Journal of

Clinical Medicine
ArticleProspective Longitudinal Changes in the Periodontal Inflamed
Surface Area Following Active Periodontal Treatment for
Chronic Periodontitis
Yoshiaki Nomura 1 , Toshiya Morozumi 2, * , Atsushi Saito 3 , Atsutoshi Yoshimura 4 , Erika Kakuta 5 ,Fumihiko Suzuki 6 , Fusanori Nishimura 7 , Hideki Takai 8 , Hiroaki Kobayashi 9 , Kazuyuki Noguchi 10 ,Keiso Takahashi 11 , Koichi Tabeta 12 , Makoto Umeda 13 , Masato Minabe 2 , Mitsuo Fukuda 14 , Naoyuki Sugano 15 ,Nobuhiro Hanada 1 , Nobuo Yoshinari 16 , Satoshi Sekino 17 , Shogo Takashiba 18 , Soh Sato 19 ,Toshiaki Nakamura 10 , Tsutomu Sugaya 20 , Yohei Nakayama 8 , Yorimasa Ogata 8 , Yukihiro Numabe 17
and Taneaki Nakagawa 21
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Citation: Nomura, Y.; Morozumi, T.;

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Saito, A.; Yoshimura, A.; Kakuta, E.;
Suzuki, F.; Nishimura, F.; Takai, H.;

8

Kobayashi, H.; Noguchi, K.; et al.
Prospective Longitudinal Changes in
the Periodontal Inflamed Surface

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Area Following Active Periodontal
Treatment for Chronic Periodontitis.
J. Clin. Med. 2021, 10, 1165. https://10

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doi.org/10.3390/jcm10061165
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Academic Editor: Denis Bourgeois
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Received: 22 February 2021
Accepted: 8 March 2021

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Published: 10 March 2021
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with regard to jurisdictional claims inpublished maps and institutional affil-

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iations.
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Copyright: © 2021 by the authors.

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Licensee MDPI, Basel, Switzerland.
This article is an open access article

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distributed under the terms andconditions of the Creative Commons*

Department of Translational Research, Tsurumi University School of Dental Medicine,
Yokohama 230-8501, Japan; nomura-y@tsurumi-u.ac.jp (Y.N.); hanada-n@tsurumi-u.ac.jp (N.H.)
Division of Periodontology, Department of Oral Interdisciplinary Medicine, Graduate School of Dentistry,
Kanagawa Dental University, Yokosuka 238-8580, Japan; minabe@kdu.ac.jp
Department of Periodontology, Tokyo Dental College, Tokyo 101-0061, Japan; atsaito@tdc.ac.jp
Department of Periodontology and Endodontology, Nagasaki University Graduate School of Biomedical
Sciences, Nagasaki 852-8588, Japan; ayoshi@nagasaki-u.ac.jp
Department of Oral Microbiology, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan;
kakuta-erika@tsurumi-u.ac.jp
Division of Dental Anesthesiology, Department of Oral Surgery, Ohu University School of Dentistry,
Koriyama 963-8611, Japan; f-suzuki@den.ohu-u.ac.jp
Section of Periodontology, Division of Oral Rehabilitation, Faculty of Dental Science, Kyushu University,
Fukuoka 812-8582, Japan; fusanori@dent.kyushu-u.ac.jp
Department of Periodontology, Nihon University School of Dentistry at Matsudo, Matsudo 271-8587, Japan;
takai.hideki@nihon-u.ac.jp (H.T.); nakayama.youhei@nihon-u.ac.jp (Y.N.);
ogata.yorimasa@nihon-u.ac.jp (Y.O.)
Department of Periodontology, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental
University, Tokyo 113-8510, Japan; h-kobayashi.peri@tmd.ac.jp
Department of Periodontology, Kagoshima University Graduate School of Medical and Dental Sciences,
Kagoshima 890-8544, Japan; kazuperi@dent.kagoshima-u.ac.jp (K.N.); toshi-n@dent.kagoshima-u.ac.jp (T.N.)
Division of Periodontics, Department of Conservative Dentistry, Ohu University School of Dentistry,
Koriyama 963-8611, Japan; ke-takahashi@den.ohu-u.ac.jp
Division of Periodontology, Department of Oral Biological Science, Niigata University Graduate School of
Medical and Dental Sciences, Niigata 951-8514, Japan; koichi@dent.niigata-u.ac.jp
Department of Periodontology, Osaka Dental University, Hirakata 573-1121, Japan;
umeda-m@cc.osaka-dent.ac.jp
Department of Periodontology, School of Dentistry, Aichi Gakuin University, Nagoya 464-8650, Japan;
fukuda-m@dpc.agu.ac.jp
Department of Periodontology, Nihon University School of Dentistry, Tokyo 101-8310, Japan;
sugano.naoyuki@nihon-u.ac.jp
Department of Periodontology, School of Dentistry, Matsumoto Dental University, Shiojiri 399-0781, Japan;
nobuo.yoshinari@mdu.ac.jp
Department of Periodontology, School of Life Dentistry at Tokyo, The Nippon Dental University,
Tokyo 102-8159, Japan; sekino-s@tky.ndu.ac.jp (S.S.); numabe-y@tky.ndu.ac.jp (Y.N.)
Department of Pathophysiology-Periodontal Science, Okayama University Graduate School of Medicine,
Dentistry and Pharmaceutical Sciences, Okayama 700-8525, Japan; stakashi@okayama-u.ac.jp
Department of Periodontology, School of life Dentistry at Niigata, The Nippon Dental University,
Niigata 951-8580, Japan; s-sato@ngt.ndu.ac.jp
Division of Periodontology and Endodontology, Department of Oral Health Science, Hokkaido University
Graduate School of Dental Medicine, Sapporo 060-8586, Japan; sugaya@den.hokudai.ac.jp
Department of Dentistry and Oral Surgery, School of Medicine, Keio University, Tokyo 160-8582, Japan;
tane@z6.keio.jp
Correspondence: morozumi@kdu.ac.jp; Tel.: +81-46-822-8855

Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Abstract: Periodontal disease is a chronic inflammatory disease of the periodontal tissue. The periodontal
inflamed surface area (PISA) is a proposed index for quantifying the inflammatory burden resulting

J. Clin. Med. 2021, 10, 1165. https://doi.org/10.3390/jcm10061165

https://www.mdpi.com/journal/jcm

J. Clin. Med. 2021, 10, 1165

2 of 13from periodontitis lesions. This study aimed to investigate longitudinal changes in the periodontal
status as evaluated by the PISA following the active periodontal treatment. To elucidate the prognostic
factors of PISA, mixed-effect modeling was performed for clinical parameters, tooth-type, and levelsof periodontal pathogens as independent variables. One-hundred-twenty-five patients with chronic
periodontitis who completed the active periodontal treatment were followed-up for 24 months, withevaluations conducted at 6-month intervals. Five-times repeated measures of mean PISA values were130+/−173, 161+/−276, 184+/−320, 175+/−417, and 209+/−469 mm2 . Changes in clinical parametersand salivary and subgingival periodontal pathogens were analyzed by mixed-effect modeling. Plaqueindex, clinical attachment level, and salivary levels of Porphyromonas gingivalis were associated withchanges in PISA at the patient- and tooth-level. Subgingival levels of P. gingivalis and Prevotella intermedia
were associated with changes in PISA at the sample site. For most patients, changes in PISA were within
10% of baseline during the 24-month follow-up. However, an increase in the number of bleeding sites ina tooth with a deep periodontal pocket increased the PISA value exponentially.
Keywords: periodontal inflamed surface area; periodontal pathogen; mixed effect modeling;follow-up study

1. Introduction
Periodontal disease is a chronic inflammatory disease of the periodontal tissue. Its
progression has been associated with a regression of the mean clinical parameters of mostof the oral sites, including teeth. Most patients are generally stable or exhibit a linear typeof progression [1–4]. However, a small fraction exhibit burst-type progressions [1,5,6].
The active periodontal treatment aims to reduce inflammatory response by removing
pathogenic bacterial deposits. Following the active treatment, supportive periodontal
therapy (SPT) is employed to reduce the probability of periodontal disease progression.
The long-term successful SPT prevents tooth loss [7]. The reported rate of tooth loss is 10%of teeth [8–15] in 20% of patients [16] over a period of 10 years. Further, tooth loss has beenshown to occur in a small fraction of patients during SPT [8,17]. Therefore, monitoring of
periodontal conditions during SPT is important for a successful treatment [18]. Several
indices have been routinely used for this purpose. The probing pocket depth (PD) andclinical attachment level (CAL) of the periodontal pocket are morphological outcomes of
periodontal disease. However, these outcomes do not quantify the proportion of inflamed
periodontal tissue. Additionally, summary statistics of these outcomes using the meanor maximum values results in information loss [1]. The mean values of these outcomes
obscure the small number of sites that exhibit a substantial progress, and the maximum
value represents only one of 168 probing sites. Therefore, site-level evaluation is importantin the clinical setting. During the follow-up period, the periodontal tissue should be
evaluated in terms of the morphological and inflammatory response status.
The periodontal inflamed surface area (PISA) is a convenient index that quantifies
the surface area of the bleeding pocket epithelium in square millimeters and is calculatedusing conventional clinical parameters of periodontal health, namely, bleeding on probing(BOP) combined with either PD or CAL, and gingival recession [19]. Thus, PISA may
represent the inflammation status of the patients as one continuous variable, in which thecalculation of mean or maximum value is not necessary. Several studies have utilized the
PISA for evaluating the periodontal status. However, these studies have focused on the
cross-sectional correlation of the PISA and non-communicable diseases [20–28]. There have
only been a few longitudinal studies regarding the changes in PISA [29].
This study aimed to investigate the changes in periodontal status, as evaluated by the
PISA during the longitudinal follow-up care after an active treatment, and to identify thefactors that affect the PISA.

