Association Between Periodontal Disease, Lung Volumes, and Airflow Limitation in the Population-Based Study of Health in Pomerania (SHIP)

Aus dem Zentrum für Zahn-, Mund- und Kieferheilkunde (Geschäftsführender Direktor: Univ.-Prof. Dr. Dr. h.c. Georg Meyer) Abteilung Parodontologie (Leiter: Univ.-Prof. Dr. med. dent. Thomas Kocher) In der Poliklinik für Zahnerhaltung, Parodontologie und Endodontologie (Direktor: Univ.-Prof. Dr. Dr. h.c. Georg Meyer) und dem Bereich Pneumologie und Infektiologie (Leiter: Univ.-Prof. Dr. med. Ralf Ewert) der Universitätsmedizin der Ernst-Moritz-Arndt-Universität Greifswald

Association between Periodontal Disease, Lung Volumes, and Airflow Limitation in the Population-based Study of Health in Pomerania (SHIP)

Inaugural-Dissertation zur Erlangung des akademischen Grades Doktor der Zahnmedizin (Dr. med. dent.) der Universitätsmedizin der Ernst-Moritz-Arndt-Universität Greifswald 2012

vorgelegt von: Stefanie Richter geb. am: 22. April 1984 in: Lauchhammer

Dekan: Prof. Dr. rer. nat. Heyo Klaus Kroemer

1. Gutachter: Prof. Dr. med. dent. Thomas Kocher

2. Gutachter: Prof. Dr. med. dent. Ulrich Schlagenhauf

Ort, Raum: Greifswald, Klinikum Sauerbruchstraße, Raum O 0.221

Tag der Disputation: 23. Januar 2013

Ich widme diese Arbeit in Liebe und Dankbarkeit meinen Eltern

Table of Contents

1. Introduction 1 1.1. Periodontal Diseases 2 1.1.1. Prevalence and Pathogenesis of Periodontal Diseases 2 1.1.2. Relevant Risk Factors for Periodontal Diseases 4 1.2. Lung Anatomy and Function 11 1.2.1. Anatomy 11 1.2.2. Lung Function Examination 14 1.2.3. Pulmonary Diseases 17 1.2.4. Association between Lung Function and Systemic Diseases 21 1.3. Evidence from Clinical and Epidemiological Studies 21 1.3.1. Association between Periodontitis and Pulmonary Dysfunction assessed by Spirometry Data 21 1.3.2. Association between Oral Infections and Pulmonary Outcomes 29 1.4. Aims of the study 33

2. Material and Methods 34 2.1. Study of Health in Pomerania (SHIP) 34 2.2. Data Assessment 35 2.2.1. Periodontal Health Assessment 35 2.2.2. Assessment of Lung Function Variables 36 2.2.3. Assessment of Covariates 37 2.2.4. Blood Sampling 38 2.3. Statistical Analyses 38

3. Results 40 3.1. Characteristics of Study Participants 40 3.2. Association between mean CAL and Pulmonary Variables 42 3.3. Association between mean PD and Pulmonary Variables 46 3.4. Association between Number of Missing Teeth and Pulmonary Variables 49 3.5. Sensitivity Analyses 52

4. Discussion 56 4.1. Summary of Results 56 4.2. Confounding of the Association between Periodontitis and Lung Function 56 4.2.1. Confounding by Cigarette Smoking 56 4.2.2. Confounding by Body Height 58 4.2.3. Confounding by Socioeconomic Status 59 4.2.4. Confounding by Other Factors 59 4.3. Biological Mechanisms linking Periodontitis and Lung Function 61 4.3.1. Low-grade Systemic Inflammation 61 4.3.2. Oral Bacteria and Respiratory Pathogens 63 4.3.3. Aspiration of Salivary Secretions 66 4.3.4. Failure of Host Defence Mechanisms 67 4.3.5. Modification of Respiratory Mucosal Surfaces by Salivary Enzymes 67 4.3.6. Destruction of Salivary Film by Enzymes 68 4.4. Strengths and Limitations 68 4.5. Conclusions 69

5. Summary 70

6. Zusammenfassung 71

References 72

Appendix

I List of Abbreviations II Overview on Lung Function Parameters in SHIP III Eidesstattliche Erklärung IV Curriculum Vitae V Acknowledgements

1. Introduction Individuals with airflow limitation potentially demonstrate airway and systemic inflammation depending on the severity of lung disease, physical activity, and potential comorbidities (Rasmussen et al., 2009, Shaaban et al., 2006, Thyagarajan et al., 2006, Hancox et al., 2007, Waschki et al., 2011). Besides smoking (Tamimi et al., 2012, Stapleton et al., 2011), risk factors for lung function decline and airflow limitation include ageing and diseases usually associated with airflow limitation such as chronic obstructive pulmonary disease and infections (Guerra, 2009). Periodontal disease is one of the most prevalent human infections. The majority of adults suffer from some degree of periodontitis, with 15-20% of the adult population having severe periodontal disease (Holtfreter et al., 2009). Periodontal disease is an oral bacterial infection, that results in gingival inflammation, breakdown of the supporting connective tissue, pocket formation between the gingiva and the tooth, destruction of alveolar bone, and eventually exfoliation of teeth (Kornman, 2008, Pihlstrom et al., 2005). This inflammatory response to the commensal oral flora is primarily provoked by colonization with anaerobic gram-negative microbes. While probing depth reflects current disease status, clinical attachment loss equals the reduction in the supporting connective tissue of the tooth and reflects cumulative disease experience. To date, few studies have examined the relationship between periodontitis and respiratory diseases with selected pulmonary function parameters. Merely on the basis of spirometry

(FEVR1R, FEVR1R/FVC ratio), several cross-sectional and case-control studies reported significant associations between history of chronic obstructive pulmonary disease, lung function, and periodontal disease (Scannapieco and Ho, 2001, Hyman and Reid, 2004, Katancik et al., 2005, Deo et al., 2009, Wang et al., 2009). Furthermore, periodontal disease was related to the longitudinal decline in spirometric lung volumes in cohort studies (Garcia et al., 2001b, Hayes et al., 1998). If periodontal disease contributes to a declining lung function as an independent risk factor, it would provide a rationale to consider periodontal prevention and treatment as a therapeutic option to target a decreasing lung function or at least to support dedicated treatments of pneumological diseases. However, the mechanisms behind these observations still remain unclear and controversial. One theoretical pathway linking both diseases includes aspiration of saliva harbouring oral bacteria into the lung (Scannapieco et al., 1992) causing local damage to the small airways (Sinclair and Evans, 1994). Subjects with severe periodontal disease harbour elevated levels of periodontal pathogens in saliva, the amount of which reflects pathogen burden in periodontal pockets (Paju et al., 2009, Saygun et al., 2011). Another pathway

1 may be up-regulated inflammation in the lungs by systemic low-grade inflammation. Elevated levels of cytokines and of circulating inflammatory markers such as C-reactive protein (CRP), fibrinogen, and leukocyte counts have been observed in subjects with periodontal disease (Slade et al., 2003, Yoshii et al., 2009). Markers of systemic inflammation were also associated with reduced spirometric lung volumes (Rasmussen et al., 2009, Glaser et al., 2011, Shaaban et al., 2006, Thyagarajan et al., 2006, Hancox et al., 2007, Dahl et al., 2001). So far, the interrelationship between periodontal disease, inflammation, and lung function remains poorly understood. Thus, the major aim of this study was to further clarify the association between periodontal disease and lung function based on a highly standardized setting of comprehensive lung function test parameters within the population-based Study of Health in Pomerania (SHIP). We further evaluated i) the strength of confounding by smoking, height, and educational status and ii) the role of systemic inflammation as an intermediate factor.

1.1. Periodontal Diseases 1.1.1. Prevalence and Pathogenesis of Periodontal Diseases Periodontitis is a bacteria-induced low-grade local infection that results in gingival inflammation, breakdown of the supporting connective tissue, pocket formation between the gingiva and the tooth, destruction of alveolar bone (see Figure 1), and eventually exfoliation of teeth (Kornman, 2008, Pihlstrom et al., 2005).

Fig. 1: Radiograph of maxillary canine, first premolar, first molar, and second molar with periodontitis showing loss of bone support (male patient, 53 years old).

Evidence from numerous epidemiological studies on oral health has shown that it is the most common cause for tooth extraction during late adulthood (Beck et al., 1997, Aida et al., 2006, National Institute of Dental and Craniofacial Research, 2011). Even though statistics indicated a possible trend of a lower prevalence of periodontitis in recent years, periodontal diseases are still highly prevalent (Hugoson and Norderyd, 2008). Severe

2 forms still affect approximately 15-20% over a wide age range, including younger and older people around the world (Petersen, 2003, National Institute of Dental and Craniofacial Research, 2011, Pihlstrom et al., 2005). The cross-sectional German dental survey (DMS, 2005) detected a prevalence of 52% and 20% for moderate and severe periodontal disease, respectively (age range 35-44 years) (Holtfreter et al., 2010). In seniors (65-74 years), prevalence was even higher, affecting 48% and 40% of the elderly, respectively. In the progression of periodontal disease, times of repeated bursts of activity, which are random with respect to time and initial disease state, alternate with periods of remission and healing (Albandar, 1990, Page et al., 1997). In the absence of treatment periodontal inflammation can persist for years through unceasing stimulation of oral bacteria (Scannapieco and Ho, 2001). Periodontitis is caused by an accumulation of dental plaque, which is organized as a biofilm on the surface of the tooth crown and root (Kolenbrander et al., 2010). Up to 700 different bacteria species colonize the oral cavity (Papapanou and Lindhe, 2008). Periodontal disease is caused by various predominantly gram-negative bacteria including Aggregatibacter actinomycetemcomitans, Porphyromonas gingivalis, Tannerella forsythia, Spirochetes, Prevotella intermedia, Prevotella nigrescens, Fusobacterium nucleatum, Campylobacter rectus, Eikenella corrodens, Peptostreptococcus micros, Selenomonas species, Eubacterium species, Streptococcus intermedius, and Treponema denticola, alone or in combination (Papapanou and Lindhe, 2008, Socransky and Haffajee, 2008). Clinical features of the disease may include presence of calculus, swollen and red gum, bleeding, gingival recession, pocket formation, and eventually tooth loss (Pihlstrom et al., 2005). Inflammation of periodontal soft tissue cause periodontal pockets that serve as an entry port for various bacteria (Loos, 2005). The entrance of bacteria into the bloodstream is always abnormal, known as bacteraemia. The release of cytokines from periodontal lesions, which induces a moderate systemic inflammatory response, could thereby play the intermediate mechanism between bacterial stimulation and tissue destruction (Scannapieco, 1998, Noack et al., 2001, Loos et al., 2000, Graves, 2008, Hayashi et al., 2010). Moreover, this course of action is linked to systemic health (Scannapieco and Mylotte, 1996, Beck and Offenbacher, 2001, Li et al., 2000, Stamm, 1998, Lopez et al., 2002b, Lopez et al., 2002a). Both, elevated levels of cytokines that activate hepatocytes in the liver to produce C-reactive protein (Loos et al., 2000) and higher levels of circulating inflammatory parameters such as CRP, fibrinogen, and leukocyte counts have been observed in dentate subjects with periodontal disease (Yoshii et al., 2009, Slade et al., 2003, Takami et al., 2003, Joshipura et al., 2004) and in edentulous subjects (Russell et al., 1999).

Takami et al. (Takami et al., 2003) reported that patients with periodontal disease had significantly higher levels of CRP compared to healthy individuals (for men: OR=1.74; 95% CI=1.34, 2.25; for women: OR=1.80; 95% CI=1.00, 3.23). In another study (Yoshii et al., 2009), a significant correlation was found between periodontal disease at baseline and CRP one year later for 10,376 Japanese men with low baseline CRP (OR=1.34; 95% CI=1.12, 1.67). However, among those 4,997 Japanese men without periodontal disease, baseline CRP did not correlate with periodontal disease one year later (OR=1.16; 95% CI=0.89, 1.51) indicating that periodontitis increased the risk for high serum CRP levels in men after one year of follow-up and not the inverse. Other studies showed that the severity of periodontitis directly correlated with serum concentrations of inflammatory markers (Amabile et al., 2008) and its successful control has been associated with a significant reduction of these markers (IL-6, CRP), but not without a significant variability among subjects (D'Aiuto et al., 2004, Mattila et al., 2002). Chronic periodontal disease is a multifactorial disease. In addition to the role of properly performing oral hygiene, various factors together can lead to the onset of periodontitis and determine extent and severity (Cohen and Horning, 1998). Established risk indicators (Albandar, 2002) include smoking, age, male sex, genetic predisposition, socioeconomic status, lifestyle changes that may cause stress and depression (Genco et al., 1999), obesity (Al-Zahrani et al., 2003, Morita et al., 2011), diabetes mellitus (Mealey and Oates, 2006, Garofalo, 2008, Ryan, 2008, Garcia et al., 2001a), physical activity (Al-Zahrani et al., 2005), and alcohol intake (Shimazaki et al., 2005). Consequently, it is believed that incidence and severity of periodontal disease result from an imbalance between subgingival plaque, a highly complex structured microbial mass mainly comprising of gram-negative bacteria (Ajwani et al., 2003), the host’s defence capacity modified by genetics, and the presence of various environmental and behavioural factors (Gomes-Filho et al., 2010, Page et al., 1997, Haffajee et al., 2008).

1.1.2. Relevant Risk Factors for Periodontal Diseases Cigarette Smoking Cigarette smoking is the leading risk factor for periodontal disease (Kocher et al., 2005, Bergstrom et al., 2000b, Albandar et al., 2000, Bergstrom et al., 2000a, Tomar and Asma, 2000). Even at an early age with low tobacco consumption, smoking affects periodontal health producing an adverse effect on clinical periodontal variables and alveolar bone height and density (Rosa et al., 2008). In a 10-year prospective study of 50-year-old individuals, smoking was found to be the strongest individual risk predictor (RR=3.2; 95% CI=2.03, 5.15) (Paulander et al., 2004). For those who had continued smoking during the follow-up period, the RR was inherently increased (3.6; 95% CI=2.32, 5.57) compared to

4 those who had quit smoking before baseline examination (RR=1.3; 95% CI=0.57, 2.96). The relationship between intensity of tobacco consumption and extent of periodontal disease is dose-dependent (Bergstrom et al., 2000b). Smokers show worse clinical periodontal variables such as probing depth (PD), clinical attachment loss (CAL), and plaque index (PI), lower gingival crevicular fluid flow rate, more alveolar bone resorption, a higher prevalence of gingival recessions, and a higher risk for tooth loss (Saxer et al., 2007, Rosa et al., 2008), whereby the strongest destruction was found on the palatal and lingual surfaces, respectively, suggestive of a local effect (Adler et al., 2008, van der Weijden et al., 2001). A smoking induced decrease of blood circulation leads to a late diagnosis of the disease due to missing bleedings (clinical characteristics of gingival inflammation or bleeding on probing are less established) (Saxer et al., 2007). However, inflammation induced degradation continues to progress. One reason for this could be the vasoconstrictive effect of nicotine, which leads to an adequate subgingival environment for anaerobes (Salvi et al., 1997). Furthermore, smoking leads to a modified host response (Nishida et al., 2005) causing an impairment of the chemotaxis, reduced phagocytosis of the oral and peripheral neutrophil granulocytes as well as a reduction of anti-body production (Palmer et al., 2005, Kinane and Chestnutt, 2000). Tobacco smoking affects both cell-mediated immunity and humoral immunity (Palmer et al., 2005, Kinane and Chestnutt, 2000). However, the one mechanism by which tobacco smoke increases the susceptibility for periodontal disease is still unclear.

Age With rising age, loss of attachment increases (Albandar, 2002, Holtfreter et al., 2010, Holtfreter et al., 2009). However, regarding prevalence of PD, results differed across studies. Albandar et al. (Albandar, 2002) reported increased PD across age groups, whereas for German cohorts, PD remained constant after the age of 40 (Holtfreter et al., 2010, Holtfreter et al., 2009), see also Figure 2. Other investigations observed an increase of gingival recession but steady levels of PD in subjects with advanced age (Morris et al., 2001, Albandar et al., 1999, Albandar and Kingman, 1999, Brown et al., 1989, Yoneyama et al., 1988) supporting the suggestion that recession is common in older individuals (Yoneyama et al., 1988, Albandar et al., 1999, Albandar and Kingman, 1999, Alwaeli and Al-Jundi, 2005). It is likely that age has the cumulative effect of prolonged exposure to various risk factors (Papapanou et al., 1991). Aged individuals are more prone to suffer from multiple chronic diseases yielding an increased medication intake and longer hospital inhabitation in higher frequency, forcing patients to be bedridden. The intake of various drugs such as

5 antidepressants, antihistamines, antihypertensives, and diuretics can reduce salivary flow (hyposalivation) (Sreebny and Schwartz, 1986, Yeh et al., 1998). Furthermore, antiepileptic drugs (phenytoin), immunosuppressive agents (cyclosporine), and calcium antagonists (nifedipine) can elicit gingival hyperplasia, an intensified progression, or a decrease of periodontal disease (Kinane and Marshall, 2001, Deen-Duggins et al., 1996).

Fig. 2: Relation between probing depth and attachment loss according to age cohorts. Adapted from (Holtfreter et al., 2009).

Furthermore, with advanced age, the pathway of signal transduction of phagocytes may undergo significant changes affecting their ability to perform certain antimicrobial functions or their capacity to regulate the inflammatory response (Hajishengallis, 2010). The oral mucosa becomes more vulnerable to mechanical damage, especially when wearing dentures (Pindborg, 1986). Altogether, these factors combined with poor oral hygiene in these senile people can result in microbial growth and make the elderly highly prone to mucosal infections such as periodontitis (Gavazzi and Krause, 2002, Slade et al., 2000, Hajishengallis, 2010).

Sex Compared with women, men exhibited a stronger disposition to chronic periodontitis (Timmerman and van der Weijden, 2006, Holtfreter et al., 2009, Susin et al., 2004, Albandar, 2002). A recent meta-analysis showed that mean AL was on average 0.19 mm higher in men compared with women (Shiau and Reynolds, 2010).

To explain sex-specific differences in periodontal disease prevalence several reasons might be considered. Differences in hormone levels might be one possible cause. Female sex hormones modulate the inflammatory response (Nilsson, 2007). Anti-inflammatory as well as pro-inflammatory responses to oestrogens have been reported (Carlsten, 2005). Moreover, little health consciousness and poor knowledge of fundamental health issues in men, which manifests itself in increased smoking, careless oral hygiene, and decreased utilization of oral health care services, could be an additional reason for differences between the sexes (Albandar, 2002, Christensen et al., 2003, Roberts-Thomson and Stewart, 2003, Dunlop et al., 2002). In NHANES (Dye et al., 2008), data suggests that women attended the dentist more frequently within the last year compared with men (Figure 3). Moreover, in SHIP, females and males also showed a different pattern regarding their dental visit behaviour (Figure 4).

