mahidol university · thesis entitled the association between periodontitis and chronic kidney...

THE ASSOCIATION BETWEEN PERIODONTITIS AND CHRONIC KIDNEY DISEASE

ATTAWOOD LERTPIMONCHAI

A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF DOCTOR OF PHILOSOPHY (CLINICAL EPIDEMIOLOGY)

FACULTY OF GRADUATE STUDIES MAHIDOL UNIVERSITY

2017

COPYRIGHT OF MAHIDOL UNIVERSITY

Thesis entitled


............................................................. Mr. Attawood Lertpimonchai Candidate ............................................................. Asst. Prof. Sasivimol Rattanasiri, Ph.D. (Statistics) Major advisor ... ......................................................... ............................................................. Prof. Piyamitr Sritara, Assoc. Prof. Ammarin Thakkinstian, M.D., FRCPT, FACP, FRCP (T) Ph.D. (Clinical Epidemiology & Co-advisor Community Medicine) Co-advisor ............................................................ ............................................................ Asst. Prof. Chantrakorn Champaiboon, Assoc. Prof. Atiporn Ingsathit, D.D.S., Ph.D. (Oral Biology) M.D., Ph.D. (Clinical Epidemiology) Co-advisor Co-advisor

............................................................ ........................................................... Assoc. Prof. Varaporn Akkarapatumwong, Assoc. Prof. Ammarin Thakkinstian, Ph.D. (Science) Ph.D. (Clinical Epidemiology & Acting Dean Community Medicine) Faculty of Graduate Studies Program Director Mahidol University Doctor of Philosophy Program in Clinical Epidemiology Faculty of Medicine, Ramathibodi Hospital, Mahidol University

Thesis entitled


was submitted to the Faculty of Graduate Studies, Mahidol University

for the degree of Doctor of Philosophy (Clinical Epidemiology) on

November 1, 2017

............................................................. Mr. Attawood Lertpimonchai Candidate .. ........................................................... Asst. Prof. Chusak Okascharoen M.D., Ph.D. (Clinical Epidemiology) Chair ............................................................ .... ......................................................... Prof. Piyamitr Sritara, Asst. Prof. Sasivimol Rattanasiri, M.D., FRCPT, FACP, FRCP (T) Ph.D. (Statistics) Member Member ............................................................ ............................................................. Prof. Rangsini Mahanonda, Assoc. Prof. Atiporn Ingsathit, D.D.S., Ph.D. (Oral Biology) M.D., Ph.D. (Clinical Epidemiology) Member Member ............................................................ ............................................................. Asst. Prof. Chantrakorn Champaiboon, Assoc. Prof. Ammarin Thakkinstian, D.D.S., Ph.D. (Oral Biology) Ph.D. (Clinical Epidemiology & Member Community Medicine) Member

……………………………………... ……………………………………... Assoc. Prof. Varaporn Akkarapatumwong, Prof. Piyamitr Sritara, Ph.D. (Science) M.D., FRCPT, FACP, FRCP (T) Acting Dean Dean Faculty of Graduate Studies Faculty of Medicine Ramathibodi Mahidol University Hospital Mahidol University

iii

ACKNOWLEDGEMENTS

The success of this thesis can be succeeded by the attentive support from

my major advisor, Asst. Prof. Sasivimol Rattanasiri, the Program director, Prof Dr.

Ammarin Thakkinstian for the precious instruction and advice in all processes of my

PhD. I am heartily thankful to my co-advisors, Asso. Prof. Dr. Atiporn Ingsathit, and

Asst. Prof. Chantrakorn Champaiboon for excellence recommendations and intuitive

ideas in specific fields.

I wish to express my appreciation to Prof. Piyamitr Sritara, Prof. Rangsini

Mahanonda and Assoc. Prof. Suphot Tamsailom for introducing the EGAT project to

me and allowed to utilize the EGAT data. I also appreciate the dedication and continuing

support on statistical analysis from Dr. Win Khaing in this project.

Finally, I would like to express endless thankfulness to all my families for

their support throughout my study.

This thesis was supported by Chulalongkorn University (Government

Budget Grant 2015)

Attawood Lertpimonchai

Attawood Lertpimonchai Background and rationale / iv


ATTAWOOD LERTPIMONCHAI 5638086 RACE/D

Ph.D. (CLINICAL EPIDEMIOLOGY)

THESIS ADVISORY COMMITTEE: SASIVIMOL RATTANASIRI, Ph.D., AMMARIN

THAKKINSTIAN, Ph.D., ATIPORN INGSATHIT, M.D., Ph.D., CHANTRAKORN

CHAMPAIBOON, D.D.S., Ph.D., PIYAMITR SRITARA, M.D., FRCPT.

ABSTRACT

Periodontitis and diabetes mellitus (DM) are suspected to be risk factors of chronic

kidney disease (CKD), but whether they were directly associated or mediated across each other is

still unknown. Therefore, this study aimed to determine the direct and mediation effects of

periodontitis through DM on CKD or vice versa. This was 10-year-retrospective non-CKD cohort

study that examined 2,635 employees of the Electric Generation Authority of Thailand (EGAT). The

interested outcome was the CKD incidence, defined as estimated glomerular filtration rate (eGFR)

< 60 ml/min per 1.73 m2 at follow-up. The periodontitis extent using the percentage of proximal sites

with severe periodontitis (Clinical attachment loss ≥ 5 mm) was used as the representativeness of

periodontal status. Mediation analysis with 1000-replication bootstrap was applied to construct 2

possible causal pathways, pathway A (periodontitis → DM → CKD), and pathway B (DM →

periodontitis → CKD). The results showed that the cumulative incidence of CKD was 1.03 cases per

100 persons per year (95% CI: 0.91, 1.16). In pathway A, each increasing percentage of proximal

sites with severe periodontitis increased the adjusted odds ratio of CKD 1.010 (95% CI: 1.005, 1.015)

and 1.007 (95% CI: 1.003, 1.013), by direct effect and indirect effect through DM, respectively.

While, pathway B indicated that the participants with DM had odds of CKD incidence around 2

times higher than the non-DM, and this effect was attributed to 6.5% mediation effect via

periodontitis. It is therefore concluded that periodontitis had significant direct and indirect effect via

DM on CKD, and it is further suggested that awareness of oral and systemic morbidities from

periodontitis should be emphasized by public health practitioners.

KEY WORDS: PERIODONTITIS / CHRONIC KIDNEY DISEASE / DIABETES MILLITUS /

MEDIATION ANALYSIS

182 pages

Fac. of Grad. Studies, Mahidol Univ. Ph.D. (Clinical Epidemiology) / v

ความสมพนธของโรคปรทนตอกเสบกบโรคไตเร9อรง


อรรถวฒ เลศพมลชย 5638086 RACE/D

ปร.ด. (วทยาการระบาดคลนก)

คณะกรรมการทEปรกษาวทยานพนธ: ศศวมล รตนสร, Ph.D., อมรนทร ทกขญเสถยร, Ph.D., อตพร องคสาธต, M.D., Ph.D., จนทรกร แจมไพบลย D.D.S., Ph.D., ปยะมตร ศรธรา, M.D., FRCPT.

บทคดยอ

โรคปรทนตอกเสบและโรคเบาหวานถกเชEอวาเปนปจจยเสEยงของโรคไตเร9 อรง ท9งน9 กลไกและข9นตอนของความสมพนธของโรคปรทนตอกเสบและเบาหวานตออบตการณโรคไตเร9อรงยงไมเปนทEรแนชด การศกษาน9 จงมวตถประสงคทEจะระบความสมพนธของโรคปรทนตอกเสบและโรคเบาหวานในการเพEมความเสEยงของการเกดโรคไตเร9 อรงดวยการวเคราะหโมเดลตวแปรคEนกลาง (Mediation analysis)

โดยการศกษาน9 เปนการศกษาระยะยาว 10 ป ในกลมพนกงานการไฟฟาฝายผลตแหงประเทศไทย ทEมการทางานของไตเปนปกตจานวน 2,635 ราย ตวแปรผลลพธทEสนใจคออบตการณการเกดโรคไตเร9 อรง โดยนยามจากคาอตราการกรองไตทEนอยกวา 60 มล./นาท ตอ 1.73 ตารางเมตร ในขณะทEภาวะโรคปรทนตอกเสบใชคาการกระจายตวของโรค หรอคารอยละของตาแหนงดานประชดทEมการทาลายอวยวะยดเกาะปรทนตระดบรนแรง (CAL ≥ 5 มม.) เปนตวแปรแทนสภาวะปรทนต การวเคราะหทางสถตใชการวเคราะหโมเดลตวแปรคEนกลาง ภายใตสมมตฐานการเกดโรค 2 รปแบบ คอ รปแบบ ก (โรคปรทนตอกเสบ →

โรคเบาหวาน → โรคไตเร9อรง) และ รปแบบ ข (โรคเบาหวาน →โรคปรทนตอกเสบ → โรคไตเร9อรง) ผลการศกษาพบวาอบตการณการเกดโรคไตเร9 อรงเทากบ 1.03 ตอประชากร 100 คน ตอป จากความสมพนธรปแบบ ก พบวาทกรอยละทEเพEมข9นของตาแหนงดานประชดทEมเปนโรคปรทนตอกเสบระดบรนแรงจะเพEมความเสEยงของการเกดโรคไตเร9อรงทางตรงและทางออมผานโรคเบาหวาน 1.010 (95%

CI: 1.005, 1.015) และ 1.007 (95% CI: 1.003, 1.013) เทา ตามลาดบ ในขณะทEความสมพนธรปแบบ ข

แสดงใหเหนวาตวอยางทEเปนโรคเบาหวาน มความเสEยงในการเกดโรคไตเร9 อรงมากกวาคนทEไมไดเปนประมาณ 2 เทา โดย 6.5% เปนความเสEยงทEเกดข9นผานโรคปรทนตอกเสบ โดยสรปโรคปรทนตอกเสบและโรคเบาหวานมผลเพEมความเสEยงของการเกดโรคไตเร9 อรง ท9งทางตรงและทางออม ดงน9นโรคปรทนตอกเสบจงควรไดรบการกระตนใหเหนความสาคญ

182 หนา

vi

CONTENTS

Page ACKNOWLEDGEMENTS iii

ABSTRACT (ENGLISH) iv

ABSTRACT (THAI) v

LIST OF TABLES ix

LIST OF FIGURES xi

LIST OF ABBREVIATIONS xiii

CHAPTER I BACKGROUND AND RATIONALE 1

1.1 Background and rationale 1

1.2 Research question 3

1.3 Research objectives 3

CHAPTER II LITERATURE REVIEW 4

2.1 Epidemiology of CKD 4

2.2 Identification and classification of CKD 5

2.3 Risk factors of CKD 7

2.4 Epidemiology of periodontitis 8

2.5 Periodontal medicine 8

2.6 Periodontitis classification 9

2.7 The association between OH and periodontitis: A systematic 10

review and meta-analysis

2.8 Effect of periodontitis on renal function/CKD: Biological 19

plausibility

2.9 Association between periodontitis and CKD: 19

Epidemiological studies

2.10 Association between periodontitis and DM 22

2.11 DM as the risk factor of CKD 23

CHAPTER III METHODOLOGY 67

3.1 Study design and setting 67

vii

CONTENTS (cont.)

Page

3.2 Study subjects 69

3.3 Data collection 69

3.4 Study factor and measurements 70

3.5 Primary outcome and measurements 72

3.6 Other co-variables and measurements 72

3.7 Sample size estimation 74

3.8 Data management 74

3.9 Imputation 77

3.10 Statistical analysis 79

3.11 Ethics considerations 84

CHAPTER IV RESULTS 101

4.1 Characteristic of subjects 101

4.2 Missing data and imputation results 102

4.3 Pathway A: Periodontitis → DM → CKD 102

4.4 Pathway B: DM → Periodontitis → CKD 105

4.5 Assumption checking 107

CHAPTER V DISCUSSION 142

5.1 Main findings 142

5.2 Comparison results with previous studies 142

5.3 Data management for EGAT cohort 144

5.4 Periodontal measurement and classification 144

5.5 Multiple imputation 145

5.6 Selection of covariables for mediation analysis 146

5.7 Strengths of this study 147

5.8 Limitations 147

5.9 Clinical application 148

5.10 Suggestion for further studies 149

5.11 Conclusion 149

viii

CONTENTS (cont.)

Page

REFERENCES 150

APPENDICES 172

Appendix A Modified Newcastle-Ottawa Quality Assessment Scale 173

Appendix B The GRADE APPROACH 180

Appendix C Ethical approval 181

BIOGRAPHY 182

ix

LIST OF TABLES

Table Page

2.1 2009 CKD-EPI creatinine equations 26

2.2 Search terms and search strategy: Periodontitis and OH 27

2.3 Characteristics of included studies 28

2.4 Risk of bias assessment 34

2.5 Categorization of OH level 36

2.6 Pooling effects of fair and poor versus good OH on periodontitis 37

2.7 Subgroup and sensitivity analysis according to sources of 38

heterogeneity of fair and poor versus good OH

2.8 Pooled mean difference of OH score between periodontitis 39

and non-periodontitis

2.9 Pooled effect size of oral care habit on periodontitis 40

2.10 Sources of heterogeneity of tooth brushing meta-analysis 41

2.11 Publication bias assessment by Egger test 42

2.12 Overview of the meta-analysis 43

2.13 Search terms and search strategy: Periodontitis and CKD 44

2.14 Case-control studies: Periodontitis and CKD 45

2.15 Cross-sectional studies: Periodontitis and CKD 49

2.16 Cohort studies: Periodontitis and CKD 54

3.1 Calibration of periodontal examination (weight kappa ± 1mm) 85

3.2 Imputation models: predictors and equations 86

4.1 Pattern of participation 108

4.2 Baseline characteristics of excluded cases 109

4.3 Baseline characteristics 112

4.4 Numbers of missing data 115

4.5 Within and whole wave missing data 116

4.6 RVI, FMI and Relative efficiency in GSEM final models 117

x

LIST OF TABLES (cont.)

Table Page

4.7 Comparison of characteristic between actual and imputed dataset 118

4.8 The F-test and coefficients of various periodontitis and 127

obesity definitions

4.9 Univariate GSEM of DM model: Mediation model 128

4.10 Univariate GSEM of CKD model: Outcome model 129

4.11 Multivariate GSEM of mediation and outcome models of Pathway A: 130

Periodontitis F

4.12 Multivariate GSEM of mediation and outcome models of Pathway A: 131

CDC/AAP

4.13 Casual effects of Periodontitis on CKD through DM (Pathway A) 132

4.14 Comparisons of forward stepwise method versus disjunctive clause 133

criteria for DM Model


criteria for CKD Model

4.16 Univariate GSEM of Periodontitis model: Mediation model 135

4.17 Multivariate GSEM of mediation and outcome models of Pathway B 136

4.18 Casual effects of DM on CKD through Periodontitis (Pathway B) 137


criteria for Periodontitis Model

xi

LIST OF FIGURES

Figure Page

2.1 Flow chart of identifying and selecting studies: Periodontitis and OH 57

2.2 Pooling effects of fair and poor versus good OH on periodontitis 58

2.3 Pooling ORs of Plaque index and Plaque score on periodontitis 59

2.4 Pooling effect of oral care habits on periodontitis 60

2.5 Funnel plots of publication bias assessment 61

2.6 Contour enhanced funnel plots 62

2.7 Summary of pooled effect of OH and oral care habits on 63

periodontitis

2.8 Flow chart of identifying and selecting studies: Periodontitis and CKD 64

2.9 Bi-directional relationship between diabetes and periodontitis 65

2.10 Mechanisms of renal injury due to diabetes 66

3.1 Structural causal pathway A: Periodontitis → DM → CKD 87

3.2 Structural causal pathway B: DM → Periodontitis → CKD 88

3.3 Periodontal measurement 89

3.4 Workflow of cleaning processes for gender 90

3.5 Workflow of cleaning processes for date visit (survey date) 91

3.6 Workflow of cleaning processes for date of birth 92

3.7 Workflow of cleaning processes for marital status 93

3.8 Workflow of cleaning processes for education 94

3.9 Workflow of cleaning processes for risk behaviors including smoking 95

and alocohol drinking

3.10 Workflow of cleaning processes for height 96

xii

LIST OF FIGURES (cont.)

Figure Page

3.11 Workflow of cleaning processes for weight, waist and hip 97

circumstance

3.12 Workflow of cleaning processes for blood pressure 98

3.13 Workflow of cleaning processes for laboratory results 99

3.14 Outline of statistical analysis 100

4.1 Flowchart of included subjects 139

4.2 Causal mediation pathway diagram of Pathway A using 140

generalized structural equation modelling

4.3 Causal mediation pathway diagram of Pathway B using 141

generalized structural equation modelling

xiii

LIST OF ABBREVIATIONS

AAP American Academy of Periodontology

ACME Average causal mediation effect

ACR Albumin-to-creatinine ratio

AGE Advanced glycosylation end products

AKI Acute kidney injury

ALT Alanine aminotransferase

AP Alkaline phosphatase

ARIC Atherosclerosis Risk in Communities

AST Aspartate aminotransferase

ASVD Atherosclerotic vascular diseases

BMI Body mass index

BOP Bleeding on probing

BP Blood pressure

CAL Clinical attachment level

CBC Complete blood count

CDC Centers for Disease Control and Prevention

CI Confidence interval

CKD Chronic kidney disease

CKD-EPI Chronic Kidney Disease Epidemiology Collaboration

COPD Chronic obstructive pulmonary disease

CPI Community periodontal index

CRF Case record (report) form

CRP C-reactive protein

CVD Cardiovascular disease

DBP Diastolic blood pressure

DE Direct effect

dl Deciliter

xiv

LIST OF ABBREVIATIONS (cont.)

DLP Dyslipidemia

DM Diabetes mellitus

DOB Date of birth

EFP European Federation of Periodontology

EGAT Electricity Generating Authority of Thailand

EKG Electrocardiography

ESRD End-stage renal disease

exp Exponential

FBS Fasting blood sugar

FMI Fraction of missing information

eGFR (estimated) Glomerular filtration rate

GI Gingival index

GSEM Generalized structural equation modelling

GGT Gamma-glutamyl transpeptidase

HD Hemodialysis

HDL High density lipoprotein HIV Human immunodeficiency virus

HR Hazard ratio

HT Hypertension

IL Interleukin

KDOQI Kidney Disease Outcomes Quality Initiative

KDIGO Kidney Disease: Improving Global Outcomes

KNHANES Korea National Health and Nutrition Examination Survey

LDL Low density lipoprotein

LOA Loss of attachment index

MAR Missing at random

MDRD Modification of Diet in Renal Disease

ME Mediation effect

MeSH Medical subject headings

xv


mg Milligram

MICE Multiple imputation with chained equations

mmHg Millimeter mercury

MrOS Osteoporotic Fractures in Men

NHANES National Health and Nutrition Examination Survey

NSAIDs Non-steroidal anti-inflammatory drugs

OH Oral hygiene

OHI Oral hygiene index

OHI-S (Simplified) Oral hygiene index

OR Odds ratio

PD Peritoneal dialysis

PDI Periodontal disease index

PGE2 Prostaglandin E2

PI Plaque index

PISA Periodontal inflamed surface area

PKC Protein kinase C

PPD Periodontal pocket depth

PRISMA Preferred Reporting Items for Systematic Reviews and Meta-

analysis

PROSPERO International prospective register of systematic reviews

PSc Plaque score

RA Rheumatoid arthritis

RE (Gingival) Recession

RR Risk ratio

RRT Renal replacement therapy

RVI Relative variance increases

SBP Systolic blood pressure

ScC Serum cystatin C

SD Standard deviation

xvi


SE Standard error

SMD Standardized mean difference

TNF Tumor necrosis factor TC Total cholesterol

TE Total effect

TG Triglyceride

US United State

var Variance

WHR Waist-to-hip ratio

WHO World health organization

Fac. of Grad. Studies, Mahidol Univ. Ph.D. (Clinical Epidemiology) / 1

CHAPTER I

BACKGROUND AND RATIONALE

1.1 Background and Rationale Chronic kidney disease (CKD) is defined as reduced glomerular filtration

rate (GFR), increased urinary albumin excretion, or both1. Renal impairments lead to

complex disorders that are characterized by solute retention, abnormality of hormones

regulation, and compensatory responses in other organ systems. Once, its progression

reaches to the kidney failure, patients are required renal replacement therapies (RRT),

e.g., peritoneal dialysis (PD), hemodialysis (HD), or renal transplantation. These

treatment modalities are high costs and impact on the quality of life. Moreover, CKD is

also an important cause of other subsequent health burden including infection,

cardiovascular disease (CVD) and death2. From here, CKD is recognized as a global

health problem. Despite of intensive improvements in medicine and research, the

number of patients with CKD is expected to grow continuously, worldwide3. Promising

to reduce the burden and cost of care from CKD, periodontitis, a novel and potentially

modifiable risk factor has been extensively mentioned and explored4.

Periodontitis, the most common oral diseases, is generally described as a

specific infectious disease. It causes the gum infection and destruction of tooth-

supporting periodontium. Its morbidities are not limited only to oral cavity, but also

influence overall health through systemic inflammation5. In periodontal pockets,

gingival epithelium is infected, and injured with the numerous small ulcerations

distributing throughout the pocket wall. In a patient with moderate to severe

periodontitis, the total surface area of these ulcerations is approximately the same size

of a human palm. This ulcerated epithelium always exposed to thousands of

microorganisms in dental biofilm. And thus, intense inflammatory response occurs, then

it possibly wide spreads. Bacteria and inflammatory mediators can reach to other

internal organs through bloodstream. The evidence showed that periodontitis patients

had significantly elevated level of serum C-reactive protein (CRP), a marker of systemic

Attawood Lertpimonchai Background and rationale / 2

inflammation6. As a result, it could imply that periodontitis had potentially effects on

overall systemic health, such as, increased risk of CVD events, which was confirmed by

founded periodontal pathogens in the atheromatous plaque in vessel walls of CVD

autopsy7.

Here, we hypothesize that periodontal infection might be one of the factors

accelerating deterioration in renal function. Possible mechanisms could be direct and

indirect pathways. The direct pathway may cause by bacteria and their products which

directly damaged the nephron. Additionally, the indirect pathway referred to renal

impaired by inflammatory cytokines5.

Beyond this, previous evidences showed that periodontitis related with

glycemic control impairment8, 9. Inflammatory mediators, such as interleukin (IL),

tumor necrosis factor (TNF), and bacterial endotoxins had been reported about

influencing on insulin resistance. Hence, periodontitis could increase risk of diabetes

mellitus (DM) incidence. Simultaneously, DM has been recognized as the conventional

risk factors of CKD. Metabolic pathways, including advanced glycosylation end

products (AGEs), activation of protein kinase C (PKC), oxidative stress, and

acceleration of the polyol pathway were suggested to be mediators of diabetic

nephropathy10.

On the other hand, initiated the causation with DM, periodontitis could be

the adverse consequences of DM8, 11. Impaired immunity function, imbalance of

collagen homeostasis and altered saliva compositions aggravated periodontal

inflammation, and deteriorated supporting periodontium leading to periodontitis. The

cycle of spreading periodontal pathogens and cytokines is initiated, and then, risk of

CKD is amplified.

Toward this end, with the uncertainty of complex causal pathway, the

mediation effects of periodontitis on CKD through DM, and vice versa, have been

mentioned. Periodontitis could influence directly on CKD, and it also could affect CKD

indirectly through DM. The other side of the coin, the causative pathway could be

flipped between periodontitis and DM. DM could increase risk of CKD directly, and it

could be mediated across periodontal infection.

Current publications do not answer these questions, and thus, the gaps of

knowledge are still present. Evidences from cross-sectional studies12-25 showed the


significant association between periodontitis and CKD. For example, Kshirsagar et al 21

indicated that severe periodontitis subjects had about 1.19 to 3.85 odds of having CKD

after adjustment for traditional CKD risk factors. However, a causal relationship cannot

be claimed from the cross-sectional design. Although some previous cohorts26-31

proposed independent effects of periodontitis and DM on CKD, they did not assess

effects of periodontitis on CKD that transmitted through DM. To answer these

questions, a well-planned cohort studies in general population are needed. Therefore,

this study was conducted utilizing data from the cohort of employees of the Electricity

Generating Authority of Thailand (EGAT) to assess the causative association between

periodontitis, DM and CKD using a mediation analysis. The direct effect and indirect

(mediation) effect of paths would be identified.

1.2 Research Question Does periodontitis directly affect on renal function or its effect is transmitted

or mediated through DM, or vice versa?

1.3 Research Objectives The objectives of the study were:

1.3.1 To determine direct and indirect effects of periodontitis through DM

on developing renal function in adults with normal kidney function.

1.3.2 To determine direct and indirect effect of DM on renal function

through periodontitis in adults with normal kidney function at baseline.

Attawood Lertpimonchai Literature review / 4

CHAPTER II

LITERATURE REVIEW

2.1 Epidemiology of CKD CKD is defined by the Kidney Disease: Improving Global Outcomes

(KDIGO) guideline as abnormalities of kidney structure or function, present for more

than 3 months, with implications for health32. It is associated with impaired quality of

life and substantially reduced life expectancy at all ages. Impairment and damage of

kidneys lead to complex disorders that are characterized by solute retention, hormone

deficiencies or resistance, and compensatory responses in other organ systems33.

Prevalence and incidence of CKD were diverse among publications due to

heterogeneous of populations, calculation of estimated glomerular filtration rate

(eGFR), and proteinuria measurements. Despite these limitations, the prevalence of

CKD is consistently reported to be around 10–15% in high to moderate income

countries34. The low-socioeconomic people are at higher risk of developing CKD and

of disease progression. In Thailand, the prevalence of CKD is about 17.5% (95% CI:

14.6, 20.4)35.

The burden of CKD is substantial in term of mortality and morbidity. Once,

CKD progression reaches to the kidney failure, its function is no longer able to sustain

life over the long term. The RRTs, e.g., PD, HD, or renal transplantation are required.

These treatment modalities are high costs and impact on the quality of life. Globally,

only half of those patients can afford and receive the RRTs34. CKD complications may

occur at any stage of disease, and could occur from the adverse effects of CKD

treatments. Anemia, infections, CKD mineral bone disease, CVD, cancer, drug toxicity

and cognitive impairment are common which can lead to death without progression to

kidney failure2, 34.


2.2 Identification and classification of CKD According to KDIGO guideline32, eGFR and albuminuria are recommended

for CKD identification and classification. eGFR is used to determine kidney function,

while, albuminuria is the standard maker for kidney damage.

2.2.1 Estimated glomerular filtration rate

The ideal kidney function measurement or actual GFR can be measured by

renal clearance of exogenous filtration markers, such as inulin. However, it is

cumbersome and impractical. The eGFR has been alternatively proposed. Serum

creatinine and cystatin C are common biomarkers which used to assess kidney function.

Creatinine, by-product of muscle metabolism, usually produces at a constant rate and

freely filters by the glomerulus. Increasing serum creatinine represents GFR decrease.

However, it has the positive correlation with muscle mass. Hence, age, sex and ethnicity

are always used for compensation in eGFR calculation. Other factors, for example,

protein intake, physical activity, tubular secretion, extra-renal excretion and creatinine

degradation also influence serum creatinine concentration.

The serum cystatin C, the low-molecular-weight protein produces in all

nucleated cells, also has been recommended as another maker of kidney function. It is

claimed the advantage about less affected by muscle mass and diet. However, its

concentration is also influenced by age, sex, inflammation, corticosteroid use, smoking

and hyperthyroidism36.

The 2009 CKD-EPI creatinine equations (Table 2.1), the 2012 CKD-EPI

cystatin C equations, or the 2012 CKD-EPI creatinine–cystatin C equations are

recommended as the eGFR estimating equations37. If data available, a combination of

creatinine and cystitis C in eGFR estimation might improve accuracy, especially for the

lower ranges of GFR32.

2.2.2 Proteinuria

Proteinuria is clinically used for identifying the kidney damage with

increased glomerular permeability allowing the filtration of macromolecules that should

remain within the circulation. The urinary dipsticks or measurement of the albumin or

total protein concentration in a spot urine sample is the conventional methods for


proteinuria. These reagent strip devices primarily detect albumin by a colorimetric

reaction with the dipstick-impregnated reagent based on the albumin concentration

within the sample. However, it has some limitations. First, it is low validity at detecting

low concentration but clinically significance of urinary albumin. Second, false positive

is occasionally founded in concentrated or highly alkaline urine, after use of iodinated

contrast agents, or in case of gross hematuria. To compensate for variations in urine

concentration in spot-check samples, it is helpful to compare the amount of albumin in

the sample against its concentration of creatinine. This is termed the albumin-to-

creatinine ratio (ACR)1, 2.

2.2.3 CKD classification

In 2002, the Kidney Disease Outcomes Quality Initiative (KDOQI)

organization, proposed a diagnosis and classification guideline for CKD. Using eGFR,

mainly, severity of CKD was classified into five stages. The presence of proteinuria was

only mandatory for stages 1 and 2 or patients with eGFR > 60 ml/min per 1.73 m2.

Patients with stage 3 or higher were defined as CKD without proteinuria results.

However, recent studies showed that the presence of proteinuria increased risk of

mortality and morbidity. Moreover, risk of death and other complications were

discrepancy among stage 3 patients2. Therefore, in 2012, KDIGO revised the diagnosis

and classification guideline and recommended that individuals should be classified

according to eGFR and proteinuria. In addition, the diagnostic criteria for stage 3 should

be divided into 2 sub-stages, i.e., stage 3a (GFR: 45 – 59 ml/min per 1.73 m2), and stage

3b (GFR: 30 – 44 ml/min per 1.73 m2).