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2. Materials and Methods
2.1. Study Design and Ethics Approval
This study is part of a clinical research project performed by The Japanese Society of
Periodontology. A 24-month follow-up study was performed with 163 patients who hadcompleted an active periodontal treatment (such as initial therapy or periodontal surgery) at
17 facilities in Japan. The end point of those active periodontal treatments was periodontal
examination. If the lesion was considered to be dormant, such as a probing pocket depth of
4 mm or more without BOP, the disease was considered stable [30]. In addition, those patientswere the candidates for this study. The follow-up patients were seen trimonthly, and thesupragingival plaque and calculus were removed if detected. The details of changes in theclinical parameters during the follow-ups have been described in our pervious report [1,31,32].
The study was conducted in compliance with the principles outlined in the Helsinki Declaration.
A written informed consent was obtained from each study participant, and the protocol wasapproved by the Institutional Review Board of each participating institution.
2.2. Inclusion Criteria
The inclusion criteria were as follows: The severity of periodontitis on the initial
visit was generalized moderate–to-severe chronic periodontitis (generalized periodontitis,
stage III or IV, grade B) [33,34], patients who were systemically healthy; aged >30 years;>20 remaining teeth; and not taking antibiotics, anti-inflammatory drugs or immunosuppressive drugs for 3 months before the start of follow-up.
2.3. Diagnosis and Evaluation
Each patient was diagnosed according to the Guidelines of the American Academyof Periodontology [33]. Oral examinations were carried out by one examiner from each
institute (T.M., A.Y., F.S., H.T., H.K., K.N., M.M., M.F., N.S., N.Y., S.S., S.S., T.N., T.S.,
Y.O., Y.N. and T.N.). Each examiner was a periodontist licensed by the Japanese Societyof Periodontology. Intra- and inter-examiner calibration sessions were conducted using
periodontal disease models (P15FE-500HPRO-S2A1-GSF) at the beginning and middle ofthe study period. In brief, full-mouth PD and gingival recession were measured twice,and repeatability for the CAL was assessed. The examiner was judged to have madereproducible measurements after reaching a percentage of agreement within ±1 mm
between repeated measurements of at least 95% of measurements.
The PD, BOP, and CAL were measured at six sites per tooth (mesiobuccal, buccal,
distobuccal, mesiolingual, lingual, and distolingual) with a periodontal probe (CP-12 ColorCoded Probe; Hu-Friedy, Chicago, IL, USA). The PISA was calculated using the Excel sheet
program [19]. By inputting the BOP and PD data from six sites per tooth, the PISA valuesof each tooth were calculated. The sum of the PISA values is automatically calculated. Thisvalue is the summary statistics of PISA for each individual.
The plaque index (PlI) was recorded at four sites per tooth (mesial, buccal, distal, andlingual). The degree of tooth mobility was scored on a four-point scale (0–3) at the toothlevel. All clinical parameters were recorded at baseline and after 6, 12, 18, and 24 months,
the third molars were excluded.
2.4. Evaluation of Periodontal Pathogens
Three periodontal pathogens in the saliva and subgingival plaque were measured:
Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, and Prevotella intermedia.
These samples were collected from each patient at every visit. Briefly, whole saliva wascollected by asking the patient to chew on a gum base for 5 min [31], and the subgingival
plaque from the deepest pockets (except for those at the third molars) was obtained byconsecutive insertion of two sterile paper points into the periodontal pocket for 10 s per
point [32]. These three periodontal pathogens and the total bacteria in the subgingival
plaque and saliva were counted using a modification of the Invader PLUS assay [35,36].
Bacterial ratios (%) for each species were also determined.

