 for comparisons between IFG and IGT.

© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

Periodontal infection and pre-diabetes 647

els of inflammatory biomarkers, butwere less likely to be ever smokersTable 1.

Periodontal status and pre-diabetes

After multivariable adjustment, incl-uding smoking status and BMI, theodds ratios for any-IGT among par-ticipants with moderate or severeperiodontitis, relative to those withno/mild periodontitis, were 1.07 [0.50,2.25] and 1.93 [1.18, 3.17] (Table 2,model 5). In contrast, the ORs [95%CI] for any-IFG among participantswith moderate or severe periodontitis,respectively, were 1.14 [0.74, 1.77] and1.12 [0.58, 2.18] (Table 2, model 5).

The odds for any-IGT wereincreased by 105% when comparing4th quartile versus 1st–3rd quartiles ofmean PD: OR [95% CI]=2.05 [1.24,3.39] (Table 3, model 5). The ORssummarizing the relationship betweenmean AL and any-IGT were attenu-ated as were results for any-IFG(Table 3). Findings were similar whenmodelling PD and AL continuously(Table S2) and also when PD and ALwere derived from all periodontalsites, including mid-facial and mid-buccal sites (Table S3). Additionaladjustment for potential mediatorsdid not meaningfully change observedORs or the interpretation of results(Tables 2, 3, model 6).

Results from change-in-estimateanalyses to quantify the magnitudeand direction of confounding revealedimportant patterns (Tables S4–S6).For the probing depth exposure, edu-cation level, smoking status, race/eth-nicity, age and activity level were thestrongest confounders. For the ALexposure age, smoking status andeducation adjustments producedmeaningful changes in the odds ratio;the age-related confounding was sub-stantial. Confounding patterns for theCDC/AAP defined periodontitisexposure were similar to those formean AL. In all analyses smoking sta-tus, race/ethnicity and activity leveldemonstrated patterns of negativeconfounding in which adjustment forthese variables strengthened, ratherthan attenuated, the associationbetween periodontal infection andpre-diabetes. Negative confoundingwas also observed for sex adjustmentsalthough the magnitude of sex-relatedconfounding was minimal (TablesS4–S6).

When considering the relation-ship between periodontal status andeither isolated-IFG, isolated-IGT or

combined IFG+IGT in generalizedlogistic regression models, theobserved ORs were notably larger

Table 2. Odds ratios (95% CI) for prevalent pre-diabetes according to periodontal status.One thousand one hundred and sixty five men and women 30–80 years old, enrolled in TheContinuous National Health and Nutrition Examination Survey (NHANES) 2009–2010

Healthy/Mildperiodontitis(n = 665)

ModeratePeriodontitis(n = 383)

SeverePeriodontitis(n = 117)

p for lineartrend

Pre-diabetes outcome defined as any impaired glucose toleranceIGT prevalence 12% 18% 31%Model 1 Ref. 1.70 (0.88–3.3) 2.56 (1.65–3.98) <0.0001Model 2 Ref. 1.67 (0.85–3.28) 2.90 (1.80–4.68) <0.0001Model 3 Ref. 1.04 (0.52–2.06) 1.75 (1.16–2.62) 0.01Model 4 Ref. 1.04 (0.49–2.20) 2.01 (1.27–3.20) <0.01Model 5 Ref. 1.07 (0.50–2.25) 1.93 (1.18–3.17) 0.02Model 6 Ref. 1.04 (0.51–2.11) 1.87 (1.10–3.16) 0.04

Pre-diabetes outcome defined as any impaired fasting glucoseIFG prevalence 38% 48% 59%Model 1 Ref. 1.54 (1.12–2.14) 2.01 (1.28–3.14) 0.004Model 2 Ref. 1.62 (1.11–2.37) 1.99 (1.17–3.38) 0.01Model 3 Ref. 1.14 (0.79–1.65) 1.14 (0.68–1.90) 0.78Model 4 Ref. 1.11 (0.73–1.69) 1.15 (0.65–2.01) 0.86Model 5 Ref. 1.14 (0.74–1.77) 1.12 (0.58–2.18) 0.84Model 6 Ref. 1.08 (0.70–1.68) 1.05 (0.56–1.99) 0.94

Model 1: Crude; Model 2: smoking, total calorie intake, total alcohol intake, physical activ-ity, BMI; Model 3: age, sex, race/ethnicity, education level, Model 4 model 3+ smoking,total caloric intake, total alcohol intake, physical activity; Model 5: model 4+ BMI; Model6: model 5+ systolic blood pressure, total cholesterol/hdl ratio, total WBC count and CRP.