Fig. 3: Last dental visit in years. Adapted from (Dye et al., 2008).

2 Fig. 4: SHIP-1: Number of dental visits during the last 12 months (left, p=0.06, ChiP P-Test) 2 and reasons for consulting a dentist (right, p=0.04, ChiP P-Test).

Socioeconomic Status The socioeconomic status including income, education, vocational education, and living situation was associated with periodontal diseases (Albandar, 2002, Suvan et al., 2011, Borrell and Papapanou, 2005, Boillot et al., 2011) and tooth loss (Mundt et al., 2011). In a

7 meta-analysis, Boillot et al. (Boillot et al., 2011) showed that individuals with low educational attainment, which is likely to lead to low-pay occupations and settlement in deprived areas, suffer nearly 1.86 times more often from periodontal disease than those with a higher education level. Possible pathways connecting both diseases include mediation via health behaviours and confounding by common risk factors. Generally, people who are better educated, wealthier, and live in more desirable circumstances have a higher degree of periodontal health awareness, a healthier lifestyle (e.g., healthier diet, physical activity), and enjoy a better health status (e.g., higher levels of dental services use) than those who are less educated and live in deprived circumstances (Osler et al., 1998, Kumari et al., 2004, Lang et al., 2008, Alwaeli and Al-Jundi, 2005). Lower educational attainment was associated with irregular oral self-care practices (Petersen et al., 2000). These poor oral hygiene habits subsequently lead to higher levels of dental plaque (Zini et al., 2011). Moreover, main periodontal risk factors such as obesity, higher alcohol and tobacco consumption, diabetes mellitus, and low levels of physical activity have been observed to be more prevalent among subjects with a lower socioeconomic status (Osler et al., 1998, Kumari et al., 2004).

Overweight and Obesity The worldwide growing prevalence of overweight and obese persons, which has more than doubled since 1980, has raised significant health concerns (World Health Organization, 2011). 1.5 Billion of over 20-year-olds were overweight in 2008, of whom over 200 Million men and nearly 300 Million women were obese (World Health Organization, 2011). Formerly considered as a high-income country problem, this epidemic disease is now on the rise in low and middle income countries. Obesity is a significant risk indicator for several chronic health conditions (Hauner et al., 2007) including periodontal disease (Al-Zahrani et al., 2003, Morita et al., 2011, Nishida et al., 2005, Suvan et al., 2011, Sheiham et al., 2002, Linden et al., 2007, Wood et al., 2003). Moreover, being overweight or obese was associated with increased mortality (Haring et al., 2010), identified as the fifth leading risk factor for global deaths (World Health Organization, 2011). Results from NHANES (Wood et al., 2003) strengthened the notion that abdominal fat metabolism is possibly a bigger health risk factor than peripheral fat distribution or subcutaneous fat tissue (Rexrode et al., 1998). Results of a recent follow-up study confirmed an association between BMI and the development of periodontal disease after adjusting for age, smoking status, and clinical history of diabetes mellitus (Morita et al., 2011).

A number of mechanisms have been proposed (Pischon et al., 2007). Changes in host immunity and increased stress levels, which are often associated with excess weight gain early in life, were considered (Reeves et al., 2006). Interestingly, human adipose tissue not only passively stores triglycerides, but also actively expresses and releases a number of pro-inflammatory cytokines such as tumour necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6), and other adipokines (e.g., adiponectin, leptin, resistin) that are involved in the inflammatory process linking the pathophysiology of obesity, periodontal disease, and related inflammatory diseases (Pischon et al., 2007, Kershaw and Flier, 2004, Genco et al., 2005). These mediators have been associated with systemic inflammation and insulin resistance, which seem to mediate the relationship between obesity and periodontal disease (Rotter et al., 2003, Visser et al., 1999, Genco et al., 2005). Last but not least, it is generally known that being overweight is connected with unhealthy dietary patterns with insufficient micronutrients, and excess sugar and fat content. Such behaviour favours the occurrence of periodontitis (Al-Zahrani et al., 2003), however, the possible causal relationship and its underlying biological mechanisms remain to be established.

Diabetes Mellitus Diabetes mellitus (both type 1 and type 2) is a well-known risk factor for periodontal diseases (Mattout et al., 2006, Mealey and Rose, 2008, Janket et al., 2008, Kaur et al., 2009, Lalla et al., 2006a, Lalla et al., 2006b). Several systematic reviews have also addressed this issue emphasizing a higher prevalence and more severity of gingivitis and periodontitis in patients with diabetes mellitus, especially if not controlled and with a long duration of the disease (Mealey and Oates, 2006, Garofalo, 2008, Ryan, 2008, Grossi and Genco, 1998). Interestingly, diabetics with good metabolic control are not at major risk of developing periodontal disease (Garofalo, 2008). Several biological plausible mechanisms for the association of diabetes mellitus with periodontal disease have been demonstrated and include microangiopathy and altered subgingival microflora caused by hyperglycaemia, alterations in gingival crevicular fluid, decreased insulin-mediated glucose uptake by skeletal muscle and subsequently whole- body insulin resistance caused by bacterial infections, reduced efficacy of the host response with hyperproduction of inflammatory mediators and polymorphonuclear dysfunction, alterations in collagen metabolism and impaired tissue repair as well as hereditary predisposition (Albandar, 2002, Salvi et al., 1997, Garofalo, 2008, Mealey and Oates, 2006, Phillip et al., 1995, Grossi and Genco, 1998). Furthermore, it has been hypothesized that hyperglycaemia progressively glycates body proteins forming advanced glycation end products (AGE), which can stimulate phagocytes and the release of

9 inflammatory cytokines subsequently promoting periodontal tissue loss (Nishimura et al., 1998, Lalla et al., 2000b, Grossi and Genco, 1998). Moreover, there is evidence in the literature suggesting a two-way relationship between periodontal disease and systemic diseases (Garcia et al., 2001a), see Figure 5. Systemic conditions not only have oral manifestations, but also periodontal disease might affect diabetes mellitus (Grossi and Genco, 1998, Lalla et al., 2000a, Taylor, 2001, Soskolne and Klinger, 2001).

Fig. 5: Two-way relationship between periodontal disease and diabetes mellitus. Adapted from (Grossi and Genco, 1998).

The chronic inflammatory process of periodontal disease induces an increasing insulin resistance causing a degradation of the diabetic metabolic condition (Veihelmann et al., 2008). Furthermore, acute inflammation reactions affect metabolic procedures such as blood sugar regularization. There is some evidence indicating that periodontal treatment significantly improves the metabolic condition in diabetics by reduction in circulating TNF-α and serum levels of glycated haemoglobin, which reduces the request of insulin (Garofalo, 2008, Vergnes, 2010, Grossi and Genco, 1998).

Physical Activity Regular physical activity is a part of a healthy lifestyle. Several investigations established that regular exercise was significantly associated with a lower prevalence of periodontal disease, especially among never and former smokers (Al-Zahrani et al., 2003). Exercise protects against development of obesity, a recognized risk factor for periodontal disease, by increasing the kilocalories used relative to consumed kilocalories (Al-Zahrani et al., 2003). Furthermore, engaging in a recommended level of physical activity may also improve periodontal health by reducing stress (Genco et al., 1999). Another potential mechanism by which physical activity may protect against periodontitis is by increasing sensitivity to insulin and subsequently preventing the progression of diabetes mellitus (Al- Zahrani et al., 2005, Kriska et al., 2001) or by reducing the synthesis of inflammatory markers such as C-reactive protein, which is a critical process in the pathogenesis of periodontal disease (Williams et al., 2005, Loprinzi et al., 2011, Abramson and Vaccarino, 2002, Ford, 2002).

1.2. Lung Anatomy and Function 1.2.1. Anatomy The lungs (pulmones) are the essential organs of respiration (Gray and Standring, 2009) with their main function to exchange carbon dioxide for oxygen, from the bloodstream into the atmosphere and vice versa. Air enters through the nose or the mouth, then travels through the pharynx, followed by the larynx and the trachea, and subsequently reaches the more and more subdivided system of bronchi and bronchioles within the lung. Each terminal bronchiole branches out into two or more respiratory bronchioles, and each of these again into several alveolar ducts carrying a rising number of exceptionally small and thin walled air sacs, called alveoli (Weinberger et al., 2009). The airways of these terminal respiratory units are surrounded by epithelial cells that are in very close contact with the capillaries permitting the efficient exchange of gases. More precisely, the pulmonary artery carries the venous deoxygenated blood from the right heart into the lungs, where it divides into branches ending in the dense capillary netting in the walls of the alveoli (Gray and Standring, 2009). Oxygenated blood then returns to the left atrium of the heart via pulmonary veins. In contrast, bronchial arteries that branch off the thoracic aorta or upper aortic intercostal arteries supply both lungs with blood for their nutrition. Each bronchial vein then receives blood flow from superficial and deep veins and ends in the azygos vein on the right and in the highest intercostal or the accessory hemiazygos vein on the left side. However, it does not receive all the blood supplied by the bronchial artery as some of it passes into the pulmonary veins (Gray and Standring, 2009).

Similar to the progressive branching of the airways (see Figure 6), the lungs, too, are divided into lobes by interlobular fissures. There are three lobes on the right and two on the left, which are further sub-divided into segments and then into lobules.

Fig. 6: Schematic diagram of airway branching. Tr = trachea; RLL = right lower lobe bronchus; RM = right mainstem bronchus; RML = right middle lobe bronchus; RUL = right upper lobe bronchus; LLL = Left lower lobe bronchus; LM = left mainstem bronchus; LUL = left upper lobe bronchus. Adapted from (Weinberger et al., 2009).

The passage of air is driven by muscular action, the biggest muscle being the diaphragm below the lungs that separates the thorax from the abdomen (Gray and Standring, 2009). During inspiration the base of the lung descends, which causes air to flow into the airways as a result of increasing volume and decreasing pressure (see Figure 7). Expiration happens passively during normal breathing, thus all muscles are relaxed. The diaphragm consequently ascends. The thorax itself is also able to extend and contract to some degree with the help of other respiratory and accessory respiratory muscles (Weinberger et al., 2009). On the outside, the lungs are enclosed by two pleurae with pleural fluid in between. The parietal pleura line the inner rib cage and the visceral pleura coat the surface of the lungs. Between the lower ribs and the costal attachment of the diaphragm the pleural cavity projects for some distance into a potential space, the costodiaphragmatic recess, also called phrenicocostal sinus, for the lungs to expand during forced inspiration. During expiration the recess only contains pleural fluid.

Fig. 7: Muscular action of the diaphragm. Maximum expiration = position of the diaphragm during maximum expiration, the diaphragm ascends. Deep inspiration = position of the diaphragm during inspiration. The base of the lung descends. Adapted from (Baumann and Kurtz, 2009).

Human lungs are located within the thorax in two cavities between the ribs and the backbone, on either side of the heart (Gray and Standring, 2009). The right lung usually weights about 625 g, the left 567 g. However, the amount of blood and serous fluid which they may contain, results in large variation. In males the lungs are heavier with their proportion to the body being 1:37 compared to females with a ratio of 1:43 (Gray and Standring, 2009). Both lobes have a conical figure with an apex at the top and a broad concave shaped base (basis pulmonis) that rests upon the diaphragm. The diaphragm separates the right lung from the right lobe of the liver and the left lung from the left lobe of the liver, the spleen, and the stomach. It elongates higher on the right containing a greater part of the liver than on the left side (Gray and Standring, 2009). The inner mediastinal surface (facies mediastinalis) is characterized by the deep concavity of the cardiac impression, which is larger and deeper on the left than on the right lung owing to the location of the heart on the left side. Above and behind the middle of the mediastinal surface of each lung is its root (radix pulmonis), which connects the lung with the heart and the trachea comprising the bronchus, the pulmonary artery, the pulmonary veins, the bronchial arteries and veins, the pulmonary plexuses of nerves, lymphatic vessels, and bronchial lymph glands, all of which are invested by pleura (Gray and Standring, 2009).

1.2.2. Lung Function Examination Spirometry The assessment of lung function with spirometry is a frequently performed diagnostic procedure, which is used to evaluate subjects’ lung function and to identify the severity of pulmonary impairment (American College of Chest Physicians and American Thoracic Society, 1975) including medical diagnosis, assessment, and monitoring of pulmonary diseases (Schnabel et al., 2010). A spirometer measures the volume and flow of air that can be inhaled and exhaled by the lungs. The body plethysmograph and the pneumotachograph are special types of spirometers. Compared to the classically developed spirometer, the pneumotachograph can additionally determine dynamic breath volumes. Importantly, measurements strongly depend on the optimal cooperation of the patient. All measures are conducted in sitting position wearing nose clips and using a sealed mouthpiece with a multiplicity of tiny fins. From these fins a small flow resistance results within the pipe, which causes a difference of pressure between point A before the fins and point B after the fins (Baumann and Kurtz, 2009). This difference of pressure is directly proportional to the peak expiratory flow, from which one can calculate expiratory volumes with the help of integration. The processor attached to the pneumotachograph then represents the received values in diagrams. Lung function examination in SHIP is described in more detail below.

Body Plethysmography The patient is placed inside a hermetically sealed chamber and breathes through a mouthpiece that can be locked with an electrical valve for some seconds (see Figure 8). At the end of normal expiration this pipe is closed. The patient is then asked to inhale. As the patient does so, the pressure within the lungs decreases with increasing lung volume. This again increases the pressure within the box. A pressure sensor determines the

change of the air pressure (ΔPRCR) and volume (VRCR) in the chamber, which is opposite for

the change of air pressure (ΔPRAR) and volume (VRAR) within the thorax, thus the pressure in the alveoli (Baumann and Kurtz, 2009). With the help of Boyle’s Law, the unknown volume of the lung can be computed. It describes the inversely proportional relationship between the absolute pressure and volume of a gas within a closed system. The pressure of the mouth is set equal to the initial pressure of the alveoli, and is therefore, directly measurable. The known pressure and volume of the chamber are set equal to the known pressure of the alveoli and the unknown new intrathoracic volume, which covers all lung parts, even those that do not

14 participate in the respiration, at the end of expiration. The intrathoracic volume can then be calculated:

VRAR* (-ΔPRAR) = VRCR * ΔPRC

VRAR = (-ΔPRCR/ΔPRAR) * VRC

ΔPRCR = Change in the pressure of the chamber.

ΔPRAR = Change in the pressure of the lungs, also called intrathoracic pressure.

VRCR = Air volume in the chamber.

VRAR = Intrathoracic volume (ITGV), also called functional residual capacity (FRCRplethR).

Fig. 8: Body Plethysmography. VRCR = air volume in the chamber; VRAR = intrathoracic volume

(ITGV); ΔPRAR = change in the pressure of the lungs; ΔPRCR = change in the pressure of the chamber. Adapted from (Baumann and Kurtz, 2009).

Total airway resistance (RRtotR) can also be measured with body plethysmography, which is less dependent on patients’ cooperation, since the patient simply has to breathe calmly into the mouthpiece. Initially, the breath amperage (V̇ ) is measured. This time, the

pressure of the mouth is not equal to the pressure of the alveoli. Then ΔPRAR = -(VRCR/VRAR) *

ΔPRC Rcan be computed, because VRAR changes only a little during breathing, so that the

relationship VRAR/VRCR remains constant. Total airway resistance RRtotR = -ΔPRAR/V̇ = ((VRCR/VRAR) *

ΔPRCR)/V̇ .

Gas Diffusion Tests In SHIP, after a break of at least 3 minutes, diffusing capacity for carbon monoxide as

corrected for current haemoglobin and VRAR (TLCOc-VRAR) and alveolar volume (VRAR) using helium dilution (Wanger et al., 2005) was measured by a single breath manoeuvre (Macintyre et al., 2005). More precisely, the former technique evaluates how well the lungs transfer gases into the blood. The latter measures the functional residual capacity (FRC) of the lungs, the volume left in the lungs after normal expiration. With the common one breath method, the patient inhales a test gas containing a very small amount of carbon monoxide (0.2% CO) from a container as deeply as possible and then stops for 10 seconds. The amount of carbon monoxide in the breath that is exhaled can then be determined. Helium dilution is a closed system, where a spirometer is filled with a mixture of 10%

helium (He) and oxygen (OR2R). The amount of helium remains constant throughout the test. The patient is asked to breathe out passively and then breathe in the gas mixture (normal breaths). As soon as the helium mixture has spread evenly throughout the lungs, the new helium concentration can be determined. Hence, FRC can be calculated using the following equation:

CRI R* VRIR = CRFR * VRtot

CRIR * VRIR = CRFR * (VRIR + FRC)

FRC = VRtotR * (CRIR - CRFR)/CRF

CRIR = Initial helium concentration.

CRFR = Final helium concentration, measured by the spirometer.

VRIR = Volume of gas in spirometer.

VRtotR = Total gas volume (FRC + VRIR (volume of gas in spirometer)). FRC = Functional residual capacity.

In helium dilution only ventilating lung sections are included. In subjects with normal lung function, FRC equals the intrathoracic volume, measured by body plethysmography. In patients with obstructive lung disease, however, the values deviate from each other. In such cases it is more accurate to use body plethysmography that measures all air in the lung including possible pulmonary caught air, so-called trapped air. From the difference of both values the trapped air can be determined.

1.2.3. Pulmonary Diseases The lungs have a remarkable reserve volume as compared to the oxygen and carbon dioxide exchange during normal breathing. Oxygen requirements rise with physical activity and a greater lung volume can be perfused with blood for gas exchange. To a certain extent, this excess capacity is one of the reasons why individuals can smoke for years without having a noticeable decrease in lung function or why people can live with only one lung. However, a breakdown of larger parts of the lung over time unceasingly leads to a perceptible impairment of lung function. The humid environment of the lung is an optimal place for bacteria. Many respiratory diseases are the consequence of bacterial or viral infection of the lungs. Diagnoses of respiratory illnesses require lung function tests because pulmonary diseases may diminish pulmonary compliance that affects air intake. In chronic obstructive diseases expiration is reduced. Furthermore, it is well known that lung function decreases with increasing age, whereas physical activity can be beneficial (Jakes et al., 2002, Pelkonen et al., 2003, Garcia-Aymerich et al., 2008). Healthy subjects’ values are located within the normal ranges, as predicted for age, height, sex, and when indicated, weight and race. Results outside the normal range represent some kind of lung disease.

