comparison of body composition and periodontal disease using nutritional assessment techniques:...

Comparison of body compositionand periodontal disease usingnutritional assessmenttechniques: Third National Healthand Nutrition Examination Survey(NHANES III)

Nelson Wood, Roger B. Johnson,and Charles F. StreckfusDepartment of Periodontics, University of

Mississippi School of Dentistry, Jackson, MS,

USA

Wood N, Johnson RB, Streckfus CF: Comparison of body composition and periodontaldisease using nutritional assessment techniques: Third National Health and NutritionExamination Survey (NHANES III). J Clin Periodontol 2003; 30: 321–327. rBlackwell Munksgaard, 2003.

AbstractObjectives: The objective of this study was to investigate the association of bodycomposition (obesity) and periodontal disease using simple, inexpensive nutritionalassessment techniques available in the Third National Health and NutritionExamination Survey (NHANES III).

Material and Methods: Caucasian subjects, aged 18 years and above, participating inNHANES III, were used for this study. Weight, height, waist circumference, hipcircumference, skinfold thickness (S), and bioelectrical impedance analysismeasurements were performed and used in the calculation of body mass index (BMI),waist-to-hip ratio (WHR) (visceral fat), log sum of S (subcutaneous fat), and fat-freemass (FFM). Data were analyzed using SPSSs. One-way, factorial ANOVA,multivariate analyses, and regression curve analyses were performed. po0.05 wasused to reject the null hypothesis.

Results: Adjusting for age, gender, history of diabetes, current smoking, andsocioeconomic status, statistically significant correlations were found betweenperiodontitis and WHR, BMI, FFM, and in some instances S.

Conclusion: This study, indicating significant correlations between body compositionand periodontal disease (with WHR being the most significant, followed by BMI,FFM, and S), showed similarities to those observed in other obesity-related healthproblems. This strengthened arguments that periodontal disease and certain obesity-related systemic illnesses are related, with abnormal fat metabolism possibly being animportant factor.

Key words: periodontitis; body composition;waist-to-hip ratio; body mass index; free-fatmass; skinfold thickness

Accepted for publication 4 April 2002

Obesity, the most common nutritionaldisorder in America (Kopelman 2000),is a significant risk factor for numerousadult diseases, and may be a factor inthe incidence of periodontitis. Bodymass index (BMI) (Elter et al. 2000,Grossi & Ho 2000, Wood & Johnson2001), waist-to-hip circumference ratio

(WHR), body fat, and maximum oxygenconsumption (Saito et al. 1998, 2000,2001) may be factors in the incidence ofthis disease.

Conditions associated with obesity,e.g. ‘‘the metabolic syndrome’’, aclustering of dyslipidemia and insulinresistance (Vanhala et al. 1997) may

exacerbate periodontitis (Grossi & Ho2000). Long-term interest in the role ofnutrition and periodontal disease (Rus-sell et al. 1961, Russell 1963, Oles1966, Alfano 1976, Muroff et al. 1979,Carlos & Wolfe 1989) questions the roleof nutrients in periodontal diseasepathogenesis (Carlos & Wolfe 1989).

J Clin Periodontol 2003; 30: 321–327 Copyright r Blackwell Munksgaard 2003Printed in Denmark. All rights reserved

The Third National Health and Nutri-tion Examination Survey (NHANES III)includes a thorough nutritional statusassessment. The relationship betweenvarious chronic diseases and bodycomposition has been recognized, andthere has been considerable interest inassessing body composition in healthexaminations for nationally representa-tive population samples. The reasons forthe use and inclusion of anthropometricmeasurements and bioelectrical impe-dance analysis (BIA) to assess nutri-tional status, to collect comprehensivesocioeconomic and demographic infor-mation, and to administer food-fre-quency questionnaires and a detailed24-h recall have been described in theliterature (Kuczmarski 1996). These aresimple, inexpensive methods for esti-mating fat-free mass (FFM), percentbody fat, and location of body fat thatmay be used outside the laboratory(Segal et al. 1988, Kyle & Pichard2000). Many investigators have devel-oped empiric BIA equations for theprediction of FFM, total body weight,and body fat (Lukaski et al. 1985, 1986,Segal et al. 1985, 1988, Kushner &Schoeller 1986, Van Loan & Mayclin1987, Jackson et al. 1988, Lukaski &Bolonchuk 1988, Graves et al. 1989,Heitmann 1990, Deurenberg et al. 1990,1991, Roubenoff et al. 1997).