CKD classification by Cause, GFR and Albuminuria (CGA staging) was

proposed32. Briefly, 6 groups of eGFR and 3 levels of proteinuria are cross-tabulated,

resulting to 18 stages of CKD, i.e., G1A1 to G5A3. For example, G3aA2 represents the

stage of mildly to moderate decreased in kidney function (eGFR: 45-90 ml/min per 1.73 m2) and moderately increased albuminuria (ACR 30 - 300 mg/g). Here, it can be

used to inform the need for specialist referral, general medical management, and

indications for investigation and therapeutic interventions.


2.3 Risk factors of CKD

CKD is the multi-factorial disease, both inherited and many acquired factors

were mentioned to be the causative. Much epidemiological evidences showed a link

between several risk factors and the initiation and the progression of CKD38-43. In

general, DM and hypertension (HT) are the well-known important risk factors of CKD3,

33, 34. DM and HT are the main causes of CKD in all high- and low-income countries.

The consistency results from previous literatures confirmed the increasing risk of CKD

incident and more rapidly progressive CKD from both. Metabolic pathways in glycemic

control, including AGEs, activation of PKC, oxidative stress, and acceleration of the

polyol pathway were suggested to be mediators of diabetic nephropathy. Increased

systemic and intraglomerular pressure and activation of various vasoactive hormone

also seems to be alternative common pathway of glomerulosclerosis and

tubulointerstitial fibrosis10, 44.

CKD commonly clusters within families, which implies to genetic or

familial predisposition. Genetic studies have suggested links between CKD and various

alterations or polymorphisms of candidate genes encoding putative mediators, including

the renin-angiotensin system. For instance, the APOL1 gene has been intensively

identified as a major susceptibility gene for kidney disease45, 46. Male, elderly people

and some ethnicities, i.e. African and Native Americans, might also be more susceptible

to CKD, which would explain the high proportions of these population groups in CKD

and end-stage renal disease (ESRD)33.

Nephrotoxic effects from toxic dosages of herbs, non-steroidal anti-

inflammatory drugs (NSAIDs), iodinated contrast media or interactions with

conventional medicines, as well as, environmental pollution of water by heavy metals

and of soil by organic compounds (including pesticides) are other risk factors of acute

kidney injury (AKI) and CKD. Infections are other major causes of AKI and CKD,

especially in resource-limited regions. Human immunodeficiency virus (HIV), Hepatitis

B, Hepatitis C, malaria, tuberculosis and various tropical diseases, have been shown to

be associated with increased risk of CKD. Malnutrition might be associated with a

reduction in the number of nephrons, and renal disease in later life. In addition, many

cohort studies have identified smoking, alcohol consumption, obesity, hyperlipidemia,


low/high birthweight, prematurity and kidney stones as risk factors or markers in the

general population for the development of CKD44.

2.4 Epidemiology of periodontitis Periodontal disease is generally described as specific disease that involve a

tooth supporting structure. It is infectious and recognized as one of the most common

chronic inflammatory diseases in human. From the 2009 and 2010 National Health and

Nutrition Examination Survey (NHANES), over 47% of the U.S. adults who aged 30

years and older, representing 64.7 million adults, had periodontitis. Moreover, in elders

aged 65 years and older, 64% had either moderate or severe periodontitis47.

Periodontium consists of gingiva, alveolar bone, cementum and periodontal

ligament. It always exposes to oral bacteria in dental plaque. Fortunately, with the

effective immune system, the balance between bacteria and host response is maintained

without developing periodontal disease. However, when the disequilibrium occurs,

either host hyper-responsiveness or high virulence from bacteria, periodontium will be

destructed by inflammation process. In the initial stage, patients usually do not have any

symptom leading to neglect their periodontal health care. Then, the disease progresses,

patients may suffer from easily gingival bleeding, gum swelling, dull pain, gingival

abscess, and tooth mobility. Finally, patients will lose their teeth, decreasing in occlusal

function and digestive ability. Then, it will influence the overall health, leading to

decrease their quality of life48.

2.5 Periodontal medicine Current evidences showed the potential effects of periodontal disease on

overall systemic health based on concept of inflammation diffusion, including

periodontal pathogen and cytokines5. The association between atherosclerotic vascular

diseases (ASVD) and periodontitis has been established49, and then, has been extend

investigated since 1990. Systemic inflammatory makers, such as CRP, which was the

surrogate outcome of ASVD, increased with periodontitis infection. Periodontitis also

associated with the decreased endothelial dysfunction and increased carotid intima-


media thickness. In addition, periodontal pathogens could be found in the autopsy of

atheromatous plaque along vessel walls of patients with CVD7. Moreover, periodontitis

was proved as the risk factors of future ASVD events with the cohort studies50. From

this, the joint of European Federation of Periodontology and American Academy of

Periodontology (EFP/AAP) Workshop on Periodontitis and Systemic Diseases stated

the consensus that there is consistent and strong epidemiologic evidence that

periodontitis increased risk for future CVD51.

In pregnant mothers with periodontitis, oral bacteria were found in an

amniotic fluid, along with increasing of inflammatory cytokines. It could possible lead

to adverse pregnancy outcomes, such as premature labor or preterm low-birth-weight

infants52. Furthermore, periodontitis mediators, such as IL-1ß, TNF-α, associated with

increasing insulin resistance, which developed the risk of develop DM and its

complications8. Relationships between periodontitis and other systemic disease, i.e.,

CKD, chronic obstructive pulmonary disease (COPD), rheumatoid arthritis (RA),

Alzheimer's disease and erectile dysfunction, also have been reported53.

2.6 Periodontitis classification Periodontal diseases are simply categorized based on disease severity into

two distinct forms, gingivitis and periodontitis. Gingivitis is inflammation of the

gingiva, meanwhile, periodontitis is inflammation of periodontium that extends beyond

gingiva and produces destruction of periodontal ligament and alveolar bone. From the

AAP in 1999, the severity of periodontitis depends on amount of clinical attachment

loss (CAL)54. Chronic periodontitis is characterized by periodontal pocket depth (PPD)

more than 3 mm with CAL. The extent of disease has been further classified as localized

or generalized depending on the proportion of infected sites (≤ 30% or > 30%). Severity

is measured on the amount of CAL, and it is designated as slight (1-2 mm), moderate

(3-4 mm) or severe (≥ 5 mm).

Although, the AAP 1999 classification has been world-wide accepted for

the clinical circumstance, it has not been widely adopted in periodontal research.

Various case definitions of periodontitis have been proposed55. It has great impact on

the prevalence and extent of periodontal disease56. Moreover, these discrepancies


diverged the results of investigating the link between periodontitis and systemic

diseases57. Due to lack of universally accepted, the Centers for Disease Control and

Prevention (CDC), in partnership with the AAP proposed and updated the standard case

definitions58, 59. These definitions have been proposed base on their primary objective

which was to examine and identify valid nonclinical measures for population-based

surveillance of periodontitis. Some literatures in periodontal medicine adopted the

CDC/AAP definition for categorizing periodontal status. However, as aforementioned,

it has not proposed to serve for identifying the linkage between periodontitis and

systemic health. Therefore, we conducted a systematic review, which aimed to review the

periodontitis definition in periodontal researches. Association between oral hygiene

(OH) & personal oral health care and periodontitis was selected as the topic of review,

because it was the basic and common knowledge in periodontal researches. It has been

extensive explored in the previous literatures. From this, we assume that, the various of

periodontitis definitions would been more covered with this systematic review and

meta-analysis.

2.7 The association between OH and periodontitis: A systematic review

and meta-analysis

2.7.1 Methods

A systematic review and meta-analysis was conducted in order to assess

effects of association between OH and periodontitis. The review proposal was

developed following the PRISMA guidelines60, and it was registered at PROSPERO

(registration number: CRD42015019036).

2.7.1.1 Search strategy

The MEDLINE and SCOPUS databases were used to identify

relevant studies with standardized methodological filters up to May 2016. Search

strategies were mainly constructed based on the primary objective with 3 domains (i.e.,

periodontitis, OH, and general aspects for observational studies) as follows:

(“periodontitis” OR “periodontal”) AND (“poor oral hygiene” OR “plaque index” OR


“oral hygiene index” OR “plaque score”) AND (“relation” OR “association” OR “risk

factor”). The search terms and strategies are described in Table 2.2.

2.7.1.2 Inclusion criteria

Studies were screened based on titles and abstracts; if a decision

could not be made based on this information, full papers were reviewed. Any type of

observational study (e.g., cohort, case-control, or cross-sectional studies) published in

English was included if it met the following criteria: (i) assessed associations between

OH and periodontitis in either general or specific types of adult populations; (ii) had at

least 2 outcome groups, periodontitis versus non-periodontitis, or mild, moderate and

severe periodontitis versus normal periodontium; (iii) assessed OH by standard tools,

such as the Oral Hygiene Index (OHI) or Simplified Oral Hygiene Index (OHI-S)61,

Plaque Index (PI)62, plaque control record/Plaque Score (PSc)63, or a questionnaire

including the frequency of brushing, interdental cleaning and dental visits; (iv)

reported/possibly calculated the mean and standard deviation (SD) of OH scores among

periodontitis groups or a contingency table between non-periodontitis/periodontitis and

OH groups. Studies were excluded if they had insufficient data for pooling after

contacting the authors for additional data.

Two of three reviewers (AL, SR, and SA) independently

evaluated the studies for eligibility, extracted the data, and assessed the risk of bias. Any

discrepancies between reviewers were discussed and resolved by consensus.

2.7.1.3 Study factors

The primary study factor was OH, objectively measured using

OHI, PI or PSc. Secondary study factors were oral care habits, which were subjectively

assessed using questionnaires assessing the frequency of tooth brushing, interdental

cleaning, and dental visits.

2.7.1.4 Outcome

The outcome of interest was periodontitis, which was defined

according to the original studies. The definition of periodontitis was based either on

PPD, CAL or radiographs without a restricted periodontitis definition.

2.7.1.5 Data extraction

Study characteristics, including study design (cohort, case-

control or cross-sectional study), population type (general population or specific


disease), and study base (community or hospital) were extracted. Subject characteristics

(i.e., percentage of males, smoking habits and the presence of DM) and clinical data

(i.e., periodontitis definition and details of OH assessments) were also extracted.

2.7.1.6 Risk of bias assessment

The quality of the studies was assessed using the modified

Newcastle-Ottawa Quality Assessment Scale64 (Appendix A), which considers 3

domains: the representativeness of the studied subjects, the comparability between

groups, and the ascertainment of outcome and exposure. Each domain was graded by

giving stars if there was a low risk of bias. Individual studies were categorized according

to these stars as having a low, moderate and high risk of bias if the percentage of stars

was ≥ 75%, 50-74% and < 50%, respectively.

2.7.1.7 Statistical analysis

Data were pooled if there were at least 2 studies reporting the

same outcomes and study factors. Data analysis was performed separately by the type

of OH data (i.e., categorical and continuous data) as described below.

For categorical data, the odds ratio (OR) of having periodontitis

for fair versus good OH (OR1) and poor versus good OH (OR2) along with their 95%

confidence interval (CI) were estimated for each study. Given included studies with 2

or more OH groups, a multivariate random-effects meta-analysis was applied for

pooling ORs. This method considers within-study variation using Riley’s method65, 66.

For studies in which OH was divided into more than 2 groups and ORs were reported

without frequency data, the variance-covariance was assumed to be zero.

For continuous data, the mean difference in OH scores between

periodontitis and non-periodontitis groups was estimated and pooled using a

standardized mean difference (SMD). If logistic model correlation coefficients were

reported instead of the mean and SD, the beta coefficients were then pooled using the

pooling mean method.

Heterogeneity was assessed by Cochrane’s Q test and the I2

statistic. If heterogeneity was present (Q test < 0.1 or I2 ≥ 25%), a random-effects model

(Dersimonian & Laird) was used. Otherwise, a fixed-effects model was applied with the

inverse variance method.


Sources of heterogeneity were explored using a Galbraith plot

to identify outlier studies. Co-variables (i.e., population type, age, gender, smoking,

DM, index use, periodontitis definition) were then fitted one-by-one into a meta-

regression model. If there was a suggested association, a sensitivity analysis excluding

the outlier studies and/or a subgroup analysis was performed.

Finally, potential publication bias was explored using the Egger

test and a funnel plot. If either of these indicated asymmetry, a contour-enhanced funnel

plot was constructed to identify the cause of asymmetry. All analyses were performed

using STATA software version 14.2. Two-sided P < 0.05 was considered statistically

significant except for the heterogeneity test, in which P < 0.10 was used.

2.7.1.8 Grade of evidence

The system from the Grades of Recommendation, Assessment,

Development and Evaluation Working Group (GRADE Working Group)67, 68 was used

for grading the quality of evidence mainly based on the study design, risk of bias,

indirectness of evidence, publication bias, heterogeneity and imprecision of results.

2.7.2 Results

2.7.2.1 Identifying studies

A total of 2,763 studies were identified from MEDLINE and

SCOPUS, and 1934 studies remained after removing duplicates. Of these, 1,878 studies

were ineligible for reasons described in Figure 2.1, leaving 56 studies69-124 that were

eligible for review. Six studies69, 102, 103, 106, 107, 112 were excluded due to insufficient data

after contacting the authors. Of the remaining 50 studies, 45 studies70-73, 75-86, 88, 90-94, 97-

101, 104, 105, 108-111, 113-124 objectively assessed OH by oral examination. Fifteen70, 72, 77, 81-84,

86, 90-94, 101, 120 analyzed OH as categorical data, 3171, 73, 75-80, 85, 88, 97-100, 104, 105, 108-111, 113-

119, 121-124 as continuous data, and one77 as both. Eleven studies provided the association

between periodontitis and oral care habits measured by the frequency of brushing84, 87-

89, 91, 92, 95, 96, 99, 111, interdental cleaning84, 96, 99, 111 and dental visits74, 88, 89, 91, 95, 111.

2.7.2.2 Subject characteristics

The characteristics of the 50 included studies are described in

Table 2.3. Most study designs were cross-sectional, most studies investigated a general

population, and 34 studies were based in hospitals. The mean subject age ranged from


15 to 65 years. The percentages of males, smokers and diabetics are also shown in Table

2.3. While the definition of periodontitis varied across the studies, most (92%) used PPD

and/or CAL.

2.7.2.3 Risk of bias assessment

The results of the risk of bias assessments are described in Table

2.4. Most studies (72%) provided inadequate details for sample selection; hence,

representativeness was unclear. For example, some authors did not mention their

sampling methods or clearly describe their process for selecting cases and controls.

Twenty-seven studies (46%) were potentially biased due to improper statistical

adjustments for confounding factors. Almost all studies measured periodontitis via oral

examination, which was objective and valid. However, 16 studies (32%) used partial-

mouth examination protocols, 16 studies (32%) diagnosed periodontitis without data

regarding CAL, and 25 studies (50%) did not provide details about intra/inter-examiner

agreement. The numbers of studies with low, moderate and high risks of bias were 23,

19 and 8, respectively.

2.7.2.4 OH

For 15 studies with categorical OH data, 6 studies70, 84, 90, 93, 101,

120 categorized OH as good or poor, whereas 9 studies72, 77, 81-83, 86, 91, 92, 94 categorized

OH as good, fair, or poor. The criteria for classifying OH are presented in Table 2.5.

Pooled ln(ORs) determined using a multivariate meta-analysis (Figure 2.2) were 0.71

(95% CI: 0.50, 0.93) and 1.61 (95% CI: 1.22, 2.00), which yielded pooled ORs of 2.04

(95% CI: 1.65, 2.53) and 5.01 (95% CI: 3.40, 7.39), respectively, for fair and poor OH.

These results indicate that fair and poor OH increase the risk of periodontitis by

approximately 2- and 5-fold compared with good OH with an I2 of 40% and 78%,

respectively. The details of each individual study are shown in Table 2.6.

Population type appeared to be a large source of heterogeneity.

Subgroup analyses in community-based studies yielded lower heterogeneity levels, i.e.,

the I2 values were 4% and 0% for fair and poor versus good OH, respectively, with

corresponding pooled ORs of 2.23 (95% CI: 1.85, 2.69) and 4.78 (95% CI: 4.10, 5.58).

In addition, a sensitivity analysis focusing on 11 studies70, 72, 77, 81-84, 90-92, 94 of general

populations decreased the degree of heterogeneity to 22% and 49% for fair and poor

versus good OH, with pooled ORs of 2.10 (95% CI: 1.76, 2.49) and 4.21


(95% CI: 3.21, 5.51), respectively. Moreover, the periodontitis definitions and index

types used, as well as smoking behavior, also contributed to heterogeneity (Table 2.7).

Among 31 studies that measured OH on a continuous scale, 25

studies73, 79, 80, 88, 97-100, 104, 105, 108-111, 113-119, 121-124 compared OH using the mean scores

between periodontitis and non-periodontitis groups. The SMDs were highly

heterogeneous (I2 = 95.6%), with a pooled SMD of 2.04 (95% CI: 1.59, 2.50)

(Table 2.8). From these findings, it could be interpreted that periodontitis subjects had

a significantly higher OH score of 2.04 standardized units than non-periodontitis

subjects.

Six75, 77, 80, 85, 88, 97 and 371, 76, 78 studies reported the effects of PI

and PSc on periodontitis as coefficients (i.e., ln(OR)) of logistic regression models.

Pooling these corresponding effects yielded pooled ORs of 2.25 (95% CI: 1.43, 3.54)

and 1.02 (95% CI: 1.01, 1.03), and high heterogeneity was found for both (Figure 2.3).

These findings could be interpreted to indicate that each one-unit increase in the

measures of PI and PSc would increase the odds of having periodontitis by 2.25 and

1.02, respectively.

2.7.2.5 Oral healthcare habits

Ten84, 87-89, 91, 92, 95, 96, 99, 111, 484, 96, 99, 111, and 6 studies74, 88, 89, 91,

95, 111 assessed the effects of brushing, dental floss, and dental visits on periodontitis

(Table 2.9). The pooled ORs (Figure 2.4) suggested that tooth brushing and dental visits

were significantly associated with periodontitis, although the I2 values showed high

heterogeneity, at 94.5% and 60.4%, respectively. Subjects who brushed their teeth

regularly had approximately 34% significantly lower odds of having periodontitis

(pooled OR = 0.66; 95% CI: 0.47, 0.94). Smoking, the definition of regular brushing

and periodontitis were potential sources of heterogeneity (Table 2.10).

For dental visits, the sensitivity analysis was performed by

considering 4 of 6 studies that had clearly defined a regular dental visit as at least one

visit per year74, 88, 91, 111. This yielded a significant effect size of 0.56

(95% CI: 0.37, 0.83) with an I2 of 0%, indicating that subjects who regularly visited

dentists at least once a year had a 44% lower risk of periodontitis than those who did

not. The effects of interdental cleaning with dental floss on periodontitis showed little


heterogeneity (I2 = 5.1%), but the pooled OR was borderline significant (OR = 0.87;

95% CI: 0.75, 1.00).

2.7.2.6 Publication bias

Publication bias was assessed for all pooled estimates using

funnel plots (Figure 2.5) and Egger tests (Table 2.11). The results suggested symmetry

except for the mean differences in OH score, PSc, and dental visits. Contour-enhanced

funnel plots were further constructed (Figure 2.6), and these indicated that the

asymmetry of the funnels might be due to both heterogeneity and publication bias.

2.7.2.7 Quality of evidence

The scoring using the GRADE framework is shown in Table

2.12 and Appendix B. Based on observational studies, all pooled estimates were graded

as low quality68. For the effects of fair and poor OH on periodontitis, this was upgraded

to moderate quality because of large effect sizes and strong dose-response relationships.

The effects of brushing and dental visits were downgraded to very low quality due to

heterogeneity and publication bias, respectively.

2.7.3 Discussion

We conducted a systematic review and meta-analysis of the effects of OH

on periodontitis. The results suggest a dose-response relationship between OH and

periodontitis, with fair and poor OH significantly increasing the risk of having

periodontitis by 2- and 5-fold, respectively, compared with good OH. In contrast,

regular tooth brushing and dentist visits could reduce periodontitis by 34% and 32%,

respectively. These pooled OH effects and oral care habits are summarized in Table 2.12

and Figure 2.7.

The effect of OH on periodontitis was stronger than those of other risk

factors, such as DM125 (OR = 2.6; 95% CI: 1.0, 6.6), smoking126 (OR = 2.82; 95% CI:

2.36, 3.39) or obesity127 (ORs = 2.13; 95% CI: 1.40, 3.26). Our results also showed

protective effects of regular brushing, which were consistent with the findings of a

previous meta-analysis128, which reported a significant risk for severe periodontitis due

to infrequent brushing (OR = 1.44; 95% CI: 1.21, 1.71). However, our study could only

identify a small effect of interdental cleaning with dental floss, i.e., a non-significant

reduction of 13% in the risk of periodontitis. This result was also consistent with a


previous meta-analysis129, which found little benefit from self-performed flossing on

plaque or periodontal parameters.

Although the use of OH assessments varied between the included studies,

approximately half of them commonly used the PI with similar cutoff points. OH was

defined as poor for a PI > 2 or if the patient had a moderate accumulation of soft deposits

visible by the naked eye; OH was defined as fair for PI values ranging from 1 to 2 or if

the patient had a film of plaque adhering to the tooth as detected by disclosing solution

or probe.

To address concerns about the varying quality of the individual studies, a

sensitivity analysis was also performed, including only studies with a low risk of bias77,

81-84, 90-92, 101, 120. The results showed little difference compared with those of the main

analysis, but with much less heterogeneity.

Good OH and oral care habits should be encouraged and promoted in public

health campaigns. Dentists and dental hygienists should regularly educate, motivate,

and assess patients’ perceptions for improving oral health behaviors at the “chair side”.

Additionally, dental nurses or assistants should additionally encourage and provide

general, useful information. Repeated and individually tailored OH instructions are key

elements in achieving gingival health. The use of goal setting, self-monitoring and

planning are effective interventions for improving OH-related behaviors in periodontitis

patients. Recognizing the benefits of behavior changes, their own susceptibility, and the

harms of periodontitis are important messages in periodontitis prevention130.

Patients should be able to regularly access dental care for professional

cleaning alongside tailoring and monitoring their OH130. They should also be taught how

to efficiently perform plaque removal. Generally, mechanical plaque controlled by

twice-daily tooth brushing with a fluoride-based dentifrice is an accepted

recommendation. The proper duration of tooth brushing is also mentioned as an

important determinant of plaque removal; therefore, it should be stressed during tooth-

brushing instruction sessions131. The current scientific data showed that dental floss is a

less effective tool and requires the user to be instructed on specific skills in order to be

effective. Interdental brushes have been shown to be the most effective method for

interdental plaque removal132; however, the selection of interdental aids must be at the


clinician’s discretion based on a patient’s needs and dexterity and the characteristics of

a patient’s interdental spaces.

This study has some strengths. It includes studies of the effects of OH using

both objective and subjective assessments. The magnitudes of the effects were pooled

and reported. The results of subgroup analyses (i.e., population type, study base,

periodontitis definition and smoking) were also explored. We used rigorous pooling

methods (multivariate random-effects meta-analysis), which considered the variance-

covariance between the studies.

However, this study also has some limitations. Our pooled ORs were based

on summary data of observational studies. Some data were reported without adjusting

for potential confounders; thus, the pooled results might be prone to bias. Moreover, the

definition of periodontitis varied among studies, which resulted in high heterogeneity,

although the subgroup analyses did reduce this effect. Furthermore, the assessments of

publication bias using funnel plots and Egger tests with the low numbers of included

studies in some meta-analyses may not be valid. Failure to detect asymmetry cannot rule

out a reporting bias, or vice versa.

In conclusion, poor OH increases the risk of periodontitis by approximately

2- to 5-fold compared with good OH. Oral care habits, including regular brushing and

dental visits, can decrease the risk of periodontitis and should thus be promoted more in

public health concerns.

2.7.4 Conclusion

From our systematic review, lack of uniformity of periodontitis definitions

were founded among included studies (Table 2.3). Although, most of them relied on

clinical examination, differences in selected periodontal parameters were large. Only

PPD, only CAL, combination of PPD & CAL, PPD & BOP were used unsteadily.

Moreover, the cut-off points for PPD or CAL also were inconsistent between

definitions. Beyond the severity of disease, the extent of periodontitis also was

erratically used. For example, Varghese et al defined periodontitis when subjects had

destructed sites ≥ 30% of total sites, meanwhile, Pranckeviciene et al descripted

periodontitis with only 1 site with clinical attachment loss.


2.8 Effect of periodontitis on renal function/CKD: Biological

plausibility Periodontal infection could be one of the factors accelerating deterioration

in renal function. Possible mechanisms include direct and indirect pathways. Bacteria

and their products could invade into systemic circulation and may cause cellular directly

damage to the nephron or its vasculature. For example, Porphyromonas gingivalis

(P.g.), periodontal pathogens, could invade capillary endothelium or cell matrix by their

polysaccharide capsules and fimbria. In addition, Gingipains, the potential proteases

secreted by P.g., may cause degradation of matrix metalloproteins, collagen, and

fibronectin of the nephrons structure133. Moreover, bacterial antigen, GroEl60 heat shock

protein, could cross-reactivity with human heat shock protein 60 which results in

endothelium damaging cascade134, 135. For indirect pathway, increasing inflammatory

mediators including IL-6, TNF-α, prostaglandin E2 (PGE2) and Thromboxane B2

accelerates atherogenesis, thrombus formation and platelet aggregation leading to

ischemia, glomerulosclerosis and severe renal insufficiency.

2.9 Association between periodontitis and CKD: Epidemiological

studies

2.9.1 Methods

A systematic review was performed by identifying studies assessing the

association between periodontitis and CKD. Studies were identified from the

MEDLINE and SCOPUS database (up to April 2017). The search terms and strategies

are showed in Table 2.13. Any type of observational study (e.g. cohort, case–control or

cross-sectional) published in English was included if it met the following criteria: (i)

assessed associations between CKD and periodontitis in either general or specific types

of adult populations; (ii) primary outcomes (kidney function or damage) were assessed

by standard measurement which recommended by KDIGO guideline, such as serum

creatinine, cystatin-C, or proteinuria; (iii) outcome was categorized at least 2 groups

with one group of normal kidney function or control, while, other(s) could be any stage


of CKD; for example, ESRD versus non-ESRD, moderate and severe CKD versus

normal kidney function. (iv) assessed periodontitis using parameters from clinical

examination, such as PPD or CAL. (v) reported the mean and SD of periodontal

parameters among CKD groups or a contingency table between non-CKD/CKD and

periodontal status. Experimental studies which assessed the effects of periodontitis

treatment on kidney function were excluded. Studies which enrolled too specific groups

of participants, i.e., renal transplant, dental implant or gingival hyperplasia/overgrowth

also were excluded.

2.9.2 Results

A total of 2,544 studies were identified from MEDLINE and SCOPUS, and

2,298 studies remained after removing duplicates. Of these, 2,257 studies were

ineligible for reasons described in Figure 2.8, leaving 41 studies12-31, 136-156 that were

eligible for review.

From literatures searching, the association between periodontitis and renal

disease has been extensively explored. Among included studies, 21 studies136-156 were

case-control with the hospital-based setting. All of them aimed to compare periodontal

status between subjects with and without renal disease. Details of each study were

provided in Table 2.14. Mostly, they focused on periodontal status in ESRD patients,

and found that, periodontal status in ESRD was worse than normal kidney function.

Results were similar with other studies which determined in CKD subjects. For

example, Borawski et al137 showed significant differences in periodontal parameters

such as PI, gingival index (GI), mean PPD, mean CAL between ESRD, Pre-dialysis

CKD and general population matched with age and gender. However, some studies

could not show these significant differences. Bot et al138, and Castillo et al140 found the

non-statistical differences of PPD or CAL among groups. Similarly, Teratani et al152

reported the non-statistical significance of extent of periodontitis. The heterogeneity

may cause by the differences in population, severity & treatment modalities of renal

disease, and methodology in assess periodontal status among these studies.

Within 14 eligible studies12-25 with cross-sectional design, 2 studies19, 22

were further excluded because study subjects were duplicated from others, as well as,

methodologies also were similarly with previous studies. One study17 was excluded


because it explored the reverse relationship, i.e., set periodontitis as the outcome.

Leaving 11 studies12-16, 18, 20, 21, 23-25 (Table 2.15), they suggested the certain association

between periodontitis and CKD. From the Atherosclerosis Risk in Communities (ARIC)

study, Kshisagar et al21 showed that the odds of having CKD is significantly greater

around 2 times in individuals with periodontitis than in those without, after adjustment

for traditional CKD risk factors. Despite of differences in periodontitis definition, group

of studies12-16, 20, 23 retrieved data from the NHANES reported the consistency

association between periodontitis and CKD. Fisher et al12-14 found that adults who had

periodontitis or edentulous were approximately twice as likely to have CKD as adults

without periodontitis in NHANES III (1988-1994) population. Similarly, Ioannidou and

Swede20 also found this association but the degree of association varied among ethnic

groups, significantly associated in non-Hispanic Blacks and Mexican-Americans, but

not so for non-Hispanic Whites. Grubbs et al16 also confirmed these with NHANES in

2001-2004. They proposed the significant adjusted odds ratios of having CKD in

periodontitis as 1.55 (95%CI: 1.16, 2.07) using normal/mild periodontitis as the referent

group.