Obstructive Lung Disease Airflow can be limited by diminished elastic rebound resulting in a collapse of the airways as in emphysema or by narrowing of the airways themselves as seen in chronic bronchitis (Thurlbeck, 1990, Burrows, 1990, Reilly et al., 2008). Both conditions are associated with an extreme shortness of breath (dyspnoea) and together describe a severe form of respiratory disease, called chronic obstructive pulmonary disease (COPD), also known as chronic obstructive lung disease (COLD), chronic obstructive airway disease (COAD), chronic airflow limitation (CAL), and chronic obstructive respiratory disease (CORD). In contrast to asthma, both diseases usually get progressively worse over time and are not fully reversible. Emphysema is histologicaly defined as distention of air spaces distal to the terminal bronchioles, with destruction of the alveolar septa and loss of lung parenchyma (Longmore et al., 2004), causing a reduction of the surface area that is available for gas exchange. Under physiological conditions a surplus of blood circulating anti-elastase (e.g., α1-proteinase inhibitor) neutralizes elastin and collagen dissolving enzymes within the lung that, with infections, can be set free by neutrophil granulocytes. The inhalation of cigarette smoke can inactivate the α1-proteinase inhibitor contributing to the destruction of lung tissue (Davies, 1994). In chronic bronchitis, microorganisms aspirated into the lung,

17 such as Haemophilus influenzae, 0TStreptococcus0T pneumonia, or 0TPorphyromonas0T gingivalis are also able to produce proteases (Hayes et al., 1998). Chronic bronchitis is the result of irritation to the bronchial airways yielding increased numbers (hyperplasia) and increased sizes (hypertrophy) of mucous-secreting cells as well as inflammatory cells within the airway epithelium (Davies, 1994). This leads to narrowed airways owing to scarred and thickened airway epithelium, exorbitant sputum production, and cough on most days for three months of a year for two consecutive years (Longmore et al., 2004). Consequently patients show symptoms of wheeze, dyspnoea, tachypnoea, and use of accessory muscles of respiration. The major cause of obstructive lung diseases is a history of prolonged cigarette smoking, given that it affects the mucociliary barrier and phagocyte activity, triggering an abnormal inflammatory response in the lung (Rabe et al., 2007, Hogg et al., 2004). Other risk factors include chronic exposure to toxic air pollutants (e.g., second hand smoke), history of childhood respiratory infections, and genetic conditions (Barker et al., 1991, Sattar et al., 2004, Maritz et al., 2005). One major complication of COPD is the occurrence of an exacerbation that typically lasts for several days. It is characterized by increased airway inflammation leading to rising hyperinflation, impairment of gas exchange, and reduced expiratory air flow, which can further cause hypoventilation, insufficient tissue perfusion, and cell necrosis (Rabe et al., 2007). Symptoms may include an increasing sputum production with change in colour, change in consistency, increasing cough, dyspnoea, chest tightness, or fatigue. Possible causes may be infections with bacteria, viruses, or environmental pollutants (Murphy and Sethi, 1992, Fagon and Chastre, 1996, Reilly et al., 2008). The organisms most closely associated with exacerbations are non-typeable Haemophilus influenzae,

0TStreptococcus0T pneumonia, and Moraxella catarrhalis (Scannapieco and Ho, 2001). Poor oral health may also play a role in the process of infection of COPD patients by these bacteria (Scannapieco and Ho, 2001). Furthermore, obstructive lung disease can be caused by foreign material that was aspirated, bronchial asthma, or an oedema or tumour, respectively that exerts pressure on the lungs. In obstructive disease lung function values alter as follows (Baumann and Kurtz, 2009):

forced vital capacity (FVC) remains constant. FEVR1R value, FEVR1R/FVC ratio, and forced expiratory flow (FEF) at 25%, 50%, and 75% are lower than predicted values. Total lung capacity (TLC) remains the same or gets higher. Residual volume (RV), RV/TLC ratio, and functional residual capacity (FRC) increase because some airways do not empty normally. Expiratory reserve volume (ERV) levels off or can decrease. After using medication that expands the airways in patients with reversible obstructive lung disease

(e.g., bronchial asthma) FEVR1R often increases.

Restrictive Lung Disease A restrictive lung condition is reflected by a decreased ability of the lung to expand due to the loss of compliance. Possible causes are pneumonia, lung cancer, pulmonary fibrosis, sarcoidosis, multiple sclerosis, or scleroderma. Other restrictive conditions include chest injuries, loss of lung tissue due to surgery, and obesity. In restrictive disease lung function values alter as follows (American College of Chest Physicians and American Thoracic Society, 1975): FVC and TLC are lower than the

predicted value. FEVR1R, FEV25/50/75, FRC, and ERV level off or can decrease. FEVR1R/FVC and RV/TLC ratios level off or can increase. The RV value can either stay the same, decrease, or increase.

Ventilation-Perfusion Mismatch Effective gas exchange in the alveolar capillaries depends on the relationship between ventilation and perfusion. Inadequate perfusion can be the result of pulmonary embolism (blockage of a lung artery) or a disorder that decreases cardiac output. Consequently, residual volume is increased. Inadequate ventilation is defined as a condition in which insufficient air is available to the alveoli for normal diffusion. Hence, blood flowing through the pulmonary vessels is not oxygenated.

Pneumonia Inflammation of the lungs is known as pneumonia. It is an infection of the pulmonary parenchyma caused by bacteria, viruses, and less commonly fungi and parasites (Pommerville, 2010). Mixed infections with both, bacteria and viruses, may arise in up to 45% of cases in children and 15% in adults (Ruuskanen et al., 2011). Pneumonia can be life-threatening, especially among the very old, the very young, and immune-compromised patients (Garibaldi et al., 1981, Bentley, 1984) and it is a significant cause of morbidity and mortality in patients of all ages. Typical symptoms include cough, chest pain, fever, and shortness of breath (Mandell and Wunderink, 2008, Donowitz and Mandell, 2000). Bacterial pneumonia is the most common and treatable form of pneumonia. Its pathogenesis in adults primarily involves colonization of the oropharyngeal region by potential respiratory pathogens, aspiration of oral sections into the lower respiratory tract, and failure of host defence mechanisms to eliminate the bacteria from the lower airways that are normally sterile. Intact cough reflexes, the action of tracheobronchial secretions, and mucociliary transport of inhaled microorganisms and particulate material from the lower airways to the oropharynx, along with immune and non-immune defence factors (e.g., cell-mediated immunity, humoral immunity, polymorphonuclear leucocytes) and action of phagocytic

19 cells keep the sterility of the lower respiratory tract (Donowitz and Mandell, 2000, Mandell and Wunderink, 2008). However, factors like smoking, COPD, diabetes, malnutrition, corticosteroid use, and endotracheal or nasogastric intubation can affect the efficiency of these various defence mechanisms (American Thoracic Society, 1996). Moreover, individuals with altered consciousness (Kollef, 1999) including stroke patients, patients with Parkinson’s disease, patients abusing alcohol (Mojon, 2002), or using sedatives (American Thoracic Society, 1996) aspirate secretions of oropharyngeal flora with high frequency and in great amounts. Aspiration of small quantities of oral secretions is quite common in healthy subjects, especially during sleep (Huxley et al., 1978). There is a 20-23% increased risk for pneumonia with each decayed tooth (Terpenning et al., 2001, Langmore et al., 1998). The presence of certain bacteria in the saliva increases the risk for pneumonia 4 to 7-fold (Terpenning et al., 2001).

Classification of Pneumonia Cases of pneumonia can be classified into two broad categories: community acquired pneumonia (CAP) and hospital (nosocomial) acquired pneumonia (HAP) (Toews, 1986). Pneumonia acquired by previously relatively fit individuals at home is called community

acquired pneumonia and is usually associated with infection by 0TStreptococcus0T pneumonia in nearly 50% of cases (Anevlavis and Bouros, 2010, Sharma et al., 2007), Haemophilus influenzae (20%), Chlamydophila pneumoniae (13%), Mycoplasma pneumoniae (3%) (Sharma et al., 2007), Legionella pneumophila, and anaerobic species (Ostergaard and Andersen, 1993), all of which normally reside on the oropharyngeal mucosa. Hence, dental plaque seems to be a logical source of these bacteria, especially in patients with periodontal disease (Scannapieco and Mylotte, 1996). In contrast, nosocomial pneumonia is usually caused by bacteria that enter the oropharynx from the environment (Scannapieco, 1999) including gram-negative bacilli (e.g., Enterobacteriaceae such as Escherichia coli, Klebsiella pneumoniae, Serratia species, and Enterobacter species), Pseudomonas aeruginosa, and Staphylococcus aureus being the most prevalent (Bentley, 1984, Rosenthal and Tager, 1975, Amin et al., 2004). Poor oral hygiene, recumbence, and debilitation as well as high medication intake among hospitalized or nursing home patients result in modified salivary flow, swallowing disorders, and further increased aspiration of oral flora, impaired pulmonary clearance of aspirated material, and reduced mucosal immunity in the lung (Brown, 2007). Moreover, antibiotic treatments lead to an overgrowth of potentially pathogenic, often resistant organisms in the oral flora (Brown, 2007). In addition, the term ventilator acquired pneumonia (VAP) describes pneumonia occurring in patients having mechanical interventions such as nasogastric or endotracheal tubes.

Nearly one third of pneumonia cases in adults are induced by viruses, commonly caused by rhinoviruses and coronaviruses (Ruuskanen et al., 2011) as well as influenza virus, respiratory syncytial virus (RSV), adenovirus, and parainfluenza (Figueiredo, 2009).

1.2.4. Association between Lung Function and Systemic Diseases Lung function impairment has been found to be associated with insulin resistance (Lazarus et al., 1998, Lawlor et al., 2004), type 2 diabetes mellitus (Lawlor et al., 2004, Yeh et al., 2005), and cardiovascular diseases (Engstrom et al., 2003, Schroeder et al., 2003).

Lung function parameters such as forced expiratory volume in one second (FEVR1R) or forced vital capacity (FVC) were independently associated with general, pulmonary, and cardiovascular mortality and morbidity (Sin et al., 2005, Gray et al., 2010, Hole et al., 1996, Schunemann et al., 2000, Miller et al., 2009, Ferrie et al., 2009, Mannino et al., 2003) representing an extensive prognostic tool in general populations (Schunemann et al., 2000).

1.3. Evidence from Clinical and Epidemiological Studies 1.3.1. Association between Periodontitis and Pulmonary Dysfunction assessed by Spirometry Data At the time of writing, there is a paucity of high-quality research examining the etiological link between periodontal disease and pulmonary function. We identified seven studies (Scannapieco and Ho, 2001, Hyman and Reid, 2004, Hayes et al., 1998, Garcia et al., 2001b, Deo et al., 2009, Wang et al., 2009, Katancik et al., 2005) in which information regarding pulmonary function or severity of chronic obstructive pulmonary disease

(COPD) was estimated using spirometry data (FEVR1R, FEVR1R/FVC ratio or both). However, those studies merely refer to COPD, instead of providing a comprehensive evaluation of lung function. An overview of these seven trials is given in Table 1. Subjects with a history of COPD had higher mean CAL than subjects without COPD, with a tendency toward diminished lung function (Scannapieco and Ho, 2001). Another cross- sectional analysis of NHANES III data (Hyman and Reid, 2004) found an association between CAL and COPD in current smokers, but not in non-smokers or former smokers. However, there was no significant association when using PD as a measure of current periodontal disease, regardless of smoking. Moreover, Katancik et al. (Katancik et al., 2005) studied 860 subjects aged 70-79 years and pointed out that CAL was significantly better in those with normal lung function than in those with airway obstruction. More recently, two case control studies (Deo et al., 2009, Wang et al., 2009) showed similar results concluding a trend of diminished pulmonary function as the amount of AL

21 increased. Furthermore, periodontal disease, measured either as PD or radiographic alveolar bone loss, was related to the longitudinal decline in spirometric lung volumes (Garcia et al., 2001b, Hayes et al., 1998). More detailed information on each study is given below.

Using cross-sectional data from the National Health and Nutrition Examination Survey III (NHANES III), Scannapieco and Ho (Scannapieco and Ho, 2001) assessed the association between periodontal disease and COPD. The study population consisted of 13,792 men and women, aged 20 years and older, with at least six natural teeth. Subjects with a history of bronchitis, emphysema, or both were considered as having COPD.

Additionally, data from pulmonary function tests were available (ratio of FEVR1R/FVC). Oral health status was assessed using the DMFS/T index, gingival bleeding, gingival recession, PD, and CAL. Periodontal disease was defined as mean periodontal CAL of ≥3 mm. Periodontitis was significantly associated with COPD. Subjects having ≥3 mm mean CAL had an odds ratio of 1.45 (95% CI=1.02, 2.05) compared to subjects with <3 mm mean CAL. They revealed that subjects with a history of COPD had a higher mean CAL (1.48 ±1.35 mm versus 1.17 ±1.09 mm, p=0.0001) and were more likely to have significantly worse oral health status (6.6 ±4.7 versus 6.5 ±4.7) than subjects without respiratory disease. Moreover, lung function diminished as CAL increased.

To verify the results published by Scannapieco and Ho, another cross-sectional analysis of NHANES III data (Hyman and Reid, 2004) was conducted using mean CAL and mean PD as the measures of periodontal disease. The pulmonary evaluation was conducted by spirometry, calculating the ratio of forced expiratory volume after 1 second and forced vital

capacity (FEVR1R/FVC). Classification of COPD was based on the Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometry guidelines (Gomez and Rodriguez-

Roisin, 2002). Predicted FEVR1R was determined using Morris's equations (Morris et al., 1971). The study included 7,625 male and female participants. Smoking was considered as an effect modifier. Subjects with (without) COPD were on average 62.3 ±14.1 (47.4 ±14.2) years old. CAL was weakly associated with COPD in the dentate population aged 30 years and older, who were current smokers and had ≥4 mm mean AL (OR=3.71; 95% CI=1.74, 7.89). However, there was neither a significant association in former nor never smokers. Using mean PD, no significant associations were found regardless of smoking.

Katancik et al. (Katancik et al., 2005) evaluated data from a cross-sectional study of a subset of 860 community-dwelling, well-functioning elderly, aged 70 to 79), who were examined within the Health, Aging, and Body Composition (Health ABC) Study, USA. Periodontal evaluations included indices of CAL, PD, gingival index, and plaque index.

Subjects’ lung function was estimated by spirometry calculating the FEVR1R/FVC ratio and

then using the percent of predicted FEVR1R to categorize severity. Analyses were stratified by both current smoking status and lifetime cigarette exposure (pack years). Specifically among former smokers, a significant association between the severity of periodontal disease and airway obstruction (CAL: p=0.0003, gingival index: p=0.0361) was found. Within this group, mean CAL (3.53 ±0.47) and mean gingival index (1.36 ±0.20) were much worse in participants with severe obstructive disease than in participants with normal pulmonary function (CAL: 2.33 ±0.77, gingival index: 0.93 ±0.03). No such relation was evident in never smokers. The study had some limitations. First, the very old age of the participants could actually be the reason for the decrease in lung function. However, analyses were adjusted for age minimizing the possibility for residual confounding by age. Residual confounding due to lack of adjustment for socioeconomic status (e.g., occupational status, income) cannot be excluded either. Furthermore, not adjusting for smoking status (current or former smoker) ignored healing effects on lung function after smoking cessation. With the same pack year value as a current smoker, lung function of a former smoker might be comparable to never smokers, indicating the importance of different effects on lung function after rehabilitation. Finally, the study cohort was highly selective indicating that selection bias occurred. This exacerbated generalization of the results to the general population.

More recently, two case control studies confirmed the trend of diminished pulmonary function with severity of CAL (Deo et al., 2009, Wang et al., 2009). Both concluded that improved oral health may lower the severity of lung infection in susceptible individuals. From two hospitals, 150 COPD patients (cases) with a mean age of 41.4 ±7.5 years and 50 controls with normal pulmonary function (age 43.6 ±5.5 years) were recruited (Deo et al., 2009). Study participants were ≥20 years old and had ≥6 natural teeth. Periodontal evaluations included measurements of PD, CAL, and oral hygiene index (OHI), which comprises the amount of debris and calculus. COPD patients had significantly higher mean CAL and a higher mean OHI than healthy controls. The risk for COPD was significantly elevated for severe CAL compared with healthy controls. Also, a diminishing of lung function was found to be significantly associated with increasing severity of CAL.

Wang et al. (Wang et al., 2009) studied 306 patients with COPD (test group) and 328 patients without COPD (control group) recruited from eight Beijing hospitals (China). Subjects were 30 years of age and older, with more than 15 teeth. COPD patients had a mean age of 63.9 ±9.8 years; controls were on average 63.3 ±9.0 years old. Periodontal health was assessed by measuring radiographic ABL, CAL, PD, and number of teeth. Oral hygiene, dental care, and oral health knowledge were obtained from validated questionnaires. Poor oral health knowledge (p<0.0001 among never smokers, p=0.02 among former smokers, and p=0.04 among current smokers), oral hygiene (e.g., inappropriate tooth brushing method: p=0.03 among never smokers), and lower regular supragingival scaling (p=0.03 among never smokers and p<0.0001 among former smokers) were significantly related to an increased risk of COPD. COPD patients had fewer teeth and more plaque compared to healthy controls.

In related research, the longitudinal VA Normative Aging Study (Hayes et al., 1998) aimed to determine the risk of developing chronic obstructive lung disease in individuals with a history of periodontal disease. Chronic marginal periodontitis was assessed by radiographic measures of ABL. Mean whole-mouth ABL scores were calculated. Of the 1,118 dentate male veterans, who were medically healthy and free of COPD at study baseline, 261 subjects developed COPD over the 25-year follow-up period. After controlling for confounders, subjects in the quintile with the worst ABL (mean ABL >20% per site) had a significantly higher risk of developing COPD (OR=1.8; 95% CI=1.3, 2.5) compared to the quintile with the lowest ABL. Using ABL as a continuous variable, the relative risk for COPD associated with whole-mouth ABL was 1.6 (95% CI=1.2, 2.0). Furthermore, periodontal disease was significantly related to the longitudinal decline in spirometric lung volumes. Nevertheless, this study had some limitations. ABL was measured at baseline only. Analyses were not adjusted for socioeconomic status introducing residual confounding. Furthermore, stratification on smoking might have been useful, since smoking influences lung function assessments. Additionally, only males were included limiting possible conclusions for females.

Garcia and co-workers (Garcia et al., 2001b) extended the earlier work of Hayes et al. (Hayes et al., 1998) to a 30-year follow-up period. In addition to measuring radiographic alveolar bone loss, mean whole-mouth score for clinical periodontal pocketing was used to additionally assess periodontal disease. Moreover, to more closely investigate the interaction among tobacco consumption, periodontal disease, and COPD, logistic

24 regression analyses were stratified by smoking status: current, former, and never smokers. All analyses were adjusted for age, height, education, and alcohol consumption. Of the 1,112 medically healthy dentate men, 279 developed COPD. They found that worse periodontal status was associated with a significantly elevated risk of incident COPD in current smokers (for ABL: OR=1.63; 95% CI=1.20, 2.21) but not in subjects who had never smoked indicating the possibility of residual confounding by tobacco smoking. Furthermore, it was recognized that periodontal disease, measured either as radiographic alveolar bone loss or probing depth, was significantly related to the longitudinal decline in

spirometric lung volumes (FEVR1R ≤65% of predicted volume). Strengths of this study included the long follow-up period, the high number of subjects, and the prospective study design. Limitations included residual confounding by socioeconomic status, for example. However, only males were included in the studies limiting possible conclusions for females.