Human body fat content may influ-ence morbidity and mortality (Stevenset al. 1998), and its measurement andlocation may provide useful informationabout various disease states. Most largeepidemiological studies that have ex-amined body composition have usedQuetelet’s Index of Obesity (BMI (kg/m2)). BMI is a determinant for over-weight (but not necessarily obesity),while the WHR determines the amountof visceral fat (also called abdominal fator upper body obesity). Skinfold thick-ness (S) measurements have beenshown to be an accurate measurementof subcutaneous fat at a given location,and evidence supports the notion thatthe log sum of several skinfold sites is agood measure of total subcutaneous fatin men (Durnin & Womersly 1974,Jackson & Pollock 1977, 1978, Lohman1981) and women (Jackson et al. 1980).Several other fat deposits exist inaddition to subcutaneous fat, includingintermuscular fat, intramuscular fat andfat around the visceral organs andgastrointestinal tract of the body (peri-renal, mesenteric, and omental), essen-tial lipids in bone marrow, and central

nervous system and other organs. Thus,there may be a considerable chance forbiological variation in body fat distribu-tion (Johnson et al. 1972).

Periodontitis, a chronic inflammatoryoral disease, may have profound effectson systemic health by affecting the hostsusceptibility to systemic disease due toaccumulation of Gram-negative bacteriaand inflammatory mediators (Herzberg& Meyer 1996, Beck et al. 1998,Herzberg & Weyer 1998, Dorn et al.1999, Iacopino & Cutler 2000). Period-ontal status has been reported to be asignificant, independent predictor ofmortality (Garcia et al. 1998, Arbes etal. 1999).

Therefore, it seems reasonable toinvestigate the associations betweenvarious nutritional assessment techni-ques, BMI, WHR, lean body mass formales (LBMm), lean body mass forfemales (LFMf), FFM, which use thepublished-BIA prediction equations ofKyle (Kyle et al. 2001) and Lukaski(Lukaski et al. 1985, 1986, Lukaski1989), and skinfold thickness (the logsum of three skinfold thicknesses (S)(tricep S1subscapular S1suprailiac S)which determine fat content and loca-tion in the human body and (1) variousperiodontal disease indices (periodontalattachment loss (PAL), percentage(PAL%) and mean (PALm) (2) meanpocket depth (PDm), (3) mean gingivalbleeding index (GBm), and (4) meancalculus index (CIm). Our hypothesis isthat the incidence and severity ofperiodontal disease is greater in obesesubjects as compared to subjects withnormal weight.

Material and methods

Data for this study were obtained fromNHANES III, conducted from 1988 to1994, designed to provide estimates ofthe health status of the United States’civilian, noninstitutionalized populationaged 2 months and over (Ezzati et al.1992). For this analysis, three public-use data files–household adult (DHHS)1996a), examination (DHHS) 1996a),and clinical laboratory data (DHHS)1996b) – were obtained from a CD-ROM and merged into one data file(DHHS 1997). We chose to limit thisstudy to the Caucasian population, aged18 years and above, due to race speci-ficity and possible limitations of theexisting BIA equations. It is presentlyunknown whether race-specific equa-

tions for African-American, Hispanic-American, and Native American popu-lations would significantly improve theaccuracy of BIA in these groups.

The independent variable of interestwas the percent of periodontal sites persubject with an attachment loss (PAL)of X3 mm. Periodontal examinationswere conducted in the mobile examina-tion centers by six calibrated dentiststrained in the use of epidemiologicalindices for oral health (Arbes et al.1999). For this study, extent scores(Carlos et al. 1986), representing thepercent of sites per subject with anattachment loss of X3 mm, were calcu-lated and separated into three groups.

Normal subjects had 0–33% of siteswith PAL X3 mm; while subjects withearly periodontitis had 33–66% of siteswith PAL X3 mm; and subjects withsevere periodontitis had 67–100% ofsites with PAL X3 mm. A threshold of3 mm was used to increase the like-lihood that attachment loss was theresult of disease and not measurementerror. The analysis excluded personswho were edentulous. Other periodontalindices that were examined werePALm, PDm, GBm indices, and CIm.