Table 3. Odds ratios for prevalent pre-diabetes according to mean probing depth and meanattachment loss levels. One thousand one hundred and sixty five men and women 30–80-years old, enrolled in The Continuous National Health and Nutrition Examination Survey(NHANES) 2009–2010

Mean probing depth* p-value Mean attachment loss* p-value

<75thpercentile(n = 873)

≥75th percentile(n = 292)

<75thpercentile(n = 874)

≥75th percentile(=291)

Pre-diabetes outcome defined as any impaired glucose tolerance (IGT)IGTprevalence

14% 23% 14% 24%

Model 1 Ref. 2.06 (1.31–3.21) 0.002 Ref 1.95 (1.24–3.07) 0.004Model 2 Ref. 2.26 (1.43–3.58) <0.001 Ref 2.09 (1.39–3.16) <0.001Model 3 Ref. 1.97 (1.26–3.07) 0.003 Ref 1.31 (0.85–2.01) 0.22Model 4 Ref. 2.23 (1.37–3.62) 0.001 Ref 1.43 (0.91–2.24) 0.12Model 5 Ref. 2.05 (1.24–3.39) 0.005 Ref 1.41 (0.90–2.22) 0.14Model 6 Ref. 1.99 (1.17–3.38) 0.01 Ref 1.32 (0.84–2.09) 0.23

Pre-diabetes outcome defined as any impaired fasting glucose (IFG)IFGprevalence

39% 55% 39% 55%

Model 1 Ref. 1.62 (1.16–2.27) 0.005 Ref. 1.64 (1.10–2.45) 0.02Model 2 Ref. 1.42 (0.91–2.20) 0.12 Ref. 1.63 (1.04–2.58) 0.03Model 3 Ref. 1.22 (0.80–1.85) 0.35 Ref. 1.10 (0.68–1.78) 0.69Model 4 Ref. 1.15 (0.73–1.81) 0.55 Ref. 1.06 (0.61–1.83) 0.84Model 5 Ref. 1.03 (0.59–1.81) 0.91 Ref. 1.04 (0.59–1.81) 0.90Model 6 Ref. 0.98 (0.55–1.75) 0.94 Ref. 0.96 (0.56–1.63) 0.87

*Mean probing depth and attachment loss values are based on inter-proximal sites from allteeth present excluding 3rd molars.Model 1: Crude; Model 2: smoking, total calorie intake, total alcohol intake, physical activ-ity, BMI; Model 3: age, sex, race/ethnicity, education level, Model 4 model 3+ smoking,total caloric intake, total alcohol intake, physical activity; Model 5: model 4+ BMI; Model6: model 5+ systolic blood pressure, total cholesterol/hdl ratio, total WBC count and CRP.

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648 Arora et al.

for the isolated-IGT outcome thanfor isolated-IFG (Table 4). After fullmultivariable adjustment (as inModel 6, Tables 2, 3), when com-paring individuals with mean PD≥75th versus <75th percentile, therespective ORs [95% CI] for iso-lated-IFG, isolated-IGT or com-bined IFG+IGT were: 0.91 [0.57,1.45], 1.85 [0.73, 4.66] and 2.06[0.91, 4.66] (also shown in Table 4).Respective ORs [95% CI] whenmodelling mean AL ≥75th versus<75th percentile were as follows:1.13 [0.70, 1.84], 1.11 [0.57, 2.16],1.45 [1.01, 2.08]; these results wereadjusted for all variables in Table 4with the exception of mean PD. Thefact that the OR for mean AL pre-dicting combined IFG+IGT was sta-

tistically significant, whereas thesame OR for mean probing depthwas not – despite a larger OR – isdue to a greater NHANES designeffect for the PD versus the ALparameter estimate.

Results summarizing the observedrelationships between several otherputative pre-diabetes risk factorsincluding age, gender, educationallevel, alcohol consumption, bodymass index, systolic blood pressureand cholesterol levels are presentedin Table 4.

Conclusion

We have found clinical measures ofperiodontal infection to be associ-ated with the pre-diabetic state.

Severe periodontitis was associatedwith a 93% increase in the odds ofimpaired glucose tolerance after mul-tivariable adjustment. Findings weresimilar for mean PD. In contrast,associations between measures ofperiodontal infection and IFG wereweak and not statistically significant.