All seven trials reported similar results, which are summarized in Table 1. However, each study had certain limitations, some of which have been mentioned above. Only two studies were longitudinal in design (Garcia et al., 2001b, Hayes et al., 1998). Three studies were cross-sectional (Scannapieco and Ho, 2001, Hyman and Reid, 2004, Katancik et al., 2005), known to preclude the establishment of a definite cause and effect relationship between exposure and outcome variable. Two studies were case-control studies (Deo et al., 2009, Wang et al., 2009). In four of the seven analyses, periodontal disease was measured by clinical examination (CAL, PD, and oral hygiene index) (Deo et al., 2009, Hyman and Reid, 2004, Katancik et al., 2005, Scannapieco and Ho, 2001) that give surrogate information on the current activity of the disease. In contrast, radiographic ABL (Garcia et al., 2001b, Hayes et al., 1998, Wang et al., 2009) reflects the cumulative history of the disease and may therefore be a better variable to quantify life-time effects of periodontitis on systemic diseases (Renvert et al., 2004). Moreover, it has been implied (Beck and Offenbacher, 2001) that measurements that combine both exposure and bacterial activity, acute-phase inflammatory, or reactive mediators should be used. In addition, using only partial recording protocols (e.g., half-mouth, two instead of six measurement sites) measurements of periodontal health may underestimate prevalence and true extent of the disease (Susin et al., 2005, Beck et al., 1990, Beck et al., 1999), which all studies attempted to limit through the use of mean variables. However, an increased misclassification of periodontitis might affect risk estimates such that risk estimates are shifted toward the null effect. Also, all studies have used very diverse and imprecise definitions of COPD.

Furthermore, two studies (Hayes et al., 1998, Scannapieco and Ho, 2001) differed from the others in the way smoking was addressed. Smoking was only considered as a confounder. Because smoking could be an important effect modifier, some studies additionally stratified by smoking status (Garcia et al., 2001b, Hyman and Reid, 2004, Katancik et al., 2005, Wang et al., 2009). Hyman and Reid (Hyman and Reid, 2004) cautioned that much of the observed results may actually reflect the exposure to smoking resulting in residual confounding. Nevertheless, the results of these trials are consistent and support the hypothesis that periodontal disease is related to systemic health (Scannapieco and Mylotte, 1996, Beck and Offenbacher, 2001, Li et al., 2000, Stamm, 1998, Lopez et al., 2002b, Lopez et al., 2002a).

Table 1. Data of seven selected studies: association between periodontitis and pulmonary dysfunction assessed by spirometry. Reference (Scannapieco and Ho, 2001) (Hyman and Reid, 2004) (Katancik et al., 2005) (Deo et al., 2009) (Wang et al., 2009) (Hayes et al., 1998) (Garcia et al., 2001b) Study design cross-sectional cross-sectional cross-sectional case-control case-control 25 year follow-up 30 year follow-up Data base National Health and Nutrition Examination Survey Health, Aging, and 2 hospitals in 8 Beijing hospitals, VA Dental Longitudinal Study and Normative (NHANES) III, USA Body Composition Sawangi (M), China Aging Study, USA (Health ABC) Study, Wardha, India USA Sample size 13,792 7625 860 200 634 1118 1112 (810 with COPD) (993 with COPD) (75 with COPD) (150 with COPD) (306 with COPD) (261 with COPD) (279 with COPD) Age ≥20 years ≥30 years 70-79 years ≥20 years ≥30 years n.a. n.a. Mean age COPD: 51.2 ±17.9 COPD: 62.3 ±14.1 74.6 ±2.8 COPD: 41.4 ±7.5 COPD: 63.9 ±9.8 COPD: 45.1 ±9.7 n.a. ±SD (years) no COPD: 43.9 ±17.7 no COPD: 47.4 ±14.2 control: 43.6 ±5.5 control: 63.3 ±9.0 no COPD: 42.2 ±9.1 Sex both sexes both sexes both sexes both sexes both sexes all males all males Periodontal AL, PD, GB, gingival AL, PD AL, PD, GI, PI AL, PD, OHI ABL, AL, PD, NoT, ABL ABL, PD disease recession, DMFS/T OH, oral health definition knowledge

COPD FEVR1R/FVC, history of FEVR1R/FVC, predicted FEVR1R/FVC, predicted FEVR1R/FVC, FEVR1R/FVC, FEVR1 R≤65% of FEVR1 R≤65% of

definition bronchitis/emphysema FEVR1 R(COPD severity FEVR1 R(COPD severity predicted FEVR1R predicted FEVR1 predicted volume predicted volume

was based on GOLD based on ATS (mild: FEVR1 R≤80%, R(COPD severity was

guidelines (Global guidelines (American FEVR1R/FVC ≥70%; based on GOLD Initiative for Chronic Thoracic Society, moderate: guidelines (Global

Obstructive Lung 1995)) FEVR1R=50%-80%, Initiative for Chronic

Disease, 2010)) FEVR1R/FVC ≥70%; Obstructive Lung

severe: FEVR1 R≤50%, Disease, 2010)) with respiratory

failure: FEVR1 R≤30%,

FEVR1R/FVC ≤60%) To be continued.

Continued Table 1. Reference (Scannapieco and Ho, 2001) (Hyman and Reid, 2004) (Katancik et al., 2005) (Deo et al., 2009) (Wang et al., 2009) (Hayes et al., 1998) (Garcia et al., 2001b) Confounders age, sex, race, education, age, sex, race, BMI, age, sex, race, age, sex, education, age, sex, BMI, age, height, age, height, education, income, pack years income, years smoked, education, pack years income, number of current/former/never education, pack pack years smoking, smoking, alcohol current/former/never smoking, cigarettes/day, smoker years smoking, current/former/never consumption, diabetes smoker, history of heart current/former/never smoker/non-smoker alcohol consumption smoker, alcohol status, dental visits attack/hypertension, smoker, recent consumption dental visits antibiotic use, steroid use Main results subjects with history of current smokers with ≥4 significant association COPD subjects had COPD subjects had 261 developed 279 developed COPD; COPD had more mean AL mm mean AL had an between periodontal higher mean AL and fewer teeth and a COPD; ABL status subjects with the than subjects without (1.48 OR of 3.7 for all COPD health and airway higher mean OHI higher plaque index at baseline was an quintile having the ±1.35 mm versus 1.17 ±1.09 (6.2 for moderate, obstruction in former than those without than controls; poor independent risk worst periodontal mm, p=0.0001); with severe COPD); no smokers COPD; risk for periodontal health, factor for COPD with health at baseline had increasing AL, lung function association among COPD was elevated dental care, and oral subjects in the worst greater risk for appeared to diminish former/non-smokers; no for severe AL; lung health knowledge population quintile at developing COPD in association at any level function diminished were significantly significantly higher current smokers but of PD regardless of as AL increased related to COPD risk risk (OR=1.8; 95% not in never smokers smoking Cl=1.3, 2.5) ABL-alveolar bone loss; AL-attachment loss; BMI-body mass index; CI-confidence interval; COPD-chronic obstructive pulmonary disease; DMFS/T-number of decayed (D),

missing (M), and filled (F) surfaces (S)/teeth (T); FEVR1R-forced expiratory volume in one second; FEVR1R/FVC-forced expiratory volume in one second to forced vital capacity ratio; GB-gingival bleeding; GI-gingival index; GOLD-Global Initiative for Chronic Obstructive Lung Disease; n.a.-not annotated; OH-oral hygiene; OHI-Oral Hygiene Index; OR-odds ratio; PD-probing depth; PI-plaque index; SD-standard deviation

28 28

1.3.2. Association between Oral Infections and Pulmonary Outcomes Searching for clinical trials on a broader topic of oral infections and risk of pulmonary outcomes, we identified eight more studies. They will be described in more detail below. A possible correlation between oral and periodontal health respectively, and respiratory diseases has been proposed by several studies (Leuckfeld et al., 2008, Scannapieco et al., 1998, Hamalainen et al., 2004, Sharma and Shamsuddin, 2011). Three other studies provided evidence that aspiration of pharyngeal bacteria into the lower respiratory tract caused pneumonia (Didilescu et al., 2005, Scannapieco et al., 1992, Sumi et al., 2007). Moreover, a randomized controlled trial evaluated the effect of oral hygiene care including pharyngeal application of povidone iodine on the reduction of potential respiratory pathogens in the oral cavity of nursing home residents (Yoneyama et al., 2002).

In 1998 Scannapieco et al. (Scannapieco et al., 1998) conducted a cross-sectional study with NHANES-I data aiming to assess the association between respiratory diseases and poor oral health. Of the NHANES-I population, aged 25 to 74 years, 386 subjects, who were included in this study, reported a respiratory condition that was subsequently examined by a physician. Their respiratory illness was categorized into chronic respiratory disease, defined as having bronchitis or emphysema or an acute respiratory disease (e.g., influenza, pneumonia, acute bronchitis) and then compared to those not having a respiratory disease. Lung volumes were not measured. Oral health was assessed by determination of a simplified oral hygiene index (amount of debris and calculus), number of teeth, number of decayed teeth, and periodontal index (categorized as having neither inflammation nor destruction of supportive tissue, gingivitis, gingivitis with pocket formation, and finally advanced destruction with loss of masticatory function). Compared to those not having any respiratory disease (n=193), the 41 subjects with a confirmed chronic respiratory disease were more likely to have significantly greater oral hygiene index scores (p=0.04). Subjects with the worst OHI score were 4.5 times more likely to have a chronic respiratory disease than those with an OHI of zero. However, there was no significant association between i) poor oral health and an acute respiratory disease and ii) periodontal disease and any respiratory disease. Thus, subjects with poor oral health may have a greater risk for colonization by respiratory pathogens and therefore a greater risk for respiratory infection. The study had several limitations. Misclassification bias occurred when assessing periodontal disease or the possibility of an inaccurate report of respiratory disease by the physician, because neither laboratory nor radiographic proofs were necessary. Moreover, the sample size of diseased people was very small.

In a Norwegian retrospective study a substantial association between periodontitis and COPD was found (Leuckfeld et al., 2008). A group of 130 subjects with severe symptoms of COPD and poor lung function were compared with 50 non-COPD controls. Of the 80 patients with alveolar bone loss data, 43.8% had a mean alveolar bone loss ≥4 mm compared to 7.3% in the non-COPD group, demonstrating that chronic marginal periodontitis has a higher frequency in patients with severe COPD. Former smoking COPD patients, characterized as being older (54.9 ±4.9 years versus 47.0 ±9.8 years, p<0.001) and as having a higher number of pack years (26.9 ±14.5 versus 13.7 ±11.9, p<0.001), had more mean alveolar bone loss (4.0 ±1.4 mm versus 2.7 ±1.1 mm, p=0.001), more frequent furcation involvement (16.5% versus 2.3%, p=0.019), and fewer teeth (15.5 ±8.7 versus 22.6 ±6.4, p<0.001) than non-COPD former smokers. Of COPD patients, 11.5% were edentulous compared to 2.0% non-COPD subjects. The difference in mean alveolar bone level remained statistically significant after adjusting for age, sex, and pack years of smoking.

One recent Indian case-control study (Sharma and Shamsuddin, 2011) compared 100 hospitalized patients to 100 age-, sex-, and race-matched systemically healthy controls. Subjects were between 20 and 60 years old, with a minimum of 20 natural teeth. Respiratory disease was diagnosed as having acute respiratory disease (e.g., pneumonia, acute bronchitis, lung abscess) or exacerbation of chronic obstructive pulmonary disease, which includes chronic bronchitis and emphysema. Oral health status was assessed including probing depth, attachment loss, gingival index, plaque index, and oral health index. An association between respiratory and periodontal disease was found. Compared to the healthy group, hospitalized patients had significantly greater levels of mean probing depth (2.23 ±0.61 mm versus 1.69 ±0.43 mm, p<0.001), mean attachment loss (3.14 ±0.53 mm versus 2.44 ±0.53 mm), mean gingival index (1.79 ±0.40 versus 1.22 ±0.63), mean plaque index (1.83 ±0.39 versus 1.27 ±0.20), and mean oral health index (3.94 ±0.64 versus 2.82 ±0.53). Results presented in this study differed from Scannapieco and Ho (Scannapieco and Ho, 2001), who found no significant association between gingival bleeding and respiratory disease. Sharma and Shamsuddin’s study was limited in several ways. Results might be biased by smoking. However, there were no former smokers considered in the analyses.

Hamalainen et al. (Hamalainen et al., 2004) stated that periodontal infections and complete prostheses might be reservoirs for pathogens that can be aspirated into the lungs inducing pneumonia. At the beginning of the study in 1990, periodontal health status

(number of teeth, probing depth, gingival bleeding, and calculus) and pulmonary function

(FEVR1R) of the 203 80-year-old Finnish participants were assessed. Edentulous men had

the lowest FEVR1R values. Five years later, another evaluation of lung function was estimated of the 88 survivors. Subjects with poor periodontal status and complete prostheses had a significant decline in lung function (-9.4%), while those without periodontal disease showed no decrease (+1.0%, p=0.006).

The following studies put special attention on ill or elderly patients, studying the role of dental plaque in susceptible individuals as a reservoir for bacteria known to cause nosocomial pneumonia. The study of Didilescu et al. (Didilescu et al., 2005), conducted in Romania, examined the supragingival plaque of 34 hospitalized patients with chronic lung disease for selected respiratory and oral pathogens and subsequently compared the data with that of 31 lung- healthy outpatients. The two groups differed in their age. Lung-diseased patients ranging in age from 21 to 79 years (mean age of 58.3 ±12.4 years) compared to reference subjects with a mean age of 50.5 ±14.1 years (age 20-70 years). Subjects with tuberculosis and less than 20 teeth were excluded from the analyses. In 29 of the 34 hospitalized patients (85.3%), and in 12 of the 31 outpatients (38.7%, p<0.001), respiratory pathogens were detected in the supragingival plaque. Hospitalized chronic lung-diseased (HCLD) patients also had a higher prevalence of plaque scores than lung-healthy outpatients (2.1 ±0.7 versus 1.4 ±0.7, p<0.001). However, no significant correlation between oral hygiene and number of respiratory pathogens was found. The authors indicated that dental plaque in susceptible individuals might be a reservoir for bacteria known to cause nosocomial pneumonia. Within the HCLD group, 23 smokers had more plaque than 11 never smokers (2.3 versus 1.6, p=0.012). In HCLD patients, who were hospitalized for more than ten days, a second plaque sample was obtained demonstrating an increase in the colonization with respiratory pathogens and a decrease in the number of oral pathogens during hospitalization.

Similar results were reported by Scannapieco et al. (Scannapieco et al., 1992), who also found higher mean plaque scores in intensive care unit (ICU) patients (mean age of 63.6 ±1.6 years, range 36 to 78 years) compared to the control group (mean age of 62.4 ±1.9 years, range 33 to 77 years) (mean plaque score: 1.9 ±0.2 versus 1.4 ±0.1, p<0.005). Colonization of plaque or mucosa by respiratory pathogens was detected in 22 of 34 ICU patients (65%) compared to 4 of 25 (16%) control subjects (p<0.005). They also demonstrated that the presence and amount of dental plaque was not associated with respiratory pathogen colonization either. Furthermore, Scannapieco et al. found that the

31 use of antibiotics among ICU patients influenced the oral colonization by respiratory pathogens.

In this context, Sumi et al. (Sumi et al., 2007) examined the dental plaque of 138 dependent elderly between the ages of 65 and 91 years (mean age of 73.9 ±9.6 years) identifying twenty-one species of microorganisms. The functionally impaired elderly patients, who had at least six natural teeth and who required oral care by their caregivers, showed high scores of dental plaque with poor oral hygiene. They detected potential respiratory pathogens in 89 participants (64.5%), indicating that dental plaques must be a potential reservoir for bacteria that commonly cause respiratory disease leading to aspiration pneumonia. Due to the use of many different medications, including antibiotics, the flora might have changed in the elderly, suggesting that their denture plaque might be different from that of healthy subjects.

Yoneyama et al. (Yoneyama et al., 2002) randomly assigned 366 residents of eleven long-term care facilities in Japan to an oral care group (mean age of 82.0 ±7.8 years, F/M sex ratio: 148/36) and a no oral care group (mean age of 82.1 ±7.5 years, F/M sex ratio: 145/37). Oral care was provided by nurses or caregivers, who brushed patients’ teeth after each meal. In some cases, patients were additionally swabbed with povidone iodine (1%). Furthermore, professional care by dentists or dental hygienists was performed once a week. The placebo group performed tooth brushing by themselves once a day or irregularly. None of them requested oral care from their caregivers. The risk of new pneumonia (RR=1.67; 95% CI=1.01, 2.75; p<0.05), febrile days (temperature >37.8°C) (RR=2.45; 95% CI=1.77, 3.40; p<0.01), or death from pneumonia (RR=2.40; 95% CI=1.54, 3.74; p<0.01) was significantly higher in 182 patients not receiving oral care over the two-year-follow-up period, compared to the 184 patients, who received oral care. New pneumonia was diagnosed in 34 (19%) elderly receiving no oral care and in 21 oral care patients (11%). 54 (29%) patients without oral care suffered from febrile days compared to 27 (15%) patients in the non-oral care group. Lastly, 30 (16%) placebo patients died from pneumonia, but only 14 (7%) in the oral care group, suggesting that professional oral health care is useful for the prevention of bacterial pneumonia by reducing the numbers of potential respiratory pathogens in the oral cavity of the very old patients.

1.4. Aims of the study Thus, the major aim of this study was to further clarify the association between periodontal disease and lung function based on a highly standardized setting of comprehensive lung function test parameters within the population-based Study of Health in Pomerania (SHIP). In addition to spirometry, body plethysmography, diffusing capacity for carbon monoxide, and helium dilution were applied to gain more physiological insight. We hypothesise that periodontal diseases are associated with increased airflow limitation, restricted lung volumes, and reduced diffusing capacity after adjustment for potential interfering factors. We further evaluated i) the strength of confounding by smoking, height, and educational status and ii) the role of systemic inflammation as an intermediate factor.

2. Material and Methods 2.1. Study of Health in Pomerania (SHIP) The Study of Health in Pomerania (SHIP) is a population-based, epidemiological study in the Northeast of Germany including the cities Greifswald, Stralsund, Anklam and 29 communities (see Figure 9). The main purpose of SHIP was to estimate the prevalence of diseases, identify potential risk factors in this defined region, and examine the particular living situation after the reunification of East and West Germany (Hensel et al., 2003). Furthermore, SHIP was designed to analyse the relationship between dental, medical, social, and environmentally and behaviourally determined health factors. Details on the study design are given elsewhere (John et al., 2001, Volzke et al., 2011). The study was approved by the local Institutional Review Board by the Ethics Committee. All participants gave their written, informed consent.