With the advance statistical analysis, Fisher et al15 explored the mediation

analysis and structural equation modeling by cross-sectional data. The results showed

that periodontitis had a significant direct effect on CKD, and had indirectly effect

through DM duration and HT. In other words, periodontitis had a significant direct effect

on DM duration; DM duration had a significant direct effect on HT, and then, HT had a

significant direct effect on CKD. While the direct effect of DM on CKD was not

significant.

As far as we were concerned, there were six cohort studies26-31 (Table 2.16)

assessing effects of periodontitis on renal disease. Shultis et al31 showed the significant

effect of periodontitis on overt nephropathy and ESRD during 22 years in subjects with

DM type 2 in the Gila River Indian Community after adjusted confounders with hazard

ratios around 2.0 to 4.9. Iwasaki et al30 proposed the 2-year-retrospective cohort study

in elderly communities. Results showed that the most severe groups of periodontitis had

the risk of decrease kidney function than others 2.24 (95% CI: 1.05, 4.79) times.

Recently, Grubbs et al identified the consistent effect of periodontitis on CKD incidents

in 2 cohorts, i.e. the Jackson heart study28 with effect size of 2.48 (95% CI: 1.04, 5.88),


and Osteoporotic Fractures in Men (MrOS) cohorts29 with adjusted ORs of 2.04 (95%

CI: 1.21, 3.44).

2.9.3 Conclusion

Relationship between periodontitis and CKD has been extensive explored.

Implied from case-control studies, periodontal status seems to be worse in CKD and

ESRD than subjects without kidney disease. Their significant association was also

claimed from cross-sectional studies with various population and types of statistical

analysis. Moreover, the longitudinal studies suggest the pattern of causation from

periodontitis on kidney function. However, there were only half of cohort studies which

interested in the incidence of CKD by exclude CKD cases at baseline. Among these,

their included subjects were quite specific groups, i.e., DM patients in Pima Indian

community31, African-American28 and only male aged 65 years or older29. Studies

representing general population have still been required. Furthermore, the complexity

between periodontitis, DM and CKD is valuable to explore. The structure models such

as, mediation analysis, which could explain the causative pathway or the sequence of

association, also should be identified.

2.10 Association between periodontitis and DM A bi-directional relationship between periodontitis and DM was

comprehensively investigated157-159. Periodontal health in subjects with DM was worse

than normal, particularly in poor glycemic control160. Simultaneously, periodontitis

could increase risk of DM initiation, as well as, impaired glycemic control in DM

patients9. Biological pathways in bi-directional relationship are not completely

understood, but it hypothesizes that involved of inflammation and cytokine, function of

immunity, collagen homeostasis, and insulin resistance161 (Figure 2.9). Although,

periodontal pathogens in periodontitis subjects with DM were little different comparing

with subjects without DM, local inflammatory cytokines were significantly increased.

Altered levels of IL-1ß, IL-6, PGE2 and RANKL/OPG ratios in saliva and gingival

tissue were broadly reported162-164. Hyper-inflammatory phenotype of monocytes165,

defective neutrophils166, and disturbance T-cells resulting in elevation of bone


resorption cytokines, i.e., RANKL, IL-17, were described as alternative pathways of

periodontal destruction in DM patients167. In addition, oxidative stress due to

hyperglycemic condition produced many reactive oxygen species, and these substances

influenced bone metabolism166. AGEs impaired collagen stability, and decreased the

potential of tissue repair following destruction168.

Epidemiological studies reported the consistent significant association

between periodontitis and DM160, 169. Originally, it was studied in the Gila River

Community, a population of Pima Indian with a high prevalence of DM. Taylor et al

established the risk association of severe periodontitis on poor glycemic control in DM

type II9. After that, Demmer et al170 found that subjects with the most severe

periodontitis at baseline had a five times greater increase in their HbA1c values over

five years compared to those who did not have periodontitis (change in HbA1c: 0.106

± 0.03% versus 0.023 ± 0.02%). Effect of periodontal treatment on glycemic control

also intense investigated. Recent systematic reviews and meta-analysis showed that

periodontal treatment was associated with reductions in HbA1c, however, this positive

effect was explored only in short term171.

Conversely, prevalence and severity of periodontitis increased in DM

patients compared to general patients. Nelson et al125 showed the increase risk of further

interproximal bone loss in DM after adjusting effects of age and sex in Pima Indian

subjects. Jimenez et al172 also reported the significant effect of DM on self-reported

periodontitis incidence and tooth loss by cohort of men in 20 years of follow-up.

However, the risk of periodontitis was based on the level of glycemic control, directly.

In well controlled, periodontium and response to periodontal treatment appeared to be

little different comparing with normal subjects. While, the differences were obvious in

poor glycemic controlled160.

2.11 DM as the risk factor of CKD

DM usually causes the various macro- and micro-vascular complications. They can lead

to CVD, nephropathy, and retinopathy. In aspect of nephrology, DM has been

recognized as the important risk factor of CKD development and its progression.

Diabetic kidney disease (DKD), i.e., kidney damage from DM, is a common


complication of both types of DM. Around 20-30% of DM patients suffered from kidney

disease173. Recent systematic review of observation studies reported the risk of

developing kidney disease among those with DM174. In type I DM, the annual incidence

of albuminuria ranged from 1.3% to 3.8%, whereas it ranged from 3.8% to 12.7% for

type 2 DM. Moreover, within studies reporting the incidence of eGFR < 60 ml/min per

1.73 m2, the annual incidence ranged from 1.9% to 4.3%, and incidence of ESRD ranged

from 0.04% to 1.8%. From this, DM has been always included as the common predictor

in the final prediction models. Echouffo-Tcheugui and Kengne40 reviewed the

prediction models of the occurrence/presence of CKD, and found that DM was

presented as the predictor in 23 from 30 models. In addition, age, sex, body mass index,

BP, serum creatinine, proteinuria, and serum albumin or total protein were commonly

included in the final prediction models. The effect size of DM on CKD incidence was

estimated from the meta-analysis of the cohort and case-control studies. Among 10

included studies175 with data from more than 5 million subjects, the pooled adjusted risk

ratio of CKD associated with DM was 3.34 (95% CI: 2.27, 4.93) in women, and 2.84

(95% CI: 1.73, 4.68) in men. Interestingly, the risk of CKD also significantly increased

in prediabetes176, with the relative risk of 1.12 (95% CI: 1.02, 1.21).

Metabolic pathways, hemodynamic pathways, and inflammation were

mentioned as the pathogenic mechanisms for renal injury (Figure 2.10). Hyperglycemia

state initiated the multiple cellular processes in the metabolic pathway including

alteration in cellular energy production, stimulation aldose reductase and PKC,

generation of AGEs and reactive oxygen species, increased flux of polyols and

hexosamines, atypical expression of cyclin kinases and their inhibitors, and disturb

stabilization of factors controlling the extracellular matrix homeostasis. These processes

altered blood flow and capillary permeability, increased production of extracellular

matrix proteins, and stimulated oxidant and osmotic stress. And thus, they contribute

glomerular sclerosis and tubulointerstitial fibrosis177. Hemodynamic dysfunction

potential occurred from intraglomerular hypertension and hyperfiltration, which directly

traumatized the vascular components in nephrons178. The most important mediator

participating in the hemodynamic pathway was the renin–angiotensin–aldosterone

system (RAAS). Hyperglycemia induced the activation of the local renal RAAS and

increased the production of angiotensin II, which exerted a preferential constrictor effect


on the efferent glomerular arteriole, resulting in a higher intraglomerular capillary

pressure10. The relationships between this inflammatory state and the initiation and

evolution of DKD involved very complex pathways. Diverse inflammatory molecules

play significant roles, including toll-like receptors, chemokines, adhesion molecules,

and inflammatory cytokines (e.g., IL-1ß, IL-6, and TNF-α)179.


Table 2.1 2009 CKD-EPI creatinine equations

Gender Serum creatinine Equation for estimating GFR

Female ≤ 0.7 mg/dl (≤ 62 µmol/l) 144 x (SCr/0.7)-0.329 x 0.993Age [ x 1.159 if black]

Female > 0.7 mg/dl (> 62 µmol/l) 144 x (SCr/0.7)-1.209 x 0.993Age [ x 1.159 if black]

Male ≤ 0.9 mg/dl (≤ 80 µmol/l) 141 x (SCr/0.9)-0.411 x 0.993Age [ x 1.159 if black]

Male > 0.9 mg/dl (> 80 µmol/l) 141 x (SCr/0.9)-1.209 x 0.993Age [x 1.159 if black]

GFR, glomerular filtration rate; SCr, serum creatinine


Table 2.2 Search terms and search strategy: Periodontitis and oral hygiene

Item Domains Terms

1

Periodontitis

Periodontitis

2 Periodontal

3 Periodontitis [MeSH]*

4 1 OR 2 OR 3

5

Oral hygiene

Poor oral hygiene

6 Plaque index

7 Dental plaque index [MeSH]*

8 Oral hygiene index

9 Oral hygiene index [MeSH]*

10 Plaque score

11 5 OR 6 OR 7 OR 8 OR 9 OR 10

12

General

Risk factor

13 Association

14 Relation

15 Correlation

16 12 OR 13 OR 14 OR 15

17 4 AND 11 AND 16 *Options for MEDLINE

Attaw

ood Lertpimonchai

Literature review

/ 28 Table 2.3 Characteristics of included studies

Authors Study type

Study base Population OH

measurement Age

Male (%)

Smoking (%)

DM (%)

Periodontitis definition

Imaki70 Cross-

sectional Community General PI 38.1 100 56.1 N/A CPITN: 3-4

Norderyd71 Cross-

sectional Community General PSc 48 48.7 20 4

Radiography: bone loss > 1/3 of root length

Wakai72 Cross-

sectional Hospital General PI 51.1 82.1 34.4 N/A CPITN: 3-4

Papapanou73 Case-

control Hospital General PSc 50.9 47.3 32.2 N/A

≥ 1 site with PPD ≥ 5 mm AND CAL ≥ 3 mm

Hashim74 Cohort Community General OHI,

Dental visit 15 54.2 33.3 N/A ≥ 1 site with ≥ 4 mm increase in CAL

Tezal75 Cross-

sectional Community General PI 48.7 48.2 61.8 N/A Mean CAL ≥ 2 mm

Hugoson76 Cross-

sectional Community General PSc 65.0 52.7 42.9 N/A

Radiography: bone loss more than 1/3 of root length

Do77 Cross-

sectional Community General PI 40 40.3 28.9 N/A

≥ 2 sites with CAL ≥ 5 mm AND ≥ 1 site with PPD ≥ 4 mm

Meisel78 Cross-

sectional Community General PSc 51.0 46.6 49.5 6

4th - 5th quintiles of % sites with CAL > 4 mm

Alpagot79 Cohort Hospital HIV

patients PI 34.1 57.9 N/A 0

≥ 1 site with PPD ≥ 4 mm OR CAL ≥ 2 mm

Fac. of Grad. Studies, M

ahidol Univ.

Ph.D

. (Clinical Epidem

iology) / 29 Table 2.3 Characteristics of included studies (cont.)

Authors Study type


measurement Age Male (%)

Smoking (%)

DM (%) Periodontitis definition

Solis80 Cross-sectional Hospital General PI 37.4 35.3 23.5 0

≥ 2 sites with CAL ≥ 6 mm AND ≥ 1 site with PPD ≥ 5 mm

Wickholm81 Cross-sectional Community General PI 36.7 49.2 44.7 N/A ≥ 3 teeth with PPD ≥ 5 mm

Natto82 Cross-sectional Community General PI 36.4 64.9 70 N/A ≥ 10 sites with PPD ≥ 5 mm

Torrungruang83 Cross-sectional Community General PSc 60 74.4 14.3 15.8 Mean CAL > 2.5 mm

de Macedo84 Cross-sectional Community General PSc, Flossing,

Brushing N/A 33.8 31.4 N/A ≥ 4 teeth with PPD ≥ 4 mm AND CAL ≥ 3 mm at the same site

Khader85 Cross-sectional Hospital General PI 39.4 44.8 N/A N/A Khader’s risk score

Vandana86 Cross-sectional Hospital Dental

fluorosis OHI 25.36 68.6 N/A 0 CPITN: 3-4

Wang87 Cross-sectional Community General Brushing N/A 45.7 27.7 0 Mean CAL ≥ 3 mm

Akhter88 Case-control Hospital General PI, Brushing,

Dental visit 38.5 50 45.7 N/A ≥ 2 sites with CAL ≥ 6 AND ≥ 1 site with PPD ≥ 5 mm

Kumar89 Cross-sectional Community General Brushing,

Dental visit 33.9 100 N/A N/A CPITN: 3-4

Attaw

ood Lertpimonchai

Literature review

/ 30 Table 2.3 Characteristics of included studies (cont.)

Authors Study type



Smoking (%)


Benguigui90 Cross-sectional Community General PI 58 54.9 19.2 6.7 CDC/AAP

Bawadi92 Cross-sectional Hospital General PI, Brushing 36.4 49.4 20.3 17.9 ≥ 4 teeth with PPD ≥ 4 mm AND

CAL ≥ 3 mm at the same site

Saxlin91 Cohort Community General PI, Brushing, Dental visit 41.86 27 0 0 New teeth with PPD ≥ 4 mm

Carrilho Neto93 Cross-sectional Hospital Inpatients OHI 45.7 59.7 42.7 N/A ≥ 1 site with PPD > 4 mm

Mathur94 Cross-sectional Hospital General OHI N/A 57.3 N/A N/A N/A

Teng95 Cross-sectional Hospital Psychiatric

inpatients Brushing,

Dental visit 41 62.5 42.5 N/A CPITN: 3-4

Crocombe96 Cross-sectional Community General Brushing,

Flossing N/A 50 15 4.3 ≥ 1 site with CAL ≥ 4 mm

Mannem97 Cross-sectional Hospital General PI 52.5 44.1 34.2 N/A ≥ 4 teeth with PPD ≥ 4 mm AND

CAL ≥ 3 mm at the same site

Raja98 Cross-sectional Hospital General PI 36.5 53.3 96.7 0 ≥ 4 sites with CAL ≥ 4 mm

Vogt99 Cross-sectional Hospital Pregnancy PSc, Flossing,

Brushing 27.2 0 15.87 0 ≥ 4 teeth with PPD ≥ 4 mm AND CAL ≥ 4 mm at the same site


ahidol Univ.

Ph.D

. (Clinical Epidem


Authors Study type



Smoking (%)


Develioglu105 Case-control Hospital General PI 46.7 N/A 0 33.3 ≥ 30% sites with PPD ≥ 5 mm AND

CAL ≥ 3 mm

Fiyaz100 Case-control Hospital General OHI N/A N/A 0 0 ≥ 1 site with PPD > 4 mm

OR CAL > 1.5 mm

Palle101 Cross-sectional Hospital CVD OHI 57.2 84.1 32.3 52.2 ≥ 5 sites with CAL ≥ 5 mm

Cakmak104 Case-control Hospital General PI 38.3 49.1 0 0 ≥ 1 site with PPD ≥ 5 mm AND

CAL ≥ 4 mm

Jacob108 Case-control Hospital General PI 37.3 75.6 33.3 0 CDC/AAP

Kaur109 Case-control Hospital General PI N/A 66.7 25 0 N/A

Koseoglu110 Case-control Hospital General PI 34.0 50 0 0 ≥ 4 teeth with PPD ≥ 5 mm AND

CAL ≥ 4 mm in each jaw

Kovačević111 Cross-sectional Hospital General

OHI, Brushing, Flossing, Dental

visit 38.9 77.2 31.7 N/A CPITN: 3-4

Lavu113 Case-control Hospital General OHI 33.6 50.4 0 0 CAL > 1 mm at least 30% sites

Lutfioglu114 Case-control Hospital General PI 33.1 53.3 51.1 0 ≥ 1 site with PPD ≥ 5 mm with

radiographic evidence of bone loss

Attaw

ood Lertpimonchai

Literature review

/ 32 Table 2.3 Characteristics of included studies (cont.)

Authors Study type



Smoking (%)


Petrović119 Case-control Hospital General PI 36.1 38.8 22.4 0 ≥ 3 quadrants with ≥ 3 sites with

PPD ≥ 3 mm AND CAL ≥ 2 mm

Pranckeviciene120 Cross-sectional Hospital Type I &

II DM PI 43.86 N/A 25.9 100 ≥ 1 site with CAL > 5 mm

Meenawat115 Case-control Hospital General PI 43.2 100 41.4 0 ≥ 4 teeth with PPD > 4 mm AND

CAL > 2 mm

Mesa116 Case-control Hospital General PSc 46.3 40.3 46.8 N/A ≥ 4 teeth with PPD ≥ 4 mm AND

CAL ≥ 3 mm at the same sites

Perayil117 Case-control Hospital General OHI 43.1 43.3 0 0 ≥ 5 teeth with PPD ≥ 5 mm AND

CAL ≥ 3 mm

Pereira118 Case-control Hospital General PSc 38.4 33.7 0 0 ≥ 4 teeth with PPD ≥ 4 mm AND

CAL ≥ 3 mm

Puri121 Case-control Hospital General OHI 39.78 N/A 0 0 AAP 1999

Singh122 Case-control Hospital General PI 43.5 52.5 0 0 ≥ 1 site with PPD ≥ 5 mm AND

CAL ≥ 2 mm

Toyman123 Case-control Hospital General PI 34.6 51.2 0 0 ≥ 6 teeth with PPD ≥ 5 mm with

radiographic evidence of bone loss


ahidol Univ.

Ph.D

. (Clinical Epidem


CAL, clinical attachment loss; CDC/AAP, periodontitis definition of the Centers for Disease Control and Prevention in collaboration with the American Academy of Periodontology, CPITN, the Community Periodontal Index of Treatment Needs; CVD, cardiovascular disease; DM, diabetes mellitus; N/A, not available; OH, oral hygiene; OHI, oral hygiene index; PI, plaque index; PPD, periodontal pocket depth; PSc, plaque score

Authors Study type



Smoking (%)


Varghese124 Case-control Hospital General PI N/A 65.3 0 0 ≥ 30% sites with PPD ≥ 6 mm AND

CAL ≥ 5 mm


Table 2.4 Risk of bias assessment

Authors Selection Comparability Outcome/Exposure* Risk of

bias S1 S2 S3 S4 S5 C1 C2 O1 O2 O3 O4 O5 O6 Cohort

Hashim74 1 1 1 0 - 1 1 1 0 1 0 1 1 Low

Saxlin91 1 1 1 1 - 0 1 1 1 0 1 1 0.5 Low

Case-control

Akhter88 1 1 1 0 0.5 1 1 1 1 0 - - - Low

Fiyaz100 1 1 0 0 0 0 0 1 1 0 - - - High

Papapanou73 1 1 1 0 0.5 1 1 1 1 0 - - - Low

Cakmak104 1 1 0 0 0.5 1 1 1 1 0 - - - Mod

Develioglu105 1 0 0 0 0 1 0 1 1 0 - - - High

Jacob108 1 1 1 0 0.5 1 0 1 1 0 - - - Mod

Kaur109 0 0 0 0 0 1 0 1 1 0 - - - High

Koseoglu110 1 1 0 0 0.5 1 0 1 1 0 - - - Mod

Lavu113 1 1 1 0 0.5 1 1 1 1 0 - - - Low

Lutfioglu114 0 1 0 0 0.5 1 0 1 1 0 - - - High

Meenawat115 1 1 0 0 0 0 0 1 1 0 - - - High

Mesa116 1 1 1 1 1 1 1 1 1 0 - - - Low

Perayil117 1 1 1 0 1 1 0 1 1 0 - - - Mod

Pereira118 1 1 0 0 1 1 0 1 1 0 - - - Mod

Petrović119 1 0 1 0 0 1 0 1 1 0 - - - Mod

Puri121 1 1 0 0 0 1 0 1 1 0 - - - Mod

Singh122 1 1 0 0 0 0 1 1 1 0 - - - Mod

Toyman123 0 1 0 0 0.5 1 0 1 1 0 - - - High

Varghese124 1 1 0 0 0 1 0 1 1 0 - - - Mod

Cross-sectional

Alpagot79 0 1 - - - 0 0 1 0 1 1 - - Mod

Bawadi92 1 1 - - - 1 1 1 1 1 1 - - Low

Benguigui90 1 1 - - - 0 0 1 1 1 1 - - Low

Carrilho Neto93 0 1 - - - 1 1 1 1 0 0 - - Mod

Crocombe96 1 1 - - - 1 1 1 1 1 0 - - Low

Do77 1 1 - - - 1 1 1 0 1 1 - - Low

Hugoson76 1 1 - - - 1 1 0 0 0 1 - - Mod

Imaki70 0 1 - - - 1 1 1 0 0 0 - - Mod

Khader85 0 1 - - - 1 1 1 1 1 1 - - Low

Kovačević111 1 1 - - - 0 0 1 1 0 0 Mod

Kumar89 1 1 - - - 0 1 1 0 0 1 - - Mod

de Macedo84 0 1 - - - 1 1 1 1 1 1 - - Low

Mannem97 0 1 - - - 1 1 1 1 1 1 - - Low

Mathur94 0 1 - - - 0 0 1 1 0 0 - - High


Table 2.4 Risk of bias assessment (cont.)

Authors Selection Comparability Outcome/Exposure* Risk

of bias S1 S2 S3 S4 S5 C1 C2 O1 O2 O3 O4 O5 O6

Meisel78 1 1 - - - 1 1 1 0 1 0 - - Low

Natto82 0 1 - - - 1 1 1 1 0 1 - - Low

Norderyd71 1 1 - - - 1 1 0 0 0 0 - - Mod

Palle101 0 1 - - - 1 1 1 1 1 0 - - Low

Pranckeviciene120 0 1 - - - 0 1 1 1 1 1 - - Low

Raja98 0 1 - - - 1 1 1 1 1 1 - - Low

Solis80 0 1 - - - 1 1 1 1 1 1 - - Low

Teng95 0 1 - - - 1 1 1 0 0 0 - - Mod

Tezal75 1 1 - - - 1 1 1 1 1 1 - - Low

Torrungruang83 0 1 - - - 1 1 1 0 1 1 - - Low

Vandana86 0 1 - - - 0 0 1 0 0 0 - - High

Vogt99 0 1 - - - 0 0 1 1 1 0 - - Mod

Wakai72 0 1 - - - 1 1 1 0 0 0 - - Mod

Wang87 1 1 - - - 1 1 1 0 1 1 - - Low

Wickholm81 1 1 - - - 1 1 1 1 0 1 - - Low

*Outcome: Cohort and Cross-sectional study || Exposure: Case-control study


Table 2.5 Categorization of OH level

Authors Index Good OH Fair OH Poor OH

Mathur94 OHI < 1 1-1.99 ≥ 2

Vandana86 OHI < 1 1-1.99 ≥ 2

Carilho Neto93 OHI < 3 - ≥ 3

Palle101 OHI < 2 - ≥ 2

Bawadi92 PI < 1 1-1.99 ≥ 2

Do77 PI < 1 1-1.99 ≥ 2

Saxlin91 PI < 1 1-1.99 ≥ 2

Wakai72 PI < 1 1-1.99 ≥ 2

Wickholm81 PI < 1 1-1.99 ≥ 2

Benguigui90 PI Mean - ≥ Mean

Imaki70 PI ≤ 1 - > 1

Pranckeviciene120 PI < 1 - ≥ 1

Natto82 PI 0-0.69 0.7 -1.30 ≥ 1.31

de Macedo84 PSc 0-64 - ≥ 65

Torrungruang83 PSc 0-39 40-79 ≥ 80

OH, oral hygiene; OHI, oral hygiene index; PI, plaque index; PSc, plaque score


ahidol Univ.

Ph.D

. (Clinical Epidem

iology) / 37 Table 2.6 Pooling effects of fair and poor versus good OH on periodontitis

Authors Index Periodontitis Non-Periodontitis OR1

* 95% CI I2 OR2** 95% CI I2

Good OH

Fair OH

Poor OH Good

OH Fair OH

Poor OH

Vandana86 OHI 21 156 100 124 605 23 1.52 0.93, 2.50 25.67 13.44, 49.06

Carilho Neto93 OHI 2.10 0.37, 11.76

Mathur94 OHI 2 60 178 18 42 0 12.86 2.83, 58.38 2641.80# 122.16, 57129.99

Palle101 OHI 6.85 2.87, 16.35

Pooled 6.16 0.85, 44.90 81% 7.64 2.14, 27.36 71%

Imaki70 PI 186 553 542 330 4.88 3.94, 6.06

Wakai72 PI 144 214 68 88 87 24 1.50 1.04, 2.16 1.73 1.01, 2.96

Do77 PI 4 36 103 24 164 244 1.32 0.43, 4.03 2.53 0.86, 7.48

Wickholm81 PI 2.72 1.89, 3.92 4.81 1.99, 10.80

Natto82 PI 1.90 1.09, 3.30 3.60 1.20, 11.00

Benguigui90 PI 87 114 42 12 4.59 2.28, 9.23

Saxlin91 PI 55 44 4 33 14 0 1.89 0.90, 3.95 5.43# 0.28, 104.11

Bawadi92 PI 15 69 21 83 119 19 3.21 1.72, 5.99 6.12 2.67, 14.01

Pranckeviciene120 PI 98 122 40 6 8.30 3.38, 20.38

Pooled 2.26 1.75, 2.92 36% 4.15 3.00, 5.72 50%

Torrungruang83 PSc 218 778 398 198 342 71 2.07 1.64, 2.60 5.09 3.71, 6.99

de Macedo84 PSc 9 33 64 66 3.56 1.58, 8.02

OVERALL POOLED 2.04 1.65, 2.53 40% 5.01 3.40, 7.39 78% * Fair versus Good OH, ** Poor versus Good OH, # Calculated with continuity correction CI, confidence interval; OH, oral hygiene; OHI, oral hygiene index; OR, odds ratio; PI, plaque index; PSc, plaque score


Table 2.7 Subgroup and sensitivity analysis according to sources of heterogeneity of fair and poor versus good OH

Factors

Fair Vs Good OH Poor vs Good OH

Number

of studies

OR1 (95% CI) I2 Number

of studies

OR2 (95% CI) I2

Overall 9 2.04 (1.65, 2.53) 40% 15 5.01 (3.40, 7.39) 78%

Subgroup analysis

Type of index

OHI

PI

PSc*

2

6

1

6.16 (0.85, 44.90)

2.26 (1.75, 2.92)

-

81%

36%

-

4

9

2

7.64 (2.14, 27.36)

4.15 (3.00, 5.72)

-

71%

50%

-

Study-based

Community

Hospital

6

3

2.23 (1.85, 2.69)

2.43 (0.72, 8.17)

4%

90%

9

6

4.78 (4.10, 5.58)

6.06 (2.08, 17.68)

0%

84%

Periodontitis definition**

Using PPD only

Using CAL or x-ray only*

Using PPD with CAL

5

1

2

1.86 (1.40, 2.46)

-

2.45 (1.58, 3.79)

43%

-

0%

7

3

4

4.86 (2.12, 11.18)

-

4.42 (2.98, 6.55)

89%

-

0%

Smoking**

< 25%

≥ 25%

3

4

2.15 (1.59, 2.91)

2.19 (1.64, 2.94)

1%

40%

4

9

5.05 (3.84, 6.62)

4.05 (2.89, 5.67)

0%

53%

Sensitivity analysis

Only studies focused on

general population

8

2.10 (1.76, 2.49)

22%

11

4.21 (3.21, 5.51)

49%

* Insufficient number of studies for pooling OR via mvmeta method ** Missing data: periodontitis definition (1 study), smoking (2 studies) CAL, clinical attachment level; CI, confidence interval; OH, oral hygiene; OHI, oral hygiene index; OR, odds ratio; PI, plaque index; PPD, periodontal pocket depth; PSc, plaque score


Table 2.8 Pooled mean difference of OH score between periodontitis and non-

periodontitis

Authors Periodontitis Non-Periodontitis

MD 95% CI N Mean SD N Mean SD

Oral hygiene index

Fiyaz100 30 2.38 0.81 30 0.53 0.41 2.88 2.15, 3.61

Perayil117 30 3.89 0.68 30 0.53 0.17 6.78 5.45, 8.11

Kovacevic111 73 1.18 0.64 28 1.18 0.64 0.00 -0.44, 0.44

Puri121 20 3.70 0.86 20 2.75 0.37 1.44 0.74, 2.13

Lavu113 177 2.77 0.83 176 0.40 0.25 3.87 3.51, 4.22

Pooled I2 = 98.9 1.71 0.65, 2.78

Plaque index

Alpagot79 111 1.79 0.74 41 0.88 0.47 1.34 0.95, 1.73

Solis80 44 1.32 0.79 105 0.87 0.57 0.70 0.34, 1.06

Akhter88 140 1.80 0.70 140 1.20 0.50 0.99 0.74, 1.23

Mannem97 77 1.19 0.44 34 0.65 0.29 1.35 0.91, 1.79

Raja98 30 1.60 0.33 30 1.31 0.26 0.98 0.44, 1.51

Cakmak104 80 1.78 0.59 40 1.05 0.57 1.25 0.84, 1.66

Develioglu105 32 2.23 0.52 16 0.19 0.10 4.74 3.60, 5.88

Jacob108 30 1.35 0.39 15 0.48 0.24 2.50 1.68, 3.31

Kaur109 20 1.69 0.35 20 0.75 0.36 2.65 1.79, 3.51

Petrovic119 36 1.20 0.78 31 0.74 0.77 0.59 0.10, 1.08

Singh122 20 2.48 0.27 20 0.57 0.10 9.38 7.18, 11.58

Koseoglu110 20 1.52 0.30 40 0.93 0.47 1.41 0.82, 2.01

Lutfioglu114 32 2.29 0.97 60 1.26 1.27 0.88 0.43, 1.32

Meenawat115 24 2.49 0.23 5 0.33 0.08 10.07 7.22, 12.92

Toyman123 21 0.89 0.90 20 0.02 0.06 1.35 0.67, 2.03

Varghese124 25 1.29 0.41 25 0.29 0.27 2.88 2.08, 3.68

Pooled I2 = 98.3 0.97 0.61, 1.32

Plaque score

Papapanou73 131 51.20 24.50 74 35.00 25.60 0.65 0.36, 0.94

Vogt99 157 73.80 14.20 177 59.60 17.90 0.87 0.65, 1.10

Pereira118 31 79.00 17.90 58 49.20 26.80 1.24 0.76, 1.71

Mesa116 41 74.47 34.79 36 46.32 35.36 0.80 0.34, 1.27

Pooled I2 = 74.2 20.44 12.85, 28.04

Overall Pooled* I2 = 95.6 2.04 1.59, 2.50 * Standardized mean difference pooling

CI, confidence interval; MD, mean difference; OH, oral hygiene; OHI, oral hygiene index; PI, plaque index; PSc, plaque score