These findings advance our under-standing of the relationship betweendiabetes and clinical periodontaldisease. Most research in this areahypothesizes that associations betweenclinical periodontal disease and diabe-tes status are due to a causal contribu-tion of diabetes to periodontal tissuedestruction (Taylor et al. 1998a,b,Taylor 2001, Lalla & Papapanou2011, Demmer et al. 2012a). Whilebiologically plausible, and supportedby strong findings in several studies(Taylor 2001, Lalla & Papapanou2011), the interpretation of resultsfrom most studies has been limited ina variety of ways, such as by smallsample sizes and/or lack of compre-hensive confounder adjustment aspreviously discussed (Demmer et al.2012a). Recent findings arising from alongitudinal, population-based cohortwith the data collection necessaryto perform comprehensive covariateadjustments have reported evidence tosupport the hypothesis of diabetes as acausal risk factor for periodontitis,although findings were much weakerthan previous studies and only uncon-trolled diabetes was observed to pre-dict worsening periodontal status(Demmer et al. 2012a).

It has also been proposed that asso-ciations between periodontal infectionand diabetes might be bidirectional.For example, an impaired immuneresponse to dysbiotic subgingival bio-films among people with diabetesmight contribute to a chronic inflam-matory state and subsequently to bothclinical periodontal disease as well asheightened insulin resistance andreduced glycaemic control. The ensu-ing uncontrolled glycaemia couldfurther exacerbate periodontal destruc-tion via the formation of advancedglycation end products (Lalla & Papa-panou 2011).

More recently, the hypothesis ofbidirectional relationships betweenperiodontal infection and diabeteshas been extended to consideradverse subgingival microbial expo-sures as a causal risk factor for dia-betogenesis (Demmer et al. 2008,

Table 4. Predictors of prevalent pre-diabetes in multivariable logistic regression modelsamong 1165 men and women aged 30–80 years, enrolled in The Continuous NationalHealth and Nutrition Examination Survey (NHANES) 2009–2010

Isolated-IFG Isolated-IGT CombinedIFG+IGT

p-value*

Periodontal statusMean PD ≥75th percentile 0.91 [0.57, 1.45] 1.85 [0.73, 4.66] 2.06 [0.91, 4.66] 0.05Age (10 year increase) 1.25 [1.07, 1.46] 1.40 [0.99, 1.98] 2.13 [1.65, 2.75] <0.0001Male versus female 2.59 [1.78, 3.77] 0.54 [0.22, 1.28] 2.40 [1.08, 5.30] <0.0001

Race/ethnicityHispanic versus white 1.13 [0.60, 2.11] 0.73 [0.26, 2.04] 1.08 [0.41, 2.86] 0.57Black versus white 0.95 [0.49, 1.84] 0.71 [0.23, 2.25] 0.69 [0.31, 1.56]Other versus white 2.23 [0.78, 6.36] 0.91 [0.36, 2.28] 2.12 [0.40, 11.1]

Education levelCollege grad versus <HS 1.03 [0.57, 1.85] 0.62 [0.13, 2.89] 0.99 [0.54, 1.84] 0.001Some college versus <HS 0.86 [0.47, 1.58] 0.64 [0.27, 1.50] 0.81 [0.47, 1.40]HS grade versus <HS 1.26 [0.69, 2.30] 0.70 [0.17, 2.88] 1.33 [0.74, 2.39]

Smoking statusFormer versus never 1.02 [0.66, 1.58] 0.75 [0.32, 1.79] 0.71 [0.35, 1.42] 0.12Current versus never 0.95 [0.55, 1.64] 0.26 [0.09, 0.75] 0.71 [0.32, 1.59]

Alcohol consumption1–3 drinks/day versus none 0.81 [0.62, 1.05] 0.89 [0.39, 2.06] 0.69 [0.31, 1.54] 0.01≥4 drinks/day versus none 0.67 [0.32, 1.40] 0.15 [0.01, 1.67] 1.35 [0.51, 3.62]Caloric Intake(500 kcal/day increase)

1.10 [0.97, 1.25] 1.05 [0.84, 1.31] 1.01 [0.81, 1.27] 0.43

Physical activity levelModerate versus none 1.20 [0.76, 1.88] 0.68 [0.29, 1.59] 0.69 [0.41, 1.58] 0.10Vigorous versus none 1.19 [0.65, 2.19] 0.44 [0.14, 1.34] 0.63 [0.36, 1.11]

Body mass indexOverweight versus normal 1.76 [1.16, 2.68] 1.18 [0.50, 2.80] 2.12 [1.26, 3.58] <0.0001Obese versus normal 2.29 [1.17, 4.48] 0.92 [0.33, 2.58] 3.56 [2.23, 5.70]

Systolic blood pressure(10 mm Hg increase)

1.12 [1.00, 1.24] 1.22 [1.03, 1.45] 1.05 [0.85, 1.32] 0.04

Total cholesterol-to-HDLcholesterol ratio(1 unit increase)