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Briefly, a quantitative analysis of the total bacterial count and periodontopathic bacterial counts, including P. gingivalis, P. intermedia, and A. actinomycetemcomitans, was performed using a modification of the Invader PLUS assay [31,32,35,36]. The bacterial DNA
was extracted from the subgingival plaque samples from the deepest pockets by suspending each plaque sample in 1 mL of phosphate-buffered saline (pH 7.4) and processed using
the MagNA Pure LC Total Nucleic Acid Isolation Kit (Roche, Basel, Switzerland) according
to the manufacturer’s instructions. Similarly, bacterial DNA was extracted from the 100-µL
whole saliva samples using the MagNA Pure LC Total Nucleic Acid Isolation Kit. The
individual sequences of each bacterial species were obtained from a public database (National Center for Biotechnology Information, Bethesda, MD, USA). Primers for each species
were designed based on a region of the 16S rRNA gene. A pair of universal primers and auniversal probe were used to determine the total number of bacteria. Primary probes andInvader oligos were designed using the Invader technology creator (HOLOGIC, Madison,
WI, USA) and were based on sequences in the amplified regions [31,32,35–37].
The template DNA was added to a 15-µL reaction mixture containing primers foreach species [50 µM deoxynucleoside triphosphate (dNTP), 700 nM primary probe, 70 nM
Invader oligo, 2.5 U polymerase chain reaction (PCR) enzyme (EagleTaq DNA polymerase,
Roche, Basel, Switzerland), and the Invader core reagent kit (Cleavase XI Invader corereagent kit, HOLOGIC, Madison, WI, USA) containing a fluorescence resonance energy
transfer (FRET) mix and an enzyme/MgCl2 solution]. The reaction mixture was preheatedat 95 ◦ C for 20 min, and a two-step polymerase chain reaction (95 ◦ C for 1 s and 63 ◦ C
for 1 min) was performed for 35 cycles using an ABI PRISM 7900 thermocycler (Applied
Biosystems, Foster City, CA, USA). The fluorescence values of carboxyfluorescein (FAM;
wavelength/bandwidth: Excitation, 485/20 nm; emission, 530/25 nm) were measured at
the end of the incubation/extension step at 63 ◦ C for each cycle.
The limit of detection for this method was determined for each species with dilutionsof bacterial DNA. Standard curves were constructed based on a crossing point determinedby the fit-point method.
The proportions of the three pathogens within the total bacterial count were subsequently determined [38,39]. Bacterial ratios (%) and bacterial counts (log10 ) for each species
were also used in various comparison and diagnostic analyses.
2.5. Statistical Analysis
2.5.1. Mixed-Effect Modeling
Changes in the PISA were analyzed at the patient-level and tooth-level by mixedeffect modeling [1,40–44]. The data structure of the PISA was five-time repeated measures.
The PISA values for each tooth had a hierarchical structure in the patients. Generalized
mixed-effect modeling was applied using the following formula:
Model 1: Patient-level PISA
−L1 : PISA(Subject level )i =π0 + π1 ( Age)i + π2 (Sex )i + π4 (Salivary levels o f A. a)i + π5 (Salivary levels o f P. g)i5(m)+π6 (Salivary levels o f P. i )i + π7 (mean CAL)i + π8 ( Pli )i + ∑ π9 (time) j + ε i
m =1



ei ∼ N 0, δe2
Fixed effect;
Patient-level: Age, sex, time, salivary levels of A. actinomycetemcomitans, P. gingivalis, and P. intermedia, mean CAL, PlI, time
Random effect: Patients, random intercept
Covariance Type: AR1
Link functions: Gamma(1)

J. Clin. Med. 2021, 10, 1165

5 of 13Model 2: Tooth-level PISA
− L1 : PISA( Tooth level )ij =π0j + π1ij ( Age)ij + π2j (Sex )ij + π3j (Salivary levels o f A. a)ij + π4j (Salivary levels o f P. g)ij
+π5j (Salivary levels o f P. i )ij + ε ij

3

6

5

− L2 : π0j = β 0j + β 1j ( Mean CAL) + β 2j ( Pli ) + ∑ β 3j (tooth mobility) j + ∑ β 4j ( Tooth type) j + ∑ β 05 (time) j + r j
(m)

m =1

(m)

m =1

(m)

(1)

(2)

m =1





eij ∼ N 0, δe2 , roj ∼ N 0, δr2
Fixed effect;
Patient-level: Age, sex, days, salivary levels of A. actinomycetemcomitans, P. gingivalis, and P. intermedia
Tooth-level: Mean CAL, PlI, tooth mobility, tooth type, time
Random effect: Patients, random intercept
Covariance Type: AR1
Link functions: Gamma