Table 2.9 Pooled effect size of oral care habit on periodontitis

Authors Periodontitis Non-Periodontitis

OR 95% CI

I2 Good Habits

Bad habits Good

Habits Bad

habits

Brushing

de Macedo84 34 8 118 12 0.43 0.16, 1.14

Wang87 0.86 0.67, 1.11

Akhter88 14 126 38 102 0.30 0.15, 0.58

Kumar89 0.55 0.53, 0.57

Saxlin91 67 33 36 10 0.56 0.25, 1.27

Bawadi92 69 36 154 81 1.01 0.62, 1.64

Teng95 0.43 0.24, 0.75

Crocombe96 1356 788 1179 847 1.24 1.09, 1.40

Vogt99 56 101 68 109 0.89 0.57, 1.39

Kovacevic111 46 27 21 7 0.57 0.21, 1.51

Pooled 0.6 0.47, 0.94 94.5%

Floss

de Macedo84 8 34 28 102 0.86 0.36, 2.06

Crocombe96 0.90 0.77, 1.05

Vogt99 49 108 76 101 0.60 0.38, 0.95

Kovacevic111 21 52 7 21 1.21 0.45, 3.27

Pooled 0.87 0.75, 1.00 5.1%

Dental visit

Hashim74 0.66 0.35, 1.27

Akhter88 6 134 20 120 0.27 0.10, 0.69

Kumar89 0.99 0.98, 1.00

Saxlin91 68 33 35 11 0.65 0.29, 1.43

Teng95 0.65 0.35, 1.22

Kovacevic111 38 34 18 10 0.62 0.25, 1.53

Pooled 0.68 0.47, 0.98 60.4%

CI, confidence interval; OR, odds ratio


Table 2.10 Sources of heterogeneity of tooth brushing meta-analysis

* Missing data of smoking: 1 study

CAL, clinical attachment level; CI, confidence interval; OR, odds ratio; PPD, periodontal pocket depth

Factors Number of studies

OR (95% CI) I2

Overall 10 0.66 (0.47, 0.94) 94.5%

Smoking*

< 25%

≥ 25%

4

5

1.03 (0.78, 1.35)

0.51 (0.31, 0.83)

47%

69%

Definition of regular brushing

Once a day

Twice a day

3

7

0.57 (0.35, 0.94)

0.75 (0.54, 1.05)

78%

77%

Periodontitis definition

Using PPD only

Using CAL or x-ray only

Using PPD combined with CAL

4

3

3

0.55 (0.53, 0.57)

0.94 (0.64, 1.39)

0.67 (0.35, 1.29)

0%

80%

79%


Table 2.11 Publication bias assessment by Egger test

Pooling Number of studies Coefficient SE P-value

OH: Continuous data

Mean difference 25 6.00 1.75 0.002

OR: Plaque index 6 4.08 1.52 0.055

OR: Plaque score 3 -201.87 0.57 0.002

OH: Categorical data

OR: Poor Vs Good OH 15 0.17 0.78 0.827

OR: Fair Vs Good OH 9 0.68 1.01 0.523

Oral health care habit

Brushing 10 1.30 1.71 0.468

Floss 4 -0.32 0.96 0.771

Dental visit 6 -1.48 0.32 0.010

CI, confidence interval; OH, oral hygiene; OR, odds ratio; SE, standard error


Table 2.12 Overview of the meta-analysis

Number of studies

Pooled OR (95% CI) I2 Quality of

evidence*

OH

(a) Categorical data

- Fair versus Good OH

- Poor versus Good OH

(b) Continuous data

- PI: 1-unit increase

- PSc: 1-unit increase

- OH score

9

15

6

3

25

2.04 (1.65, 2.53)

5.01 (3.40, 7.39)

2.25 (1.43, 3.54)

1.02 (1.01, 1.03)

2.04 (1.59, 2.50)**

40%

78%

81.1%

74.2%

95.6%

Moderate

Oral healthcare habits

- Tooth brushing

- Interdental cleaning

- Dental visits

10

4

6

0.66 (0.47, 0.94)

0.87 (0.75, 1.00)

0.68 (0.47, 0.98)

94.5%

5.1%

60.4%

Very low

Low

Very low

* Quality of evidence: The Grades of Recommendation, Assessment, Development and Evaluation Working Group (GRADE Working Group) ** Pooled standard mean difference (SMD) CI, confidence interval; OH, oral hygiene; OHI, oral hygiene index; OR, odds ratio; PI, plaque index; PSc, plaque score


Table 2.13 Search terms and search strategy: Periodontitis and CKD

Item Domains Terms 1

Periodontitis

periodontitis 2 periodontal 3 alveolar bone loss 4 attachment loss 5 pocket depth 6 Periodontitis [MeSH]* 7 Chronic Periodontitis [MeSH]*

8 [1-7 | OR]

9

CKD

chronic kidney disease 10 CKD 11 renal disease 12 kidney failure 13 ESRD 14 ESKD 15 renal Insufficiency 16 glomerular Filtration Rate 17 *GFR 18 creatinine 19 proteinuria 20 albuminuria 21 microalbuminuria 22 macroalbuminuria 23 Kidney disease [MeSH]* 24 Kidney Failure, Chronic [MeSH]*

25 [9-24 | OR]

26

General

risk factor 27 risk study 28 association 29 relation 30 observational study 31 cross-sectional study 32 retrospective study 33 prospective study 34 cohort study 35 longitudinal 36 epidemiolog*

37 [26-36 | OR]

38 8 AND 25 AND 37

* Options for MEDLINE


ahidol Univ.

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. (Clinical Epidem

iology) / 45 Table 2.14 Case-control studies: Periodontitis and CKD

Authors (Year) Cases Controls Periodontitis Results

Oshrain (US, 1979) HD (n = 20)

Controls (n=20) Matched: N/A

Examination: 6 index teeth Index: PI, GI, PDI

All periodontal parameters were poorer in HD than Controls

Tollefsen (Norway, 1985) HD (n = 12)

Controls (n=12) Matched: age, number of teeth, Social status

Examination: - Full-mouth Index: PI, GI, PPD, Bone level

PI: HD > Controls GI, PPD, Bone level: NS

Rahman (Tuskey, 1992) HD (n = 52)

Controls (n=52) Matched: age, number of teeth, Social status

Examination: N/A Index: PI, GI, SBI, PDI, PPD

PI: HD > Controls GI, SBI, PDI, PPD: NS

Galvada (UK, 1999) HD (n = 105)

Controls (n=53) Matched: age, gender

Examination: - Full-mouth - 6 sites per tooth Index: PI, CAL

NS

Marakoglu (Turkey, 2003) HD (n = 36)

Controls (n=36) Matched: age, gender, PI

Examination: - Full-mouth - 6 sites per tooth Index: GI, PPD

NS

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Bots (Netherlands, 2006) ESRD (n=84)


Examination: - Half-mouth - 6 sites per tooth

Index: PI, PPD, BOP

NS

Borawski (Poland; 2007)

1. CKD (n=38) 2. HD (n = 35) 3. CAPD (n=33)


Examination: - Full-mouth - 6 sites per tooth

Index: GI, PI, PPD, CAL

All parameters: HD > Controls

Castillo (Spain, 2007) HD (n = 52)



Index: PI, CAL

NS

Marinho (Portugal, 2007)

1. CKD (n=22) 2. HD (n = 28)

Controls (n=64) Matched: age, gender, weight, education

Examination: - 6 index teeth

Index: PPD, CAL

- PPD: NS - CAL: CKD & HD > Controls

Chamani (Iran, 2009) HD (n = 55)

Controls (n=30) Matched: age, gender, PI

Examination: - Half-mouth

Index: GI, PPD, CAL, BOP

- PPD: NS - GI, BOP, CAL: HD > Controls

Throman (Sweden, 2009)

CKD & HD (n=93)



Index: CAL

CAL: Kidney disease > Controls


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Dag (Turkey, 2010) HD (n = 43)



Index: PI, GI, PPD


Bhatsange (India; 2012) HD (n = 75)



Index: OHI-S, PDI


Brito (2012, Brazil)

1. CKD (n=51) 2. HD (n = 40) 3. CAPD (n=40)



Index: PPD, CAL

% sites with CAL > 6 mm: Kidney disease > Controls

Parkar (India, 2012) HD (n = 152)



Index: OHI-S, CPI, LOA


Chhokra (India, 2013) ESRD (n = 40)



Index: OHI-S, GI, PPD, CAL

All parameters: ESRD > Controls

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Teratani (Japan, 2013) HD (n = 89)


Examination: - Full-mouth - 2 sites per tooth Index: % sites with CAL ≥ 4 mm

NS

Tiwari (India, 2013) CKD (n = 30)


Examination: - Index teeth

Index: CPI

CPI: CKD > Controls

Tadakamadla (India, 2014)

CKD (n = 64) GFR: - Creatinine - Cockcroft-Gault


Examination: - Full-mouth - 3 sites per tooth Index: OHI GI, CPI

All parameters: CKD > Controls

Zhao (China, 2014) HD (n = 102)


- Full-mouth - 6 sites per tooth CPI, CAL


Limeres (Portugal, 2016) HD (n = 44)


- 6 index teeth PI, GI, PPD, CAL


C, controls; CAL, clinical attachment level; CAPD, Continuous Ambulatory Peritoneal Dialysis; CKD, chronic kidney disease; CPI, Community Periodontal Index; ESRD, end-stage renal disease; GFR, glomerular filtration rate; GI, gingival index; HD, hemodialysis; LOA, loss of attachment index; N/A, not available; NS, not significant; OHI, oral hygiene index; PDI, Periodontal Disease Index; PI, plaque index; PPD, periodontal pocket depth


ahidol Univ.

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. (Clinical Epidem

iology) / 49 Table 2.15 Cross-sectional studies: Periodontitis and CKD

Authors (Year)

Participants and Setting Primary outcome Periodontitis Analysis Results

Kshirsagar (2005)

ARIC: - USA (1996-1998) - Community-based Participants: - General population - Age: 45-64 - n = 5,537

GFR: - Creatinine - MDRD equations Categorization 1. CKD: GFR < 60 2. non-CKD: GFR ≥ 60

Methods: - Full-mouth exam - 6 sites per tooth Categorization*: 1. Severe 2. Initial 3. Healthy

Binary logistic regression Adjusted: age/gender/center/race smoking/education DM/HT/CVD CRP

Adjusted OR (95% CI) Initial: 2.00 (1.23, 3.24) Severe: 2.14 (1.19, 3.85)

Fisher (2008a)

NHANES III: - USA (1988-1994) - Community-based Participants: - General population - age: ≥ 18 - n = 12,947


Methods: - Half-mouth exam - 2 sites per tooth Categorization 1. No periodontitis 2. Periodontitis** 3. Fully edentulous

Binary logistic regression Adjusted: age/gender/race smoking/BMI/CRP DM/HT/Lipid profile Income/education

Crude OR (95% CI) Perio: 3.93 (2.95, 5.24) Eden: 10.38 (7.87, 13.70) Adjusted OR (95% CI) Perio: 1.60 (1.16, 2.21) Eden: 1.85 (1.34, 2.56)

Fisher (2008b)




Binary logistic regression Adjusted: age/gender/race smoking/BMI/CRP DM/HT/Lipid profile Income/education

Crude OR (95% CI) Perio: 1.26 (0.78 to 2.03) Eden: 3.54 (2.54 to 4.93) Adjusted OR (95% CI) Perio: 1.20 (0.76 to 1.90) Eden: 1.61 (1.09 to 2.37)

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Authors (Year)

Participants and Setting

Primary outcome Periodontitis Analysis Results

Fisher (2009)

NHANES III: - USA (1988-1994) - Community-based Participants: - General population - age: ≥ 18 - n = 5,978/11,955 (randomly selected)



Binary logistic regression Adjusted: age/gender/race smoking/BMI/CRP DM duration/HT/Lipid profile Income/education/Hospitalized albuminuria

Crude ORs (95% CI) Perio: 4.50 (3.02 to 6.71) Eden: 10.87 (6.86 to 17.20) Adjusted ORs (95% CI) Perio: 1.60 (1.07 to 2.39) Eden: 2.03 (1.31 to 3.14)

Fisher (2011)




1. Binary logistic regression 2. Mediation analysis - Periodontitis → DM → (HT) → CKD - DM → Periodontitis → CKD Adjusted: age/gender/race smoking/BMI/CRP DM duration/HT/Lipid profile Income/education/Hospitalized albuminuria

Binary logistic regression Adjusted ORs (95% CI) Perio: 1.62 (1.17–2.26) Eden: 1.83 (1.31–2.55) Mediation analysis - DM did not affect CKD directly but mediated through Periodontitis and HT - Periodontitis had a significant direct effect on CKD, and had indirect effect through diabetes duration, and then, hypertension.


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iology) / 51 Table 2.15 Cross-sectional studies: Periodontitis and CKD (cont.)

Authors (Year)



Ioannidou (2011)

NHANES III: - USA (1988-1994) - Community-based Participants: - General population - age: ≥ 21 - n = 12,081 - Dentate only


Methods: - Half-mouth exam - 2 sites per tooth Categorization: CDC/AAP 1. No/ Mild 2. Moderate/ Severe

Binary logistic regression Adjusted: age/gender smoking/BMI DM/HT/CVD Income/education

Crude ORs (95% CI) - Non-Hispanic Black: 1.85 (1.48, 2.30) - Mexican-American: 2.77 (2.15, 3.55)

Adjusted ORs (95% CI) - Non-Hispanic Black: 1.24 (0.95, 1.62) - Mexican-American: 1.59 (1.14, 2.13)

Grubbs (2011)

NHANES: - USA (2001-2004) - Community-based Participants: - General population - age: 21-75 - n = 6,199


Methods: - Half-mouth exam - 3 sites per tooth Categorization: CDC/AAP 1. No/ Mild*** 2. Moderate/ Severe***

Binary logistic regression Adjusted: age/gender/race smoking/ DM/HT Income/education

Crude ORs (95% CI) - 2.50 (1.96, 3.19) Adjusted ORs (95% CI) - 1.55 (1.16, 2.07)

Yoshihara (2012)

Setting: - community-dwelling Participants: - Postmenopausal woman - age: 55-74 - n = 405

Cystatin C Categorization 1. Low (ScC > 0.91) 2. Normal (ScC ≤0.91)

Numbers of remaining teeth Mean CAL: - 10 index teeth - 6 sites per tooth

t-test Compare N (remaining teeth) & Mean CAL between normal and low renal function

Significant differences between renal function groups

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Authors (Year)



Han (2013) KNHANES: - Korea (2008–2009) - Community-based Participants: - General population - age: 43.4 ± 0.2 - n = 15,729

1. Kidney function: - Creatinine / MDRD Categorization 1.1 CKD: GFR < 60 1.2 non-CKD: GFR ≥ 60 2. Proteinuria (dipstick tests; ≥ 1+) 3. Hematuria (dipstick tests; ≥ 1+)

Methods: - 6 index teeth - CPI Categorization: CPI 1. No periodontitis (CPI 0-2)

2. Periodontitis (CPI 3,4)

Binary logistic regression Adjusted: age, sex, region, education, obesity, smoking, exercise, HT, DM, Lipid, CVD

Adjusted ORs (95% CI) 1. Kidney function: 1.39 (1.03–1.89) 2. Proteinuria 1.29 (0.87–1.91) 3. Hematuria 1.29 (1.15–1.46)

Salimi (2014) NHANES III: - USA (1988-1994) - Community-based Participants: - General population - age: ≥ 20 - n = 13,270

GFR: - Continuous data - - Creatinine - MDRD equations ACR: - Continuous data - - urine albumin - urine creatinine

Methods: - Half-mouth exam - 2 sites per tooth Categorization: CDC/AAP 1. No/ Mild 2. Moderate 3. Severe

Linear regression Adjusted: Age/gender/race Income/education Smoking CVD/HT/HbA1C Total cholesterol Serum lead

1. GFR - Moderate (sig.) R2 = 0.33, β = -0.013 - Severe (sig.) R2 = 0.33, β = -0.001

2. ACR - Moderate (sig.) R2 = 0.16, β = 0.022 - Severe (sig.) R2 = 0.16, β = 0.003


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. (Clinical Epidem

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Authors (Year)



Yoshihara (2016)

Setting: - Japan - Community-based Participants: - postmenopausal never smoking women - age: 55-74 - n = 332

Cystatin C Categorization 1. Low function (ScC > 0.91) 2. Normal function (ScC ≤0.91)

Methods: - Full-mouth - 2 sites / tooth (buccal / lingual) Categorization: PISA Highest quartile Vs Others

Binary logistic regression Adjusted: Osteocalcin, CRP, job, regular check-up, use of inter dental brush or floss, the number of remaining teeth and age

Adjusted ORs (95% CI) 2.44 (1.23, 4.85)

* Periodontitis definition (Kshirsagar, 2005) (1) Severe periodontitis: two or more interproximal sites with CAL ≥ 6 mm that are not on the same tooth AND one or more interproximal sites with PPD ≥ 5 mm (2) Initial periodontitis: two or more interproximal sites with CAL ≥ 4 mm (not on the same tooth) (3) Healthy/gingivitis: individuals not meeting the other 2 criteria. ** Periodontitis definition (Fisher, 2008) Periodontitis: 1 or more sites with CAL ≥ 4 mm and bleeding on the same tooth (bleeding as an indicator of active inflammation) *** Periodontitis definition (CDC/AAP) (1) Severe periodontitis: two or more interproximal sites with CAL ≥ 6 mm that are not on the same tooth AND one or more interproximal sites with PPD ≥ 5 mm (2) Moderate periodontitis: two or more interproximal sites with CAL ≥ 4 mm, or two or more interproximal sites with PPD ≥ 5 mm, not on the same tooth. (3) Normal /Mild periodontitis ACR urine albumin to creatinine ratio; BMI, body mass index; CAL, clinical attachment level; CI, confidence interval; CKD, chronic kidney disease; CPI, Community Periodontal Index; CRP, C-reactive protein; CVD, cardiovascular disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; HbA1C, hemoglobin A1C; HT, hypertension; OR, odds ratio; PISA, Periodontal inflamed surface area; PPD, periodontal pocket depth; SBP, systolic blood pressure; ScC, serum cystatin C; TG, triglyceride; WBC, white blood cell count

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Authors (Year)


Primary outcome Periodontitis Duration

(year) Analysis Results

Shultis (2007)

Gila River Indian: - Arizona, US - community-based Participants: - Type II DM only - Preserved kidney function, eGFR ≥ 60 AND ACR < 300 - age: ≥ 25 - n: 529

1. MA: ACR ≥ 300 2. ESRD

Number of missing teeth & Panoramic radiographs Categorization*: 1. none/mild 2. moderate 3. severe 4. fully edentulous

9.4

(0.03-21.6) Cox proportional hazard model Adjusted: Age/gender DM duration/BMI Smoking/ DM control

Adjusted HRs (95%CI)

1) MA - Moderate 2.0 (1.2, 3.5) - Severe 2.1 (1.2, 3.8) - Edentulous 2.6 (1.4, 4.6)

2) ESRD - Moderate 2.3 (0.6, 8.1) - Severe 3.5 (0.96, 12.4) - Edentulous 4.9 (1.4, 17.4)

Iwasaki (2012)

Niigata: - community-based Participants: - elderly Japanese - 75-year-old - n = 317

Decreased kidney function: - eGFR - creatinine - Japanese equation Categorization: 1) Decreased 2) No decreased

Full-mouth examination Categorization PISA: The highest quartile Vs Others

2 Logistic regression Adjusted: Baseline kidney function/ gender Income/education Smoking/Alcohol Proteinuria/obesity HT/Hyperglycemia

Adjusted ORs (95% CI)

- 2.24 (1.05, 4.79)


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. (Clinical Epidem

iology) / 55 Table 2.16 Cohort studies: Periodontitis and CKD (cont.)

Authors (Year)




Chen (2015)

Taipei City: - National Health Examination - community-based Participants: - elderly Chinese - age > 65 - n = 100,263

Decline ≥ 30% of eGFR (creatinine: CKD-EPI)

CPI index (highest sextant) - 0-2 (No periodontitis) - 3-4 (Periodontitis)

3.8 ± 1.7

Logistic Regression Adjusted: age, sex, BMI, smoking, alcohol use, HT, DM, CVD WBC, Hb, level, HDL, triglycerides, uric acid, urea nitrogen, and albumin, baseline eGFR, urinary protein

Adjusted ORs (95% CI) - 1.59 (1.37, 1.86)

Grubbs (2015)

The Jackson Heart Study: - Mississippi, US - community-based - D-ARIC study Participants: - African Americans - preserved kidney function (eGFR ≥ 60) - age: 65.4 ± 5.2 - n = 699

eGFR < 60 OR Decline > 5% annualized loss of eGFR

Full-mouth examination Categorization: 1) CDC/AAP: Severe Vs Non-severe 2) PISA: The highest quartile Vs Others

4.8 ± 0.6

Multivariable Poisson regression Adjusted: age, gender, diabetes, hypertension, smoking, income

Adjusted ORs (95% CI) - CDC/AAP: 4.18 (1.68, 10.39) - PISA: 2.48 (1.04, 5.88)

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* Periodontitis definition (Shultis, 2007) 1) None/mild periodontitis, defined as ≥ 24 teeth, of which < 6 had 25–49% bone loss and none had ≥50% bone loss 2) Moderate periodontitis, defined as ≥15 teeth, of which < 7 had 50–74% bone loss and < 4 had ≥ 75% bone loss 3) Severe periodontitis, defined as 1–14 teeth or greater bone loss than in previous categories 4) Edentulous

ACR urine albumin to creatinine ratio; BMI, body mass index; CAL, clinical attachment level; CI, confidence interval; CKD, chronic kidney disease; CPI, Community Periodontal Index; CRP, C-reactive protein; CVD, cardiovascular disease; DM, diabetes mellitus; eGFR, estimated glomerular filtration rate; HbA1C, hemoglobin A1C; HT, hypertension; MA, macroalbuminuria; OR, odds ratio; PISA, Periodontal inflamed surface area; PPD, periodontal pocket depth; SBP, systolic blood pressure; ScC, serum cystatin C; TG, triglyceride; WBC, white blood cell count

Authors (Year)




Grubbs (2016)

MrOS study: - US - Hosipital-based Participants: - Male only - age: 73.4 ± 4.8 - n = 761 - preserved kidney function (eGFR ≥ 60)

eGFR < 60 OR Decline > 5% annualized loss of eGFR

Half-mouth examination Categorization: 1) CDC/AAP: Severe Vs Non-severe 2) European Workshop: Severe Vs Non-severe

4.9 ± 0.3

Multivariable Poisson regression Adjusted: age, diabetes, hypertension, smoking, race, education

Adjusted ORs (95% CI) - CDC/AAP: 1.10 (0.63, 1.91) - European Workshop: 2.04 (1.21, 3.44)

Chang (2017)

MDSCAN: - Taiwan - Hosipital-based Participants: - Referred patients to a multidisciplinary subspecialty - age: 53.1 ± 8.4 - n = 1,486

Progression of color intensity in GFR and albuminuria grid from KDIGO guideline 2012

Not mentioned details of examination Categorization: Mean PPD: - < 3.8 mm - 3.8 – 4.5 mm - ≥ 4.5 mm

4.9

(2.4-7.3)

Multiple Cox regression model Adjusted: age, DM, ACR, sex, HT, creatinine, smoking, and betel nut chewing.

Adjusted HRs (95% CI) Mod periodontitis: 2.06 (1.31, 3.20) Severe Periodontitis: 3.10 (1.99, 4.58)


Figure 2.1 Flow chart of identifying and selecting studies: Periodontitis and OH

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Figure 2.2 Pooling effects of fair and poor versus good OH on periodontitis


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Figure 2.3 Pooling ORs of Plaque index and Plaque score on periodontitis


Figure 2.4 Pooling effect of oral care habits on periodontitis


Figure 2.5 Funnel plots of publication bias assessment

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Figure 2.6 Contour enhanced funnel plots


Figure 2.7 Summary of pooled effect of OH and oral care habits on periodontitis


Figure 2.8 Flow chart of identifying and selecting studies: Periodontitis and CKD

41 articles eligible for inclusion

MEDLINE

6 2 2

SCOPUS

1 9 2 0

Screening Titles and Abstracts

2 2 9 8

Excluded:

- 871 Reviews/Case reports/Letters

- 612 No kidney fn/No control group of CKD

- 410 No periodontal examination

- 159 Non-human

- 74 Children

- 48 Other gingival diseases

- 37 Renal transplants

- 34 Studies of periodontal treatment

- 11 Insufficient data

- 1 Non-English

Deleted Duplicates (244)

Case-control study(21)

Cross-sectional study(14)

Cohort study(6)

Cross-sectional study(11)

2 Duplicated study subjects & methodology

1 Reverse relationship (CKD → Periodontitis)


Figure 2.9 Bi-directional relationship between diabetes and periodontitis

Diabetes Periodontitis

Hyperglycemia

Hyperlipidemia

Insulin resistance

Defective host response

Disturb collagen homeostasis

AGE/RAGE in PMNs, Monocyte

Increase inflammatory cytokines (e.g. IL-1, TNF-α)

Decrease growth factors


Figure 2.10 Mechanisms of renal injury due to diabetes

Metabolic pathways Haemodynamic pathways Inflammation

Hyperglycemia Renin-Angiotensin-Aldosterone System

Diabetes

Mechanisms for renal injury

Cellular and Extracellular matrix-related effects

DifferentiationProliferationHypertrophy

Apoptosis

↑ Collagen & fibronectin↑ Connective tissue↑ Inhibition metalloproteinases↓ Matrix degradation

Renal functional and structural changes

Diabetic Kidney Disease


CHAPTER III

METHODOLOGY

3.1 Study design and setting A sub-cohort of a prospective EGAT cohort was conducted by retrieving

data for 10-year follow up period. Details of the EGAT cohorts were described

elsewhere180, briefly, the EGAT project included 3 parallel cohorts; EGAT1, EGAT2

and EGAT3. In total, 9,082 EGAT employees were randomly selected, and enrolled in

1985, 1998 and 2009 for EGAT1, EGAT2, EGAT3, respectively. The EGAT1

(n=3,499) and EGAT3 (n=2,584) were the headquarters which located in Nonthaburi

province, while, the EGAT2 (n=2,999) included urban employees (n=1,998) and

employees who worked at hydro-electric plants in remote areas (n=1,001). All

participants underwent medical examinations and completed self-administered

questionnaire every 5 years, except only 12-year-gap between the 1st (in 1985) and the

2nd (in 1997) surveys of EGAT1. In 2002, the 3rd survey of EGAT1 (called EGAT1/3),

periodontists were invited to collaborate with the EGAT cohort by setting up a half-

mouth examination. Then, the examination has been expanded to the full-mouth

examination since 2003 (EGAT2/2; 2nd survey of EGAT2).

This study was conducted using the EGAT2 cohort. The EGAT2/2 was

used as the baseline, whereas the EGAT2/3 (in 2008) and EGAT2/4 (in 2013) were

defined as the follow-up visit at 5 and 10 years. Two casual pathways were constructed

with the mediation analysis. Rationale and setting details of each pathway were shown

as follows:

3.1.1 Pathway A: Periodontitis → DM → CKD

3.1.1.1 Rationale

Initially, we aimed to determine the effects of periodontitis on

CKD, therefore periodontitis was set as the independent factor and CKD was the

interested outcome. As for biological background, a systemic inflammation from

Attawood Lertpimonchai Methodology / 68

periodontitis could induce the resistance of insulin receptors, then, causes the DM157. In

the same time, the hyperglycemic condition also increase risk of CKD181. Hence, we

hypothesize that periodontitis could directly affect on CKD, and it could affect through

DM (Figure 3.1).

3.1.1.2 Setting

To determine the direct and indirect effects of periodontitis in

the causal pathway A, ideally, subjects with CKD or DM in 2003 should be excluded,

remaining the non-CKD and non-DM cohorts at baseline. New case of DM and CKD

would be occurred during follow up in 2008 and 2013. The mediation effect through

DM would be identified if there was occurrence of DM, and CKD subsequently. To

apply these to ours setting, we allowed the first 5-year-interval (EGAT 2/2 to EGAT

2/3) for DM onset that was affected by periodontitis. Then, the second 5-year-interval

(EGAT 2/3 to EGAT 2/4) was a duration of CKD development after DM diagnosed.