1.15 [1.05, 1.26] 1.27 [1.07, 1.51] 1.12 [0.87, 1.45] 0.002

CRP level 1–3 mg/l 0.78 [0.59, 1.03] 0.82 [0.29, 2.33] 0.63 [0.26, 1.15] <0.001>3 mg/l 1.41 [0.88, 2.25] 1.91 [0.97, 3.76] 1.61 [0.79, 3.26]White blood cell count(1 9 109 cells increase)

1.07 [0.94, 1.21] 1.12 [0.88, 1.42] 1.11 [0.88, 1.41] 0.78

*p-Value derived from Wald chi-square values used to test the null hypothesis of no differ-ence in the odds of pre-diabetes across levels of exposure.IFG, Impaired fasting glucose; IGT, Impaired glucose tolerance; PD, Probing Depth.

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Periodontal infection and pre-diabetes 649

2010a, 2012b). The current findingsbolster this hypothesis by buildingon previous research and demon-strating an association between peri-odontal infection and pre-diabetes.Although the temporality of associa-tions cannot be established in cross-sectional data, it is unlikely thatIFG or IGT could have been a driv-ing causal factor in the developmentof severe periodontitis as most previ-ous research suggests that severedysglycaemia observed in uncon-trolled diabetes is necessary to havea meaningful influence on periodon-tal tissue destruction (Taylor et al.1998a, Tsai et al. 2002, Demmeret al. 2012a). Nevertheless, it is pos-sible that hyperglycaemia in the pre-diabetic state might contribute tocompositional shifts in the subgingi-val microbiome and incipient gingi-val inflammation.

The hypothesis that infectionmight contribute to diabetogenesis isbiologically plausible and fits logi-cally into the larger frameworkregarding host inflammatory pheno-type as a risk factor for insulin resis-tance and diabetes development(Demmer et al. 2008, 2010a, 2012b).Chronically elevated systemic inflam-mation has been shown to predictinsulin resistance, (Pradhan et al.2003, Park et al. 2009) impaired glu-cose metabolism (Chakarova et al.2009) and incident T2DM (Pradhanet al. 2001, Hu et al. 2004). Accord-ingly, several exogenous inflamma-tory stimuli such as air pollution(Kramer et al. 2010), tobacco smoke(Foy et al. 2005) and pollutants (Lee& Jacobs 2006, Lee et al. 2007,2010) have also been linked toT2DM risk. Regarding the potentialfor periodontal infections to triggera chronic inflammatory response, ithas been suggested that “keystone”pathogens such as P. gingivalismight possess the ability to evadeand/or subvert the host immune sys-tem in a manner that enables key-stone organisms to persist in thesubgingival space, subsequently shift-ing the microbial community compo-sition towards a state of dysbiosisand chronic inflammation (Hajishen-gallis et al. 2011, 2012). Accordingly,there is a large body of researchreporting that individuals with clini-cal evidence of current periodontalinflammation also have elevated lev-els of systemic inflammation and

randomized controlled trials haveshown that anti-infective periodontaltreatment can lead to reductions insystemic inflammation (Lockhartet al. 2012, Demmer et al. 2013).

In these data, after multivariableadjustment (excluding potential medi-ators), the ORs summarizing associa-tions between periodontal infectionand outcomes that included impa-ired glucose tolerance (i.e. IGT withor without IFG) ranged from ~1.4–2.0 depending on the exposure mod-elled. In contrast, ORs summarizingassociations between infection andIFG ranged from 0.98–1.05. This sug-gests that IGT outcomes might bemore relevant vis-�a-vis infection andpotentially supports the mechanisticinvolvement of inflammation. Previ-ous studies have demonstratedincreased CRP levels to be morestrongly linked to IGT than IFG out-comes (Muntner et al. 2004, Chakar-ova et al. 2009, Capaldo et al. 2013)and our own current results also showhigher CRP levels among participantswith isolated-IGT versus isolated-IFG. Although, we did not observestrong attenuation of our findingsafter adjustment for inflammation,findings of this nature are not uncom-mon among studies of periodontalinfection and diabetes risk. As previ-ously discussed, it is possible thatCRP and WBC might be sufficient,but not necessary mediators in thecausal pathway from microbial expo-sures to impaired glucose regulationand a larger set of inflammatorybiomarkers might be required to ade-quately consider mediation hypothe-ses (Demmer et al. 2012b). Futurestudies with more comprehensive bio-marker assessments during longitudi-nal follow-up will be necessary tobetter inform the potential for inflam-matory mediation.