Established risk factors for period-ontal disease and obesity were selectedas covariables. The covariables wereage, gender, smoking status (currentsmoker), a history of diabetes (self-reported by ‘‘Has the doctor ever toldyou that you have diabetes?’’), andsocioeconomic status (poverty incomeratio (unimputed income)).

Weight was measured in kilograms(kg), height in centimeters (cm), andBMI in kg/m2. Waist circumferencemeasurements were taken at the levelof the umbilicus in centimeters (cm),and hip measurements were taken at thegreatest circumference of the buttocksarea in centimeters (cm). Waist circum-ference measurements were divided byhip circumference measurements toobtain the WHR. The measurement ofS (cm) was made at specific subscapu-lar, tricep, and suprailiac sites (Lukaski1987). A licensed physician supervisedall measurements. Total body resistivityand reactance were measured with afour-terminal portable impedance ana-lyzer (RJL Systems, Detroit, MI, USA)(Lukaski et al. 1985). Resistance (R) tothe flow of a 50-kHz injected currentwas measured on a 0–1000 O scale andreactance (Xc) was measured on a0–200 O scale. Empirically derivedformulas provided by the manufacturer

322 Wood et al.

of the BIA instrument were used tocalculate estimated LBM for men(LBMm) and women (LBMf) (Segal etal. 1988). Formulas are described in theAppendix.

Pearson correlation coefficients (r)comparing FFM using BIA requiredprediction equations of Kyle (Kyle etal. 2001), Lukaski (Lukaski et al. 1986,Lukaski 1989), and the manufacturer’sBIA prediction equations of LBMm andLBMf n5 17,660. Due to the very highmulticolinearity between these equa-tions (all r’s40.957), we decided touse the new Geneva BIA equation forthe prediction of FFM of Kyle (Kyle etal. 2001) (FFMBIA-K), which has beenvalidated using dual X-ray absorptome-try (DXA) (r5 0.986–0.987) in healthyadults aged 22–94 years with BMIsbetween 17.0 and 33.8 kg/m2 (Kyle etal. 2001) (Table 1). From hereon, FFM

refers to fat-free mass derived fromFFMBIA-K.

Data were analyzed using SPSSs

version 10.1. Initially, periodontitis(PAL) severity and BMI, WHR, S, andFFM were compared by one-wayand factorial analysis of variance. Inall multivariate analyses, adjustmentswere made for age, gender, smokingstatus, history of diabetes, and socio-economic status. In the logistic regres-sion models for goodness-of-fit, thefollowing variables were recoded: gen-der was recoded as male (code 1) andfemale (code 0); history of diabetes,(yes (code 1), no (code 0)); and currentsmoking history, (yes (code 1), no (code0)). Regression curve analyses wereperformed for all periodontal indicesand compared to nutritional assessmentdata. po0.05 was used to reject the nullhypothesis.

Results

Characteristics of the study population(mean7sem) are presented in Table 2.Male subjects were significantly hea-vier, taller, and had higher WHRs thanfemale subjects in each age category,while R, Xc, and S were significantlyhigher in all female age categories(po0.05). Our study demonstrated thatan increasing percentage of PAL wassignificantly associated with WHR(F5 253.32) and BMI (F5 19.65) atpo0.01, and FFM (F5 4.11) atpo0.05, but not S (Table 3). However,adjusted PALm was significantly corre-lated with WHR (0.2459) BMI (0.1512),and S (0.0866) at po0.01, but not FFM.Adjusted PDm scores were significantlycorrelated with WHR (0.1596), BMI(0.1306), and S (0.1100) at po0.01, butnot FFM. Adjusted GBm scores were

Table 1. Pearson correlation coefficients (r) among bioelectrical impedance analysis (BIA) published (PUB) prediction equations, and themanufacturer’s (MAN) BIA prediction equations (n5 17,660)

Fat-Free MassPUB

(Kyle et al. 2001)Fat-Free MassPUB

(Lukaski 1986, 1989)Lean body

mass menMAN

Lean bodymass womenMAN

Fat-Free MassPUB

(Kyle et al. 2001)0.990 0.958 0.989

Fat-Free MassPUB

(Lukaski et al. 1986, Luskaski 1989)0.963 0.978

Lean body mass menMAN 0.985

po0.001 for all correlations.