Fig. 9: Catchment area of SHIP.

Briefly, a two-stage cluster sampling method adopted from the World Health Organization (WHO) MONICA Project in Augsburg, Germany (Keil et al., 1988) was adopted. From the entire population of 212,157 inhabitants, 7008 adults aged 20-79 years with German citizenship and main residency within the target region were randomly selected. Within each of the twelve 5-year-strata, 292 females and 292 males were sampled. After removing 746 subjects (126 died, 615 moved away, five had severe medical problems), 6262 inhabitants were invited. Of those, 4308 individuals (response 68.8%) participated in the baseline study (SHIP-0) with examinations held between 1997 and 2001.

Follow-up examinations (SHIP-1) were performed 5 years after and including 3300 subjects aged 25-85 years, equating to a response of 83.6% (without migrated or deceased people) (Haring et al., 2009). Of these, 1809 subjects (885 men, 924 women) participated in a sub-study including body plethysmography, spirometry, helium dilution, and diffusing capacity for carbon monoxide. The mean time interval between core examination of SHIP-1 and lung function examination was 0.6 months (range 0.0-2.5 months). Exclusion criteria comprised missing lung function data (n=9), missing dental examinations (n=8), regular intake of non-steroidal anti-rheumatics or steroids (n=274), hs-CRP >10 mg/l (n=42) indicating acute and active infection, systemic inflammatory processes or physical trauma (Spitzer et al., 2010), and missing confounder data (n=11). The total sample size was 1465.

2.2. Data Assessment 2.2.1. Periodontal Health Assessment Here, we describe the variables assessing the periodontal disease status. Periodontal disease was assessed by probing depth (PD), clinical attachment loss (CAL), and tooth count. Probing depth reflects the extent of current disease. Probing depth equals the distance from the free gingival margin to the bottom of the periodontal pocket (see Figure 10). Clinical attachment loss reflects the extent of cumulative disease. CAL equals the distance from the cemento-enamel junction (CEJ) to the pocket base. If recession was present at the examined site, clinical attachment loss was directly measured. In the case of subgingival located CEJ, attachment loss was calculated as probing depth minus the distance between free gingival margin (FGM) and CEJ. CAL was not recorded when CEJ determination was indistinct (e.g., wedge-shaped defects, fillings, crown margins). Measurements were taken alternately on the right or left side using a PCP2 manual probe (Hu-Friedy Co., Chicago, IL, USA) at four sites (midbuccal, midpalatinal/midlingual, mesiobuccal, and distobuccal). Third molars were excluded. Measurements were mathematically rounded to the closest millimetre. For each subject the mean CAL/PD over all measurement sites, the percentage of sites per mouth with CAL/PD ≥4 mm, and the number of missing teeth (NoMT) were determined. Data collection of bleeding on probing (BOP) and presence of supragingival plaque was subsequently carried out, half-mouth at the central incisor, canine, and first molar (four sites) and expressed as the percentage of positive sites. In case of missing teeth, the next distal tooth was assessed.

Biannual calibration exercises were conducted on subjects not associated with the study yielding intra-class correlations between 0.70-0.89 per examiner and an interrater correlation of 0.90 for attachment level measurements.

Healthy Diseased Gesund Krank

GHG

RezessionRecession ClinicalA attachment(klinischer ProbingS Attachmen losst- verlustverlust) ) depth(Sondieru(Sondierun - ngsgstiefe)

AlveolarknochenAlveolar bone

ParodontalesPeriodontal ligament Ligament

Fig. 10: Graphical representation of gingiva height (GH), gingival recession, probing depth, and clinical attachment loss. Further to this, the use of the periodontal probe PCP2 was graphically presented. Adapted from (Gätke, 2010).

2.2.2. Assessment of Lung Function Variables Lung function examinations were performed using a body plethysmograph equipped with a pneumotachograph (VIASYS Healthcare, MasterSceen Body/Diff., JAEGER, Hoechberg, Germany), thus meeting the American Thoracic Society (ATS) criteria (Nelson et al., 1990) and recommendations of the European Respiratory Society (ERS) (Miller et al., 2005, Wanger et al., 2005). Experienced, trained, and certified physicians demonstrated the required manoeuvres beforehand and encouraged and supervised participants throughout the tests. All measures were conducted in sitting position, wearing nose clips, and using a sealed mouthpiece with a multiplicity of tiny fins. Lung function variables were measured continuously throughout baseline breathing and forced manoeuvres, recording pressure-

flow and pressure-pressure diagrams to obtain total airway resistance (R RtotR) (Glaser et al., 2011). After spontaneous breathing against a sealed mouthpiece, followed by deep

36 expiration, expiratory reserve volume (ERV) and inspiratory vital capacity (RslowRVC) were

measured. Functional residual capacity (FRCRplethR) was obtained during expiration, again by breathing against a shutter (Glaser et al., 2011). Residual volume (RV) was computed

by subtracting ERV from FRCRplethR, and total lung capacity (TLC) by adding RV and RslowRVC.

Ratios of FEVR1R/FVC (%) and RV/TLC (%) were calculated.

Forced vital capacity (FVC), forced expiratory volume in one second (FEVR1R), and maximal expiratory flow at 25% of FVC (MEF25) were determined by spirometry in all subjects who did not smoke or use inhalers one hour prior to the test. Study volunteers had to achieve at least three forced spirometric manoeuvres to obtain a minimum of two acceptable and reproducible values, according to ATS-ERS guidelines (e.g., FRC variability ≤5%; RAW

variability ≤5%; FEVR1R, RslowRVC, FVC variability <150 ml) (Miller et al., 2005, Wanger et al., 2005). FVC measures the volume of air that can forcibly be blown out after full inspiration.

FEVR1R measures the volume of air expelled in the first second during the FVC manoeuvre (both measured in litres) (Hayes and Kraman, 2009).

After a break of at least 3 minutes, alveolar volume (VRAR) using helium dilution (Wanger et al., 2005) and diffusing capacity for carbon monoxide as corrected for current

haemoglobin and VRA R(TLCOc-VRAR) was measured by a single breath manoeuvre (Macintyre et al., 2005).

2.2.3. Assessment of Covariates Height and weight were determined using calibrated scales and body mass index (BMI) was calculated. Waist circumference (WC) was measured to the nearest 0.1 cm of the narrowest part of the waist midway between the lower rib margin and the iliac crest in the horizontal plane in standing position. Previous history of diseases was based on self-reported physician’s diagnosis. Use of medication influencing lung function was recorded according to the Anatomical Therapeutic Chemical (ATC) code (World Health Organization, 2008). The following drugs were considered: steroids (ATC A11ED, A14A, D07, G01B, H02, J01XC, M01BA, N02CB, R01AD, S01BA, S01BB, S01BC, S01BX, S01CA, S01CB, S02B, S02C, S03B, S03C) and non-steroidal anti-rheumatics (ATC M01). Diabetes mellitus was defined as self-reported physician’s diagnosis or intake of anti-diabetic medication (ATC A10) in SHIP-0 or as self- reported physician’s diagnosis or intake of anti-diabetic medication (ATC A10) between baseline and follow-up (SHIP-1). Asthma and chronic obstructive pulmonary disease (COPD) were defined using self-reported physician’s diagnosis. Information on socioeconomic status and behavioural characteristics were retrieved from the computer-guided interview. School education was categorised based on the East German three-level school system (<10, 10, >10 years). High physical activity was

37 classified as at least 2-3 hours per week. Participants were categorised into never smokers, occasional smokers, former smokers with <20 or ≥20 pack years, and current smokers with <20 or ≥20 pack years (Boulet et al., 2008).

2.2.4. Blood Sampling For laboratory examinations, non-fasting blood samples were obtained from the cubital vein in supine position. High sensitivity C-reactive protein (hs-CRP; Dade Behring, Eschborn, Germany), plasma fibrinogen according to Clauss (Electra 1600 analyser, Instrumentation Laboratory, Barcelona, Spain), and leukocyte counts (Coulter® MaxM™, Coulter Electronics, Miami, USA) were determined. To adjust diffusing capacity for the potential influence of blood haemoglobin, haemoglobin concentrations from each subject were determined on the same day that lung function measurements took place. Hence, a sample of capillary blood was taken from the ear lobe and immediately transferred to the blood gas and haemoglobin analyser (Radiometer ABL 510, Radiometer, Copenhagen, Denmark).

2.3. Statistical Analyses th th Continuous data were expressed as median (25P P; 75P P quartile). Categorical data were presented as numbers (percentages). Differences between subjects with low and high mean CAL were tested using Mann-Whitney-U-tests (continuous data) or Chi square tests (categorical data). Linear regression models were used to assess the association between periodontal and pulmonary variables. First-degree fractional polynomials (FPs) were applied to explore and graph nonlinear associations (Royston et al., 1999). To assess goodness of fit, 2 deviances were compared using χP P distributions with according degrees of freedom (Royston et al., 1999). If none of the FP models fitted the data significantly better than the linear model, linear regression was applied. Starting with the exposure-effect estimate for mean CAL from the model including age, sex, and time between core and pulmonary examination, confounders making the most difference to the estimate were added one by one (forward selection) (Greenland and Rothman, 2008). A change in estimate of >5% was considered important. Final models were adjusted for age, sex, time between core and pulmonary examination, diabetic status, waist circumference, presence of asthma, physical activity, smoking status, school education, and height (as age- and sex-specific quintiles). Presence of COPD did not change estimates for mean CAL by >5%. To assess change in estimates more closely, major confounders were stepwise included into models and the relative change in estimates for periodontal exposures was assessed.

Major confounders included smoking status, school education, and body height. Furthermore, to assess the possibility that systemic inflammation might link periodontal disease with lung function, fibrinogen and hs-CRP were additionally included in the full model (M6). Relative change in estimate for mean CAL (exposure) was estimated to assess a putative intermediate position of systemic inflammation. In sensitivity analyses, waist circumference was substituted by weight and BMI (excluding height). Further to this, subjects were restricted to never smokers and periodontal variables were substituted by the percentage of surfaces with CAL or PD ≥4 mm. As recommended for epidemiological studies with missing data, multiple imputations of missing lung function data via chained equations were performed to reduce potential bias due to missing values in complete case analyses (van Buuren et al., 1999). Using the procedure ice with 20 runs (Sterne et al., 2009), parameters of lung function were predicted by age, height, waist circumference, congestive heart failure (self-reported physician’s diagnosis, echocardiographic evidence of left ventricular or valvular dysfunction, or in accordance to the Rotterdam Study heart failure definition (Bleumink et al., 2004)), systolic blood pressure, diabetes, evidence of coronary artery disease (e.g., myocardial infarction, signs of ischemia in ECG), related medications (e.g., chronic use of non-steroidal anti rheumatics and steroid intake), and pack years in strata of sex and smoking status (Arnold and Kronmal, 2003). Afterwards regression analyses were repeated using the imputed data. Statistical significance was considered at a two-sided p<0.05. Statistical analyses were performed using STATA/SE 12.0 (StataCorp, 2011).

3. Results 3.1. Characteristics of Study Participants Compared with subjects with low mean CAL (Table 2), subjects with high mean CAL were less often well educated (high school education: 17.8% versus 26.4%, p<0.001), were more often current smokers (28.7% versus 14.6%), and presented higher levels of systemic inflammation (hs-CRP: 1.3 (0.6; 2.2) versus 1.0 (0.6; 2.2), p=0.002; fibrinogen: 3.0 (2.6; 3.6) versus 2.9 (2.5; 3.4), p=0.001; leucocytes: 6.5 (5.5; 7.7) versus 6.3 (5.2; 7.2), p<0.001).

Table 2. Characteristics of the study population stratified by mean CAL (n=1270). Low mean CAL ** High mean CAL ** p* Male sex 50.9% 51.0% 0.95 Age at core examination in years 50.1 (39.9; 60.3) 50.1 (39.8; 60.2) 0.99 School education <10 years 18.7% 26.7% 10 years 54.9% 55.5% >10 years 26.4% 17.8% <0.001 Smoking status never smoker 49.6% 39.3% occasional smoker 4.1% 2.2% former smoker with <20 pack years 28.4% 24.2% former smoker with ≥20 pack years 3.3% 5.6% current smoker with <20 pack years 12.3% 17.3% current smoker with ≥20 pack years 2.3% 11.4% <0.001 Diabetes mellitus (yes) 4.5% 6.7% 0.10 Physical activity (high) 22.8% 20.0% 0.23 2 Body mass index in kg/mP 26.7 (24.1; 29.8) 27.3 (24.5; 30.8) 0.03 Height in cm 171 (164; 178) 170 (164; 177) 0.12 Weight in kg 79.4 (69.0; 89.7) 79.2 (69.0; 91.8) 0.44 Waist circumference in cm 104 (99; 110) 105 (99; 111) 0.14 Medical variables COPD 1.3% 1.8% 0.46 Asthma 3.7% 7.8% 0.002 Coronary artery disease 4.4% 5.3% 0.46 Laboratory variables High-sensitive CRP in mg/L 1.0 (0.6; 2.2) 1.3 (0.6; 2.7) 0.002 Fibrinogen (Clauss) in g/L 2.9 (2.5; 3.4) 3.0 (2.6; 3.6) 0.001 Leukocyte count in Gpt/L 6.3 (5.2; 7.2) 6.5 (5.5; 7.7) <0.001 th th Data are given as percentage or median (25P P and 75P P percentile). 2 * Mann-Whitney-U-test for continuous data and χP P test for categorical data. ** classified as ≤ (low) or > (high) age- and sex-specific median for mean CAL. Physical activity was defined as sportive activity of at least 2-3 hours per week. Coronary artery disease was defined as myocardial infarction or signs of ischemia in ECG; CRP-C-reactive protein; COPD-chronic obstructive pulmonary disease (subject’s reported physician’s diagnosis).

Regarding lung function variables (Table 3), subjects with high mean CAL presented a

deteriorated lung function. Values of FEVR1R (3298 ml (2754; 3961) versus 3413 ml (2886; 4119), p=0.01), MEF25 (1.50 l/s (1.09; 1.97) versus 1.59 l/s (1.19; 2.08), p=0.009),

RV/TLC (35.5% (29.6; 41.9) versus 34.5% (28.5; 40.2), p=0.007), and TLCOc-VRAR (1.405 mmol/kPa/min/l (1.267; 1.596) versus 1.452 mmol/kPa/min/l (1.315; 1.605), p=0.008) differed significantly between subjects with low versus high mean CAL (p<0.05).

Table 3. Lung function and periodontal variables stratified by mean CAL (n=1270). Low mean CAL ** High mean CAL ** p* (n=641) (n=629) Lung function variables Time between core and 0.57 (0; 2.33) 0.63 (0; 2.73) 0.11 pulmonary examination in Months Dynamic lung volumes FVC in ml 3999 (3368; 4743) 3884 (3269; 4671) 0.06

FEVR1R in ml 3413 (2886; 4119) 3298 (2754; 3961) 0.01 Obstruction/Airflow limitation

FEVR1R/FVC in % 85.8 (82.2; 88.9) 85.2 (81.5; 88.8) 0.054 MEF25 in l/s 1.59 (1.19; 2.08) 1.50 (1.09; 1.97) 0.009

RRtotR in kPa/s·l 0.21 (0.17; 0.26) 0.22 (0.17; 0.28) 0.17 Static lung volumes

FRCRplethR in ml 3.54 (3.02; 4.15) 3.55 (2.99; 4.18) 0.75 TLC in ml 6300 (5481; 7207) 6322 (5403; 7223) 0.82 RV/TLC in % 34.5 (28.5; 40.2) 35.5 (29.6; 41.9) 0.007 Diffusion

TLCOc-VRAR in mmol/kPa/min/l 1.452 (1.315; 1.605) 1.405 (1.267; 1.596) 0.008 Periodontal variables Mean CAL in mm 0.94 (0.37; 1.75) 3.03 (1.96; 4.25) <0.001 %CAL ≥4 mm in % 1.9 (0; 10.0) 35.4 (7.7; 66.7) <0.001 Mean PD in mm 1.89 (1.71; 2.09) 2.46 (2.05; 2.97) <0.001 %PD ≥4 mm in % 2.3 (0; 6.8) 15.9 (4.2; 32.7) <0.001 Tooth count 24 (22; 27) 22 (15; 26) <0.001 Bleeding on Probing in % sites 8.3 (0; 20.8) 20.8 (0; 41.7) <0.001 Plaque in % sites 8.3 (0; 25.0) 18.8 (4.2; 45.8) <0.001 th th Data are given as percentage or median (25P P and 75P P percentile). 2 * Mann-Whitney-U-test for continuous data and χP P test for categorical data. ** classified as ≤ (low) or > (high) age- and sex-specific median for mean CAL.

FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-maximal expiratory flow at 25%

of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity derived body plethysmography; TLC-

total lung capacity; RV/TLC-residual volume to total lung capacity ratio; TLCOc-VRAR-diffusing capacity for carbon monoxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution).

Compared with subjects with low mean CAL, subjects with high mean CAL presented higher levels of mean CAL (3.03 mm (1.96; 4.25) versus 0.94 mm (0.37; 1.75), p<0.001), mean PD (2.46 mm (2.05; 2.97) versus 1.89 mm (1.71; 2.09), p<0.001), tooth count (22 (15; 26) versus 24 (22; 27), p<0.001), and BOP (20.8% (0; 41.7) versus 8.3% (0; 20.8), p<0.001).

3.2. Association between mean CAL and Pulmonary Variables In the overall population (n=1270, Table 4), significant associations of mean CAL with

dynamic (FVC (Figure 11a), FEVR1R (Figure 11b)) and static lung volumes (FRCRplethR) and

airflow limitation (FEVR1R/FVC, MEF25, RRtotR, and RV/TLC (Figure 11c)) were seen after full

adjustment for potential confounders. Associations between mean CAL and RRtotR and 3 FRCRplethR were cubic (XP P). Association of mean CAL with FEVR1R and RV/TLC were of 2 quadratic nature (XP P). Associations of mean CAL with FVC, FEVR1R/FVC, MEF25, TLC, and

TLCOc-VRAR were best described using linear forms of mean CAL (X).

Fig. 11: Associations between mean CAL and a) forced vital capacity (FVC), b) forced

expiratory volume in one second (FEVR1R), and c) residual volume to total lung capacity ratio (RV/TLC) in the overall population (black) and in never smokers (red) according to M5 in Table 3. Confidence limits (dashed lines) and P values from fully adjusted regression analyses are given.

Restricted subjects to never smokers (n=565, Table 4), mean CAL was significantly

(p<0.05) associated with dynamic lung volumes (FVC and FEVR1R, both cubic) and airflow limitation (RV/TLC, cubic), see also Figure 11. Associations between mean CAL and remaining lung variables were linear and non-significant.