ORs were generally larger foranalyses modelling either mean PDor CDC/AAP periodontitis as theprimary exposure when compared tomean AL. This is likely due to thefact that mean AL was notablylower among participants in theupper 25th percentile of AL as com-pared to participants with severeperiodontitis. In contrast, mean PDwas similar among individuals withsevere periodontitis and those in theupper 25th percentile of PD.

Our apparently null results forthe IFG outcome are in contrast to

positive findings among >12,000 par-ticipants in NHANES III (Choiet al. 2011) in which both AL andPD were related to an ~20–50%increase in odds of IFG. However,the previous NHANES III publica-tion did not measure IGT and it ispossible that the finding was drivenby a higher prevalence of combinedIFG & IGT relative to our currentsample.

Periodontal infection was associ-ated with impaired glucose toleranceafter comprehensive multivariableadjustment. The strongest confound-ers in these data appeared to be age,smoking, race/ethnicity, educationlevel and activity level. After adjust-ment for these variables, furtheradjustment did not meaningfullychange the strength of association.Importantly, adjustment for bodymass index – the strongest knownrisk factor for pre-diabetes – alsohad only marginal influence on thestrength of associations. The factthat smoking was a negative con-founder (i.e. smoking adjustmentstrengthened rather than attenuatedresults) is notable as smoking hasfrequently been suspected as a prom-inent source of positive confounding(i.e. smoking adjustment attenuatesresults) in studies concerning peri-odontal infection and non-periodon-tal outcomes such as cardiovasculardisease and cancer (Hujoel et al.2002). Therefore, the pattern of neg-ative confounding substantially mini-mizes the potential for our reportedORs to be overestimated due toresidual confounding related totobacco exposure. The pattern ofnegative confounding by smokingarises from the fact that smoking isoften inversely related to metabolicoutcomes while it is positivelyrelated to periodontal disease; in ourcurrent report, the odds of isolated-IGT were lower among currentsmokers relative to never smokers(see results). Similarly, previous stud-ies found smoking to be a negativeconfounder of the relationshipbetween periodontal infection and5-year change in haemoglobin A1clevel. That pattern was the result ofinverse associations between baselinesmoking status and longitudinal A1cchange (Demmer et al. 2010a).

We have found clinical indicatorsof periodontal infection to be associ-ated with impaired glucose tolerance

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650 Arora et al.

among a nationally representativesample of US adult men and women.The exact temporality of associationscannot be determined in these cross-sectional data nor are there sufficientdata to carefully explore the role ofinflammation as an underlying bio-logical mechanism. Longitudinalstudies that collect broader panels ofinflammatory biomarkers will beimportant for answering these ques-tions. Nevertheless, the findings aresuggestive of a potential role forperiodontal infections in the aetiol-ogy of impaired glucose regulation.If replicated in future studies, thepublic health implications would besubstantial given the high prevalenceof inflammatory periodontal infec-tions in the general population(Demmer & Papapanou 2010).

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Supporting Information

Additional Supporting Informationmay be found in the online versionof this article:

Table S1. General characteristics ofstudy participants overall and accord-ing to periodontal status.Table S2. Odds ratios for prevalentpre-diabetes according to continuousmeasures of mean probing depth andmean attachment loss.Table S3. Odds ratios for prevalentpre-diabetes according to full-mouthmeasures of mean probing depth andmean attachment loss levels.Table S4. Evaluation of changes inodds ratios produced by serial addi-tion of potential confounders tologistic models examine the relation-ship between mean probing depthand impaired glucose tolerance.Table S5. Evaluation of changes inodds ratios produced by serialaddition of potential confounders tologistic models examine the relation-ship between mean attachment lossand impaired glucose tolerance.

Table S6. Evaluation of changes inodds ratios produced by serial addi-tion of potential confounders tologistic models examine the relation-ship between CDC/AAP definedperiodontitis and impaired glucosetolerance.

Address:Ryan T. DemmerDepartment of EpidemiologyColumbia University722 W. 168th St.New York, NY 10032USAE-mail: rtd2106@columbia.edu

Clinical Relevance

Scientific rationale for the study:Periodontal infections have beenhypothesized as a potential riskfactor for poor metabolic outcomes,but limited data are available explor-ing whether periodontal infectionsare differentially associated with

impaired glucose tolerance orimpaired fasting glucose among diabe-tes-free individuals.Principal findings: Periodontitis wasassociated with increased odds ofimpaired glucose tolerance but notimpaired fasting glucose.

Practical implications: Future rese-arch studies are merited to under-stand whether periodontal infectionsare more strongly associated withspecific glucose metabolism pheno-types and whether the observed asso-ciations are causal or confounded.

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652 Arora et al.