Table 2. Characteristics of the study population. mean7SEM (n)

Age Categories (years)

Characteristics Gender 18–34 35–49 50–64 651

Age (years) Male 26.070.1 (2225) 41.470.1 (1457) 57.570.1 (1164) 75.870.2 (1765)Female 25.970.1 (2299) 41.370.1 (1590) 57.370.1 (1185) 75.870.2 (1879)

Weight (kg) Male 5771 (2203) 5671 (1447) 5671 (1161) 5571 (1751)Female 4971 (2279)n 4971 (1577)n 4971 (1175)n 5071 (1857)n

Height (cm) Male 15771 (1969) 15671 (1278) 15671 (1026) 15571 (1532)Female 14771 (2021)n 14771 (1403)n 14671(1044)n 14771 (1683)n

BMI (kg/m2) Male 23.770.1(1954) 23.470.2(1273) 23.370.2(1026) 23.370.2(1526)Female 23.570.2(2008) 23.670.2(1395) 23.870.2(1037) 23.670.2(1665)

WHR Male 0.94970.002(1851) 0.94970.002(1202) 0.94870.003(960) 0.94870.002(1452)Female 0.88670.002(1894)n 0.88870.002(1305)n 0.88470.002(978)n 0.88770.002(1570)n

Resistance (O) Male 49472 (1270) 49572 (821) 49873 (671) 49672 (970)Female 60172 (1237)n 60173 (862)n 59773 (618)n 59973 (1021)n

Reactance (O) Male 65.270.4(1269) 64.770.5(821) 65.470.5(670) 65.670.4(969)Female 72.970.4 (1236)n 73.070.5(861)n 72.970.6(618)n 73.370.5(1020)n

Log sum S Male 1.6070.01(1836) 1.5970.01(1180) 1.5970.01(948) 1.597.01(1427)Female 1.7070.01(1852)n 1.7170.01(1285)n 1.7070.01(960)n 1.727.01(1543)n

FFMBIAPUB�K 50.970.2(2492) 50.670.3(1676) 50.970.3(1285) 50.570.2(1979)

nPo0.05 for gender.

BMI5 body mass index (kg/m2), WHR5waist circumference to hip circumference ratio, log sum S5 logsum of skinfold thickness (tricep1subscapular1suprailiac), FFMBIAPUB�K5 fat-free mass using a published prediction equation (Kyle et al. 2001).

Body composition and periodontal disease 323

significantly correlated with WHR(0.2081), FFM (0.1872) and BMI(0.0945) at po0.005, but not S. Ad-justed CIm were significantly correlatedwith WHR (0.2510), BMI (0.1770), andS (0.1270) at po0.01, and FFM(� 0.0687) at po0.05. All correlationswere adjusted for age, gender, a historyof diabetes, current smoking, and socio-economic status (Table 4).

Regression curve estimations showed,dramatic changes. BMI versus GBm

showed a steep incline at GBm (0–0.71),with BMI increasing from 18 to 25 kg/m2

over this distance, and the curve thenleveled off. Also, S versus GBm showed asteep incline from GBm 0 to 0.40increasing from 1.38 to 1.80 over thisdistance, and then the curve leveled offand eventually turned downward. On theCIm versus FFM curve, the FFM(mean7 sem) started out small towardthe left side of the curve, and steadilyincreased as the CIm increased (Table 5).

DISCUSSION

Visceral fat accumulation (abdominalobesity) that is observed in upper bodyobesity (WHRX0.8 for females andX0.9 for males) is associated with morehealth problems than lower body obe-sity (Nakamura et al. 1994, Banerji et al.1995, Rexrode et al. 1998) and sub-cutaneous fat (Nakamura et al. 1994),regardless of BMI (Rexrode et al. 1998).Our data demonstrate that this patternalso exists for periodontal disease.When the percent of PALX3 mm persite per subject was compared to upperbody obesity (WHR), BMI, FFM, andsubcutaneous fat (represented by the logsum of S), our data show that WHR hasthe highest F ratio (F5 253.32), fol-lowed by BMI (F5 19.651) and FFM(F5 4.11); subcutaneous fat (S) was notsignificant. Also, adjusted-PALm, PDm,GBm, and CIm correlations show asimilar pattern, with WHR demonstrat-ing the highest Pearson (r) correlation,followed by BMI, then S, and FFM.Thus, the patterns of fat distributionin terms of periodontal pathogenesis

Table 3. Comparison of body composition and periodontal attachment loss (PAL) severity

Mean7SEM (n)