Table 4. Linear regression evaluating the associations between mean clinical attachment loss (CAL, exposure) and pulmonary variables (outcomes) using fractional polynomials (FP) in the overall population, in never smokers, and using imputed data. Regression coefficients (B) with 95% confidence intervals (CI) for mean CAL are reported. Overall population (n=1270) FP B (95% CI) FVC in ml X -242.3 (-451.5; -33.2) * 2 FEVR1R in ml XP -537.8 (-778.3; -297.3) ***

FEVR1R/FVC in % X -3.79 (-6.09; -1.48) ** MEF25 in l/s X -0.27 (-0.49; -0.05) * 3 RRtot Rin kPa/s * l † XP 0.074 (0.027; 0.121) ** 3 FRCRplethR in ml † XP 1.00 (0.60; 1.40) *** TLC in ml † X 227.3 (-84.4; 539.0) 2 RV/TLC in % ‡ XP 8.9 (5.4; 12.4) ***

TLCOc-VRAR in mmol/kPa/min/l $ X -0.024 (-0.150; 0.102) Never smokers (n=565) FP B (95% CI) 3 FVC in ml XP -971.9 (-1648.2; -295.7) ** 3 FEVR1R in ml XP -716.9 (-1285.9; -147.9) *

FEVR1R/FVC in % X 0.69 (-2.90; 4.28) MEF25 in l/s X -0.02 (-0.36; 0.32)

RRtot Rin kPa/s * l † X -0.001 (-0.053; 0.052)

FRCRplethR in ml † X 0.22 (-0.20; 0.64) TLC in ml † X 115.4 (-404.0; 634.9) 3 RV/TLC in % ‡ XP 16.7 (8.3; 25.0) ***

TLCOc-VRAR in mmol/kPa/min/l $ X 0.093 (-0.123; 0.310) Imputed data (n=2208) FP B (95% CI) FVC in ml X -255.4 (-471.5; -39.2) * 2 FEVR1R in ml XP -424.8 (-643.7; -205.9) ***

FEVR1R/FVC in % X -2.25 (-4.49; -0.01) * MEF25 in l/s X -0.21 (-0.40; -0.03) * 3 RRtot Rin kPa/s * l XP 0.054 (-0.0003; 0.109) 3 FRCRplethR in ml XP 0.49 (0.12; 0.87) * TLC in ml X 12.0 (-301.9; 326.0) 2 RV/TLC in % XP 5.7 (2.7; 8.8) ***

TLCOc-VRAR in mmol/kPa/min/l X -0.042 (-0.153; 0.069) Linear regression using fractional polynomials for mean clinical attachment loss (CAL) adjusted for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, asthma, smoking status, height, and school education; X=mean CAL/10. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values, $ twelve additional missing values in the overall population model and seven in never smokers.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-

maximal expiratory flow at 25% of FVC; FRCRplethR-functional residual capacity derived body plethysmography;

RRtotR-total airway resistance; TLC-total lung capacity; RV/TLC-residual volume to TLC ratio; TLCOc-VRAR- diffusing capacity for carbon dioxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution).

Using data imputation methods for non-participate of lung function examinations, n=2208 subjects were available for analysis. Functional forms (linear, quadratic, or cubic) for mean CAL were adapted from the overall population model (see Table 4). Data imputation

confirmed associations with dynamic (FVC, p<0.05; FEVR1R, p<0.001) and static lung

volumes (FRCRplethR, p<0.05) and airflow limitation (FEVR1R/FVC, p<0.05; MEF25, p<0.05; RV/TLC, p<0.001).

To assess the strength of confounding by smoking, height, and school education more closely, stepwise adjusted models were evaluated (Table 5). After adjustment for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, and asthma (M1), mean CAL was significantly related to all pulmonary variables (p<0.05), except TLC. Adjustment for smoking (M2) reduced coefficients for

mean CAL by >5% for all variables, except RRtotR. Relative change in coefficients for mean

CAL was 13% for FVC, 12% for FEVR1R, 23% for FEVR1R/FVC, 30% for MEF25, 0% for RRtotR,

12% for FRCRplethR, 50% for TLC, and 96% for TLCOc-VRAR. Further adjustment for height (M3) led to a more pronounced change in coefficients of

mean CAL for FVC, FEVR1R, MEF25, FRCRplethR, and TLC compared with an adjustment for

school education (M4). For FEVR1R/FVC, RRtotR, RV/TLC, and TLCOc-VRAR adjustment for school education (M4) led to a more pronounced change in coefficients of mean CAL compared with an adjustment for height (M3). Nevertheless, both confounders led to pronounced changes in estimates of mean CAL (>5%), regardless of the lung function variable assessed, and were thus major confounders. In the full model (M5, marked grey), mean CAL was significantly associated with FVC

(p<0.05), FEVR1R (p<0.001), FEVR1R/FVC (p<0.01), MEF25 (p<0.05), RRtotR (p<0.01), FRCRplethR (p<0.001), and RV/TLC (p<0.001). Increased mean CAL was related to lower levels of

FVC (Figure 11a) and FEVR1R (Figure 11b), and higher levels of RV/TLC (Figure 11c). Further, fibrinogen and hs-CRP were considered as intermediates linking periodontal disease with lung function (M6). Inclusion of fibrinogen and hs-CRP relevantly changed coefficients for non-significant associations between mean CAL for TLC (5%) and

TLCOc-VRAR (50%). For all the other lung function variables, inclusion of fibrinogen and hs-CRP changed coefficients of mean CAL by less than 5%.

Table 5. Linear regression evaluating associations between mean clinical attachment loss (CAL, exposure) and pulmonary variables (outcomes) using fractional polynomials (FP). Regression coefficients (B) with 95% confidence intervals (CI) for mean CAL are reported (n=1270). FP M1 M2= M3= M1+smoking M2+height FVC in ml X -394.2 (-626.8; -161.5) ** -342.3 (-581.7; -103.0) ** -256.6 (-462.2; -51.0) * 2 FEVR1R in ml XP -726.5 (-993.0; -460.1) *** -637.5 (-907.5; -367.6) *** -561.8 (-798.3; -325.3) ***

FEVR1R/FVC in % X -4.84 (-7.05; -2.62) *** -3.73 (-5.99; -1.47) ** -3.76 (-6.03; -1.50) ** MEF25 in l/s X -0.43 (-0.64; -0.21) *** -0.30 (-0.52; -0.08) ** -0.26 (-0.48; -0.05) * 3

P RRtot Rin kPa/s⋅l † X 0.083 (0.036; 0.130) ** 0.083 (0.035; 0.130) ** 0.078 (0.031; 0.124) ** 3 FRCRplethR in ml † XP 1.09 (0.64; 1.54) *** 0.96 (0.50; 1.41) *** 1.03 (0.64; 1.43) *** TLC in ml † X 174.2 (-173.3; 521.7) 86.9 (-270.0; 443.8) 212.4 (-93.8; 518.6) 2 RV/TLC in % ‡ XP 10.5 (7.1; 13.9) *** 9.2 (5.7; 12.6) *** 9.0 (5.6; 12.5) ***

TLCOc-VRAR in X -0.138 (-0.263; -0.014) * -0.006 (-0.130; 0.119) -0.015 (-0.139; 0.109) mmol/kPa/min/l $ M4= M5= M6= M2+school education full model M5+Fibrinogen+hs-CRP FVC in ml X -284.7 (-527.9; -41.6) * -242.3 (-451.5; -33.2) * -240.2 (-450.0; -30.3) * 2 FEVR1R in ml XP -571.7 (-845.9; -297.6) *** -537.8 (-778.3; -297.3) *** -537.2 (-778.2; -296.2) ***

FEVR1R/FVC in % X -3.80 (-6.11; -1.50) ** -3.79 (-6.09; -1.48) ** -3.84 (-6.15; -1.53) ** MEF25 in l/s X -0.29 (-0.52; -0.07) * -0.27 (-0.49; -0.05) * -0.27 (-0.49; -0.05) * 3

P RRtot Rin kPa/s⋅l † X 0.076 (0.028; 0.124) ** 0.074 (0.027; 0.121) ** 0.074 (0.028; 0.121) 3 FRCRplethR in ml † XP 0.98 (0.52; 1.43) *** 1.00 (0.60; 1.40) *** 1.00 (0.60; 1.41) *** TLC in ml † X 165.4 (-197.3; 528.1) 227.3 (-84.4; 539.0) 237.8 (-73.8; 549.4) 2 RV/TLC in % ‡ XP 9.0 (5.5; 12.5) *** 8.9 (5.4; 12.4) *** 9.0 (5.5; 12.5) ***

TLCOc-VRAR in X -0.019 (-0.145; 0.108) -0.024 (-0.150; 0.102) -0.012 (-0.138; 0.115) mmol/kPa/min/l $ Linear regression using fractional polynomials for mean clinical attachment loss (CAL); X=mean CAL/10. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values, $ twelve additional missing values.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-

maximal expiratory flow at 25% of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity

derived body plethysmography; TLC-total lung capacity; RV/TLC-residual volume to TLC ratio; TLCOc-VRAR- diffusing capacity for carbon dioxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution). M1: adjusted for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, and asthma M2: M1 additionally adjusted for smoking status M3: M2 additionally adjusted for height M4: M2 additionally adjusted for school education M5: M2 additionally adjusted for height and school education (full model, marked grey) M6: M5 additionally including fibrinogen (cont.) and hs-CRP (cont.)

3.3. Association between mean PD and Pulmonary Variables Considering mean PD as the periodontal exposure (Table 6), relevant associations with

FEVR1R (B=-55.8 (-94.2; -17.5), p<0.01), FEVR1R/FVC (B=-55.8 (-94.2; -17.5), p<0.01),

FRCRplethR (B=0.066 (0.010; 0.121), p<0.05), and RV/TLC (B=0.8 (0.3; 1.4), p<0.01) were seen in the overall population. All associations were of linear form. Restricting subjects to never smokers, the association with FVC missed statistical significance (B=-64.7 (-133.0; 3.5), p=0.06). Associations between mean PD and other lung function variables were non- significant. Evaluation of models after data imputation (n=2331) confirmed results. Mean

PD was significantly related to FEVR1R (B=-42.5 (-82.5; -2.5), p<0.05) and MEF25 (B=-0.04

(-0.07; -0.003), p<0.05). The association of Mean PD with FEVR1R/FVC missed statistical significance (p=0.06). To assess the strength of confounding by smoking, height, and school education more closely, stepwise adjusted models were also evaluated with mean PD as the exposure variable (Table 7). After adjustment for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, and asthma (M1), mean PD

was significantly related to FVC, FEVR1R, FEVR1R/FVC, MEF25, FRCRplethR, RV/TLC, and

TLCOc-VRAR (p<0.05). Associations between mean PD and lung function variables were all linear. Again, adjustment for smoking (M2) reduced coefficients for mean PD by >5% for all lung function variables. For most lung function variables, adjustment for height led to higher relative changes in coefficients for mean PD than adjustment for school education in most models.

In the full model (M5, marked grey), mean PD was significantly related to FEVR1R (p<0.01),

FEVR1R/FVC (p<0.01), FRCRplethR (p<0.05), and RV/TLC (p<0.01).

Table 6. Linear regression evaluating the associations between mean probing depth (PD, exposure) and pulmonary variables (outcomes) using fractional polynomials (FP) in the overall population, in never smokers, and using imputed data. Regression coefficients (B) with 95% confidence intervals (CI) for mean PD are reported. Overall population (n=1329) FP B (95% CI) FVC in ml X -37.2 (-80.7; 6.3)

FEVR1R in ml X -55.8 (-94.2; -17.5) **

FEVR1R/FVC in % X -0.68 (-1.16; -0.20) ** MEF25 in l/s X -0.04 (-0.09; 0.001)

RRtotR in kPa/s·l † X 0.003 (-0.004; 0.009)

FRCRplethR in ml † X 0.066 (0.010; 0.121) * TLC in ml † X 12.3 (-52.2; 76.8) RV/TLC in % ‡ X 0.8 (0.3; 1.4) **

TLCOc-VRAR in mmol/kPa/min/l $ X -0.022 (-0.048; 0.004) Never smokers (n=594) FVC in ml X -64.7 (-133.0; 3.5)

FEVR1R in ml X -43.7 (-101.6; 14.3)

FEVR1R/FVC in % X 0.17 (-0.56; 0.90) MEF25 in l/s X -0.02 (-0.09; 0.05)

RRtotR in kPa/s·l † X -0.003 (-0.014; 0.008)

FRCRplethR in ml † X 0.011 (-0.074; 0.095) TLC in ml † X -47.5 (-152.0; 57.0) RV/TLC in % ‡ X 0.7 (-0.2; 1.6)

TLCOc-VRAR in mmol/kPa/min/l $ X -0.006 (-0.049; 0.038) Imputed data (n=2331) FVC in ml X -33.7 (-76.5; 9.2)

FEVR1R in ml X -42.5 (-82.5; -2.5) *

FEVR1R/FVC in % X -0.43 (-0.87; 0.02) MEF25 in l/s X -0.04 (-0.07; -0.003) *

RRtotR in kPa/s·l X 0.003 (-0.004; 0.010)

FRCRplethR in ml X 0.020 (-0.041; 0.082) TLC in ml X -5.4 (-71.1; 60.3) RV/TLC in % X 0.4 (-0.1; 1.0)

TLCOc-VRAR in mmol/kPa/min/l X -0.015 (-0.039; 0.008) Linear regression using fractional polynomials for mean probing depth (PD) adjusted for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, asthma, smoking status height, and school education; X=mean PD. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values, $ twelve additional missing values in the overall population and seven in never smokers.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-

maximal expiratory flow at 25% of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity

Table 7. Linear regression evaluating the associations between mean probing depth (PD, exposure) and pulmonary variables (outcomes) using fractional polynomials (FP). Regression coefficients (B) with 95% confidence intervals (CI) for mean PD are reported (n=1329). FP M1 M2= M3= M1+smoking M2+height FVC in ml X -73.6 (-122.3; -24.8) ** -61.8 (-111.7; -11.9) * -39.6 (-82.8; 3.6)

FEVR1R in ml X -95.3 (-137.7; -53.0) *** -76.6 (-119.7; -33.4) ** -58.2 (-96.3; -20.1) **

FEVR1R/FVC in % X -0.87 (-1.34; -0.40) *** -0.65 (-1.13; -0.17) ** -0.67 (-1.14; -0.19) ** MEF25 in l/s X -0.08 (-0.12; -0.03) ** -0.05 (-0.10; -0.01) * -0.04 (-0.09; 0.001)

RRtot Rin kPa/s⋅l † X 0.005 (-0.002; 0.011) 0.004 (-0.002; 0.011) 0.003 (-0.004; 0.009)

FRCRplethR in ml † X 0.07 (0.01; 0.13) * 0.05(-0.02; 0.11) 0.07 (0.01; 0.12) * TLC in ml † X -6.0 (-78.5; 66.6) -22.7 (-96.9; 51.4) 9.6 (-54.4; 73.6) RV/TLC in % ‡ X 1.2 (0.6; 1.7) ** 0.9 (0.3; 1.4) ** 0.9 (0.3; 1.4) **

TLCOc-VRAR in X -0.044 (-0.069; -0.018) ** -0.019 (-0.044; 0.007) -0.021 (-0.046; 0.005) mmol/kPa/min/l $ FP M4= M5= M6= M2+school education full model M5+Fibrinogen+hs-CRP FVC in ml X -53.5 (-103.7; -3.4) * -37.2 (-80.7; 6.3) -36.4 (-80.0; 7.1)

FEVR1R in ml X -69.5 (-112.8; -26.1) ** -55.8 (-94.2; -17.5) ** -55.2 (-93.6; -16.7) **

FEVR1R/FVC in % X -0.67 (-1.15; -0.19) ** -0.68 (-1.16; -0.20) ** -0.68 (-1.16; -0.19) ** MEF25 in l/s X -0.05 (-0.10; -0.01) * -0.04 (-0.09; 0.001) -0.04 (-0.09; 0.001)

RRtot Rin kPa/s⋅l † X 0.004 (-0.003; 0.010) 0.003 (-0.004; 0.009) 0.002 (-0.004; 0.009)

FRCRplethR in ml † X 0.05 (-0.02; 0.11) 0.07 (0.01; 0.12) * 0.07 (0.01; 0.12) * TLC in ml † X -11.4 (-86.0; 63.3) 12.3 (-52.2; 76.8) 11.8 (-52.6; 76.1) RV/TLC in % ‡ X 0.9 (0.3; 1.4) ** 0.8 (0.3; 1.4) ** 0.8 (0.3; 1.4) **

TLCOc-VRAR in X -0.021 (-0.047; 0.005) -0.022 (-0.048; 0.004) -0.020 (-0.046; 0.005) mmol/kPa/min/l $ Linear regression using fractional polynomials for mean probing depth (PD); X=mean PD. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values, $ twelve additional missing values.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-

maximal expiratory flow at 25% of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity

derived body plethysmography; TLC-total lung capacity; RV/TLC-residual volume to TLC ratio; TLCOc-VRAR- diffusing capacity for carbon dioxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution). M1: adjusted for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, and asthma M2: M1 additionally adjusted for smoking status M3: M2 additionally adjusted for height M4: M2 additionally adjusted for school education M5: M2 additionally adjusted for height and school education (full model) M6: M4 additionally including fibrinogen (cont.) and hs-CRP (cont.)

3.4. Association between Number of Missing Teeth and Pulmonary Variables Considering the NoMT as the exposure (Table 8, overall population (n=1465)), relevant

associations were found for FVC (B=-8.8 (-13.5; -4.2), p<0.001), FEVR1R (B=-9.8 (-14.0;

-5.7), p<0.001), FEVR1R/FVC (B=-0.69 (-1.19; -0.20), p<0.01), RRtotR (B=0.002 (0.001; 0.003),

p<0.001), FRCRplethR (B=0.11 (0.05; 0.17), p<0.001), and RV/TLC (B=1.0 (0.5; 1.6),

p<0.001). Associations with FVC, FEVR1R and RRtotR were cubic, while others were linear. Restricting analyses to never smokers (n=650), NoMT was significantly associated with

FVC (B=-65.9 (-128.6; -3.3), p<0.05) and TLCOc-VRAR (B=0.048 (0.007; 0.088), p<0.05). Evaluation of imputed data (n=2746) confirmed the associations of NoMT with FVC (cubic,

B=-7.8 (-12.2; -3.3), p<0.01), FEVR1R (cubic, B=-8.0 (-11.9; -4.2), p<0.001), FEVR1R/FVC

(linear, B=-0.54 (-0.93; -0.13), p<0.05), RRtotR (cubic, B=0.002 (0.001; 0.002), p<0.01), and RV/TLC (linear, B=0.9 (0.3; 1.5), p<0.01).