However, typically, the onset of overt nephropathy detected by deterioration of GFR or

proteinuria is about 10 and 15 years after the onset of the DM10. As a result, the 5-year-

interval in our setting was too short follow up time. The effect of DM on CKD incidents

might be underestimated with improper follow-up duration. Therefore, we had to

compromised by including DM cases at baseline and assuming that all cases of DM

were influenced by periodontitis. From here, the duration for estimation the risk effect

of DM on overt nephropathy would be allowed to 10 years. In addition, in case that DM

and CKD were diagnosed simultaneously, we assumed that DM developed before CKD.

3.1.2 Pathway B: DM → Periodontitis → CKD

3.1.2.1 Rationale

As for a bi-directional association of periodontitis and DM, not

only periodontitis influences the glycemic control as mentioned in pathway A, but DM

might also increase risk of periodontitis11. Therefore, this causal pathway hypothesized

that periodontitis may be the mediator in the causation of DM on CKD (Figure 3.2).

3.1.2.2 Setting

In the pathway B, DM, periodontitis and CKD were set as the

independent variable, mediator, and interested outcome, respectively. Similarity with

the pathway A, preferably, CKD and periodontitis at baseline in 2003 should be


excluded. However, the prevalence of periodontitis was approximate 60%. To exclude

all periodontitis cases may cause insufficient the power of analysis. Therefore, subjects

with periodontitis at baseline were compromise enrolled.

3.2 Study subjects All studied subjects in EGAT 2 were included into this study if they

participated at least one health survey in 2003, 2008, and 2013. Subjects were excluded

if they were diagnosed as CKD at baseline in 2003 (eGFR < 60 ml/min per 1.73m2),

absence of ALL periodontal examinations due to (1) refusal, (2) systemic conditions

which required antibiotic prophylaxis before dental procedure including congenital

heart disease or valvular heart disease, previous history of bacterial endocarditis or

rheumatic fever, total joint replacement and end-stage renal disease, and (3) fully

edentulous subjects

3.3 Data collection In each survey, self-administered questionnaires were used to collect

general demographic data (age, gender, educational level, income, marital status),

behavioral data (smoking status, alcohol consumption, exercise/physical activity),

family history of illness, underlying diseases (DM, HT, stroke, CKD, dyslipidemia

(DLP), tuberculosis, allergy, autoimmune diseases, osteoarthritis, recent fracture or

Parkinson’s disease), and use of medication. Physical examinations, i.e., blood pressure

(BP), heart rate, weight, height and waist & hip circumference, were performed by

clinicians and trained personnel from Ramathibodi Hospital. Laboratory tests under

fasting state were carried out included glucose, total cholesterol, low-density lipoprotein

(LDL), high-density lipoprotein (HDL) and triglycerides, creatinine, total protein,

albumin, total bilirubin, direct bilirubin, aspartate aminotransferase (AST), alanine

aminotransferase (ALT), alkaline phosphatase (AP), gamma-glutamyl transpeptidase

(GGT) and a complete blood count (CBC). Electrocardiograms and chest X-rays were

routinely recorded in every survey.


3.4 Study factor and measurements

3.4.1 Periodontal examination

Dental examinations were carried out by experienced periodontists from the

Department of Periodontology, Faculty of Dentistry, Chulalongkorn University in

mobile dental units. Examinations were comprised of the number of missing teeth and

retained roots, periodontal examinations, and evaluation of treatment needs. Periodontal

examinations included PPD, and gingival recession (RE) which were carried out on all

fully erupted teeth, except third molars and retained roots. PPD and RE were measured

using a PCP-UNC15 probe on six sites per tooth, i.e., mesio-buccal, mid-buccal, disto-

buccal, mesio- lingual, mid-lingual, and disto-lingual. These measurements were made

in millimetres and were rounded to the nearest whole millimetre. CAL was calculated

from PPD and RE (Figure 3.3), and represented the distance from cemento-enamel

junction to the tip of a periodontal probe83.

Calibration and standardization for periodontal measurements were carried

out among six to eight examiners before the survey. The weighted kappa coefficients

(±1 mm) was used to determine the agreement of inter-examiner and intra-examiner

(Table 3.1). Between each pair of examiners, the kappa ranged from 0.72 to 1.00 for

PPD and 0.67 to 1.00 for CAL/RE. The weighted kappa coefficients (±1 mm) within

each examiner ranged from 0.85 to 1.00 for PPD and from 0.80 to 1.00 for CAL.

3.4.2 Periodontitis classification

Due to lacking uniformity of periodontitis definition in periodontal

medicine researches, we proposed classifications of periodontitis in various criteria as

follows:

3.4.2.1 CDC/AAP definitions

Using PPD and CAL58, periodontitis was categorized into 3

groups as normal/mild, moderate, and severe periodontitis. Moreover, severe

periodontitis versus non-severe periodontitis (normal/mild/moderate) was also an

alternative categorization.


- Severe periodontitis: two or more interproximal sites with

CAL ≥ 6 mm that are not on the same tooth AND one or more interproximal sites with

PPD ≥ 5 mm

- Moderate periodontitis: two or more interproximal sites with

CAL ≥ 4 mm, or two or more interproximal sites with PPD ≥ 5 mm, not on the same

tooth.

- Normal /Mild periodontitis

3.4.2.2 Using the disease extent in continuous scale

Although, the CDC/AAP definition is the standard criteria, the

amounts of inflammation seem to be discrepancies within the same category. For

instance, subjects who had only 2 teeth of severe periodontitis would be grouped as the

same as subjects who had whole-mouth with severe form. Therefore, the disease extent

defined as the percentage of disease site/periodontitis sites, ranged from 0 to 100%, was

also adopted in this study. Definitions for periodontitis site are shown as follow:

- Periodontitis A: PPD ≥ 4 mm

- Periodontitis B: PPD ≥ 6 mm

- Periodontitis C: PPD ≥ 4 mm AND CAL ≥ 3 mm

- Periodontitis D: PPD ≥ 4 mm AND CAL ≥ 5 mm

- Periodontitis E: CAL ≥ 3 mm (proximal sites only)

- Periodontitis F: CAL ≥ 5 mm (proximal sites only)

Example 1: Subjects, who had 20 teeth, they would have total

120 measured sites (6 sites per tooth). If 24 sites were found with PPD ≥ 4 mm, the

extent of periodontitis would equal 20%, when defined disease site as Periodontitis A.

Example 2: Subjects, who had 20 teeth, they would have total

80 measured proximal sites (4 sites per tooth). If 24 proximal sites were found with CAL

≥ 5 mm, the extent of periodontitis would equal 30%, when defined disease site as

Periodontitis F.


3.5 Primary outcome and measurements The outcome of interest was the incidence of CKD stage III1, which was

defined as subjects who had preserved kidney function in 2003, and subsequently their

eGFR < 60 ml/min per 1.73m2 in 2008 or 2013. Serum creatinine was measured using

IDMS-standardized enzymatic assay on the Vitros 350 analyzer (Ortho-Clinical

Diagnostics, USA). The eGFR was then calculated using the Chronic Kidney Disease

Epidemiology Collaboration (CKD-EPI: 2009) equations37 (Table 3.2).

3.6 Other co-variables and measurements

3.6.1 Self-administered data

3.6.1.1 Demographic data: age, gender, marital status,

education, income

3.6.1.2 Smoking: Smoking habit was categorized as (1) never

smokers (2) quit smokers, and (3) current smokers, based on multiple questions in

questionnaires including past/current smoking habits, quantity and duration of smoking,

age at start or quit smoking.

3.6.1.3 Alcohol drinking: Alcohol classification was similar

with smoking habits. History of alcohol consumption, along with, frequency, duration,

and type of alcohol were considered.

3.6.1.4 Exercise (Physical activity): Frequency of exercise was

used to categorized physical activity as (1) none exercise (2) 1-2 times/week, and

(3) ≥ 3 times/week

3.6.1.5 NSAIDs use: Users were identified from current

medication, and then, classified as “Yes” or “No”.

3.6.1.6 Underlying diseases and current medication: DM, HT

and DLP were identified from physical examinations and laboratory, along with the

prescribed treatments.

- DM was diagnosed if an individual had fasting blood sugar

(FBS) ≥ 126 mg/dl or had been taking anti-diabetic drugs182.


- HT was diagnosed if the participant had systolic blood pressure

(SBP) ≥ 140 mmHg or diastolic blood pressure (DBP) ≥ 90 mmHg, or had been taking

prescribed BP lowering drugs183.

- DLP was classified using the guidelines for the management

of dyslipidemias184. Subjects were classified as having DLP if they had HDL < 50 mg/dl

in male or HDL < 40 mg/dl in female OR LDL ≥ 160 mg/dl OR triglyceride ≥150 mg/dl

OR used any lipid-lowering medications.

3.6.1.7 Family history of DM, HT, DLP

3.6.2 Physical examination data

3.6.2.1 Body measurement: Height was measured in

centimetres and weight was measured in kilograms with dressed in normal clothing

without shoes. Hip and waist circumferences were measured in centimetres using

measuring tape by trained staffs.

3.6.2.2 Body mass index (BMI): BMI was calculated from the

recorded weight in kilograms divided by squared height in meters. BMI was

categorized185 as (1) underweight (<18.5 kg/m2), (2) normal (18.5-24.9 kg/m2), and (3)

overweight (≥ 25 kg/m2).

3.6.2.3 Waist-hip ratio (WHR): WHR was calculated from the

recorded waist in centimetres divided by hip in centimetres. WHR was categorized as

normal and obesity with cut-off value of ≥ 0.9 in male or ≥ 0.85 in female186.

3.6.2.4 Blood pressure: SBP and DBP were measured in sitting

position after 5-minute-rest using a calibrated automatic blood pressure monitors.

3.6.3 Laboratory data

Blood samples were collected after 12-hour overnight fasting. Blood

glucose was measured by plasma samples in mg/dl (Peridochrome, Boehringer

Mannheim, Mannheim, Germany). Serum total cholesterol, triglyceride, LDL, HDL

were measured in mg/dl using enzymatic-calorimetric assays (Boehringer Mannheim,

Mannheim, Germany).


3.7 Sample size estimation Sample size was estimated based on testing two independent proportions.

Type I and type II errors were set at 5% and 20%, respectively. The ORs of presence

versus absence periodontitis that could be detected was set at 1.5. The ratio of subjects

with normal periodontium versus subjects with periodontitis was set at 1:2, as for

approximating from EGAT2/2. As for previous studies39, 41, 187, 188 which was similar to

this study in terms of CKD definition and time horizon (10-12 years), the incidence of

CKD varied from 9.1 to 16.9%. Therefore, a total of 502 subjects with normal

periodontium and 1,004 subjects with periodontitis were required to detect the risk ratio

of 1.5.

The availability was explored from existed data. Among 2,651 participants

in 2003, 1,821 participants had completed data in 2008 and 2013. Among them, 1759

participants were free from CKD at baseline. As a result, a total of non-CKD participants

with completed data would be sufficient to detect the risk ratio of 1.5. Moreover, the

plan with the imputation should lead to increase more power of test.

3.8 Data management

3.8.1 Data acquirement

3.8.1.1 Demographic and medical records

Demographic and medical data were retrieved from the EGAT

databases. These were merged with the Excel worksheets of the civil registrations for

additional data.

3.8.1.2 Periodontal databases

Periodontal databases were constructed, all periodontal

parameters for EGAT2/2, 2/3 and 2/4 were computerized as follows:

- Build the periodontal databases: Databases were constructed

using Epidata version 3.1, separately by EGAT2/2, 2/3 and 2/4, because there were some

variables were differently measured for each survey. Sequencing of data entry were

designed with “tooth by tooth” system. It means that users had to entry all parameters

including PPD, and RE, tooth by tooth. If a tooth was missing, the system would not


allow users to entry any data for that tooth. In addition, databases were encoded the

specific value/range for each variable to prevent data entry error.

- Data entry (Periodontal parameters): Manual checking on case

record forms (CRF) was done by a data manager before entering data. This process

included revise of the readable of handwriting, minor missing data and consistence of

every parameters. If handwriting was not clear, the query was done directly to the

recorder. Then, data were independently entered twice by two persons. These two data

sets were then validated, any inconsistence was checked and corrected. Finally, all

records were manually checking and edited based on the original CRF, again.

3.8.2 Data cleaning

All interested variables along with the periodontal data were retrieved from

the main databases. They were renamed systematically across three EGATs in order to

combine them all together. Then, data cleaning was performed by the data cleaning

TEAM, which consisted of Asso.Prof. Ammarin Thakkinstian, Asst.Prof. Sasivimol

Rattanasiri, Dr. Attawood Lertpimonchai and Miss Sukanya Siriyotha. Regular meeting

at least twice a month was organized to solve any incorrectness or unclear data. Data

were summarized and cross-checked using STATA software. Any inconsistency or

outliners were verified and checked with the CRFs to make sure that data were valid.

All variables were assigned as the time-varying variables, if possible. Only gender and

height were considered as the fixed variables. The cleaning process of each variable was

described in Figure 3.4 to Figure 3.13

3.8.2.1 Gender

Gender was expected to be consistent among all databases.

Heterogeneity was solved by rechecking the original CRF (Figure 3.4).

3.8.2.2 Examination date

The date of examination was used to calculated “age”. It had to

be within that survey periods. If not or missing data presented, it was recoded as the

middle time of that survey (Figure 3.5).

3.8.2.3 DOB

Although, the DOB form the civil registration was available, its

validity was questioned because some errors were detected, such as inconsistency date


format, unreliable year of birth. Hence, we planned to merge the civil registration data

with the databases for improving the reliable (Figure 3.6). Similar with the gender, DOB

was expected to be consistent among all EGAT and the civil registration databases. If

the discrepancies were presented, it would be solved with the majority.

3.8.2.4 Marital status

Marital status was checked after combined data from 3 periods

together. Subjects, who were married or divorced, and subsequently became single

would be detected. Then, it would be discussed within the TEAM (Figure 3.7).

3.8.2.5 Education

The level of education cannot be decreased. Thus, the non-sense

declinations were detected and made decision with the TEAM (Figure 3.8).

3.8.2.6 Risk behaviors (smoking and alcohol drinking)

First, smoking and alcohol drinking habits were classified

within each period with multiple questions in questionnaire. If inconsistency was

present, the TEAM would consider the surrounding details of that behavior, as much as,

we could (Figure 3.9). Once, data was cleaned in each period, it would be merged and

judged with the same logic as the marital status was. For example, current smokers could

not become never smoker later.

3.8.2.7 Body measurement

Height (Figure 3.10), weight, waist and hip were summarized

and checked for outliers (exceed mean ± 4SD). If outliers presented, the original CRF

was proven. Moreover, the change within subject overtime would be checked after

merge data across periods. The astonishing change of weight, waist and hip would be

list, and then, its possibility would be validated by comparing with other relevant

variables (Figure 3.11).

3.8.2.8 Blood pressure

Concurrently present of SBP and DBP, within the proper range

of BP, SBP > DBP, and appropriate pulse pressure were used as the guideline to certify

BP (Figure 3.12).

3.8.2.9 Laboratory results

All laboratory results, which were reported in continuous data,

were checked for outliers (i.e., exceed mean ± 4SD). If outliers existed, the TEAM


would discuss the likelihood of that values (Figure 3.13). If it was not reasonable, it was

replaced with missing data.

3.8.3 Completed data with carried forward/backward methods

To complete database as much as possible, the forward/backward carry

over methods were used to replace missing data for some variables, i.e., marital status,

education, smoking and alcohol drinking. Some missing data in some circumstances

were replaced with previous or subsequent data. For example, never smokers in EGAT

2/4 whose smoking status in EGAT 2/2 and 2/3 were missing. These missing data was

replaced with the “never smokers”. The processes of carried forward/backward methods

is shown in Figure 3.7 to Figure 3.9.

3.9 Imputation

3.9.1 Imputation methods

Missing data were assumed as missing at random (MAR); the multiple

imputation using chain equation (MICE) for longitudinal data was performed for both

within-wave and whole-wave missing data189-191. Frequency and patterns or

distributions of missing variables were explored to check for the MAR assumption.

Detail of predicted variables for each chain equation was summarized in Table 3.2. Data

for all 3 periods (i.e., EGAT 2/2, 2/3 and 2/4) were combined as one dataset using wide

format, i.e., one record per one subject. This format allowed us to use the same factor

as the predictors of itself in different time. For example, serum creatinine at EGAT 2/2

could be assigned as the predictor of missing serum creatinine at EGAT 2/3.

3.9.2 Variable types in the imputation model

All analyzed variables were included in the imputation model including,

age, gender, marital status, education, income, smoking, alcohol drinking, exercise,

NSAIDs use, height, weight, waist and hip circumferences, blood pressure, family

history of DM, HT and DLP, serum glucose, cholesterol, HDL, LDL, triglyceride, uric

acid, creatinine, PPD and CAL in every site. According to the concept of MICE, some


variables in the imputed model must have completed data which called “regular

variables”. The MICE initially used them as the anchor of beginning in the chain

equations to generate set of imputed data for incomplete variables or “imputed

variables”. Here, age, gender and family history of DM, HT and DLP were assumed as

the regular variables, meanwhile, the remaining variables were registered as imputed

variables192.

3.9.3 Imputation modeling

Each model was constructed for each imputed variable based on its own data

type/distribution of that data and thus link function. In addition, predictors of each

imputed model were selected individually based on biological plausibility. Type of

models and its predictors were selected as described in the Table 3.2. In brief,

imputation modeling was selected as follows: the ordinal logit model was used for

imputation of ordinal variable (i.e., income), the multi-nominal logistic regression

model was used for imputation of nominal variable (i.e., exercise), the logistic

regression model was used for imputation of dichotomous variable (i.e., medication of

lipid drug), and the linear regression model was used for imputation of continuous

variables (i.e., height, weight, waist circumference, hip circumference, SBP, DBP).

Although, the conventional imputation could be performed and all missing

data were filled, some imputed values were still unreliable by producing outliner values

such as 8 mg/dl of serum glucose, or declination of education level. In order to solve

this problem, an interval linear regression model was applied for all laboratory results

by setting up the possible upper and lower boundary for each variable in each period.

Moreover, the interval linear regression was also applied for conditional variables, i.e.,

marital status, education, smoking and alcohol drinking by pretending them as the

continuous data, and the upper and lower boundary were set based on logical reasoning

similar with the cleaning processes. Furthermore, the truncated regression, which

regularly uses for imputing continuous variable with a restricted range, was modeled for

the PPD and RE.


3.9.4 Numbers of imputations

Among eligible subjects, the missing data of studied variables range from

0.38% to 18.30%, thus, twenty imputations were initially constructed191. The sufficient

of imputations were assessed in the estimation models based on performances of

imputation measured by the largest fraction of missing information (FMI) of coefficient

estimates due to missing data.

3.9.5 Data management after multiple imputation

Composited variables or “passive variables”, i.e., BMI, WHR, DM, HT,

CAL, and periodontal status, were calculated and/or classified from the imputed

variables. The categorical variables which were imputed by interval linear regression

model were nearest rounded to be the integer. Then, all imputed variables were cleaned

and checked with the cleaning processes similar with the original data, again.

3.10 Statistical analysis Characteristics of studies subjects were described using mean and SD or

median (range) for continuous data, where appropriates; frequency and percentage for

categorical data. The outline of all analysis was summarized in Figure 3.14

3.10.1 Mediation analysis: Periodontitis → DM → CKD

A mediation analysis was conducted using rationale and statistical

procedures outlined by Baron and Kenny193. Analyses were done and reported into 2

parts separately for each casual pathway. According to pathway A (Figure 3.1), effect

of periodontitis on CKD through DM as the mediator, mediation analysis for

categorical data194, 195 were applied. With conceptual framework of mediation analysis,

DM was assigned as the mediator of periodontitis and CKD if a) periodontitis has a

statistically significant effect on DM (mediation model) b) DM is significantly

associated with the CKD incident after controlling for the periodontitis effect (outcome

model)


3.10.1.1 Equations and calculations

To explore these, two equations from causal pathway were

constructed as below.

path a

logit !"#$!" = a0 + ax1 + ∑ ek zk

path b, c’

logit %&!#$%&! = c0 + c’x1 + bm1 + ∑ ek zk

where

x1 periodontitis

m1 0, 1 for non-DM and DM

zk confounders

For mediation model, DM was regressed on periodontitis, called

path a. For the outcome model, CKD was regressed on DM and periodontitis, called

path b and c’, respectively. The generalized structural equation model (GSEM) was

applied to constructed these two equations using logit link functions for both taking into

account for within and between variations of imputed data sets, and also longitudinal

data. Then, the average causal mediation effect (ACME) was estimated using product-

of-coefficient method. Then, the total and direct effects were estimated. Simultaneously,

the ORs of these effects also calculated. Formulas are shown as follow196, 197:

Mediation effect:

ACME = ab

Total effects (TE):

TE = ab + c’


Percent of total effect mediated through DM (% MEdm):

%)*+, = ./

./ + 1′

Percent of direct effect (% DE):

%3* = 1′./ + 1′

The ORs of mediation effect (ORsACME):

ORs = expab

3.10.1.2 Selection of variables

The GSEM198, 199 was used to constructed the outcome (CKD)

and mediator (DM) model as mentioned above. In the CKD model, periodontitis and

DM were considered as exposure and mediator, respectively. Other relevant risk factors

of CKD were included as confounders including education, income, marital status,

smoking, alcohol drinking, exercise, NSAIDs use, obesity, HT, DLP, and uric acid. Age

and gender were not included in the CKD model because they had been already

accounted in the eGFR calculation. In DM model, periodontitis was recognized as the

exposure with other confounders, i.e., age, gender, education, income, marital status,

smoking, alcohol drinking, exercise, obesity, HT, DLP, and family history of DM.

All proposed periodontitis definitions, i.e., CDC/AAP

definition, and the periodontitis extent in continuous scale (Periodontitis A to F), were

applied in the univariate analysis logit model one-by-one. The definition which was

given the maximum F-test was selected for periodontitis representativeness. Similarly,

the maximum F-test criteria was applied for selecting BMI or WHR to be a represent

for obesity.

The methods of variables selection for final model is another

issue that have been recently widely discussed200-204. The conventional stepwise method

is standard and generally approach for epidemiological researches. However, when

applied the stepwise to claim causation, its rationale is questioned in reflection the

biology of the process202. Moreover, it may induce a bias and inefficiency as a result of

overfitting in the outcome regression model200, 204. The disjunctive cause criterion203 was

introduced and claimed the advantages for cause–effect relationships, in particularly,


the treatment effect and the mediation analysis. Confounders were selected if they

associated with either mediator or outcome. Therefore, in this study, the selection of

confounders for the multivariate GSEM model or final model was further performed the

confounder selection with 3 methods as follows.

- Conventional forward stepwise

The final model of outcome and mediator were constructed,

separately. Factors with p-value < 0.10 in the univariate analysis for each model were

selected and forwardly included in the final model one by one. Only significant variables

were kept. As a result, the two models might contain different confounders. - Partial disjunctive clause criteria

The significant factors in the multivariate of either CKD or DM

model from conventional stepwise were combined as the set of confounders. Then, all

of them were considered to include into both models. Although, some variables might

be significant in the DM model, but not for the CKD model, or vice versa, they might

be left to modified others and thus should be included.

- Modified fully disjunctive clause criteria

With this method, a set of confounders that were significant in

univariate analysis from either DM or CKD model was considered and included in both

models. It would be the loosest criteria resulting in being the highest number of co-

variables in final model.

3.10.2 Mediation analysis: DM → Periodontitis → CKD

A causal pathway B was constructed as the inverses association of

periodontitis and DM. Here, DM was considered as the independent variable, and

periodontitis was considered as the mediators. Steps of analyses were performed

similar to the previous approaches for pathway A; except that periodontitis was the

mediator instead of DM (switched between independent variable and mediator). Thus,

the CKD and periodontitis model were constructed. In the periodontitis model, if the

disease extent (continuous data,) was selected to representing periodontitis, the model

might be built by the linear regression model. In addition, age, gender, education,

income, smoking, alcohol drinking, exercise, HT and DLP were recognized as


confounders for periodontitis model. Then, the mediation effect estimation and

processes of variable selection were performed similar with previous one.

3.10.3 Bootstrapping

A bootstrap analysis with 1,000 replications was applied to test the

mediation effect without requiring the assumption of normality205. For each bootstrap

sample, the mediation effect, percent of total effect mediated through mediator, ORs of

mediation effect were estimated. Then, the estimations and their 95% CI were

determined using bias-corrected bootstrap technique.

3.10.4 Checking assumptions

To test the mediation effect, the basic assumptions according to the essential

steps of the Baron and Kenny should been checked. The significant effect of X regressed

on M, and then the significant effect of M regressed on Y should be presented.

Moreover, the sequential ignorability assumption was the additional assumption for

mediation analysis proposed by Imai, Keele, and Yamamoto206. First, given the

observed pretreatment/exposed confounders, assignment of treatment/exposure was

assumed to be ignorable/independently, i.e., statistically independent of exposure (here

was periodontitis) and confounders for the mediation model. The second assumption

states that the mediator was ignorable in the outcome model given the observed

exposure and pretreatment confounders. In other words, we assumed that the following

two statements of conditional independence hold:

[Yi (t', m), Mi(t)] ||�Ti | Xi = x

Yi (t', m) || Mi(t) | Ti = t, Xi = x

However, our interested independent variable was not randomly allocated

like a randomized controlled trial (RCT), it was just observed. Therefore, adjusting

confounders should lead to meet these two assumptions and the ignorability assumption

should be assumed195. Here, after estimated the effect size in the final multivariate model

with stepwise method, the residuals from the mediators and outcome models were post-


estimated. Then, Pearson's correlation was used to test the independence, assuming the

low correlation represented the higher chance of ignorability.

In addition, the misspecification assumption from causal order, causal

direction, unmeasured variables and imperfect measurement were other assumptions for

the mediation analysis. However, testing them with statistical approaches seem to be

difficult or untestable195. The reliable of conceptual framework and background

knowledge were used to support the assumption.

All analyses were performed using Stata version 14.2. All analyses were

performed based imputed data under the “mi estimate” commands, which considered

within and between imputed data variations. P-value of less than 0.05 was considered

as a threshold for statistical significance.

3.11 Ethics considerations This retrospective study used the demographic, medical and dental data

from EGAT projects which are belonging to Faculty of Medicine Ramathibodi hospital,

Mahidol University and Faculty of Dentistry, Chulalongkorn University. The

permission to access the database was asked directly to the Head of EGAT projects

(Prof. Piyamitr Sritara), as well as, the Head of Periodontal Department, Faculty of

Dentistry, Chulalongkorn University (Prof. Rangsini Mahanonda). Before making

decision about the permission, they were clearly informed about the objectives, benefits

and methodology of this study. This study was approved by Institutional Review Board

of Ramathibodi’s Ethical Committee on June 27, 2008 with MURA2008/809/S41.

(Appendix C).


Table 3.1 Calibration of periodontal examination (weight kappa ± 1mm)

CAL, clinical attachment level; PPD, periodontal pocket depth; RE, recession

PPD RE / CAL

Inter-examiner Intra-examiner Inter-examiner Intra-examiner

EGAT 2/2 0.72 - 0.90 0.85 - 0.96 0.69 - 0.79 0.80 - 0.97

EGAT 2/3 0.77 - 0.89 0.87 - 0.91 0.67 - 0.94 0.90 - 0.96

EGAT 2/4 0.74 - 1.00 0.87 - 1.00 0.78 - 1.00 0.87 - 1.00

Attaw

ood Lertpimonchai

Literature review

/ 86

Table 3.2 Imputation models: predictors and equations

Predictors

Model* Se

x

Age

Educ

atio

n

Inco

me

Mar

ital s

tatu

s

Smok

ing

Alc

ohol

Exer

cise

Wei

ght

Hei

ght

Wai

st

Hip

SBP

NSA

IDs

Lipi

d D

rug

Glu

cose

Cho

lest

erol

HD

L

LDL

Trig

lyce

ride

Uric

Aci

d

Cre

atin

ine

FM-H

T

FM-D

M

FM-D

LP

Education Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Income Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö ologit Marital status Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Smoking Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Alcohol Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Exercise Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö mlogit Height Ö Ö Ö Ö Ö Ö regress Weight Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö regress Waist Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö regress Hip Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö regress SBP Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö regress DBP Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö regress Lipid Drug Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö logit Glucose Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Cholesterol Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg HDL Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg LDL Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Triglyceride Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Uric Acid Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg Creatinine Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö intreg PPD Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö truncreg RE Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö Ö truncreg

* Type of imputation equation for each variable: intreg, interval regression; ologit, ordered logistic regression; mlogit, multinomial logistic regression; regress, linear regression; logit, logistic regression; truncreg, truncated regress DBP, diastolic blood pressure; FM-DLP, family history of dyslipidemia; FM-DM, family history of diabetes; FM-HT, family history of hypertension; HDL, high-density lipoprotein; LDL, low-density lipoprotein; PPD, periodontal pocket depth; RE gingival recession; SBP, systolic blood pressure


Figure 3.1 Structural causal pathway A: Periodontitis → DM → CKD

( A )

c

c'

ba

Periodontitis (X) CKD (Y)

Periodontitis (X)

DM (M)

CKD (Y)

( B )


Figure 3.2 Structural causal pathway B: DM → Periodontitis → CKD

( A )

c

c'

ba

DM (X) CKD (Y)

DM (X)

Periodontitis (M)

CKD (Y)

( B )


Figure 3.3 Periodontal measurement CEJ, cemento-enamel junction


Figure 3.4 Workflow of cleaning processes for gender CRF, case record form

Yes No

Check data entry error with original CRF

Consistency?