% PAL of X3 mm per subjecta Age cats. total n 18–34Male1Female

35–49 yearsMale1Female

50–64 yearsMale1Female

651yearsMale1Female

BMI (Kg/m2)0–33 3426 25.570.1 (1599) 27.770.2 (939) 28.170.2 (503) 27.270.2 (385)

3985 25.470.1 (1784) 27.970.2 (1099) 28.170.2 (608) 26.970.2 (494)33–66 471 25.670.8 (30) 27.070.5 (79) 27.770.4 (154) 26.870.3 (208)

444 28.471.9 (12) 28.670.9 (51) 28.670.6 (112) 26.770.3 (269)67–100 319 25.070.2 (2) 28.771.1 (37) 26.870.4 (98)** 26.370.3 (182)**

197 28.771.6 (23) 29.171.0 (46) 25.970.5(128)WHR (cm)

0–33 3328 0.91570.002 (1559) 0.97170.002 (910) 1.00370.003 (489) 1.01170.003(370)3879 0.84070.002 (1745) 0.86870.003 (1064) 0.90470.003 (593) 0.92670.003 (477)

33–66 454 0.93470.010 (30) 0.98770.007 (78) 1.00370.005 (149) 1.01470.005(197)329 0.89670.023 (11) 0.88970.011 (51) 0.91370.007 (110) 0.92370.006 (157)**

67–100 297 1.00570.035 (2) 1.00870.017 (36)* 1.01370.008 (93) 1.01570.004 (166)187 0.90770.013 (21) 0.93870.012 (44)** 0.94770.007 (122)**

S0–33 3246 1.7070.01 (1536) 1.7770.01 (878) 1.7870.02 (470) 1.7470.02 (362)

3353 1.8370.01 (1687) 1.9570.02 (1019) 1.9470.02 (574) 1.8470.02 (73)33–66 438 1.7470.08 (29) 1.7170.04 (73)** 1.7470.02 (142) 1.6870.01 (194)

313 2.1670.25 (11) 1.9570.08 (46) 2.0270.06 (104) 1.8270.04 (152)67–100 291 1.6970.12 (2) 1.8270.09 (35)* 1.7170.02 (92) 1.777.01 (162)*

184 1.8870.11 (19) 1.9570.08 (45) 1.8170.04 (120)FFMBIAPUB�K (Kg)

0–33 6806 52.170.2 (2992) 53.770.2 (1943) 52.970.3 (1050) 49.470.4 (821)33–66 749 55.271.4 (41) 54.570.9 (122) 54.370.7 (242) 50.070.6 (344)67–100 477 59.073.5 (2) 56.371.7 (56) 54.570.9 (132) 49.770.6 (287)

BMI5 body mass index (kg/m2); WHR5waist circumference (cm) to hip circumference (cm) ratio; S5 log 10 sum of (tricep S1subscapularS1suprailiac S); FFMBIAPUB�K5 free-fat mass (kg)5 � 4.1041(0.518� height2/resistance)1(0.231�weight)1(0.130� reactance)1(4.229� sex:men5 1, women5 0).a%PAL5 percent of sites with periodontal attachment loss of X3 mm per subject.*po0.01; **po0.05 for same gender.

Table 4. Adjusteda-Pearson correlation coefficients (r) among body composition and variousperiodontal indices in Caucasians (df5 1,011)

Periodontalattachmentloss (mean)

Pocket depth(mean)

Gingival bleeding(mean)

Calculus index(mean)

BMI 0.1512n 0.1306n 0.0945n 0.1770n

WHR 0.2459n 0.1596n 0.2081n 0.2510n

Log sum of S 0.0866n 0.1100n 0.0413 0.1270n

FFMBIA�K 0.0553 � 0.0560 0.1872n � 0.0687nn

npo0.01; nnpo0.05. BMI5Body mass index (kg/m2), WHR5waist-to-hip circumference ratio,Log sum of S5 log sum of skinfold thickness (tricep1subscapular1suprailiac), FFMBIA�K5 fat-free mass using a published prediction equation (Kyle et al. 2001).aAdjusted for age, gender, a history of diabetes, a history of current smoking, and socioeconomicstatus.

324 Wood et al.

follow those observed with other obe-sity-related health problems. Upperbody fat localization is a significant riskfactor for type II diabetes, dyslipidemia,hypertension seen in the ‘‘metabolicsyndrome’’ (Grossi & Genco 1998),along with various cancers. Thisstrengthens the argument that period-ontal disease and certain systemic ill-nesses are related, and that fatmetabolism may play a key role inthese relationships.