Also for NoMT, we assessed the strength of confounding by smoking, height, and school education more closely (Table 9). After adjustment for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, and asthma

(M1), NoMT was significantly related to FVC, FEVR1R, FEVR1R/FVC, MEF25, RRtotR, FRCRplethR, and RV/TLC (p<0.05). Associations between NoMT and lung function variables were

linear (FEVR1R/FVC, MEF25, FRCRplethR, TLC, RV/TLC, and TLCOc-VRAR) or cubic (FVC, FEVR1R,

and RRtotR). Adjustment for smoking (M2) reduced coefficients for NoMT by >5% for all lung

function variables, except RRtotR (0%). Height and school education were confirmed as major confounders. In the full model (M5, marked grey), NoMT was significantly related to FVC

(p<0.001), FEVR1R (p<0.001), FEVR1R/FVC (p<0.01), RRtotR (p<0.001), FRCRplethR (p<0.001), and RV/TLC (p<0.001). Inclusion of inflammatory markers did not change coefficients of NoMT by more than 5%. Thus, inflammation does not appear to be an intermediate between NoMT and diminished lung function in this study.

Table 8. Linear regression evaluating the associations between number of missing teeth (NoMT, exposure) and pulmonary variables (outcomes) using fractional polynomials (FP) the overall population, in never smokers, and using imputed data. Regression coefficients (B) with 95% confidence intervals (CI) for NoMT are reported. Overall population (n=1465) FP B (95% CI) 3 FVC in ml XP -8.8 (-13.5; -4.2) *** 3 FEVR1R in ml XP -9.8 (-14.0; -5.7) ***

FEVR1R/FVC in % X -0.69 (-1.19; -0.20) ** MEF25 in l/s X -0.03 (-0.08; 0.01) 3 RRtotR in kPa/s·l † XP 0.002 (0.001; 0.003) ***

FRCRplethR in ml † X 0.11 (0.05; 0.17) *** TLC in ml † X 4.8 (-61.6; 71.3) RV/TLC in % ‡ X 1.0 (0.5; 1.6) ***

TLCOc-VRAR in mmol/kPa/min/l $ X 0.011 (-0.015; 0.037) Never smokers (n=650) FP B (95% CI) FVC in ml X -65.9 (-128.6; -3.3) *

FEVR1R in ml X -44.6 (-98.0; 8.8)

FEVR1R/FVC in % X 0.22 (-0.47; 0.91) MEF25 in l/s X 0.01 (-0.05; 0.07)

RRtotR in kPa/s·l † X 0.003 (-0.007; 0.013)

FRCRplethR in ml † X 0.03 (-0.06; 0.11) TLC in ml † X -49.5 (-150.1; 51.1) RV/TLC in % ‡ X 0.5 (-0.2; 1.4)

TLCOc-VRAR in mmol/kPa/min/l $ X 0.048 (0.007; 0.088) * Imputed data (n=2746) FP B (95% CI) 3 FVC in ml XP -7.8 (-12.2; -3.3) ** 3 FEVR1R in ml XP -8.0 (-11.9; -4.2) ***

FEVR1R/FVC in % X -0.54 (-0.93; -0.13) * MEF25 in l/s X -0.03 (-0.07; 0.01) 3 RRtotR in kPa/s·l XP 0.002 (0.001; 0.002) **

FRCRplethR in ml X 0.05 (-0.01; 0.10) TLC in ml X -27.9 (-87.9; 32.1) RV/TLC in % X 0.9 (0.3; 1.5) **

TLCOc-VRAR in mmol/kPa/min/l X 0.001 (-0.020; 0.022) Linear regression using fractional polynomials for number of missing teeth (NoMT) adjusted for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity, asthma, smoking status, height, and school education; X=NoMT/10. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values, $ five additional missing values in the overall population and nine in never smokers.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-

maximal expiratory flow at 25% of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity

Table 9. Linear regression evaluating the associations between number of missing teeth (NoMT, exposure) and pulmonary variables (outcomes) using fractional polynomials (FP). Regression coefficients (B) with 95% confidence intervals (CI) for number of missing teeth are reported (n=1465). FP M1 M2= M3= M1+smoking M2+height 3 FVC in ml XP -12.8(-18.1; -7.6) *** -11.4 (-16.7; -6.1) *** -9.1 (-13.7; -4.5) *** 3 FEVR1R in ml XP -14.2 (-18.8; -9.6) *** -12.0 (-16.6; -7.4) *** -10.1 (-14.2; -6.1) ***

FEVR1R/FVC in % X -0.97 (-1.45; -0.49) *** -0.66 (-1.15; -0.18) ** -0.68 (-1.17; -0.20) ** MEF25 in l/s X -0.07 (-0.11; -0.03) ** -0.04 (-0.08; 0.004) -0.03 (-0.08; 0.01) 3 RRtot Rin kPa/s * l † XP 0.002 (0.001; 0.003) *** 0.002 (0.001; 0.003) *** 0.002 (0.001; 0.003) ***

FRCRplethR in ml † X 0.12 (0.06; 0.18) *** 0.09 (0.02; 0.15) ** 0.11 (0.05; 0.17) ** TLC in ml † X -8.5 (-82.1; 65.2) -31.6 (-106.4; 43.2) -1.6 (-66.5; 63.4) RV/TLC in % ‡ X 1.4 (0.8; 2.0) *** 1.1 (0.5; 1.6) *** 1.0 (0.5; 1.6) ***

TLCOc-VRAR in X -0.010 (-0.036; 0.016) 0.015 (-0.010; 0.040) 0.013 (-0.012; 0.039) mmol/kPa/min/l $ FP M4= M5= M6= M2+school education full model M5+Fibrinogen+hs-CRP 3 FVC in ml XP -10.4 (-15.8; -5.0) *** -8.8 (-13.5; -4.2) *** -8.8 (-13.5; -4.1) *** 3 FEVR1R in ml XP -11.1 (-15.8; -6.4) *** -9.8 (-14.0; -5.7) *** -9.8 (-14.0; -5.6) ***

FEVR1R/FVC in % X -0.69 (-1.18; -0.19) ** -0.69 (-1.19; -0.20) ** -0.70 (-1.19; -0.20) ** MEF25 in l/s X -0.04 (-0.08; 0.01) -0.03 (-0.08; 0.01) -0.03 (-0.08; 0.01) 3 RRtot Rin kPa/s * l † XP 0.002 (0.001; 0.003) *** 0.002 (0.001; 0.003) *** 0.002 (0.001; 0.003) ***

FRCRplethR in ml † X 0.09 (0.03; 0.16) ** 0.11 (0.05; 0.17) *** 0.11 (0.05; 0.17) *** TLC in ml † X -10.8 (-87.2; 65.5) 4.8 (-61.6; 71.3) 9.2 (-57.2; 75.7) RV/TLC in % ‡ X 1.1 (0.5; 1.6) *** 1.0 (0.5; 1.6) *** 1.1 (0.5; 1.7) ***

TLCOc-VRAR in X 0.012 (-0.014; 0.038) 0.011 (-0.015; 0.037) 0.013 (-0.013; 0.039) mmol/kPa/min/l $ Linear regression using fractional polynomials for number of missing teeth (NoMT); X=NoMT/10. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values, $ fourteen additional missing values.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-

maximal expiratory flow at 25% of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity

derived body plethysmography; TLC-total lung capacity; RV/TLC-residual volume to TLC ratio; TLCOc-VRAR- diffusing capacity for carbon dioxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution). M1: adjusted for age, sex, time between core and pulmonary examination, diabetes, waist circumference, physical activity and asthma M2: M1 additionally adjusted for smoking status M3: M2 additionally adjusted for height M4: M2 additionally adjusted for school education M5: M2 additionally adjusted for height and school education (full model, marked grey) M6: M4 additionally including fibrinogen (cont.) and hs-CRP (cont.)

3.5. Sensitivity Analyses Several sensitivity analyses were performed to assess stability of the results for the association between periodontal diseases and lung function diseases. Substituting waist circumference for weight (Table 10) did not change coefficients for mean CAL, mean PD, or NoMT relevantly. Results presented in Tables 4 (mean CAL), 6 (mean PD), and 8 (NoMT) were confirmed. If mean CAL was the exposure, functional

forms were linear (FVC, FEVR1R/FVC, MEF25, TLC, RV/TLC, and TLCOc-VRAR), quadratic

(FEVR1R and MEF75), or cubic (RRtotR and FRCRplethR). After full adjustment, mean CAL was

significantly associated with FVC (linear, B=-258.1 (-467.7; -48.5)), FEVR1R (quadratic,

B=-560.0 (-800.4; -319.6))R, RFEVR1R/FVC (linear, B=-3.88 (-6.17; -1.59)), MEF25 (linear,

B=-0.288 (-0.509; -0.068)), RRtotR (cubic, B=0.079 (0.032; 0.125)), FRCRplethR (cubic, B=0.96 (0.55; 1.37)), and RV/TLC (linear, B=6.56 (3.90; 9.22)). If mean PD was the exposure, functional forms were consistently linear. After full

adjustment for confounders, mean PD was significantly associated with FEVR1R (B=-59.9

(-98.4; -21.4))R, RFEVR1R/FVC (B=-0.66 (-1.14; -0.18)), MEF25 (B=-0.047 (-0.093; -0.001)),

FRCRplethR (B=0.068 (0.012; 0.124)), and RV/TLC (B=0.85 (0.29; 1.40)). The association between mean PD and FVC was of borderline significance (p=0.06).

After full adjustment, the number of missing teeth was significantly related to FEVR1R/FVC

(B=-0.69 (-1.18; -0.19)), RRtotR (B=0.002 (0.001; 0.003)), FRCRplethR (B=0.10 (0.04; 0.16)), and RV/TLC (B=1.06 (0.48; 1.63)).

Table 11 presents results on the association between extent measures of CAL and PD on lung function variables. For extent CAL ≥4 mm, associations with pulmonary variables

were linear (FVC, FEVR1R/FVC, MEF25, RRtotR, TLC, and TLCOc-VRAR), quadratic (RV/TLC), or

cubic (FEVR1R and FRCRplethR). Extent CAL ≥4 mm was significantly associated with FVC

(linear, B=-170.9 (-289.5; -52.3)), FEVR1R (cubic, B=-295.4 (-410.6; -180.1)), FEVR1R/FVC

(linear, B=-2.29 (-3.60; -0.98)), MEF25 (linear, B=-0.18 (-0.31; -0.06)), FRCRplethR (cubic, B=0.39 (0.22; 0.56)), and RV/TLC (quadratic, B=4.65 (3.04; 6.25)). For extent PD ≥4 mm, associations with lung function variables were linear for all variables. Extent PD ≥4 mm was significantly associated with FVC (B=-177.5 (-342.2;

-12.9)), FEVR1R (B=-236.8 (-382.1; -91.4)), FEVR1R/FVC (B=-2.43 (-4.26; -0.60)), MEF25

(B=-0.21 (-0.38; -0.04)), FRCRplethR (B=0.28 (0.07; 0.49)), and RV/TLC (B=3.88 (1.77; 5.98)).

Table 10. Associations between mean clinical attachment loss, mean probing depth, and number of missing teeth and pulmonary variables using fractional polynomials (FP). Mean CAL (n=1270) FP B (95% CI) FVC in ml X -258.1 (-467.7; -48.5) * 2 FEVR1R in ml XP P -560.0 (-800.4; -319.6) ***

FEVR1R/FVC in % X -3.88 (-6.17; -1.59) ** MEF25 in l/s X -0.288 (-0.509; -0.068) ** 3 RRtotR in kPa/s·l † XP 0.079 (0.032; 0.125) ** 3 FRCRplethR in ml † XP 0.96 (0.55; 1.37) *** TLC in ml † X 250.9 (-61.8; 563.7) RV/TLC in % ‡ X 6.56 (3.90; 9.22) ***

TLCOc-VRAR in mmol/kPa/min/l $ X -0.034 (-0.160; 0.092) Mean PD (n=1329) FP B (95% CI) FVC in ml X -42.4 (-86.2; 1.3)

FEVR1R in ml X -59.9 (-98.4; -21.4) **

FEVR1R/FVC in % X -0.66 (-1.14; -0.18) ** MEF25 in l/s X -0.047 (-0.093; -0.001) *

RRtotR in kPa/s·l † X 0.002 (-0.004; 0.009)

FRCRplethR in ml † X 0.068 (0.012; 0.124) * TLC in ml † X 8.7 (-56.1; 73.6) RV/TLC in % ‡ X 0.85 (0.29; 1.40) **

TLCOc-VRAR in mmol/kPa/min/l $ X -0.022 (-0.048; 0.004) NoMT (n=1465) FP B (95% CI) 3 FVC in ml XP P -9.2 (-13.9; -4.5) 3 FEVR1R in ml XP -10.2 (-14.4; -6.0)

FEVR1R/FVC in % X -0.69 (-1.18; -0.19) ** MEF25 in l/s X -0.037 (-0.081; 0.008) 3 RRtotR in kPa/s·l † XP 0.002 (0.001; 0.003) ***

FRCRplethR in ml † X 0.10 (0.04; 0.16) ** TLC in ml † X 3.8 (-63.2; 70.8) RV/TLC in % ‡ X 1.06 (0.48; 1.63) ***

TLCOc-VRAR in mmol/kPa/min/l $ X 0.013 (-0.013; 0.039) Linear regression using fractional polynomials for mean probing depth (PD), mean clinical attachment loss (CAL), and number of missing teeth (NoMT) adjusted for age, sex, diabetes, height (age- and sex-specific quintiles) and weight, physical activity, COPD, asthma, smoking status, school education, and time between core and pulmonary examination. X=mean CAL/10; X=mean PD; X=NoMT/10. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values; $ twelve additional missing values.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF25-

maximal expiratory flow at 25% of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity derived body plethysmography; TLC-total lung capacity; RV/TLC-residual volume to total lung capacity ratio;

TLCOc-VRAR-diffusing capacity for carbon dioxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution).

Table 11. Associations between extent CAL ≥4 mm (CAL4) and extent PD ≥4 mm (PD4) and pulmonary variables using fractional polynomials (FP). Extent CAL ≥4 mm (n=1270) Extent PD ≥4 mm (n=1329) FP B (95% CI) FP B (95% CI) FVC in ml X -170.9 (-289.5; -52.3) ** X -177.5 (-342.2; -12.9) * 3 FEVR1R in ml XP P -295.4 (-410.6; -180.1) *** X -236.8 (-382.1; -91.4) **

FEVR1R/FVC in % X -2.29 (-3.60; -0.98) ** X -2.43 (-4.26; -0.60) ** MEF25 in l/s X -0.18 (-0.31; -0.06) ** X -0.21 (-0.38; -0.04) *

RRtotR in kPa/s·l X 0.012 (-0.006; 0.029) X 0.010 (-0.014; 0.035) 3 FRCRplethR in ml XP 0.39 (0.22; 0.56) *** X 0.28 (0.07; 0.49) * TLC in ml X 125.2 (-52.2; 302.6) X 110.6 (-133.9; 355.1) 2 RV/TLC in % XP P 4.65 (3.04; 6.25) *** X 3.88 (1.77; 5.98) ***

TLCOc-VRAR in mmol/kPa/min/l X 0.017 (-0.054; 0.089) X -0.027 (-0.125; 0.071) Linear regression using fractional polynomials for mean probing depth (PD), mean clinical attachment loss (CAL), and number of missing teeth (NoMT) adjusted for age, sex, diabetes, waist circumference, height (age- and sex-specific quintiles), physical activity (at least 2-3 hours/week), COPD, asthma, former and current smoking, school education, and time between core and pulmonary examination. X=%PD/CAL≥4 mm/100. * p<0.05; ** p<0.01; *** p<0.001; † one missing value; ‡ three missing values; $ twelve additional missing values.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF25-

TLCOc-VRAR-diffusing capacity for carbon dioxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution).

We additionally considered indicators of gingival bleeding (percentage of sites with bleeding on probing (BOP)) and oral hygiene (percentage of sites presenting plaque) as exposures (Table 12). Associations between BOP or plaque and pulmonary variables were consistently linear. None of the associations between BOP and lung function variables were statistically significant. Associations between plaque and FVC (B=-160.6

(-279.6; -41.6)), FEVR1R (B=-154.5 (-259.8; -49.3)), FRCRplethR (B=0.15 (0.00; 0.30)), and RV/TLC (B=2.63 (1.11; 4.16)) were statistically significant (p<0.05).

Table 12. Associations between bleeding on probing (BOP, %) and plaque (%) and pulmonary variables using fractional polynomials (FP). %BOP (n=1329) %Plaque (n=1330) FP B (95% CI) FP B (95% CI) FVC in ml X -88.9 (-208.0; 30.1) X -160.6 (-279.6; -41.6) **

FEVR1R in ml X -79.9 (-185.2; 25.4) X -154.5 (-259.8; -49.3) **

FEVR1R/FVC in % X -0.20 (-1.52; 1.13) X -0.61 (-1.94; 0.71) MEF25 in l/s X -0.005 (-0.130; 0.120) X -0.068 (-0.193; 0.058)

RRtotR in kPa/s·l † X -0.006 (-0.024; 0.011) X 0.003 (-0.015; 0.021)

FRCRplethR in ml † X 0.08 (-0.07; 0.23) X 0.15 (0.00; 0.30) * TLC in ml † X -79.5 (-256.1; 97.1) X 27.9 (-149.2; 205.0) RV/TLC in % ‡ X 1.24 (-0.28; 2.77) X 2.63 (1.11; 4.16) **

TLCOc-VRAR in X 0.026 (-0.045; 0.096) X 0.039 (-0.031; 0.110) mmol/kPa/min/l $ Linear regression using fractional polynomials for mean probing depth (PD), mean clinical attachment loss (CAL), and number of missing teeth (NoMT) adjusted for age, sex, diabetes, waist circumference, height, physical activity, COPD, asthma, smoking status, school education, and time between core and pulmonary examination. X=BOP/100; X=Plaque/100. * p<0.05; ** p<0.01; † one missing value; ‡ three missing values, $ twelve additional missing values.

CI-confidence interval; FVC-forced vital capacity; FEVR1R-forced expiratory volume in one second; MEF-

maximal expiratory flow at 75, 50, and 25% of FVC; RRtotR-total airway resistance; FRCRplethR-functional residual capacity derived body plethysmography; TLC-total lung capacity; RV/TLC-residual volume to total lung

capacity ratio; TLCOc-VRAR-diffusing capacity for carbon dioxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution).

4. Discussion 4.1. Summary of Results In this study, periodontal disease was independently associated with dynamic and static lung volumes and with airflow limitation but not with diffusing capacity. These associations, however, were heavily confounded by height and smoking. Low-grade systemic inflammation as assessed by serum fibrinogen levels and hs-CRP seemed to mediate associations between periodontal disease and lung function only to a minor part.