Model 3: PISA of the sampling teeth for subgingival periodontal pathogens−L1 : PISA(Sampling teeth)i =π0 + π1 (% o f A.a in subgingival taotal bacteria)i+π2 (% o f A.a in subgingival taotal bacteria)i + π3 (% o f P. g in subgingival taotal bacteria )i
5

(1)

(m)

+π4 (% o f P. i in subgingival taotal bacteria)i + ∑ π5 (time) j + ε i
m =1



ei ∼ N 0, δe2
Fixed effect;
Patient-level: Proportion of A. actinomycetemcomitans, P. gingivalis, and P. intermedia in the total subgingival bacteria, time
Random effect: Patients
Covariance Type: AR1
Link functions: Gamma

2.5.2. Cluster Analysis
A cluster analysis was carried out using K-means. The data used for the cluster
analysis were sequential data obtained from five-time repeated measures of PISA at thepatient level. The parameter settings for the cluster analysis were: Distance measure: Log
likelihood, and the criteria of clusters were Bayesian Information Criterion (BIC) [−2 ln (L)+ k ln (L)]. The generated cluster was used for the analysis.
All analyses were performed using SPSS Statistics version 24.0 (IBM, Tokyo, Japan).
3. Results
3.1. Patient Characteristics
Among the 163 patients enrolled in the study, 38 patients were disqualified for lackof data due to a variety of causes such as earthquake, failure to comply with visit, use of
antimicrobial agents for acute periodontal abscesses, and tooth extraction for root fracture.
Thus, data from a total of 125 patients, 3107 teeth, and their five-time repeated measure
PISA values were obtained. A total of 15,535 data were analyzed from the teeth. The
study population comprised 50 men and 75 women and the mean age was 59.2 ± 8.7 years.

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Clinical examinations and measurement of periodontal pathogens were carried out at every
visit. Five-time repeated measures of these parameters were analyzed.
3.2. Analysis of the Change in PISA at the Patient-Level
Factors correlated with the PISA were identified using generalized mixed-effect modeling.
The results are shown in Table 1. The salivary levels of P. gingivalis, mean CAL, and PlI wereidentified as significant factors affecting the PISA. Time was not statistically significant.
Table 1. Patient-level analysis of factors affecting the periodontal inflamed surface area (PISA).

Sex
Age (years)
Salivary levels of A. a (%)
Salivary levels of P. g (%)
Salivary levels of P. i (%)
Mean CAL (mm)
PlITime (6 months)
Intercept

Coefficient (95% CI)

p-Value−0.124 (−0.507–0.258)
0.012 (−0.010–0.033)−3.275 (−39.368–32.818)
1.141 (0.148–2.136)
0.206 (−0.282–0.694)
0.737 (0.540–0.934)
1.069 (0.637–1.500)
0.008 (−0.030–0.045)
1.338 (0.009–2.677)

0.524
0.276
0.859
0.025
0.408
<0.001<0.001
0.693
0.048

Fitness index, BIC: 1317; AICC, 1326; PlI: Plaque index; CAL: Clinical attachment level; A. a: Aggregatibacter actinomycetemcomitans; P. g: Porphyromonas gingivalis; P. i: Prevotella intermedia; CI: Confidence interval;
AICC = −2 L + 2 k (k − 1)/(n – k − 1);BIC = −2 L + k ln (n); L: Likelihood; k: Number of parameters; n: Sample size.

3.3. Analysis of the Change in PISA at the Tooth-Level
The sum of the PISA of each tooth was calculated as a dependent variable. To determine the effect of tooth type, a multilevel mixed-effect model was used for the analysis. As
shown in Table 2, the salivary levels of P. gingivalis were identified as a significant factor at
the patient-level. The CAL, the PlI, and tooth mobility >2 were identified as significant factors at the tooth-level. Regarding the tooth type, the maxillary molar and premolar tended
to increase the PISA during the follow-up period. Time was not statistically significant.
Table 2. Tooth-level analysis of factors affecting the PISA.
Coefficient (95% CI)

p-Value

Patient-level variables−0.002 (−0.010–0.007)−0.076 (−0.225–0.074)−3.624 (−17.577–10.329)
0.669 (0.323–1.015)−0.028 (−0.230–0.173)

Age
Sex
Salivary levels of A. a (%)
Salivary levels of P. g (%)
Salivary levels of P. i (%)