An additional important finding inthis study was that of the nonlinearrelationships between various period-ontal disease indices and the nutritionalassessment data utilized in this study.The categorizing and/or groupingdata in studies of this type may lead tovery misleading results, and curveextrapolations cannot be trusted in thesecases.

Obesity has been shown to affect hostimmunity (Tanaka et al. 1993, Stallone1994). Obese, hypertensive rats havebeen shown to have a higher incidenceof periodontitis than normal rats, andhave intimal periodontal blood vesselthickening, indicating diminished bloodflow (Perlstein & Bissada 1977). Theplasminogen-activating system has beenshown to play an important role ingingival inflammation (Kinnby et al.1999). Plasminogen activator inhibitor-1 (PAI-1) has an increased gene expres-sion in visceral fat (Shinomura et al.1996) and induces agglutination ofblood, increasing the risk for ischemicvascular disease. Thus, PAI-1 may alsodecrease periodontal blood flow inobesity, promoting initiation of period-ontitis and progression.

Hyperlipidemia frequently accompa-nies infectious diseases (Feingold et al.1992, Hardardottir et al. 1994), and asingle dose of bacterial endotoxin (li-popolysaccharide (LPS)) can induceliver adipose tissue-lipid metabolicchanges (Feingold et al. 1992). Gram-

negative LPSs from periodontal pocketscan mediate adipose tissue TNF-arelease, and it may possibly be asso-ciated with hepatic dyslipidemia, whichwould result in various obesity-relatedhealth problems. An association be-tween periodontitis and hyperlipidemiahas been reported (Cutler et al. 1999),and periodontal treatment has beenshown to have a favorable effect ondiabetic control (Grossi & Genco 1998).

Tumor necrosis factor alpha (TNF-a),recently reported in adipose tissue, hasbeen shown to cause liver injury inobese patients (Yang et al. 1997), andto be directly associated with insulinresistance (Hotamisligil et al. 1996,Uysal et al. 1997). It also has an effecton cytokines and prostaglandins inperiodontal tissue (Gemmell et al.1997). All these factors indicatea periodontitis–dyslipidemia relation-ship.

Body fat distribution – ‘‘where fat?’’in addition to ‘‘how fat?’’ – is likely tobe a critical epidemiological factor indiseases of the oral cavity. The associa-tion between these diseases and the roleof fat should be investigated in moredetail.

Appendix

Manufacturer’s prediction equation:

LBMm¼ 6:493

þ0:4936ðheight2=resistanceÞþ0:332ðweightÞ;

LBMf¼ 5:091

þ0:6483ðheight2=resistanceÞþ0:1699ðweightÞ:

A new Geneva BIA equation valid forthe prediction of FFMBIA�K with dualX-ray absorptometry (DXA)(r5 0.986–0.987) in healthy adults aged22–94 years with BMIs between 17.0

and 33.8 kg/m2 (Kyle et al. 2001):

FFMBIA�K¼ �4:104

þð0:518�height2=

resistanceÞþð0:231�weightÞþð0:130�reactanceÞþð4:229�sex : men¼ 1;

women ¼ 0Þ:FFMBIA�L was also calculated with thefollowing equation of Lukaski (Lukaskiet al. 1986, Lukaski 1989), which hasbeen validated with hydrodensitometry(Lukaski et al. 1986):

FFMðkgÞ ¼ �4:03 þ 0:734ðHt2=RÞþ0:116ðweightÞþ0:096ðXcÞþ0:984ðsexÞ;

where Ht is height in cm, R is resistancein O, weight is in kg, Xc is reactance inO, and sex5 0 for women and 1 for men.