No

Recheck with the Civil Registration

Yes; data entry error

Edited data

CLEANED DATA

CLEANED DATA

MERGE DATASETS

GENDER


Figure 3.5 Workflow of cleaning processes for date visit (examination date)

Yes No

Replaced with the median time of that survey

Within actual survey date?

CLEANED DATA

DATE VISIT (Examination date)

Yes No

Missing data?

CLEANED DATA

Replaced with the median time of that survey

CLEANED DATA


Figure 3.6 Workflow of cleaning processes for date of birth CRF, case record form

Match with given age?

Yes No

Replaced with missing

Consistency?

Can we assume from the majority?

Data entry error ? (checked with original CRF)

Yes (Data entry error)

Edited data

No

W I T H I N each dataset

MERGE DATASETS

Yes No

CLEANED DATA


Yes (Data entry error) No

Edited data

DATE OF BIRTH

(EGAT Database)

DATE OF BIRTH

(Civil Registration Database)

YesNo

CLEANED DATA

Assume data from the Civil Registration

CLEANED DATA


Figure 3.7 Workflow of cleaning processes for marital status CRF, case record form; TEAM, data cleaning team

MERGE DATASETS

MARITAL STATUS

Reasonable overtime sequence?

No. For example, married subjects became singles Yes

Discussed with TEAM

CLEANED DATA


Edited data

No


CLEANED DATA

MARITAL STATUS was single in EGAT 2/4 ?

No Yes

Replaced 2/2 & 2/3 as “single”

CLEANED DATA & CARRIED BACKWARD

F i l l i n g m i s s i n g d a t a


Figure 3.8 Workflow of cleaning processes for education CRF, case record form; TEAM, data cleaning team

No Yes

Check data entry error with original CRF

Education level decreased overtime?

No Yes; data entry error

Edited data

CLEANED DATA

CLEANED DATA

MERGE DATASETS

EDUCATION

Discussed with TEAM

EDUCATION 24 was ≤ secondary school (the lowest level) ?

NoYes

EDUCATION 22 was ≥ Bachelor's degree (the highest level) ?

NoYes

EDUCATION 23 was missing ANDEDUCATION 22 equals EDUCATION 24 ?

Replaced EDUCATION2/2 & 2/3 with

“ ≤ secondary school ”

Replaced EDUCATION 2/3 & 2/4 with

“ ≥ Bachelor's degree ”

NoYes

Replaced EDUCATION 2/3 with EDUCATION 2/2

CLEANED DATA & CARRIED FORWARD/BACKWARD



Figure 3.9 Workflow of cleaning processes for risk behaviors including smoking and alcohol drinking CRF, case record form; TEAM, data cleaning team

Inconsistency among past/current habits and details of smoking or alcohol drinking ?

No YesData entry error ?

(checked with original CRF)


Edited data

No


MERGE DATASETS

SMOKING | ALCOHOL DRINKING

Discussed with TEAM(considered every details of

habits, such as, amount, frequency, start/quit age)

Reasonable overtime sequence?

No. For example, current smokers became never smokers Yes

Discussed with TEAM(considered every details of

habits, such as, amount, frequency, start/quit age)

CLEANED DATA


Edited data

No


CLEANED DATA

SMOKING 2/4 / ALCOHOL 2/4 was never smokers / never drinkers ?

No Yes

Replaced 2/2 & 2/3 as “never”

CLEANED DATA & CARRIED BACKWARD



Figure 3.10 Workflow of cleaning processes for height cm, centimeter; CRF, case record form; obs., observes; SD, standard deviation; TEAM, data cleaning team

Within mean ± 4 S.D. ?

Yes No

Replaced with missing

From 1 dataset

How many ‘height’ present ?

From 2 datasets From 3 datasets

Average height

Difference between Max and Min > 10 cm ?

Yes No

Average height

Drop the outlier, then average height from 2 obs.



Edited data

No


CLEANED DATA

CLEANED DATA

CLEANED DATA

CLEANED DATA

MERGE DATASETS



Edited data

HEIGHT


Figure 3.11 Workflow of cleaning processes for weight, waist and hip circumstance cm, centimeter; CRF, case record form; kg, kilograms; SD, standard deviation; TEAM, data cleaning team

Within mean ± 4 SD ?

Yes NoData entry error ?

(checked with original CRF)


Edited data

No


MERGE DATASETS

WEIGHT | WAIST | HIP

Discussed with TEAM(comparing with height, weight, waist and hip)

Identifying impossible “Change” in body measurement over-time

Was there ∆weight ≥ 10 kg or ∆waist ≥ 15 cm or ∆hip ≥ 10 cm?

Yes No

Discussed with TEAM(comparing with height, weight, waist and hip)

Data was POSSIBLE?

YesNo

Replace the inconsistency with missing value

CLEANED DATA


Edited data

No


CLEANED DATA

CLEANED DATA


Figure 3.12 Workflow of cleaning processes for blood pressure BP, blood pressure; CRF, case record form; DBP, diastolic blood pressure; obs., observes; SBP, systolic blood pressure; SD, standard deviation; TEAM, data cleaning team

Ø SBP & DBP, simultaneously presented ?

Ø Within mean ± 4 SD ?

Ø Systolic > Diastolic ?

Ø Pulse pressure > 10 mmHg

Yes No

1 measurement

How many times of measurement ?

2 measurements 3 measurements

Average BP

Difference among SBP > 10 mmHg ?

Yes No

Average BP

Drop the SBP & DBP which SBP was the outlier,

then average SBP & DBP from 2 obs.



Edited data

No

CLEANED DATA

CLEANED DATA

CLEANED DATA

CLEANED DATA



Edited data

BLOOD PRESSURE

Discussed with TEAM


Figure 3.13 Workflow of cleaning processes for laboratory results HDL, high-density lipoprotein; LDL, low-density lipoprotein; SD, standard deviation; TEAM, data cleaning team

Within mean ± 4 SD ?

Yes No

No

Replaced with missing value

Yes (Possible value)

L A B O R A T O R Y R E S U L T S

CLEANED DATA

CLEANED DATA

Discussed with TEAM

Data was POSSIBLE?

CLEANED DATA

Serum glucose, cholesterol, HDL LDL,

Triglyceride, uric acid, creatinine


Figure 3.14 Outline of statistical analysis

CKD, chronic kidney disease; DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension; NSAIDs, nonsteroidal anti-inflammatory drugs

DM Model:

- Periodontitis

- Age

- Gender

- Marital status

- Income

- Education

- Smoking

- Alcohol drinking

- Exercise

- HT

- DLP

- Family history o DM

Statistical analysis

Pathway A:

Periodontitis(Exposure)

CKD(Outcome)

DM(Mediator)

Pathway B:

DM(Exposure)

CKD(Outcome)

Periodontitis(Mediator)

CKD Model:

- Periodontitis

- DM

- Marital status

- Income

- Education

- Smoking

- Alcohol drinking

- NSAIDs use

- Exercise

- HT

- DLP

- Uric acid

Forward Stepwise

Final model: Mediation analysis

Partial disjunctive clause

Modified fully disjunctive clause

Periodontitis Model:

- DM

- Age

- Gender

- Marital status

- Income

- Education

- Smoking

- Alcohol drinking

- Exercise

- HT

- DLP

CKD Model:

- Periodontitis

- DM

- Marital status

- Income

- Education

- Smoking

- Alcohol drinking

- NSAIDs use

- Exercise

- HT

- DLP

- Uric acid

Forward Stepwise

Final model: Mediation analysis

Partial disjunctive clause

Modified fully disjunctive clause


CHAPTER IV

RESULTS

4.1 Characteristic of subjects Numbers of registered subjects for EGAT 2/2, 2/3 and 2/4 were of 2,686,

2,288 and 2,037, respectively. A total number of subjects who participated at least one

survey during 2003 to 2013 was 2,795. The patterns that subjects participated with the

EGAT survey were shown in Table 4.1. Among them, 1,821 (65%) subjects were

completely attended, with 14% and 21% of subjects absented from 1 and 2 times,

respectively.

From total, 126 subjects were excluded because they had CKD at baseline

(EGAT 2/2). Among excluded cases, 122 subjects were diagnosed based on actual data,

while 4 subjects, whose kidney functions at baseline were missing, were excluded from

the average imputed data. Among the non-CKD cohort, 6 subjects were further excluded

from systemic conditions that could not obtain the periodontal examination. 15 subjects

were fully edentulous and 13 subjects refused to attend all periodontal examinations. As

results, 2,635 participants were finally included for analysis (Figure 4.1).

Characteristics of excluded cases are shown in Table 4.2.

The baseline characteristics are shown in Table 4.3. With mean age of

47.7 ± 4.9 years, approximately three quarters were males and a half of them had

experiences of smoking and alcohol drinking. Prevalence of DM, HT and DLP at

baseline were 7.69%, 27.25 % and 68.54%, respectively. With the CDC/AAP

periodontitis definition, the prevalence of moderate and severe periodontitis

approximated 50% and 30%, respectively. In addition, each subject, had about 2% of

total sites with PPD ≥ 6 mm, and 10-14% of proximal sites with CAL ≥ 5 mm, by

average.

A total number of new CKD was respectively 167 and 105 for EGAT 2/3

and 2/4 survey, with a total CKD subjects of 272. As a result, a cumulative incidence of

CKD was 1.03 cases per 100 persons per year (95% CI: 0.91, 1.16). Moreover, CKD

Attawood Lertpimonchai Results / 102

incidence increased with the severity of periodontitis, in which the cumulative incidence

of CKD among normal/mild periodontitis, moderate periodontitis and severe

periodontitis were 0.72, 0.96 and 1.39 cases per 100 persons per year, respectively.

4.2 Missing data and imputation results Among twenty variables, age, gender and family history were completed

and considered/assigned as “regular” variables. After intensively cleaned and filled in

missing data with the forward/backward carried over methods, missing data was still

presented, which ranged from 0.38% to 18.30% with a median of 16.25% (Table 4.4).

The most frequent missing variables was periodontal status (18.30%) whereas the least

frequent missing variable was height (0.38%). When explored the sources of missing

data (Table 4.5), most of them were absent because of loss to follow-up which called

the whole-wave missing data. On the other hand, the within-wave missing data (i.e., the

missing data of participated subjects) was typically less than 5%. Distribution of these

missing values was explored and the results showed arbitrary patterns, thus, MAR was

assumed. Twenty imputations with MICE were completely generated to fill in all

missing observes, which were set based on the percentage of missing data with the

maximum of 18.30%. Then, the FMI from final GSEM models (Table 4.6) were

considered to confirm that our twenty imputations were adequate efficiency and power.

The highest FMI was about 23% for income in the CKD model, although we set at 20

imputations, it should be sufficient with the relative efficiency of about 99%. The

characteristics of each variable between actual and imputed data were compared which

were not much different (Table 4.7).

4.3 Pathway A: Periodontitis → DM → CKD Potential causal relationships between periodontitis, DM and CKD were

assessed following a causal diagram described in Figure 3.1. Mediation equation

(periodontitis → DM) and outcome equation (periodontitis + DM → CKD) were

constructed as follows:


4.3.1 Dealing with periodontitis and obesity variables

The F-test of simple logistic regression for the outcome model was

performed to select which classification/definition of periodontitis should be the most

suitable in term of explanation the outcome. The F-tests and coefficients of periodontitis

defined by various definitions were shown in Table 4.8. The direction of this causative

association was consistent among all classifications, in which the disease extent of

proximal sites with CAL ≥ 5 mm (Periodontitis F) was achieved the highest F-test when

compared with other definitions. Thus, it was selected for the representative of

periodontal status in further analysis. To compare with other studies, the standard

periodontitis definition defined by CDC/AAP was also alternatively selected.

Same procedures had been performed for BMI and WHR to represent

obesity. Obese group defined with WHR was finally chosen because it yielded the

largest F-test (Table 4.8).

4.3.2 Selection variables based on conventional method

4.3.2.1 Mediation model

This mediation model was performed to estimate the effect of

periodontitis on DM. The GSEM with logit link was used within the assumption that

periodontitis was the cause of DM. Periodontitis and other DM risk factors (i.e., age,

gender, income, education, marital status, exercise, smoking, alcohol, obesity, HT, DLP

and family history of DM) were considered in the univariate analysis of GSEM (see

Table 4.9) indicating p-value for all factors were < 0.1, thus should be included all in

the next step. A forward selection was performed and finally kept only periodontitis and

6 co-variables including age, education level, obesity, HT, DLP and family history of

DM.

4.3.2.2 Outcome model

A univariate GSEM with logit link was used to assess whether

periodontitis and DM along with other co-variables (i.e., marital status, income,

education, smoking, alcohol, NSAIDs use, exercise, obesity, HT, DLP, uric acid) were

associated with CKD, see Table 4.10. Almost all, including periodontitis (exposure),

DM (mediator), marital status, income, smoking, alcohol, NSAIDs use, obesity, HT,


DLP and serum uric acid were significantly associated from univariate analysis. A

stepwise forward selection was performed to explore the final model which strict

contained periodontitis and DM. The final model (Table 4.11) suggested periodontitis,

DM, HT, income and serum uric acid were associated with incident CKD,

independently.

The multivariate GSEMs were simultaneously constructed for

both mediation and outcome models, see Table 4.11. Results indicated the significant

independent effect of periodontitis on DM with adjusted ORs of 1.011

(95% CI: 1.006, 1.015). In addition, the coefficients of the effects of periodontitis and

DM on CKD after controlling for other co-variables were 0.010 (95% CI: 0.005, 0.015)

and 0.689 (95% CI: 0.385, 0.994), respectively.

For CDC/AAP periodontitis definitions, the univariate analysis

from GSEM showed that only severe periodontitis had significant risk effect on DM

(Table 4.9). However, it was not remained significant in the multivariate analysis

(Table 4.12). In the CKD model, the risk effect of periodontitis on CKD incidence was

also not statistically significant in the multivariate GSEM with forward stepwise

(Table 4.12).

4.3.2.3 Estimations of mediation effects

Mediation effects were estimated by products of coefficients of

mediation and outcome models, see Figure 4.2. A 1000-replication bootstrap suggested

the significant ORs from mediation (indirect) effect (Periodontitis → DM → CKD) and

direct effect (Periodontitis → CKD) of 1.007 (95% CI: 1.003, 1.013), and 1.010

(95% CI: 1.005, 1.015), respectively. The percentage of periodontitis effect contributed

through DM mediator was 42.38% (Table 4.13). From these, it could be interpreted that

every one percent increasing of periodontitis extent would increase risk of CKD through

DM around 0.7%, and it would directly increase risk of CKD about 1.0%. Suppose

subjects who had 50% of proximal sites with severe periodontitis, these subjects were

about 50% higher risk to directly develop CKD, and about 35% higher risk to develop

CKD through DM when compared with normal periodontium.


4.3.3 Selection of co-variables based on disjunctive clause criteria

The partial disjunctive and modified fully disjunctive clause criteria were

performed to select co-variables in DM and CKD final models using GSEM.

Coefficients were shown and compared with the conventional forward stepwise method

in Table 4.14 and Table 4.15. With the partial disjunctive clause criteria, the income and

uric acid, which were selected from the stepwise of CKD, were added to the DM model.

In addition, education, obesity, DLP and family history of DM were added to the CKD

model. While, the modified fully disjunctive clause criteria included periodontitis and

all co-variables in both models. Comparing among 3 methods, the effect sizes of

periodontitis on CKD were very much similar, i.e., 0.010 (95% CI: 0.005, 0.015), 0.011

(95% CI: 0.006, 0.016) and 0.012 (95% CI: 0.006, 0.018) for conventional, partial and

modified fully disjunctive clause criteria, respectively. Only the uric acid was additional

significant in the DM model with coefficients of -0.190 (95% CI: -0.266, -0.155) and -0.229 (95% CI: -0.313, -0.144) from the partial and modified fully disjunctive clause

criteria, respectively. While, the direction and magnitude of other co-variables were

very not much different.

4.4 Pathway B: DM → Periodontitis → CKD

The inverse association between periodontitis and DM was also assessed.

Here, DM was considered as the exposure, and periodontitis was considered as the

mediator. The periodontitis equation was additionally constructed with adjustment for

co-variables. Whereas, the outcome model was the same as the pathway A.

4.4.1 Selection variables based on conventional method

4.4.1.1 Mediation model

The mediator model was performed to estimate the effect of DM

on periodontitis. Instead of the logit link, The GSEM was constructed with the Gaussian

family and identity link, because the disease extent, which was the continuous data, was

used representing periodontitis


Given periodontitis was caused by DM, it was also taking to

account simultaneously with other risk factors, including age, gender, income,

education, marital status, exercise, smoking, alcohol, obesity, HT, and DLP. Results

from the univariate analysis is shown in Table 4.16, indicating all factors significantly

related with periodontitis. Furthermore, the multivariate model from forward stepwise

selection implied that DM, age, gender, education exercise and smoking were

significant (Table 4.17). Here, it was interpreted that with identical other co-variables,

subjects with DM had the percentage of proximal sites with CAL ≥ 5 mm about 4.8%

significantly higher than non-DM.

4.4.1.2 Estimations of mediation effects

The average causal mediation effect (ACME) were estimated

using product-of-coefficient method (Figure 4.3). A 1000-replication bootstrap yielded

significant mediation (indirect) effect (DM → Periodontitis → CKD) and direct effect

(DM → CKD) of 0.048 (95% CI: 0.021, 0.096), 0.689 (95% CI: 0.366, 0.982),

respectively. The ORs of having CKD in subjects with DM was 2.09 (95% CI: 1.52, 2.83) comparing with non-DM. Within this ORs, the percentage of DM

effect contributed through periodontitis mediator was 6.53% (Table 4.18).

4.4.2 Selection of co-variables based on disjunctive clause criteria

The partial disjunctive and modified fully disjunctive clause criteria were

performed also in the periodontitis model. The effect size and pattern of existed co-

variables in stepwise were approximate in both disjunctive clause criteria (Table 4.19).

Low income, middle income and uric acid were also considered as significant factors in

the partial disjunctive clause with the effect size of 2.36 (95% CI: 0.42, 4.31), 1.51 (95% CI: 0.05, 2.26) and -0.65 (95% CI: -1.16, -0.14), respectively. Moreover, the

modified fully disjunctive clause criteria included NSAIDs as the protective factors of

periodontitis with coefficients of -1.73 (95% CI: -3.31, -0.16).


4.5 Assumption checking

The sequential ignorability assumption was tested in both pathways.

Because the post-estimation command of GSEM for residuals estimation was not

compatible with the imputed data, only actual data was used to test this assumption.

Pearson's coefficients of correlation between residuals of DM and CKD model, as well

as, periodontitis and CKD model were -0.0146 and -0.0192, respectively, which

indicated very low correlation between the two residuals. This could be implied that the

two residuals were independent and thus ignorability assumption should be held.


Table 4.1 Pattern of participation

Pattern EGAT 2/2

(2,686)

EGAT 2/3

(2,288)

EGAT 2/4

(2,037)

Total

(2795) %

A 1 0 0 352 12.59

B 0 1 0 25 0.90

C 0 0 1 23 0.82

A-C (1 time of participation) 400 14.31

D 1 1 0 381 13.63

E 1 0 1 132 4.72

F 0 1 1 61 2.19

D-F (2 times of participation) 574 20.54

G (Completed participation) 1,821 65.15


Table 4.2 Baseline characteristics of excluded cases

Characteristics

EGAT 2/2

n = 2,532

All excluded

cases

n = 160

CKD at

baseline

n = 126

Periodontal

reasons

n = 34

Age (year) 47.65 ± 4.85 51.10 ± 5.21 51.07 ± 5.22 51.25 ± 5.27

Gender

Male

Female

1848 (73.0)

684 (27.0)

121 (75.62)

39 (24.38)

95 (75.40)

31 (24.60)

26 (76.47)

8 (23.53)

Marital status

Single

Married

Divorce/Widows

238 (9.49)

2081 (82.97)

189 (7.54)

11 (7.19)

128 (83.66)

14 (9.15)

8 (6.50)

105 (85.37)

10 (8.13)

3 (10.00)

23 (76.67)

4 (13.33)

Income (Baht/month)

< 20,000

20,000 – 49,999

≥ 50,000

312 (12.47)

1318 (52.68)

872 (34.85)

18 (11.76)

85 (55.56)

50 (32.68)

13 (10.57)

70 (56.91)

40 (32.52)

5 (16.67)

15 (50.00)

10 (33.33)

Education

≤ Secondary school

Vocational/Diploma

≥ Bachelor’s degree

688 (27.39)

825 (32.84)

999 (39.77)

64 (41.03)

45 (28.84)

47 (30.13)

51 (40.80)

36 (28.80)

38 (30.40)

13 (41.94)

9 (29.03)

9 (29.03)

Smoking

Never smokers

Quit smokers

Current smokers

1340 (53.22)

622 (24.70)

556 (22.08)

83 (52.87)

43 (27.39)

31 (19.74)

70 (56.45)

32 (25.81)

22 (17.74)

13 (39.40)

11 (33.33)

9 (27.27)

Alcohol

Never drinkers

Quit drinkers

Current drinkers

1182 (47.04)

211 (8.40)

1120 (44.57)

73 (46.79)

20 (12.82)

63 (40.39)

60 (48.39)

17 (13.71)

47 (37.90)

13 (40.62)

3 (9.38)

16 (50.00)


Table 4.2 Baseline characteristics of excluded cases (cont.)

Characteristics

EGAT 2/2

n = 2,532

All excluded

cases

n = 160

CKD at

baseline

n = 126

Periodontal

reasons

n = 34

Exercise (times/week)

None

1 - 2

≥ 3

728 (29.00)

670 (26.69)

1112 (44.30)

42 (27.27)

34 (22.08)

78 (50.65)

30 (24.39)

29 (23.58)

64 (52.03)

12 (38.71)

5 (16.13)

14 (45.16)

NSAIDs use

Yes

No

246 (9.80)

2264 (90.20)

20 (12.99)

134 (87.01)

17 (13.82)

106 (86.18)

3 (9.68)

28 (90.32)

Height (cm) 163.61 ± 7.67 163.58 ± 7.73 163.39 ± 7.90 164.28 ± 7.12

Weight (kg) 65.88 ± 11.48 68.28 ± 11.85 68.94 ± 12.21 65.64 ± 9.96

Waist circumference (cm) 85.97 ± 9.87 89.23 ± 9.79 89.67 ± 9.77 87.5 ± 9.85

Hip circumference (cm) 96.49 ± 6.57 97.46 ± 6.75 97.86 ± 6.83 95.87 ± 6.25

BMI (kg/m2) 24.56 ± 3.61 25.45 ± 3.74 25.78 ± 3.84 24.13 ± 3.03

Waist to hip ratio 0.89 ± 0.07 0.91 ± 0.06 0.91 ± 0.06 0.91 ± 0.06

Diabetes Mellitus

Yes

No

194 (7.69)

2329 (92.31)

24 (15.58)

130 (84.42)

16 (13.01)

107 (86.99)

8 (25.81)

23 (74.19)

Hypertension

Yes

No

689 (27.52)

1815 (72.48)

66 (43.14)

87 (56.86)

55 (44.72)

68 (55.28)

11 (36.67)

19 (63.33)


Table 4.2 Baseline characteristics of excluded cases (cont.)

Characteristics

EGAT 2/2

n = 2,532

All excluded

cases

n = 160

CKD at

baseline

n = 126

Periodontal

reasons

n = 34

Family history of DM

Yes

No

929 (36.69)

1603 (63.31)

54 (33.75)

106 (66.25)

44 (34.92)

82 (65.08)

10 (29.41)

24 (70.59)

Total cholesterol (mg/dl) 233.79 ± 41.99 249.04 ± 53.50 249.93 ± 52.05 245.52 ± 59.69

HDL (mg/dl) 53.15 ±14.42 51.04 ± 14.43 51.57 ± 14.98 48.94 ± 12.01

LDL (mg/dl) 151.72 ± 38.74 164.14 ± 50.34 165.19 ± 47.71 159.52 ± 61.45

Triglyceride (mg/dl)** 126 (27, 1362) 140 (35, 1147) 140 (35, 892) 151 (71, 1147)

Creatinine (mg/dl) 1.01 ± 0.17 1.44 ± 0.91 1.55 ± 0.99 1.02 ± 0.14

Uric acid (mg/dl) 5.60 ± 1.47 6.26 ± 1.71 6.40 ± 1.69 5.68 ± 1.69

eGFR (ml/min per 1.73m2) 83.37 ± 12.73 58.39 ± 14.94 52.82 ± 9.32 80.34 ± 12.54

Periodontitis (CDC/AAP)

No / Mild Periodontitis

Moderate Periodontitis

Severe Periodontitis

429 (17.24)

1268 (50.94)

792 (31.82)

17 (15.18)

66 (58.93)

29 (25.89)

N/A

% sites with PPD ≥ 4 mm** 4.17 (0, 93.75) 4.42 (0, 56.25)

% sites with PPD ≥ 6 mm** 0 (0, 63.64) 0 (0, 22.92)

% sites with PPD ≥ 4 mm & CAL ≥ 5 mm** 0.88 (0, 91.67) 0.76 (0, 55.21)

% proximal sites with CAL ≥ 3 mm** 41.67 (0, 100) 43.75 (0, 100)

% proximal sites with CAL ≥ 5 mm**

2.68 (0, 100) 2.66 (0, 80.56)

Values are mean ± SD for continuous data, and frequency (%) for categorical data, except where specified.

Total numbers of subjects of each variable may be different depended on missing data.

** Median (range)

BMI, body mass index; CAL, clinical attachment level; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; FM-DM, family history of diabetes; FM-HT, family history of hypertension; HDL, high-density lipoprotein; LDL, low-density lipoprotein; N/A, not available; PPD, periodontal pocket depth


Table 4.3 Baseline characteristics

Characteristics EGAT 2/2 n = 2,532

EGAT 2/3 n = 2,183

EGAT 2/4 n = 1,948

Age (year) 47.65 ± 4.85 52.26 ± 4.59 56.88 ± 4.53

Gender

Male

Female

1848 (73.0)

684 (27.0)

1567 (71.8)

616 (28.2)

1368 (70.2)

580 (29.8)

Marital status

Single

Married

Divorce/Widows

238 (9.49)

2081 (82.97)

189 (7.54)

178 (8.22)

1801 (83.15)

187 (8.63)

144 (7.46)

1586 (82.18)

200 (10.36)

Income (Baht/month)

< 20,000

20,000 – 49,999

≥ 50,000

312 (12.47)

1318 (52.68)

872 (34.85)

135 (6.38)

634 (29.96)

1347 (63.66)

189 (9.82)

248 (12.88)

1488 (77.30)

Education


Vocational/Diploma


688 (27.39)

825 (32.84)

999 (39.77)

488 (22.49)

736 (33.92)

946 (43.59)

357 (18.50)

633 (32.80)

940 (48.70)

Smoking

Never smokers

Quit smokers

Current smokers

1340 (53.22)

622 (24.70)

556 (22.08)

1138 (52.47)

586 (27.02)

445 (20.51)

1047 (54.22)

622 (32.21)

262 (13.57)

Alcohol

Never drinkers

Quit drinkers

Current drinkers

1182 (47.04)

211 (8.40)

1120 (44.57)

782 (36.09)

312 (14.40)

1073 (49.51)

530 (27.45)

433 (22.42)

968 (50.13)


Table 4.3 Baseline characteristics (cont.)


EGAT 2/3 n = 2,183

EGAT 2/4 n = 1,948


None

1 - 2

≥ 3

728 (29.00)

670 (26.69)

1112 (44.30)

1055 (48.80)

351 (16.23)

756 (34.97)

622 (32.24)

272 (14.10)

1035 (53.66)

NSAIDs use

Yes

No

246 (9.80)

2264 (90.20)

312 (14.40)

1855 (85.60)

170 (8.81)

1760 (91.19)

Height (cm) 163.61 ± 7.67 163.55 ± 7.71 163.49 ± 7.82

Weight (kg) 65.88 ± 11.48 66.24 ± 11.23 66.93 ± 11.88

Waist circumference (cm) 85.97 ± 9.87 87.14 ± 9.48 88.23 ± 10.13

Hip circumference (cm) 96.49 ± 6.57 95.72 ± 6.50 98.00 ± 6.94

BMI (kg/m2) 24.56 ± 3.61 24.72 ± 3.59 25.00 ± 3.82

Waist to hip ratio 0.89 ± 0.07 0.91 ± 0.07 0.90 ± 0.007

Central obesity

Yes

No

1283 (51.40)

1213 (48.60)

1434 (66.39)

726 (33.61)

1156 (60.05)

769 (39.95)

Diabetes Mellitus

Yes

No

194 (7.69)

2329 (92.31)

254 (11.71)

1915 (88.29)

305 (15.67)

1641 (84.33)

Hypertension

Yes

No

689 (27.52)

1815 (72.48)

827 (38.20)

1338 (61.80)

1049 (54.35)

881 (45.65)

Dyslipidemia

Yes

No

1723 (68.54)

791 (31.46)

1640 (75.75)

525 (24.25)

1468 (75.63)

473 (24.37)


Table 4.3 Baseline characteristics (cont.)