0.736
0.320
0.611
<0.001
0.782

Tooth-level variables
Mean CAL/teeth (mm)PlI/teeth
Tooth mobility

Tooth-type

0
1
2 and 3
Mandibular anterior
Mandibular premolar
Mandibular molar
Maxillary anterior
Maxillary premolar
Maxillary molar
TimeIntercept

0.293 (0.266–0.320)
0.234 (0.157–0.310)
Reference−0.044 (−0.136–0.048)
0.697 (0.472–0.923)
Reference−0.033 (−0.124–0.058)−0.016 (−0.105–0.074)−0.116 (−0.197–−0.035)
0.444 (0.359–0.530)
0.680 (0.590–0.770)−0.004 (−0.022–0.014)
1.516 (0.972–2.061)<0.001<0.001
0.348<0.001
0.477
0.732
0.005
<0.001<0.001
0.632<0.001

Fitness index, AICC: 11,732.53; BIC: 11,745.40; PlI: Plaque index; CAL: Clinical attachment level; A. a: Aggregatibacter actinomycetemcomitans; P. g: Porphyromonas gingivalis; P. i: Prevotella intermedia; AICC = −2 L + 2 k (k − 1)/(n – k − 1); BIC = −2 L + k ln (n); L: Likelihood; k: Number of parameters; n: Sample size.

J. Clin. Med. 2021, 10, 1165

7 of 133.4. Classification of the Changes in the PISA

J. Clin. Med. 2021, 10, 1165

The change in the PISA was dependent on the tooth-type (Figure S1). The PISA ofmaxillary molars increased throughout the follow-up period, while the PISA of othertooth-types were relatively stable.
A cluster analysis was carried out to characterize the changes in the PISA. The resultsof the cluster analysis and changes in the PISA in each cluster are shown in Figure 1. Most
8 of 14of the patients were classified as Cluster 1, and the PISA of this cluster was stable. A
minority of patients were classified to other clusters, which exhibited drastic changes in
the PISA during the follow-up period.

Figure 1. Changes in the PISA according to the clusters. For 117 (93.6%) patients, the PISA was stable during the 24-monthfollow-up
period. However,a minority
exhibiteda drastic
increaseor thefluctuation
instablethe PISA.
Figure 1. Changes
in the PISA
accordingoftopatientsthe clusters.
For 117(93.6%)
patients,
PISA wasduring the 24-monthfollow-up period. However, a minority of patients exhibited a drastic increase or fluctuation in the PISA.

A minority of the teeth may have had an effect on the overall PISA. The teeth withPISAA>500
were ofidentified
changesin effect
the PISAof theseteeth
areTheillustratedinminority
the teethandmaythehave
had anon theoverall
PISA.
teeth with
Figure
2.
A
total
of
seven
teeth
(maxillary
2nd
molarsormandibular
2nd
molars)had
PISA
PISA >500 were identified and the changes in the PISA of these teeth are illustrated in
>500
at 2.
least
onceofduringfollow-up
period.
drastic
increase in 2nd
the overall
FigureA totalseventheteeth(maxillary
2nd Themolarsor mandibularmolars)PISA
had
was
primarily
dependentontheincreaseinthenumberofBOPsites.
PISA >500 at least once during the follow-up period. The drastic increase in the overall

PISA was primarily dependent on the increase in the number of BOP sites.

J. Clin. Med. 2021, 10, 1165

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J. Clin. Med. 2021, 10, 1165

9 of 14

Figure 2. Changes
in the PISA at the tooth-level in teeth with PISA > 500 during the 24-month
Figure 2. Changes in the PISA at the tooth-level in teeth with PISA > 500 during the 24-month folCommented [MRed lines indicate
the maxillary2nd
2nd molars
andandblue lines
follow-up period.low-up
Red period.
lines indicate
the maxillary
molarsblueindicatelines mandibular
indicate mandibular
Please make a
2nd molars. Seven teeth exceed 500 PISA. The numbers indicate the mean probing depth of each
2nd molars. Seventeeth
exceedPISA.
numbers
thechanges
meanin probing
depth of each
toothand numberof 500bleedingsites Theon probing.
In theseindicate
cases, drastic
the PISA werepredominantly
dependent
on theincrease inIn
thethesenumberof bleedingsiteschanges
on probing
tooth and numberof bleedingsites onprobing.
cases,
drastic
in. the PISA werepredominantly dependent on the increase in the number of bleeding sites on probing.
3.5. Association between Periodontal Pathogens and the Change in the PISA