ZusammenfassungVergleich von Korperzusammensetzung undParodontitis mittels Techniken der Ernahrung-serfassung: Dritte Nationale Studie zur Ge-sundheitheits- und Ernahrungserfassung(NHANES III)Zielsetzung: Untersuchung der Zusammen-hange zwischen Korperszusammensetzung(Fettleibigkeit) und Parodontitis mittels einfa-cher, kostengunstiger Techniken zur Ernah-rungserfassung, die im Rahmen der drittenNationalen Studie zur Gesundheitheits- undErnahrungserfassung (NHANES III) zuganglichwaren.Material und Methoden: Individuen kauka-sischer Abstammung im Alter von X 18 Jahren,die an der NHANES III teilnahmen, wurdenuntersucht. Gewicht, Korpergro�e, Bauch- undHuftumfang, Hautfaltendicken- (S) und BIA-Messungen wurden erhoben und zur Berech-nung des Korpermassenindex (BMI), derBauch-zu-Huften-Relation (WHR) (viszeralesFett), log der Summe von S (Unterhautfett) undfreie Fettmasse (FFM) verwendet. Die Datenwurden mittels SPSSs ausgewertet. Einseitige,faktoriale ANOVA, multivariate Analysen undRegressionskurvenanalysen wurden durchge-fuhrt. Po0,05 wurde festgelegt, um die Null-hypothese zu verwerfen.Ergebnisse: Nach Korrektur fur Alter,Geschlecht, Vorgeschichte von Diabetes, ak-tuellem Rauchen und soziookonomischem Sta-tus wurden statistisch signifikanteKorrelationen zwischen Parodontitis undWHR, BMI, FFM und in manchen Fallen Sgefunden.Schlussfolgerungen: Diese Studie zeigte sig-nifikante Zusammenhange zwischen Korperzu-sammensetzung und Parodontitis; dabei warWHR der wichtigste Faktor gefolgt von BMI,FFM und S. Dies zeigt Parallelen zu anderenmit Fettleibigkeit einhergehenden Gesundheit-sproblemen und starkt die Argumentation, dass

Table 5. Regression curve estimations without the constant term (x35R2)

Periodontalattachmentloss (mean)

Pocket depth(mean)

Gingival bleeding(mean)

Calculus index(mean)

BMI 0.956 0.960 0.797 0.762WHR 0.983 0.988 0.810 0.778Log sum of S 0.944 0.949 0.772 0.744FFMBIA�K 0.955 0.961 0.775 0.756

All curves are cubic (x3), and all correlation coefficients are squared (R2).Po0.001 for all correlations.


Parodontitis und bestimmte systemische Erk-rankungen in Verbindung stehen, die mitFettleibigkeit einhergehen, wobei ein abnorma-ler Fettstoffwechsel moglicherweise ein wichti-ger Faktor ist.

Resume

Comparaison entre la composition corporelle etla maladie parodontale. En utilisant destechniques d’estimation nutritionnelle. Troi-sieme enquete nationale d’examen de la sante etde la nutrition. (NHANES III)Objectifs: L’objectif de cette etude etaitd’etudier l’association entre la compositioncorporelle (Obesite) et la maladie parodontaleen utilisant des techniques de mises enevidences nutritionnelles peu onereuses etsimples disponibles dans le troisieme bilan del’examen national de sante et de nutrition.(NHANES III).Materiels et Methodes: des sujets caucasiensages de 18 ans et plus, et ayant participe aNHANES III, furent utilises pour cette etude.Le poids, la taille, le tour de taille, le tour dehanche, l’epaisseur des plis de la peau (S) et desmesures du BIA furent utilises pour le calcul del’indice de masse corporel (BMI), le ratio taille-hanche (WHR)(graisse viscerale), log dessommes des S (graisses sous-cutanees), etmasse de graisse libre (FFM). Les donnees ontete analysees par SPSSs. Des analyses multi-variees ANOVA factoriel, a sens unique, et desanalyses de courbe de regression ont eterealisees. Po0.05 a ete utilise pour rejeterl‘hypothese nulle.Resultats: En ajustant pour l’age, le sexe,l’historique de diabete, la condition tabagique etsocio-economique, des correlations statistique-ment significatives ont ete trouvees entre laparodontite et WHR, BMI, FFM, et aussiparfois avec S.Conclusion: cette etude indique des correla-tions significatives entre la composition corpor-elle et la maladie parodontale. WHR est lefacteur le plus significatif suivi de BMI, FFM etS, et il y a des similarites avec d’autresproblemes de sante en relation avec l’obesite.Ceci renforce les arguments selon lesquels lamaladie parodontale et des maladies system-iques en relation avec l’obesite sont associees,avec comme facteur important une anomalie dumetabolisme graisseux.

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Address:

Nelson Wood

Department of Periodontics

University of Mississippi School of

Dentistry

2500 North State Street

Jackson, MS 39216-4505, USA

Fax:11 601 984 6120

e-mail: [email protected]


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