EGAT 2/3 n = 2,183

EGAT 2/4 n = 1,948

Family history of DM

Yes

No

929 (36.69)

1603 (63.31)

853 (39.07)

1330 (60.93)

780 (40.04)

1168 (59.96)

Total cholesterol (mg/dl) 233.79 ± 41.99 231.33 ± 42.09 217.73 ± 43.89

HDL (mg/dl) 53.15 ±14.42 51.03 ± 12.41 57.53 ± 15.55

LDL (mg/dl) 151.72 ± 38.74 149.94 ± 30.11 144.86 ± 40.02

Triglyceride (mg/dl)** 126 (27, 1362) 128 (31, 1133) 121.5 (37, 1280)

Creatinine (mg/dl) 1.01 ± 0.17 1.02 ± 0.19 0.98 ± 0.22

Uric acid (mg/dl) 5.60 ± 1.47 5.83 ± 1.45 6.06 ± 1.45

eGFR (ml/min per 1.73m2) 83.37 ± 12.73 80.13 ± 13.60 80.45 ± 13.53


Non / Mild Periodontitis



429 (17.24)

1268 (50.94)

792 (31.82)

227 (10.97)

1092 (52.78)

750 (36.25)

272 (14.31)

1002 (52.74)

626 (32.95)

% sites with PPD ≥ 4 mm** 4.17 (0, 93.75) 4.17 (0, 88.67) 3.97 (0, 95.83)

% sites with PPD ≥ 6 mm** 0 (0, 63.64) 0 (0, 62.67) 0 (0, 75.00)

% sites with PPD ≥ 4 mm

& CAL ≥ 5 mm** 0.88 (0, 91.67) 1.45 (0, 88.67) 1.19 (0, 95.83)

% proximal sites with CAL

≥ 3 mm** 41.67 (0, 100) 56.00 (0, 100) 55.10 (0, 100)


≥ 5 mm** 2.68 (0, 100) 5.36 (0, 100) 4.35 (0, 100)

Values are mean ± SD for continuous data, and frequency (%) for categorical data, except where specified.

Total numbers of subjects of each variable may be different depended on missing data.

** Median (range)

BMI, body mass index; CAL, clinical attachment level; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; FM-DM, family history of diabetes; FM-HT, family history of hypertension; HDL, high-density lipoprotein; LDL, low-density lipoprotein; N/A, not available; PPD, periodontal pocket depth


Table 4.4 Numbers of missing data

Variables Number of Observed

% missing data

Marital status 6,619 16.27

Education 6,784 14.18

Income 6,543 17.23

Smoking 6,731 14.85

Alcohol 6,678 15.52

Exercise 6,601 16.50

NSAIDs use 6,607 16.42

Weight 6,598 16.53

Height 7,875 0.38

Waist 6,583 16.72

Hip 6,582 16.74

Blood Pressure 6,594 16.58

Glucose 6,637 16.04

Cholesterol 6,639 16.02

HDL 6,639 16.02

LDL 6,547 17.18

Triglyceride 6,639 16.02

Uric acid 6,639 16.02

Creatinine 6,623 16.22

Periodontal status 6,458 18.30

HDL, high-density lipoprotein; LDL, low-density lipoprotein; NSAIDs, nonsteroidal anti-inflammatory drugs

Attaw

ood Lertpim

onchai

Results / 116

Table 4.5 Within and whole wave missing data

HDL, high-density lipoprotein; LDL, low-density lipoprotein; NSAIDs, nonsteroidal anti-inflammatory drugs

Table 4.5 Within and whole wave missing data

Variables

PERIO-CKD cohort (N = 2,635)

EGAT 2/2 (2,532) EGAT 2/3 (2,183) EGAT 2/4 (1,948)

Within wave Whole wave (Total) Within wave Whole wave (Total) Within wave Whole wave (Total)

Observe Missing Observe Missing % Missing Observe Missing Observe Missing % Missing Observe Missing Observe Missing % Missing

Marital status 2,508 24 2,516 119 4.52 2,166 17 2173 462 17.53 1,930 18 1,930 705 26.76

Education 2,512 20 2,540 95 3.61 2,170 13 2,293 342 12.98 1,930 18 1,951 684 25.96

Income 2,502 30 2,502 133 5.05 2,116 67 2,116 519 19.70 1,925 23 1,925 710 26.94

Smoking 2,518 14 2,568 67 2.54 2,169 14 2,232 403 15.29 1,931 17 1,931 704 26.72

Alcohol 2,513 19 2,545 90 3.42 2,167 16 2,202 433 16.43 1,931 17 1,931 704 26.72

Exercise 2,510 22 2,510 125 4.74 2,162 21 2,162 473 17.95 1,929 19 1,929 706 26.79

NSAIDs use 2,510 22 2,510 125 4.74 2,167 16 2,167 468 17.76 1,930 18 1,930 705 26.76

Weight 2,505 27 2,505 130 4.93 2,165 18 2,165 470 17.84 1,928 20 1,928 707 26.83

Height 2,527 5 2,625 10 0.38 2,181 2 2,625 10 0.38 1,945 3 2,625 10 0.38

Waist 2,496 36 2,496 139 5.28 2,161 22 2,161 474 17.99 1,926 22 1,926 709 26.91

Hip 2,496 36 2,496 139 5.28 2,161 22 2,161 474 17.99 1,925 23 1,925 710 26.94

Blood Pressure 2,504 28 2,504 131 4.97 2,163 20 2,163 472 17.91 1,927 21 1,927 708 26.87

Glucose 2,523 9 2,523 112 4.25 2,168 15 2,168 467 17.72 1,946 2 1,946 689 26.15

Cholesterol 2,524 8 2,524 111 4.21 2,169 14 2,169 466 17.69 1,946 2 1,946 689 26.15

HDL 2,524 8 2,524 111 4.21 2,169 14 2,169 466 17.69 1,946 2 1,946 689 26.15

LDL 2,432 100 2,432 203 7.70 2,169 14 2,169 466 17.69 1,946 2 1,946 689 26.15

Triglyceride 2,524 8 2,524 111 4.21 2,169 14 2,169 466 17.69 1,946 2 1,946 689 26.15

Uric acid 2,524 8 2,524 111 4.21 2,169 14 2,169 466 17.69 1,946 2 1,946 689 26.15

Creatinine 2,511 21 2,511 124 4.71 2,168 15 2,168 467 17.72 1,944 4 1,944 691 26.22

Periodontal status 2,489 43 2,489 146 5.54 2,069 114 2,069 566 21.48 1,900 48 1,944 691 26.22

LDL, Low-density lipoprotein; HDL, High-density lipoprotein; NSAIDs, Nonsteroidal anti-inflammatory drugs


Table 4.6 RVI, FMI and Relative efficiency in GSEM final models

RVI FMI Relative efficiency

DM Model

Periodontitis 0.020091 0.019735 0.999014 Age 0.019497 0.019162 0.999043 HT 0.168423 0.146011 0.992752 DLP 0.052441 0.050076 0.997502 Obesity 0.150097 0.132062 0.99344 Family history of DM 0.011175 0.011064 0.999447 Education

≤ High school 0.027203 0.026555 0.998674 Vocation/Diploma

0.037777

0.036536

0.998177

Periodontitis Model

DM 0.009181 0.009106 0.999545 Age 0.020628 0.020254 0.998988 Gender: male 0.008426 0.008363 0.999582 Smoking

Quit smokers 0.057689 0.054838 0.997266 Current smokers 0.020825 0.020443 0.998979

Education ≤ High school 0.050939 0.048705 0.997571 Vocation/Diploma 0.038244 0.036973 0.998155

Exercise (times/week) 1 - 2 0.074013 0.069378 0.996543 ≥ 3 0.09528 0.087718 0.995633

CKD Model

Periodontitis 0.031003 0.030163 0.998494 DM 0.018715 0.018406 0.999081 Uric acid 0.085909 0.079719 0.99603 HT 0.148691 0.130975 0.993494 Income (Baht/month)

< 20,000 0.320169 0.247168 0.987792 20,000 - 49,999 0.306239 0.238835 0.988199

CKD, chronic kidney disease; DLP dyslipidemia; DM, diabetes mellitus; FMI, fraction of missing information; GSEM, generalized structural equation model; HT, hypertension; RVI, relative variance increase


Table 4.7 Comparison of characteristic between actual and imputed dataset


Imputed data

Proportion / Mean SE

Marital status

Single

Married

Divorce/Widows

238 (9.49)

2081 (82.97)

189 (7.54)

9.50

82.80

7.70

0.006

0.007

0.005

Income (Baht/month)

< 20,000

20,000 – 49,999

≥ 50,000

312 (12.47)

1318 (52.68)

872 (34.85)

12.54

52.88

34.58

0.007

0.010

0.009

Education


Vocational/Diploma


688 (27.39)

825 (32.84)

999 (39.77)

27.43

33.14

39.43

0.009

0.009

0.010

Smoking

Never smokers

Quit smokers

Current smokers

1340 (53.22)

622 (24.70)

556 (22.08)

52.93

25.05

22.02

0.010

0.008

0.008

Alcohol

Never drinkers

Quit drinkers

Current drinkers

1182 (47.04)

211 (8.40)

1120 (44.57)

46.42

9.95

43.63

0.010

0.006

0.010


None

1 - 2

≥ 3

728 (29.00)

670 (26.69)

1112 (44.30)

28.97

26.68

44.35

0.009

0.009

0.010


Table 4.7 Comparison of characteristic between actual and imputed dataset (cont.)


Imputed data


NSAIDs use

Yes

No

246 (9.80)

2264 (90.20)

9.85

90.15

0.006

0.006

Height (cm) 163.61 ± 7.67 163.59 0.149

Weight (kg) 65.88 ± 11.48 65.94 0.224

Waist circumference (cm) 85.97 ± 9.87 86.04 0.194

Hip circumference (cm) 96.49 ± 6.57 96.49 0.129

BMI (kg/m2) 24.56 ± 3.61 24.58 0.070

Waist to hip ratio 0.89 ± 0.07 0.89 0.001

Central obesity

Yes

No

1283 (51.40)

1213 (48.60)

51.78

48.22

0.010

0.010

Diabetes Mellitus

Yes

No

194 (7.69)

2329 (92.31)

7.75

92.25

0.005

0.005

Hypertension

Yes

No

689 (27.52)

1815 (72.48)

27.57

72.43

0.008

0.008

Dyslipidemia

Yes

No

1723 (68.54)

791 (31.46)

68.82

31.18

0.009

0.009




Imputed data


Total cholesterol (mg/dl) 233.79 ± 41.99 233.85 0.828

HDL (mg/dl) 53.15 ±14.42 53.03 0.281

LDL (mg/dl) 151.72 ± 38.74 151.61 0.797

Triglyceride (mg/dl) 154.47 ± 116.60 155.79 2.297

Creatinine (mg/dl) 1.01 ± 0.17 1.01 0.004

Uric acid (mg/dl) 5.60 ± 1.47 5.61 0.029

eGFR (ml/min per 1.73m2) 83.37 ± 12.73 83.38 0.278





429 (17.24)

1268 (50.94)

792 (31.82)

16.53

50.58

32.89

0.007

0.010

0.009

% sites with PPD ≥ 4 mm 10.50 ± 14.89 10.82 0.290

% sites with PPD ≥ 6 mm 1.98 ± 5.31 1.95 0.102

% sites with PPD ≥ 4 mm & CAL ≥ 5 mm

6.13 ± 12.21 6.37 0.239

% proximal sites with CAL ≥ 3 mm

45.88 ± 29.22 46.72 0.564

% proximal sites with CAL ≥ 5 mm 10.17 ± 17.75 10.46 0.348




Imputed data


Marital status

Single

Married

Divorce/Widows

178 (8.22)

1801 (83.15)

187 (8.63)

7.92

82.74

9.34

0.005

0.008

0.006

Income (Baht/month)

< 20,000

20,000 – 49,999

≥ 50,000

135 (6.38)

634 (29.96)

1347 (63.66)

7.16

31.17

61.67

0.006

0.010

0.010

Education


Vocational/Diploma


488 (22.49)

736 (33.92)

946 (43.59)

24.71

33.68

41.61

0.009

0.009

0.010

Smoking

Never smokers

Quit smokers

Current smokers

1138 (52.47)

586 (27.02)

445 (20.51)

49.86

28.56

21.58

0.010

0.009

0.008

Alcohol

Never drinkers

Quit drinkers

Current drinkers

782 (36.09)

312 (14.40)

1073 (49.51)

33.08

19.34

47.58

0.009

0.009

0.010


None

1 - 2

≥ 3

1055 (48.8)

351 (16.23)

756 (34.97)

48.06

16.26

35.69

0.011

0.008

0.010




Imputed data


NSAIDs use

Yes

No

312 (14.40)

1855 (85.6)

15.07

84.93

0.008

0.008

Height (cm) 163.55 ± 7.71 163.59 0.149

Weight (kg) 66.24 ± 11.23 66.41 0.227



BMI (kg/m2) 24.72 ± 3.59 24.77 0.072

Waist to hip ratio 0.91 ± 0.07 0.91 0.001

Central obesity

Yes

No

1434 (66.39)

726 (33.61)

67.80

32.20

0.010

0.010

Diabetes Mellitus

Yes

No

254 (11.71)

1915 (88.29)

12.45

87.55

0.006

0.006

Hypertension

Yes

No

827 (38.20)

1338 (61.80)

38.82

61.18

0.010

0.010

Dyslipidemia

Yes

No

1640 (75.75)

525 (24.25)

77.11

22.89

0.009

0.009




Imputed data



HDL (mg/dl) 51.03 ± 12.41 50.68 0.254

LDL (mg/dl) 149.94 ± 30.11 149.68 0.826


Creatinine (mg/dl) 1.02 ± 0.19 1.02 0.005

Uric acid (mg/dl) 5.83 ± 1.45 5.88 0.030

eGFR (ml/min per 1.73m2) 80.13 ± 13.60 79.80 0.346





227 (10.97)

1092 (52.78)

750 (36.25)

8.71

45.80

45.49

0.005

0.010

0.010

% sites with PPD ≥ 4 mm 9.95 ± 14.38 12.06 0.289

% sites with PPD ≥ 6 mm 2.07 ± 5.72 2.04 0.104


& CAL ≥ 5 mm 6.70 ± 12.38 8.36 0.253


≥ 3 mm 56.35 ± 28.47 59.15 0.521


≥ 5 mm 14.04 ± 20.46 16.09 0.404




Imputed data


Marital status

Single

Married

Divorce/Widows

144 (7.46)

1586 (82.18)

200 (10.36)

6.55

80.48

12.97

0.005

0.008

0.007

Income (Baht/month)

< 20,000

20,000 – 49,999

≥ 50,000

189 (9.82)

248 (12.88)

1488 (77.30)

13.51

14.69

71.80

0.009

0.008

0.011

Education


Vocational/Diploma


357 (18.50)

633 (32.80)

940 (48.70)

22.72

33.93

43.35

0.008

0.010

0.010

Smoking

Never smokers

Quit smokers

Current smokers

1047 (54.22)

622 (32.21)

262 (13.57)

47.43

35.18

17.39

0.010

0.010

0.008

Alcohol

Never drinkers

Quit drinkers

Current drinkers

530 (27.45)

433 (22.42)

968 (50.13)

22.58

30.20

47.22

0.009

0.011

0.011


None

1 - 2

≥ 3

622 (32.24)

272 (14.10)

1035 (53.66)

32.32

13.09

54.59

0.011

0.008

0.012




Imputed data


NSAIDs use

Yes

No

170 (8.81)

1760 (91.19)

9.80

90.20

0.007

0.007

Height (cm) 163.49 ± 7.82 163.59 0.149

Weight (kg) 66.93 ± 11.88 66.96 0.239



BMI (kg/m2) 25.00 ± 3.82 24.97 0.078

Waist to hip ratio 0.90 ± 0.007 0.90 0001

Central obesity

Yes

No

1156 (60.05)

769 (39.95)

62.46

37.54

0.010

0.010

Diabetes Mellitus

Yes

No

384 (18.96)

1641 (81.04)

16.93

83.07

0.007

0.007

Hypertension

Yes

No

1206 (57.79)

881 (42.21)

55.42

44.58

0.010

0.010

Dyslipidemia

Yes

No

1428 (75.12)

473 (24.88)

77.94

22.06

0.009

0.009




Imputed data



HDL (mg/dl) 57.53 ± 15.55 56.60 0.315

LDL (mg/dl) 144.86 ± 40.02 143.28 0.959


Creatinine (mg/dl) 0.98 ± 0.22 0.99 0.006

Uric acid (mg/dl) 6.06 ± 1.45 6.11 0.031

eGFR (ml/min per 1.73m2) 80.45 ± 13.53 79.93 0.405





272 (14.31)

1002 (52.74)

626 (32.95)

10.36

42.04

47.60

0.006

0.010

0.010

% sites with PPD ≥ 4 mm 10.58 ± 16.23 14.45 0.336

% sites with PPD ≥ 6 mm 2.17 ± 6.34 2.29 0.116


& CAL ≥ 5 mm 6.93 ± 14.12 10.04 0.297


≥ 3 mm 56.35 ± 26.47 60.93 0.493


≥ 5 mm 13.46 ± 21.11 17.62 0.438

Values are mean ± SD for continuous data, and frequency (%) for categorical data, except where specified. Total numbers of subjects of each variable may be different depended on missing data. BMI, body mass index; CAL, clinical attachment level; CKD, chronic kidney disease; eGFR, estimated glomerular filtration rate; FM-DM, family history of diabetes; FM-HT, family history of hypertension; HDL, high-density lipoprotein; LDL, low-density lipoprotein; N/A, not available; PPD, periodontal pocket depth


Table 4.8 The F-test and coefficients of various periodontitis and obesity definitions

Factors Overall F-test

b t p 95% CI

LL UL

PERIODONTITIS

CDC/AAP 3gr 8.55

Normal/Mild periodontitis - Reference

Moderate periodontitis - .4429876 1.62 0.105 -.0922116 .9781869

Severe periodontitis - .9738453 3.46 0.001 .4226753 1.525015

CDC/AAP 2gr 14.43

Non-severe periodontitis - Reference

Severe periodontitis - .5998399 3.80 < 0.001 .2902356 .9094441

Periodontitis Extent

Periodontitis A 16.49 .0184575 4.06 < 0.001 .0095483 .0273667

Periodontitis B 6.35 .0282988 2.52 0.012 .0062878 .0503099

Periodontitis C 16.53 .0184865 4.07 < 0.001 .0095756 .0273974

Periodontitis D 26.89 .0257024 5.19 < 0.001 .0159878 .0354171

Periodontitis E 37.97 .0187561 6.16 < 0.001 .0127903 .024722

Periodontitis F 40.62 .0229052 6.37 < 0.001 .015861 .0299494

OBESITY

BMI 3 gr 8.55

Underweight (< 18.5) - .307886 0.57 0.569 -.7535123 1.369284

Normal (18.5 – 24.9) - Reference

Overweight/Obese (≥ 25.0) - .6407238 3.79 < 0.001 .3087904 .9726572

BMI 2 gr 14.15

Non-obese (< 25) - Reference

Overweight/Obese (≥ 25.0) - .6277721 3.76 < 0.001 .3003383 .9552058

WHR 15.73

Normal - Reference

Obese (M > 0.90 | F > 0.85) - .7214715 3.97 < 0.001 .3640045 1.078939

b, coefficient; BMI, body mass index; CI, confidence interval; F, female; M, male; p, p-value; t, t-test; WHR, waist-hip-ratio


Table 4.9 Univariate GSEM of DM model: Mediation model

Factors b SE t p 95% CI

LL UL

Periodontitis F 0.051 0.005 9.66 < 0.001 0.041 0.062



0.200

0.262

0.76

0.445

-0.313

0.713

Severe Periodontitis 1.246 0.278 4.49 < 0.001 0.702 1.791

Age (year) 0.368 0.024 15.46 < 0.001 0.321 0.414

Sex: male 1.173 0.258 4.55 < 0.001 0.668 1.677

Income (Baht/month)

< 20,000 -0.209 0.253 -0.83 0.409 -0.707 0.288

20,000 - 49,999 -0.884 0.171 -5.16 < 0.001 -1.220 -0.548

Education

≤ High school 1.227 0.288 4.26 < 0.001 0.662 1.793

Vocation/Diploma 1.251 0.259 4.84 < 0.001 0.744 1.758

Marital status

Married 0.621 0.409 1.52 0.129 -0.180 1.422

Divorce / widows 1.206 0.473 2.55 0.011 0.279 2.134


1 - 2 -0.580 0.228 -2.54 0.011 -1.028 -0.132

≥ 3 0.308 0.178 1.73 0.085 -0.042 0.657

Smoking

Quit smokers 1.301 0.227 5.73 < 0.001 0.856 1.746

Current smokers 0.813 0.259 3.13 0.002 0.304 1.322

Alcohol

Quit drinkers 1.915 0.257 7.46 < 0.001 1.412 2.419

Current drinkers 1.425 0.237 6.02 < 0.001 0.961 1.890

Obesity 1.681 0.216 7.77 < 0.001 1.256 2.106

HT 2.158 0.179 12.06 < 0.001 1.807 2.509

DLP 0.953 0.225 4.24 < 0.001 0.512 1.394

Family history of DM 1.550 0.212 7.32 < 0.001 1.135 1.965 b, coefficient; CI, confidence interval; DLP dyslipidemia; DM, diabetes mellitus; HT, hypertension; p, p-value; SE, standard error; t, t-test


Table 4.10 Univariate GSEM of CKD model: Outcome model


LL UL

Periodontitis F 0.023 0.004 6.37 < 0.001 0.016 0.030


Moderate Periodontitis 0.443 0.273 1.62 0.105 -0.092 0.978

Severe Periodontitis 0.974 0.281 3.46 0.001 0.423 1.525

DM 1.174 0.198 5.93 < 0.001 0.786 1.562

Sex: male 0.611 0.204 2.99 0.003 0.211 1.012

Income (Baht/month)

< 20,000 0.591 0.219 2.7 0.007 0.161 1.022

20,000 - 49,999 -0.667 0.188 -3.55 < 0.001 -1.036 -0.298

Education

≤ High school 0.238 0.215 1.11 0.268 -0.183 0.660

Vocation/Diploma 0.343 0.206 1.67 0.095 -0.060 0.746

Marital status

Married 0.811 0.367 2.21 0.027 0.091 1.530

Divorce / widows 1.101 0.432 2.55 0.011 0.254 1.947


1 - 2 -0.461 0.245 -1.88 0.06 -0.942 0.020

≥ 3 0.219 0.178 1.24 0.217 -0.129 0.568

Alcohol

Quit drinkers 1.116 0.231 4.83 < 0.001 0.663 1.570

Current drinkers 0.661 0.201 3.3 0.001 0.268 1.055

Smoking

Quit smokers 0.811 0.199 4.08 < 0.001 0.421 1.200

Current smokers 0.324 0.230 1.41 0.16 -0.128 0.775

NSAIDs 0.470 0.226 2.07 0.039 0.023 0.916

Obesity 0.721 0.182 3.97 < 0.001 0.364 1.079

HT 1.402 0.167 8.4 < 0.001 1.075 1.730

DLP 0.653 0.193 3.38 0.001 0.274 1.032

Uric acid (mg/dl) 0.688 0.062 11.06 < 0.001 0.566 0.810 b, coefficient; CI, confidence interval; CKD, chronic kidney disease; DLP dyslipidemia; DM, diabetes mellitus; HT, hypertension; NSAIDs, nonsteroidal anti-inflammatory drugs; p, p-value; SE, standard error; t, t-test


Table 4.11 Multivariate GSEM of mediation and outcome models of Pathway A:

Periodontitis F

Factors b SE t p

95% CI

LL UL

DM

Mod

el

Periodontitis 0.011 0.002 4.81 < 0.001 0.006 0.015

Age (year) 0.050 0.008 6.13 < 0.001 0.034 0.066

Education

≤ High school 0.550 0.150 3.66 < 0.001 0.255 0.844


Obesity 1.076 0.124 8.69 < 0.001 0.833 1.319

Family history of DM 0.938 0.116 8.09 < 0.001 0.711 1.165

HT 0.833 0.106 7.82 < 0.001 0.624 1.042

DLP 0.595 0.128 4.64 < 0.001 0.343 0.846

CK

D M

odel

Periodontitis 0.010 0.003 3.90 < 0.001 0.005 0.015

DM 0.689 0.155 4.44 < 0.001 0.385 0.994

Income (Baht/month)

< 20,000 0.278 0.175 1.59 0.112 -0.065 0.622

20,000 - 49,999 -0.476 0.155 -3.08 0.002 -0.780 -0.172

HT 0.748 0.141 5.31 < 0.001 0.472 1.024

Uric acid (mg/dl) 0.467 0.044 10.51 < 0.001 0.380 0.554

b, coefficient; CI, confidence interval; CKD, chronic kidney disease; DLP dyslipidemia; DM, diabetes mellitus; HT, hypertension; p, p-value; SE, standard error; t, t-test


Table 4.12 Multivariate GSEM of mediation and outcome models of Pathway A:

CDC/AAP


LL UL

DM

Mod

el


Moderate Periodontitis -0.207 0.337 -0.61 0.54 -0.867 0.454

Severe Periodontitis 0.222 0.372 0.6 0.55 -0.507 0.952

Age (year) 0.267 0.026 10.37 < 0.001 0.216 0.318

Education

≤ High school 1.513 0.368 4.12 < 0.001 0.792 2.234


Obesity 1.506 0.280 5.39 < 0.001 0.957 2.056

Family history of DM 2.732 0.311 8.78 < 0.001 2.122 3.342

HT 1.393 0.228 6.12 < 0.001 0.946 1.840

DLP 0.638 0.282 2.26 0.024 0.084 1.192

CK

D M

odel


Moderate Periodontitis 0.221 0.287 0.77 0.441 -0.341 0.783

Severe Periodontitis 0.486 0.296 1.64 0.101 -0.094 1.066

DM 0.903 0.211 4.28 < 0.001 0.490 1.316

Income (Baht/month)

< 20,000 0.336 0.230 1.46 0.145 -0.116 0.787

20,000 - 49,999 -0.661 0.195 -3.39 0.001 -1.045 -0.278

HT 0.934 0.178 5.24 < 0.001 0.584 1.285

Uric acid (mg/dl) 0.625 0.062 10.13 < 0.001 0.504 0.746

b, coefficient; CI, confidence interval; CKD, chronic kidney disease; DLP dyslipidemia; DM, Diabetes mellitus; HT, hypertension; p, p-value; SE, standard error; t, t-test


Table 4.13 Casual effects of Periodontitis on CKD through DM (Pathway A)

Effects Pathway b SE Bias 95% CI*

LL UL

Direct Periodontitis ⟶ CKD 0.01003 0.00258 0.00005 0.00463 0.01494

Indirect Periodontitis ⟶ DM ⟶ CKD 0.00738 0.00228 -0.00006 0.00381 0.01314

Percent of direct effect 57.62

Percent of total effects mediated 42.38

* bias-corrected bootstrapped

b, coefficient; CI, confidence interval; CKD, chronic kidney disease; DM, Diabetes mellitus; SE, standard error;


ahidol Univ.