Mixed-effectmodeling
was employedevaluatethe local
association
3.5. Association between
Periodontal
PathogensandtotheChangein thePISA between periodontal pathogens and the tooth-level PISA. The results are shown in Table 3. The PISA

of thesample teeth
wasemployedincluded as a todependent
variable.
periodontal
pathogens,P.
Mixed-effectmodeling
wasevaluate
the Thelocal
association
betweenperigingivalis
P. tooth-levelintermedia, werePISA.
observed
to beresults
significantly
withthe PISA
odontal pathogens
andandtheTheare associatedshown in
Table
3. of
The PISA
the sample teeth. Time was not a significant factor for PISA. The list of the sample teeth isof the sample teeth
included
as a dependent variable. The periodontal pathogens,shownwasin Table
S1.
P. gingivalis and P. intermedia, were observed to be significantly associated with the PISA of
Table 3. Association between subgingival periodontal pathogens and the changes in the PISA of
the sample teeth.
Time was not a significant factor for PISA. The list of the sample teeth is
the sample sites.
shown in Table S1.
PISA
Coefficient (95% CI)

p-Value

0.116 (0.024–0.208)

0.014

Table 3. Association between
in the PISA of theA. asubgingival periodontal−0.221pathogens(−1.054–0.612) and the changes0.602
P. g
0.033 (0.022–0.044)<0.001
sample sites.
P. i

PISA

A. a
P. g
P. i
Time
Intercept

Coefficient (95% CI)

p-Value−0.221 (−1.054–0.612)
0.033 (0.022–0.044)
0.116 (0.024–0.208)
0.033 (−0.010–0.077)
2.847 (2.377–3.070)

0.602<0.0010.014
0.130<0.001

Fitness index: For PISA, AICC: 734, BIC: 741; CAL: Clinical attachment level, A. a: Aggregatibacter actinomycetemcomitans, P. g: Porphyromonas gingivalis, P. i: Prevotella intermedia, CI: Confidence interval. AICC = − 2 L + 2 k (k − 1)/(n – k − 1), BIC = −2 L + k ln (n). L: Likelihood; k: Number of parameters; n: Sample size.

4. Discussion
This study aimed to investigate the changes in the periodontal status, as evaluated by
the PISA during a longitudinal follow-up, and to identify the factors that associate with
the PISA.

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The mixed-effect models used in this study did not include BOP and PD as independent variables as the PISA was calculated using these variables. The PlI and CAL
were observed to be significantly associated with the PISA at both the patient-level andtooth-level. Thus, the results indicated that the PISA was associated with the oral hygiene
status and the morphology of periodontal tissue. A previous report has indicated thatloss of CAL is associated with poor oral hygiene [45], change in the PD is associated with
BOP [46], the PlI is associated with tooth loss [47], and changes in the PD are associatedwith tooth-type and tooth mobility [48]. These findings suggest that changes in the clinicalparameters after an active periodontal treatment are interactive, and that deteriorations inthese clinical parameters ultimately lead to tooth loss.
As shown in Tables 1–3, time was not significantly associated with the PISA. Thisfinding suggests that the PISA was a stable clinical parameter during the follow-up period.
In contrast, it has been previously reported that time is significantly associated with changes
in the CAL, with a positive coefficient in the mixed-effect modeling [1]. During the followup period in our study, the CAL deteriorated gradually. In the site-level analysis, mostof the CAL measurements were stable. A small fraction of sites with progressive loss of
CAL affected the overall CAL. As shown in Figure 1, most of the patients were classified
as Cluster 1 and the PISA of this cluster was stable. However, a drastic increase in the
PISA in one tooth had a large effect on the PISA at the patient-level. In cases of deep
periodontal pockets, the increase in the number of bleeding sites increased the PISA value
exponentially. In this sense, the PISA is more sensitive than BOP%. As shown in Figure 2,
the drastic increase in the PISA was derived from the increase in bleeding sites rather thandeep periodontal pockets. Thus, PISA is a sensitive clinical parameter for identifying the
progression of inflamed periodontal tissue.
The salivary levels of P. gingivalis were significantly associated with the PISA at
the patient-level and tooth-level. Additionally, the subgingival levels of P. gingivalis and
P. intermedia were significantly associated with the PISA of the periodontal pocket ofthe sample sites. Previous studies have shown that bacteria levels in the subgingival
plaque and in whole saliva are also significantly associated with the PISA [49–54]. Further,levels of P. gingivalis in the subgingival plaque from the deepest pockets have shown to
be associated with the progression of periodontitis [32], and levels of P. gingivalis and
P. intermedia in the subgingival plaque have shown to be associated with the progressionof periodontal inflammation [55]. P. intermedia is also correlated with BOP and deeper
pockets [56,57]. One study reports that reinfection of the treated sites by P. gingivalis
and/or P. intermedia diminishes the effect of therapy during the follow-up [58], and that anincrease in periodontal pathogens increases the PISA. Several studies have also indicatedthat the adjunctive use of antimicrobial agents during the follow-up enhances the effect of
mechanical debridement [59]. Therefore, when the PISA increases during the follow-up
period, monitoring periodontal pathogen levels and the adjunctive use of antimicrobial
agents may be useful for successful long-term treatment.
Our study has some limitations. First, there was no data regarding the initial mean
PISA values of the patients before the periodontal treatment. Thus, we were unable toobserve any change in the PISA over the entire treatment period of the patient. Second,
all patients were systemically healthy. We enrolled patients with periodontitis and without systemic disease as the effect of systemic inflammation can serve as a bias for PISA.
Therefore, the association between PISA and the etiology of periodontal disease due tothe acquired risk factors was not elucidated. Third, intra- and inter-examiner calibration
sessions were conducted using a periodontal disease model rather than in patients due tothe large number of examiners required in a multicenter study [60].
5. Conclusions
For most of the patients, changes in the PISA during the follow-up period after anactive periodontal treatment were within 10% of the baseline. However, an increase in the

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number of bleeding sites in a tooth with a deep periodontal pocket was associated with an
exponential increase in the PISA.
Supplementary Materials: The following are available online at https://www.mdpi.com/2077-038
3/10/6/1165/s1. Figure S1: Changes in PISA by tooth-type during the follow-up period; Table S1:
List of sample teeth for the periodontal pathogen.
Author Contributions: Conceptualization, Y.N. (Yoshiaki Nomura) and T.M.; methodology, Y.N.
(Yoshiaki Nomura); software, Y.N. (Yoshiaki Nomura); validation, E.K. and N.H.; formal analysis,
Y.N. (Yoshiaki Nomura); investigation, T.M., A.S., A.Y., F.S., F.N., H.T., H.K., K.N., K.T., M.U.,
M.M., M.F., N.S., N.Y., S.S. (Satoshi Sekino), S.S. (Soh Sato), T.N. (Toshiaki Nakamura), T.S., Y.N.
(Yohei Nakayama), Y.O., Y.N. (Yukihiro Numabe) and T.N. (Taneaki Nakagawa); data curation, S.T.;
writing—original draft preparation, Y.N. (Yoshiaki Nomura); writing—review and editing, Y.N.
(Yoshiaki Nomura), T.M. and K.T.; visualization, Y.N. (Yoshiaki Nomura); supervision, T.M.; project
administration, T.M. and T.N. (Taneaki Nakagawa); funding acquisition, T.N. (Taneaki Nakagawa).
All authors have read and agreed to the published version of the manuscript.
Funding: This research was funded by a clinical research project grant from the Japanese Society of
Periodontology for the diagnosis of periodontitis.
Institutional Review Board Statement: The names of the ethics committee members and their reference numbers are as follows: The regional ethical committee of the Faculty of Dentistry, Niigata
University (20-R17-08-06); Keio University School of Medicine, Ethics Committee (20080096); EthicalCommittee of Kagoshima University Medical and Dental Hospital (20-58); The Ethics Committee,
Nagasaki University Graduate School of Biomedical Sciences (0846-2); Ethical Committee of Kyushu
University Faculty of Dental Science (20-11); The Ethics Committee of Osaka Dental University(80712); Ethics Committee of Aichi Gakuin University, School of Dentistry (158); The Ethics Committee of Matsumoto Dental University (0090); The Institutional Review Board of Nippon Dental
University (2-1-22); Ethical Committee of Nihon University School of Dentistry (EP08D016); Dental
Research Ethics Committee of Tokyo Medical and Dental University (660); Ethics Committee in Nihon
University School of Dentistry at Matsudo (EC 08-014); Ethics Committee of Tokyo Dental College(208); The Ethical Review Committee of The Nippon Dental University School of Life Dentistry at
Niigata (151); Ohu University Research Ethics Committee (52); Institutional Review Board for ClinicalResearch of Hokkaido University Hospital (008-0113).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: The data presented in this study are available on request from thecorresponding author. The data are not publicly available due to privacy.
Acknowledgments: The authors thank Hiromasa Yoshie, Toshihide Noguchi, Masamitsu Kawanami,
Koichi Ito, Yuichi Izumi, Yoshitaka Hara, Osamu Fujise, Yuzo Abe, Tomoo Kono, Asako Makino-Oi,
and Chie Fukaya, for their advice and helpful comments.
Conflicts of Interest: The authors declare no conflict of interest.

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