Ph.D

. (Clinical Epidem

iology) / 133 Table 4.14 Comparisons of forward stepwise method versus disjunctive clause criteria for DM Model

Forward stepwise method Partial disjunctive clause Modified fully disjunctive clause

Coefficient 95% LCI 95% UCI Coefficient. 95% LCI 95% UCI Coefficient. 95% LCI 95% UCI

DM-Model Periodontitis 0.011 0.006 0.015 0.011 0.007 0.015 0.011 0.006 0.015 Age 0.050 0.034 0.066 0.052 0.036 0.069 0.044 0.026 0.062 Gender (male) 0.314 -0.037 0.664 Income (Baht/month)

< 20,000 0.039 -0.257 0.334 0.012 -0.290 0.314 20,000 - 49,999 0.109 -0.086 0.305 0.083 -0.115 0.281

Education ≤ High school 0.550 0.255 0.844 0.489 0.166 0.812 0.508 0.184 0.832 Vocation/Diploma 0.494 0.230 0.759 0.477 0.205 0.748 0.482 0.210 0.753

Marital status Married -0.189 -0.615 0.237 Divorce / widows -0.121 -0.642 0.401

Exercise (times/week) 1 - 2 -0.069 -0.331 0.194 ≥ 3 0.088 -0.118 0.293

Alcohol drinking Quit drinkers 0.315 0.003 0.626 Current drinkers 0.119 -0.157 0.395

Smoking Quit smokers 0.121 -0.168 0.410 Current smokers -0.121 -0.456 0.215

Obesity 1.076 0.833 1.319 1.183 0.935 1.431 1.115 0.866 1.364 HT 0.833 0.624 1.042 0.909 0.700 1.118 0.859 0.648 1.070 DLP 0.595 0.343 0.846 0.646 0.394 0.898 0.620 0.368 0.873 Family history of DM 0.938 0.711 1.165 0.941 0.713 1.170 0.926 0.697 1.156 NSAIDs use 0.675 0.452 0.897 Uric acid -0.190 -0.266 -0.115 -0.229 -0.313 -0.144

DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension; NSAIDs, nonsteroidal anti-inflammatory drugs

Attaw

ood Lertpimonchai

R

esults / 134 Table 4.15 Comparisons of forward stepwise method versus disjunctive clause criteria for CKD Model

DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension; NSAIDs, nonsteroidal anti-inflammatory drugs



CKD-Model Periodontitis 0.010 0.005 0.015 0.011 0.006 0.016 0.012 0.006 0.018 DM 0.689 0.385 0.994 0.751 0.438 1.065 0.688 0.368 1.008 Income (Baht/month)

< 20,000 0.278 -0.065 0.622 0.366 -0.017 0.749 0.323 -0.065 0.710 20,000 - 49,999 -0.476 -0.780 -0.172 -0.408 -0.728 -0.088 -0.394 -0.719 -0.069

Education ≤ High school -0.311 -0.689 0.066 -0.338 -0.717 0.041 Vocation/Diploma -0.066 -0.380 0.249 -0.070 -0.388 0.248

Marital status Married 0.381 -0.162 0.924 Divorce / widows 0.582 -0.060 1.223

Exercise (times/week) 1 - 2 -0.191 -0.579 0.197 ≥ 3 0.104 -0.179 0.387

Alcohol drinking Quit drinkers 0.124 -0.254 0.503 Current drinkers -0.216 -0.567 0.134

Smoking Quit smokers 0.037 -0.298 0.372 Current smokers -0.219 -0.627 0.189

Obesity -0.024 -0.341 0.293 0.002 -0.318 0.321 HT 0.748 0.472 1.024 0.757 0.472 1.041 0.722 0.439 1.006 DLP 0.073 -0.248 0.394 0.055 -0.268 0.377 Family history of DM -0.268 -0.548 0.013 -0.259 -0.542 0.024 NSAIDs use 0.200 -0.181 0.582 Uric acid 0.467 0.380 0.554 0.466 0.376 0.556 0.477 0.382 0.571


Table 4.16 Univariate GSEM of Periodontitis model: Mediation model

Factors F b SE t p 95% LCI 95% UCI

DM 143.700 7.248 0.605 11.990 < 0.001 6.063 8.433

Age (year) 989.030 0.740 0.024 31.450 < 0.001 0.693 0.786

Sex: male 158.660 10.287 0.817 12.600 < 0.001 8.686 11.888

Income (Baht/month) 37.420

< 20,000 -0.537 0.527 -1.020 0.309 -1.573 0.499

20,000 - 49,999 -2.720 0.320 -8.510 < 0.001 -3.348 -2.093

Education 56.660

≤ High school 8.722 0.870 10.030 < 0.001 7.013 10.431


Marital status 28.590

Married 4.867 0.984 4.950 < 0.001 2.939 6.795

Divorce / widows 8.708 1.173 7.420 < 0.001 6.407 11.008

Exercise (times/week) 18.880

1 - 2 -2.422 0.411 -5.900 < 0.001 -3.228 -1.616

≥ 3 -0.189 0.393 -0.480 0.631 -0.963 0.585

Smoking 126.450

Quit smokers 9.923 0.632 15.710 < 0.001 8.684 11.162

Current smokers 10.988 0.731 15.030 < 0.001 9.554 12.423

Alcohol 116.600

Quit drinkers 7.279 0.480 15.160 < 0.001 6.337 8.221

Current drinkers 5.044 0.452 11.170 < 0.001 4.158 5.929

Obesity 46.890 2.585 0.378 6.850 < 0.001 1.844 3.326

HT 103.250 3.510 0.345 10.160 < 0.001 2.833 4.188

DLP 11.730 1.355 0.396 3.420 0.001 0.579 2.131

b, coefficient; CI, confidence interval; DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension; p, p-value; SE, standard error; t, t-test


Table 4.17 Multivariate GSEM of mediation and outcome models of Pathway B

Factors b SE t p 95% LCI 95% UCI

Peri

odon

titis

M

odel

DM 4.801 1.131 4.24 < 0.001 2.584 7.018

Age 0.636 0.044 14.51 < 0.001 0.550 0.722

Gender: male 4.204 0.637 6.59 < 0.001 2.954 5.453

Education

≤ High school 9.911 0.878 11.29 < 0.001 8.190 11.632



1 - 2 -1.607 0.616 -2.61 0.009 -2.814 -0.400

≥ 3 -1.273 0.576 -2.21 0.027 -2.403 -0.143

Smoking

Quit smokers 3.748 0.764 4.90 < 0.001 2.250 5.246

Current smokers 14.063 1.058 13.29 < 0.001 11.988 16.137

CK

D

Mod

el

Periodontitis 0.010 0.003 3.90 < 0.001 0.005 0.015

DM 0.689 0.155 4.44 < 0.001 0.385 0.994

Income (Baht/month)

< 20,000 0.278 0.175 1.59 0.112 -0.065 0.622

20,000 - 49,999 -0.476 0.155 -3.08 0.002 -0.780 -0.172

HT 0.748 0.141 5.31 < 0.001 0.472 1.024

Uric acid 0.467 0.044 10.51 < 0.001 0.380 0.554

b, coefficient; CI, confidence interval; CKD, chronic kidney disease; DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension; p, p-value; SE, standard error; t, t-test


Table 4.18 Casual effects of DM on CKD through Periodontitis (Pathway B)

Effects Pathway b SE Bias 95% CI*

LL UL

Direct DM ⟶ CKD 0.68944 0.15423 -0.00440 0.36568 0.98245

Indirect DM ⟶ Periodontitis ⟶ CKD 0.04816 0.01833 0.00049 0.02130 0.09555

Percent of direct effect 93.47

Percent of total effects mediated 6.53

* bias-corrected bootstrapped

b, coefficient; CI, confidence interval; CKD, chronic kidney disease; DM, Diabetes mellitus; SE, standard error

Attaw

ood Lertpimonchai

R

esults / 138 Table 4.19 Comparisons of forward stepwise method versus disjunctive clause criteria for Periodontitis Model



Periodontitis-Model DM 4.801 2.584 7.018 4.652 2.436 6.869 4.766 2.546 6.986 Age 0.636 0.550 0.722 0.662 0.567 0.758 0.654 0.554 0.754 Gender (male) 4.204 2.954 5.453 5.298 3.743 6.853 5.578 3.899 7.258 Income (Baht/month)

< 20,000 2.364 0.417 4.311 2.314 0.359 4.269 20,000 - 49,999 1.151 0.045 2.257 1.079 -0.035 2.194

Education ≤ High school 9.911 8.190 11.632 9.028 7.199 10.857 9.141 7.298 10.984 Vocation/Diploma 4.961 3.645 6.277 4.681 3.352 6.010 4.781 3.441 6.121

Marital status Married -1.060 -3.210 1.091 Divorce / widows -0.901 -3.633 1.831

Exercise (times/week) 1 - 2 -1.607 -2.814 -0.400 -1.732 -2.941 -0.522 -1.654 -2.862 -0.445 ≥ 3 -1.273 -2.403 -0.143 -1.391 -2.520 -0.262 -1.346 -2.477 -0.215

Alcohol drinking Quit drinkers 0.498 -1.184 2.181 Current drinkers -1.180 -2.672 0.311

Smoking Quit smokers 3.748 2.250 5.246 3.784 2.292 5.277 3.994 2.462 5.526 Current smokers 14.063 11.988 16.137 13.927 11.854 15.999 14.289 12.171 16.408

Obesity 0.265 -0.854 1.383 HT -0.172 -1.371 1.027 -0.040 -1.236 1.157 DLP -0.299 -1.510 0.912 NSAIDs use -1.734 -3.305 -0.164 Uric acid -0.648 -1.159 -0.138 -0.618 -1.138 -0.097

DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension; NSAIDs, Nonsteroidal anti-inflammatory drugs


Figure 4.1 Flowchart of included subjects CKD, chronic kidney disease; EGAT, Electricity Generating Authority of Thailand; Perio, periodontitis

EGAT 2/2 EGAT 2/3 EGAT 2/4

EGAT 2/2 – 2/4

Non-CKD c o h o r t

• 126 CKD at baseline

2,669

Perio-CKD c o h o r t

2,635

Periodontal exclusion

• 6: Medical condition

• 15: Fully edentulous

• 13: Refused

n = 2,795

n = 2,686 n = 2,288 n = 2,037

Attaw

ood Lertpim

onchai

Results / 140

Figure 4.2 Causal mediation pathway diagram of Pathway A using generalized structural equation modelling

Values represent unstandardized coefficients CKD, chronic kidney disease; DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension

0.689DM

Bernoulli

logit

CKD

Bernoulli

logit

PeriodontitisAge Family history of DM

Education: ≤ High school

Education: Vocation/Diploma

Income: < 20,000

Income: 20,000-49,999

HT Uric acidObesity

DLP


ahidol Univ.

Ph.D

. (Clinical Epidem

iology) / 141

Figure 4.3 Causal mediation pathway diagram of Pathway B using generalized structural equation modelling

Values represent unstandardized coefficients CKD, chronic kidney disease; DLP, dyslipidemia; DM, diabetes mellitus; HT, hypertension

0.010Periodontitis

Gaussian

identity

CKD

Bernoulli

logit

DMAge Male

Education: ≤ High school

Education: Vocation/Diploma

Income: < 20,000

Income: 20,000-49,999

HT Uric acid

Exercise: 1-2 times/week

Exercise: ≥ 3 times/week

Smoking: Quit smokers

Smoking: Current smokers

Attawood Lertpimonchai Discussion / 142

CHAPTER V

DISCUSSION

5.1 Main findings The conceptual framework of causative association between periodontitis,

DM and CKD were constructed using a mediation analysis indicating periodontitis and

DM were risk factors of CKD incidence. Both had the significant direct effect, as well

as, indirect (mediation) effect through each other. From the pathway A, periodontitis →

DM → CKD, the finding suggested that every increasing one percent of proximal sites

with severe periodontitis, the odds of developing CKD would be directly increased and

indirectly transmitted thought DM 1.010 and 1.007, respectively. The pathway B, DM

→ periodontitis → CKD, suggested that subjects with DM would had odds of CKD

incidence higher than non-DM around 2 times, and this effect was attributed to the

mediation effect via periodontitis 6.5%.

5.2 Comparison results with previous studies Similar with previous cross-control studies12-25, our results could show the

significant association of CKD and its risk factors including periodontitis and DM. But

with our cohort design which excluded CKD cases at baseline, the temporal relationship

cloud be further firmly established. All cases of interested outcome were subsequence

of periodontitis and DM.

Our results were consistent with previous six cohort studies26-31, which

found the causative association between periodontitis and CKD. However, half of

them26, 27, 30 mainly focused on kidney function declination not the CKD incidence. Both

CKD and non-CKD cases also were included into these studies. Chen et al 27 defined

the primary outcome as decline ≥ 30% of eGFR. Chang et al 26 used the progression of

color intensity in GFR and albuminuria grid (CGA staging) as the outcome. While,

Iwasaki et al 30 interested in worsening of eGFR category, which defined as ≥ 60,


30-59, and < 29 ml/min per 1.73 m2, here, the interpretation should be done with caution,

because decrease of significantly different size would be classified similarly, for

example, eGFR declination from 90 to 32 ml/min per 1.73 m2, and 31 to 28 ml/min per

1.73 m2.

Besides, there were 3 cohort studies, which interested outcomes were new

cases of altered kidney function28, 29, 31. All of them revealed the significant effects of

periodontitis, which were similar with ours. Shultis et al31 showed that the incidences

of macroalbuminuria were 2.0, 2.1, and 2.6 times as high in individuals with moderate

or severe periodontitis or those who were edentulous, respectively, compared with those

with none/mild periodontitis. However, they defined periodontal status from numbers

of remaining teeth and radiography without clinical periodontal parameters, which

might be affect the internal validity. Likewise, the MrOS cohorts29 applied the random

half-mouth protocol for periodontal examination which may be underestimated the

prevalence of periodontitis207, and then, the association between periodontitis and CKD

might be bias. Moreover, eligible subjects from these 3 cohorts were quite specific

groups, i.e., African-American28, elderly male (hospital-based)29, and Pima Indian with

type II of DM31. Generalization and implication to other populations should be done

with caution. Furthermore, the important difference between conventional and

mediation analysis is the perspective on the third variable. The conventional analysis,

as previous cohort studies, claiming the casual association regarded DM as the

confounder of periodontitis effect. While, the mediation analysis treated

DM/periodontitis as the intermediate variable (mediator). It has been claimed the

advantage in refining and understanding a possible pathway. In other words, the

mediation analysis could determine a process of how one variable effect the outcome208.

Comparing with the previous bi-directional mediation analysis using cross-

sectional survey data, Fisher et al15 found significant direct and indirect effect of

periodontitis on CKD through DM duration and HT. In addition, they also found reverse

association between diabetic duration and periodontitis, both direct and indirect

associations through HT and CKD. Our data and this previous study were different.

First, we considered only CKD incidence whereas Fisher et al considered old and new

CKD cases. Second, interestingly, the direct effect of DM on CKD was not significant

in the previous study. DM status (non-DM / well-control DM / poor-control DM) and


DM duration (assuming equal zero if subjects were non-DM) were used as

representation of DM, and both were not significant in the full-model of CKD. Hence,

they concluded that DM duration influenced CKD indirectly through HT and

periodontitis, but unexpectedly does not directly impact CKD. While, most previous

studies and ours study reported the independent and significant effect of DM on CKD.

5.3 EGAT data management The EGAT study, the first cohort study of chronic disease in Thailand, was

set up by a group of cardiologists at Ramathibodi Hospital, Bangkok, since 1985. Until

now, the cohort is actively surveyed and followed up every 5 years. Thus, the EGAT

database has continuously growth. From then to now, the data management, including

data entry process, format of database, data cleaning process, has been changed over

time depended on data managers. In this study, we retrieved and explored 3 periods of

the EGAT2 cohort, and found that validity of data should be systematically manage. In

EGAT2/2 and 2/3, the processes of data entry seem to be problematic. Data entry error

was occasionally detected. Process of data cleaning by recheck with the original CRFs

was strongly suggested. In addition, the consistency of variables is another issue that

should be aware. Within each survey, some replies from the questionnaire were

contradict to each other. Revise the questionnaire with the trained staff at the survey

site, before the end of survey, may reduce these problems. The official team of data

cleaning should be authorized to methodically solve and judge data queries. Moreover,

to validate the suspected cases, for example, single FBS ≥ 126 mg/dl or rapidly decrease

in kidney function, should be followed and documents with the proper planning.

5.4 Periodontal measurement and classification As for our results, we found that periodontitis defined form disease extent

could show the significant effect on DM and CKD incidence, meanwhile, CDC/AAP

definition was not. Various case definitions of periodontitis have been proposed for

periodontal research55. It has great impact on the prevalence and extent of periodontal

disease56. Moreover, these discrepancies diverged the results of investigating the link


between periodontitis and systemic diseases57. Due to lack of universally accepted, the

CDC and AAP proposed and updated the standard case definitions58, 59. These

definitions have been proposed for surveillance, mainly, to determine total prevalence

of periodontitis. Some previous studies16, 20, 28, 29 applied them for investigate the

relationship between periodontitis and kidney function. However, we postulate that the

plausible link between periodontitis and the systemic disease based on inflammation.

With CDC/AAP definition, in some cases, the amounts of inflammation seem to be

discrepancies within the same category. For example, subjects who had only 2 teeth

with severe periodontitis would be grouped as the same as subjects who had whole-

mouth with severe form.

Therefore, periodontitis was also alternatively dealt as continuous data by

using the disease extent. The percentage of disease sites were calculated from full-

mouth periodontal data. Six definitions of disease sites that we extracted and

summarized from previously systematic review were proposed. Results showed that the

percentage of proximal sites with severe periodontitis or CAL ≥ 5mm was the most

suitable to our primary outcome. The cut-off point of CAL at 5 mm or above was quoted

from amount of attachment loss at severe level in the AAP-1999 classification of

periodontal disease54. Only the proximal sites were considered to rule out the attachment

loss due to non-periodontitis cause, i.e., traumatic brushing at buccal and lingual sites.

5.5 Multiple imputation Missing data is undesirable, inevitable, and unignorably. It is common

problem in both observational or clinical trials study particularly with long term follow

up. There are several ways to dealing with, i.e., list-wise deletion (complete case

analysis), simple imputation with average or mode, simple regression substitution, and

MICE209, 210. With current literatures, the MICE is recommended, because results from

MICE are more valid comparing with other methods189, 210. However, currently there is

no standard guideline which method we should apply for multiple imputation in

longitudinal data. For our study, we tried to impute missing variables by using

themselves at different periods as the predictors. Moreover, to impute some conditional

variables, i.e., marital status, education level, smoking, and alcohol drinking, by


conventional MICE might be achieved some inappropriate values, for example, reversed

education level. Although, the Stata does not provide options that supported to those

conditions, alternative models for imputation, including interval linear regression model

and the truncated regression model, could be applied. The Stata is quite flexible for

imputation. Users can construct the individual imputation model for each imputed

variable with own model and predictors. It should be noted that the computations were

somewhat time-consuming, for example, with m=20 in our dataset, generating the

imputed dataset lasted about 14 hours on a personal computer (macOS Sierra, 3.3 GHz

Intel Core i7, 8 GB RAM, with 4-core Stata 14.2 MP). The high specification of

computer with the Stata multi-core version are recommended.

Comparing with the complete case analysis (i.e., actual data without

imputation), ours results from MICE were quite approximate in term of significant

factors and their coefficients, but MICE decreased uncertainty (lower standard errors),

in other words, increased precision of results.

The number of imputation, firstly, were set based on the percentage of

missing data which ranged from 0.38% to 18.30%. Then, FMI from final models were

considered to confirm that our twenty imputations were adequate efficiency and power.

A rule of thumb suggested that the number of imputation should be larger than

FMI × 100. Therefore, twenty imputations would be insufficient for this dataset.

5.6 Selection of co-variables for mediation analysis To claim the causative association, the RCT is the ideal study design. With

the randomization technique, the balances of measured and unmeasured co-variables

among groups are claimed. However, the risk factors or exposure to periodontitis could

not be randomly allocated. Therefore, confounding bias may play a role in the causal

pathway. Selection of co-variables to include and adjust in the mediation analysis is

therefore important process. For our study, the disjunctive clause criteria have been

proposed by adopted the conceptual framework of the treatment effect analysis. This

criterion can be applied to the mediation analysis by considering the mediator as the

treatment. It consists of controlling for all co-variables that satisfy (i) the co-variates are

associated with exposure, or (ii) the co-variates are associated with the outcome, or


both203. In other words, with the disjunctive clause criteria, we try to use the relevant

co-variables as much as we can to estimate the effect size. It believed that the more co-

variables that we consider could be better in explanation the unmeasured variables.

In our results, the pattern of associations and coefficients of study factor and

mediator were approximated between conventional stepwise and the disjunctive clause

criteria in all models, i.e., CKD model, DM model and periodontitis model.

Interestingly, some variables were added with the significant effect size in the

disjunctive clause criteria. It might be hidden valuable, which was not presented in the

conventional stepwise analysis, for instance, the protective effect of NSAIDs in the

periodontitis model.

5.7 Strengths of this study We constructed a bi-directional causal pathway of periodontitis, DM, and

CKD using mediation analysis. Data from EGAT cohort had been used, only new cases

of CKD were considered, thus a temporal relationship was claimed. The EGAT cohort

survey is still active with a good data management. Although some missing data

occurred, the complex MICE for longitudinal study were constructed to deal with

missing data, and it increased the precision of results. Next, the periodontal status was

examined with the gold standard protocol, full-mouth examination with six sites per

tooth, therefore misclassification of periodontal disease severity should be minimized.

Finally, many periodontitis definitions were used to identified the association with the

outcome, and the best match was proposed.

5.8 Limitations Some limitations in our study should be pointed out. CKD was classified

based on eGFR without information of micro- or macro-proteinuria. Moreover,

proteinuria by itself, was also worsening factor for kidney function, hence the CKD

incident and effect size of other co-variables might be bias. Second, the cohort had only

3 surveys with 5-year interval. With the large gaps between surveys, uncertainty of

outcome and variables among visits were present. Thus, the time varying co-variables


analysis was used to compensate for this bias. Third, our studied population might not

represent the general Thai population. EGAT employees represented older adults with

higher education and income than average Thai people. The generalization of our

findings should be made with caution. Finally, our mediation effect might be only a

small part under the complexity of association between these diseases and other chronic

conditions. Other relevant diseases or behaviors might be the candidates of other

mediators or moderators. Future investigation will be required to clarify.

5.9 Clinical application Systemic chronic inflammation from periodontitis is a risk factor not only

for DM and CKD, but also, CVD and all-cause mortality. In general population, almost

50% of adults had periodontitis, with about 10% having severe periodontitis47. It means

that large populations were at risk to develop the subsequent burdens from periodontitis.

Worse than that, awareness among patients and health-care providers was low211. Our

results pointed out the important of this oral disease. These messages may encourage

the communication between physicians and dentists. Regularly refer patients with

chronic disease such as DM to the periodontal clinic is recommended to minimize the

oral inflammation. For general dentists, not only the destructive periodontium, but also

the systemic burden form periodontitis should be emphasized. Advice for medical

check-up among periodontitis patients is also suggested to detect kidney deterioration,

as well as, other adverse events from periodontitis. To public, the basic knowledge about

periodontitis should be promoted to increase awareness. Prevention and treatment of

periodontal disease are effective and inexpensive modalities. Motivation the personal

oral health care, routine dental check-up, and professional cleaning are efficient in

control the oral inflammation, and minimized spreading systemic inflammation from

periodontitis.


5.10 Suggestion for further studies To overcome our limitations, the prospective cohort study which directly

designed based on our primary research questions is recommended. Community-based

with properly sampling technique is more suitable and more representativeness of study

subjects. The study duration more than 10 to 15 years is suggested to permit the CKD

development after DM onset. In addition, the annual follow-up periods should be

proposed to serve time-varying co-variables pattern, and allow the advance “time to

event” statistical analysis. Most importantly, microproteinuria should be additionally

used for outcome verification, as well as, be considered as another risk factor of CKD.

Moreover, periodontal parameters indicated the gingival inflammation, such as, GI,

BOP and periodontal inflamed surface area (PISA), should be used to characterize the

inflammation that could be spreading to overall system.

We currently proposed the effect of periodontitis on CKD initiation.

Similarly, its progression may be also influenced by oral inflammation. The cohort of

CKD patients could be used to emphasize the important of oral health. Furthermore,

investigation the protective effect of periodontal treatment on CKD initiation and

progression is also interesting and valuable.

5.11 Conclusion In conclusion, periodontitis and DM had the significant direct and indirect

effect via each other on increasing CKD incidence. Oral and systemic morbidities from

periodontitis should be emphasized among nephrologists, general practitioners, and

patients. Its treatment and prevention also should be promoted in public.

Attawood Lertpimonchai References / 150

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Attawood Lertpimonchai Appendices / 172

APPENDICES


APPENDIX A

MODIFIED NEWCASTLE-OTTAWA QUALITY ASSESSMENT

SCALE (COHORT STUDIES)

Selection (Maximum:��)

1) Representativeness of the Exposed Cohort (depends on the study design)

a) Truly representative of the general adults in the community/hospital �

b) Somewhat representative of the average general adults in the

community/hospital �

c) Selected group of users e.g. cohorts of DM / CKD patients

d) No description of the derivation of the cohort

2) Selection of the Non-Exposed Cohort

a) Drawn from the same community/hospital as the exposed cohort �

b) Drawn from a different source

c) No description of the derivation of the non-exposed cohort

3) Ascertainment of Exposure (Oral hygiene)

a) Full-mouth examination (Plaque and/or calculus) �

b) Partial-mouth or index teeth (Plaque and/or calculus) �

c) Record only the highest score of plaque and/or calculus (½�)

d) Self-report oral hygiene with ≥ 3 questions of OH assessment (¼�)

e) Subjective judgment or no description

4) Demonstration that Outcome of Interest Was Not Present at Start of Study

a) Yes �

b) No


Comparability (Maximum:��)

Confounders must be adjusted for in the analysis.

a) Study controls and/or adjusted for SMOKING in the analysis. �

b) Study controls for any additional one or more factor(s) (age, gender,

socio-status, DM, etcetera) �

Statements of no differences between groups or that differences were not

statistically significant, are not sufficient for establishing comparability.

Outcome (Maximum:��)

1) Assessment of outcome: Periodontitis assessment

a) Clinical measurement �

b) Radiography only or No description

2) Proper representative of periodontal status

a) Full-mouth examination AND ≥ 4 sites per tooth �

b) Partial-mouth examination OR < 4sites per tooth

3) Proper “Periodontitis” definition

a) Define “Periodontitis” with details of clinical attachment level (CAL)

with or without periodontal probing depth (PD) �

b) Define “Periodontitis” without clinical attachment level (CAL)

4) Validation of periodontal measurement

a) Mention about intra/inter-examiners calibration �

b) No statement


5) Was follow-up long enough for outcomes to occur: (1 year)

a) Yes �

b) No

6) Adequacy of follow up of cohorts

a) Complete follow up�

b) ≥ 80% follow up, and provided description of those lost or some

characteristics of BOTH groups. �

c) ≥ 80% follow up, without description of those lost (½�)

d) Follow up rate < 80% or No statement


Modified Newcastle-Ottawa Quality Assessment Scale

(Cross-sectional studies)


1) Representativeness of the samples

a) Clearly define the sampling method with random technique �

b) Consecutive samples from the study setting �

c) Selected group of samples e.g. volunteers

d) No description of the derivation of the samples

2) Ascertainment of Exposure (Oral hygiene)




d) Self-report oral hygiene with ≥ 3 questions of OH assessment (¼�)



Confounders must be adjusted for in the analysis.







Outcome (Maximum:� ��)

1) Assessment of outcome: Periodontitis assessment

a) Clinical measurement �

b) Radiography only or No description




3) Proper “Periodontitis” definition

a) Define “Periodontitis” with details of clinical attachment level (CAL) with

or without periodontal probing depth (PD) �


4) Validation of periodontal measurement


b) No statement


Modified Newcastle-Ottawa Quality Assessment Scale

(Case-control studies)


1) Is the case (periodontitis) and control (non-periodontitis) definition adequate?

a) Define “Periodontitis” with details of clinical attachment level (CAL)

with or without periodontal probing depth (PD) �





3) Is the periodontal measurement valid?


b) No statement

4) Representativeness of the cases

a) Consecutive or obviously representative series of cases �

b) Potential for selection biases or not stated

5) Selection of Controls: Non-periodontitis

a) Consecutive selection or using any random sampling from the same

source and the same time periods as the cases �

b) Drawn from the same source and the time periods as the cases without

specified sampling method (½�)

c) No description



1) Comparability of cases and controls based on the design or analysis. They must be

matched in the design and/or adjusted confounders in the analysis.






Exposure (Maximum:��)

1) Ascertainment of exposure




d) Self-report oral hygiene


2) Same method of ascertainment for cases and controls

a) Yes � b) No

3) Non-Response rate

a) Same rate for both groups �

b) Non-respondents described

c) Rate different and no designation

Attaw

ood Lertpimonchai

A

ppendices / 180

APPENDIX B

THE GRADE APPROACH

Pooling Study design

(No. of studies) Risk of

bias1 Inconsistency2 Indirectness Imprecision Other considerations (+/-)

Quality of Evidence

Fair and poor versus good OH

Observational (15): - Cohort (1) - Cross-sectional (14)

Not serious Not serious Not serious Not serious (+) Large effect3

(+) Dose-response gradient4

(+)(+)(+)( ) MODERATE

Brushing

Observational (10): - Cohort (1) - Case control (1) - Cross-sectional (8)

Not serious Serious Not serious Not serious (+)( )( )( ) VERY LOW

Interdental cleaning Observational (4): - Cross-sectional (4) Not serious Not serious Not serious Not serious (+)(+)( )( )

LOW

Dental visits

Observational (6): - Cohort (2) - Case control (1) - Cross-sectional (3)

Not serious Not serious Not serious Notserious (-) Publication bias5 (+)( )( )( ) VERY LOW

NOTE: 1 Risk of bias: The results of modified Newcastle-Ottawa Quality Assessment Scale were used for considered (see Table S2). Proportions of low, moderate and high risk of bias studies were 10:3:2, 6:4:0, 2:2:0 and 3:3:0 for fair and poor versus good OH, brushing, interdental cleaning and dental visit, respectively. From these, the numbers of low risk of bias studies were ≥ 50% for each pooling, hence, we graded them as “Not serious”. 2 Inconsistency: Heterogeneity of all poolings were moderate to high level and might be problematic except interdental cleaning. Fortunately, sources of heterogeneity could be identified properly. Results from pooled effect of OH in “community-based studies” and pooled effect of dental visit among studies that clearly defined a regular dental visit as least once a year were quite consistent. While, the heterogeneity of brushing still had presented as moderate to high level after exploring possible sources (Table S8). Therefore, only brushing was considered as “Serious” for inconsistency, then the quality of evidence was downgraded to very low level. 3, 4 Large effect & Dose-response gradient: The effect of OH on periodontitis was significant with the large pooled OR about 2 to 5 times. Moreover, it showed the dose-response relation, in other words, risk of periodontitis increased with worsening OH level (poor > fair > good OH). Therefore, the quality of evidence was upgraded from low to moderate level. 5 Publication bias: The publication bias was suspected in the pooled effect of dental visit from the Egger test (Table S9) and the contour enhanced-funnel plot (Figure S2-c). The quality of evidence was downgraded to very low level.


APPENDIX C

ETHICAL APPROVAL


BIOGRAPHY

NAME Mr. Attawood Lertpimonchai

DATE OF BIRTH 27 April 1983

PLACE OF BIRTH Bangkok, Thailand

INSTITUTIONS ATTENDED Doctor of Dental Surgery (D.D.S.), Faculty of

Dentistry, Chulalongkorn University (2001-2006),

Master of Science (M.Sc.) in

Periodontics, Department of

Periodontology, Faculty of Dentistry,

Chulalongkorn University (2009-2011)

SCHOLARSHIP RECEIVED Faculty of Dentistry, Chulalongkorn University

RESEARCH GRANTS Chulalongkorn University (Government Budget

Grant 2015) HOME ADDRESS 20 Prachanivej 2, Samakkee 58/18, Thasai,

Amphur Muang, Nonthaburi, 11000

EMPLOYMENT ADDRESS Faculty of Dentistry, Chulalongkorn University,

Henri Dunant, Pathum Wan, Bangkok, 10330

PHONE (66) 81-726-6969

E-MAIL [email protected]

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