In never smokers, lung volume (FVC, FEVR1R) and RV/TLC ratio declined as the periodontal status deteriorated. Compared with previously published data (Scannapieco and Ho, 2001, Hyman and Reid, 2004, Hayes et al., 1998, Garcia et al., 2001b, Deo et al., 2009, Wang et al., 2009, Katancik et al., 2005), our study offers more accurate definitions of airflow limitation providing a comprehensive evaluation of lung function. Our findings are consistent with previous publications, which presented associations between periodontal disease and airflow limitation in populations with COPD (Scannapieco and Ho, 2001, Hyman and Reid, 2004, Hayes et al., 1998, Garcia et al., 2001b, Deo et al., 2009, Wang et al., 2009, Katancik et al., 2005).

4.2. Confounding of the Association between Periodontitis and Lung Function 4.2.1. Confounding by Cigarette Smoking Smoking mainly confounds the association between periodontitis and lung function (Page, 2001, Terpenning, 2001, Hyman and Reid, 2004). Not only is cigarette smoking the leading risk factor for periodontal disease (Tomar and Asma, 2000, Albandar, 2002) but also for emphysema, chronic bronchitis, and lung infections resulting in the decline of pulmonary function (Garcia et al., 2001b, Hyman and Reid, 2004, Katancik et al., 2005). Further to this, studies have shown that tobacco use is associated with the pathophysiology of chronic, low-grade inflammation (Aronson et al., 2008, Gan et al., 2005). On one side, smoking is involved in the pathogenesis and progression of periodontal diseases (Bergstrom, 2004, Tomar and Asma, 2000). The peripheral vasoconstrictive effect of tobacco smoke and nicotine reduces the delivery of oxygen and nutrients to the gingival tissue (Baab and Oberg, 1987) and alters gingival inflammatory response (Bergstrom, 2004). Cigarette smoking can also impair immunologic function (Barbour et al., 1997). On the other side, smoking affects respiratory defences, promotes chronic lung diseases resulting in respiratory infections, damages the airway epithelium, and has

56 effects on lung clearance by suppressing coughing and the protective waving action of cilia in the airways (Terpenning, 2001). Cigarette smoking is known to hamper lung clearance by directly suppressing coughing, compromising the protective mucociliary action in the airways, and phagocyte activity (Page, 2001). Here, there is more damage to the large airways and to the airway epithelium causing high levels of sputum production in patients, especially in the mornings (known as chronic bronchitis) (Terpenning, 2001). The mechanism behind this increased risk is poorly understood, but it has been suggested that tobacco use suppresses the production of protective IgG2 antibodies and blocks phagocytosis and killing of bacteria by neutrophils (Page, 1998). Chronic bronchitis counts as one of the most frequent illnesses of all. Its prevalence within the German population is among 15-25%. Every second smoker aged over 40 years suffers from chronic bronchitis (Davies, 1994). Moreover, the effect depends on the non-specific airway reactivity in each patient, which can be influenced by early childhood exposure to allergens. Hence, the damage from smoking may differ in each patient (Mullen et al., 1986). Furthermore, smoking causes irreversible damage of the alveoli that results in air trapping, also known as emphysema. It is the result of chronically enlarged alveoli being unable to fully discharge their contents (Terpenning, 2001). Primarily the smallest airways, parenchyma, and pulmonary vessels are unsounded in smokers (Rodriguez-Roisin et al.,

2009) affecting TLCOc-VRAR and MEF25 values. Moreover, the concerned patient then shows a high residual volume after expiration. The underlying mechanism was explained earlier (see 1.2.3. Pulmonary Diseases). Finally, it has been suggested that smoking will induce endothelial dysfunction in pulmonary vessels ensuing in diminished pulmonary blood volume and pulmonary vasoconstriction (Wang et al., 2010). In this study, inclusion of smoking status strongly attenuated coefficients in models that evaluated lung volumes, airway limitation, and diffusion capacity. Due to consideration of smoking status and number of pack years for adjustment, residual confounding by smoking was reduced considerably in this study. However, residual confounding by the number of cigarettes smoked appears to be an aspect consistently observed in the current literature, where associations between periodontitis and lung function were only reported among current smokers. Periodontal disease was significantly related to the longitudinal decline of lung volumes (Garcia et al., 2001b, Hayes et al., 1998) and presence of COPD (Hyman and Reid, 2004) in current smokers compared to non-smokers, which raises the question whether the observations may actually be the result of residual confounding by smoking (Garcia et al., 2001b). Moreover, never smokers may have been exposed to various environmental smokes in their occupations or daily lives (Scannapieco, 1999). Thus, confounding by passive smoking cannot be excluded either.

Since tobacco use can be considered a significant independent risk factor for both periodontitis and impaired lung function, residual confounding by smoking cannot be ruled out totally and exacerbates interpretation of results (Hyman and Reid, 2004). Hujoel et al. (Hujoel et al., 2002) suggested that the periodontitis - systemic disease relationship should be studied among healthy never smokers. To avoid residual confounding by active smoking experience, we picked up this aspect in our study and also restricted analyses to never smokers, which halved the number of pulmonary variables associated with mean CAL. In never smokers mean CAL was only related to variables assessing dynamic (FVC

and FEVR1R) and static lung volume (RV/TLC). This aspect underlines the importance of major confounding by smoking and smoking intensity on the relationship between periodontal diseases and lung function. Future studies on the relationship of periodontal and systemic diseases ought to include a separate analysis for never smokers and should be conducted on a variety of populations to validate the observed associations and to determine their level of generalization (Hyman and Reid, 2004).

4.2.2. Confounding by Body Height In this study, adjustment for body height changed coefficients of mean CAL for static and dynamic lung volumes and MEF25 by more than 10%. This leads to the question whether body height might be related to both lung volume and periodontitis. Body height reflects intrauterine interactions, birth weight as well as weight, and environmental factors in early childhood (e.g., nutrition, illness, psychosocial stress), which consequently constrain the growth of the airways and may be reflected in reduced lung volumes. Evidence in the literature suggests that intrauterine interactions or respiratory tract infections and environmental exposures in early childhood, which retard foetal weight gain and postnatal growth, might also have an impact on the potency of inflammatory responses to infections (Barker et al., 1991, Sattar et al., 2004, Maritz et al., 2005, Marossy et al., 2007). Sattar et al. (Sattar et al., 2004) examined a large population of middle-aged men and women and found an inverse relationship between birth weight and systemic inflammation, as measured by CRP concentration, in adult life. After adjusting for potential confounders, CRP decreased by 10.7% for every 1-kg increase in birth weight (95% CI=3.0, 17.8). These intrauterine infections or childhood diseases can retard growth and cause lifelong increased morbidity by keeping a higher susceptibility for subsequent inflammatory diseases (Meisel et al., 2007, Knossalla, 2009). Furthermore, they might constrain the growth of the airways and reduce lung function development. Studies (Barker et al., 1991, Stein et al., 1997) indicated that lower birth weight and lower weight

58 at one year was associated with worse lung volumes and death from chronic obstructive pulmonary disease in adulthood. Moreover, body height, which is related to lung capacity and under a certain degree of genetic control, too, is associated with birth weight as well as weight and environmental factors in early childhood (e.g., nutrition, illness, psychosocial stress) (Wadsworth et al., 2002, Gunnell, 2002). In paediatrics, growth is considered one of the most important indicators of overall health status (Ceballos, 2008). One assumes that body heights of adults reflect the net nourishing quality during their childhood, whereby the life circumstances during the first three years of life affect the final size of adults particularly strongly (Knossalla, 2009). Results of the Study of Health of Pomerania (SHIP) (Meisel et al., 2007, Knossalla, 2009) showed that body height was associated with the severity of periodontal disease, suggesting that individuals born with a high susceptibility to infectious and inflammatory diseases in childhood may present impaired length growth. Reaching adulthood, growth comes to a halt, but the individual remains susceptible to inflammatory sequelae. Thus, smaller persons tend to develop more severe periodontitis. Periodontal disease offers a one-of-a-kind possibility for retracing its history of disease, being a diagnostic measure for each individual during life (Knossalla, 2009). Loss of attachment constantly increases, reflecting the sum of all damaging and protective influences.

4.2.3. Confounding by Socioeconomic Status The socioeconomic status is associated with lung function independently of smoking (Prescott et al., 2003, Prescott et al., 1999). In line with this, adjustment for education in our study attenuated associations between periodontal disease and airflow limitation,

quantified by FEVR1R/FVC, RRtotR, RV/TLC, and diffusion, quantified by TLCOc-VRAR, more pronounced compared with body height. However, having only adjusted for education, residual confounding by other components assessing the socioeconomic status cannot be ruled out. Thus, for future studies comprehensive adjustment for socioeconomic status is recommended to avoid residual confounding.

4.2.4. Confounding by Other Factors There are other important confounders that need to be considered when analysing the possible link between periodontal disease and pulmonary function. These include age, sex, the lack of being able to properly perform oral hygiene or general hygiene altogether, nutrition, obesity, diabetes mellitus, alcohol intake, and lifestyle changes that may cause stress and depression (Gomes-Filho et al., 2010, Azarpazhooh and Leake, 2006, Scannapieco and Ho, 2001). Of these, some will be discussed shortly.

It is well known that pulmonary function deteriorates with increasing age. With respect to advanced age, as noted above, it is believed that older individuals are more prone to suffer from multiple chronic diseases yielding in increased medication intake and longer hospital inhabitation in higher frequency. The intake of various drugs such as antidepressants, antihistamines, antihypertensives, and diuretics can reduce salivary flow (Sreebny and Schwartz, 1986, Abe et al., 2001). Altogether, these factors combined with poor oral hygiene in these senile people can result in microbial infections such as periodontitis and pneumonia due to aspiration of oral organisms (Gavazzi and Krause, 2002, Abe et al., 2001), for which elderly individuals show an increased susceptibility to (Hajishengallis, 2010). With advanced age, the pathway of signal transduction of phagocytes may undergo significant changes affecting their ability to perform certain antimicrobial functions or their capacity to regulate the inflammatory response (Hajishengallis, 2010). Moreover, Scannapieco and Ho (Scannapieco and Ho, 2001) suggested that poor periodontal health would likely work in concert with other factors such as continued smoking, environmental pollutants, viral infection, allergy, and genetic factors to increase the risk for respiratory diseases. The presence of periodontal disease may even modify environmental conditions to permit mucosal colonization and infection by respiratory pathogens (Scannapieco and Mylotte, 1996, Fourrier et al., 1998, Scannapieco, 1999). The peak lung function in early adulthood is also related to sex, race/ethnicity, cigarette smoking, exposure to environmental tobacco smoke, and particulate air pollution (Blanc and Toren, 2007, Viegi et al., 2006, Apostol et al., 2002). Obesity and physical activity may also confound the association between periodontitis and pulmonary lung function. On one hand, obesity (Al-Zahrani et al., 2003, Ylostalo et al., 2008) and physical activity (Al-Zahrani et al., 2005, Shimazaki et al., 2010) were related to periodontal disease linking the association between periodontitis and lung function. On the other hand, little physical activity (Jakes et al., 2002, Williams et al., 2005, Pelkonen et al., 2003, Garcia-Aymerich et al., 2008, Sabia et al., 2010, Loprinzi et al., 2011) and excess body fat have been found to be associated with higher levels of CRP (Aronson et al., 2008) and reduced pulmonary function (Rasmussen et al., 2009, Poulain et al., 2006, Fukahori et al., 2010, Thyagarajan et al., 2008, Zammit et al., 2010), suggesting that BMI and physical activity may even be capable of influencing lung function indirectly via a chronic low-grade inflammation (Rasmussen et al., 2009). In this context, it was shown that adipose tissue serves as an active endocrine organ, able to release large numbers of cytokines and bioactive mediators that play important roles in the pathogenesis of numerous obesity related diseases (Van Gaal et al., 2006).

Last but not least, it is likely that there are other so far unknown factors that may confound the association between periodontal disease and pulmonary disease that were not addressed so far.

4.3. Biological Mechanisms linking Periodontitis und Lung Function Research on the relationship between periodontitis and systemic diseases has been given increasing attention over recent decades and established that periodontal infection might be a possible risk factor for various systemic conditions including pulmonary impairment. A number of studies have found results favouring such an association (Scannapieco and Ho, 2001, Hyman and Reid, 2004, Hayes et al., 1998, Garcia et al., 2001b, Deo et al., 2009, Wang et al., 2009, Katancik et al., 2005, Scannapieco et al., 1998, Leuckfeld et al., 2008, Sharma and Shamsuddin, 2011, Hamalainen et al., 2004, Mojon et al., 1997, Azarpazhooh and Leake, 2006). Even though the mechanisms by which poor periodontal health may influence respiratory function still remains unclear and controversial, several potential mechanisms have been proposed to explain the biological plausibility for an association between periodontal disease and lung function (Travis et al., 1994, Scannapieco, 1999, Scannapieco and Rethman, 2003, Scannapieco et al., 2003). Of those potential mechanisms, the role of low-grade systemic inflammation was specifically addressed in this study. Due to the cross-sectional study design, other mechanisms could not be addressed in this study, but are comprehensively presented in the following paragraphs.

4.3.1. Low-grade Systemic Inflammation Low-grade systemic inflammation, specifically addressed in this study, might provide a further plausible link. On one hand, both elevated levels of cytokines and higher levels of circulating inflammatory parameters such as CRP, fibrinogen, and leukocyte counts have been observed in dentate subjects with periodontal disease (Yoshii et al., 2009, Slade et al., 2003, Takami et al., 2003, Joshipura et al., 2004) and in edentulous subjects (Russell et al., 1999). Takami et al. (Takami et al., 2003) reported that patients with periodontal disease had significantly higher levels of CRP compared to healthy individuals (for men: OR=1.74; 95% CI 1.34, 2.25; for women: OR=1.80; 95% CI=1.00, 3.23). In another study (Yoshii et al., 2009), a significant correlation was found between periodontal disease at baseline and CRP one year later for 10,376 Japanese men with low baseline CRP (OR=1.34; 95% CI=1.12, 1.67). However, among those 4,997 Japanese men without periodontal disease, baseline CRP did not correlate with periodontal disease one year later (OR=1.16; 95% CI=0.89, 1.51) indicating that periodontitis increased the risk for high serum CRP levels in men after one year of follow-up and not the inverse. Other studies

61 showed that the severity of periodontal disease directly correlated with serum concentrations of inflammatory markers (Amabile et al., 2008) and its successful control has been associated with a significant reduction of these markers (IL-6, CRP), but not without a significant variability among subjects (D'Aiuto et al., 2004, Mattila et al., 2002). On the other hand, markers of systemic inflammation positively correlated with reduced spirometric lung volumes (Rasmussen et al., 2009, Glaser et al., 2011, Shaaban et al., 2006, Thyagarajan et al., 2006, Hancox et al., 2007, Dahl et al., 2001) suggesting that the chronic inflammatory burden of periodontal disease and the host response to this inflammation may lead to impaired lung function independently of the effects of smoking, obesity, cardiorespiratory fitness, or asthma. These studies indicated that different inflammatory markers may be similar in predicting changes in lung function. However, C-reactive protein (CRP) is the best studied and the most widely used clinically significant serum marker of systemic inflammation, endothelial cell damage, and infection. It is an acute-phase reactant primarily produced in the liver to the response of interleukin (IL)-6 (Loos et al., 2000, Gabay and Kushner, 1999). CRP levels can rise with age, obesity, diabetes mellitus, or smoking (Pearson et al., 2003). Epidemiological studies have reported a strong association between C-reactive protein and increased mortality independently of potential interfering factors such as age and smoking (Tice et al., 2003). Findings of Rasmussen (Rasmussen et al., 2009) showed a significant association between higher levels of CRP at the age of 20 years and a greater reduction in spirometry

values (FEVR1R, FVC) between the ages 20 and 29 years independently of sex, smoking, body mass index, cardio respiratory fitness, and asthma. Further, it has been suggested that inflammation leads to muscle wasting (Cesari et al., 2004, Fabbri et al., 2008), which is again associated with reduced lung function (Ito and Barnes, 2009, Janssens et al., 1999). Nevertheless, it is still debated whether inflammation leads to impaired lung function or the inverse (Fogarty et al., 2007, Olafsdottir et al., 2007). It has been hypothesized that cytokines such as interleukin (IL)-1α, IL-1β, IL-6, IL-8, and tumour necrosis factor (TNF)-α originating from inflamed periodontal tissues (e.g., epithelial cells, endothelial cells, fibroblasts, macrophages, white cells, peripheral mononuclear cells) may modify the respiratory epithelium to promote adherence and colonization by respiratory pathogens to these mucosal surfaces (Wilson et al., 1996, Scannapieco et al., 2001, Reddi et al., 1996), see also Figure 13. Subsequently, the respiratory epithelium may release cytokines and attract neutrophils, which in turn infiltrate airway parenchyma and release proteolytic enzymes and toxic oxygen radicals that damage the epithelium (Birkedal-Hansen, 1993, Pesci et al., 1998). As a consequence, the resultant inflamed mucosal epithelium may be more prone to infection (Mojon, 2002, Hedges et al., 1995, Svanborg et al., 1996, Scannapieco, 1999). This mechanism has

62 been observed for pathogens such as Haemophilus influenzae and 0TStreptococcus0T pneumonia that commonly cause respiratory tract infections (Hakansson et al., 1996, Khair et al., 1996). Thus, systemic inflammation might only be of minor importance when considering the link between periodontal diseases and lung volume. Nevertheless, results are in agreement with others showing that hs-CRP and fibrinogen were associated with TLC (Rasmussen et al., 2009, Glaser et al., 2011, Shaaban et al., 2006, Thyagarajan et al., 2006, Hancox et al., 2007, Dahl et al., 2001).

4.3.2. Oral Bacteria and Respiratory Pathogens Oral bacteria play an essential role in the pathogeneses of pulmonary impairment. Because of its humidity and temperature, the oral cavity provides an optimal environment for bacterial colonization hosting a wide variety of different species of bacteria with varying 3 degrees of virulence (Sachdeo et al., 2008). One mmP P of plaque, for instance, contains 6 more than 10P P bacteria with 300 different anaerobic and facultative anaerobic species (Slots et al., 1988). Moreover, poor oral hygiene results in high supragingival plaque accumulation and higher concentrations of oral pathogens on both teeth and oral mucosa as well as in the saliva, being jointly responsible for periodontitis with pocketing (Aas et al., 2005, Russell et al., 1999, Scannapieco et al., 1992, Mojon and Bourbeau, 2003). Under specific conditions, dental plaque can harbour respiratory pathogens and promote their growth (Mojon, 2002, Scannapieco and Mylotte, 1996). Hence, the oral cavity may be a potential reservoir for respiratory pathogens leading to aspiration pneumonia (Rajasuo et al., 1996, Yoneyama et al., 1999, Scannapieco et al., 1998, Sumi et al., 2007, Fourrier et al., 1998). The presence of the following predisposing factors can foster such an occurrence: cigarette smoking, antibiotic treatment, diabetes mellitus, congestive heart failure, congenital defects in host defence, presence of underlying disease (e.g., chronic obstructive pulmonary disease (COPD)), immunosuppression, depressed consciousness, mechanical ventilation, enteral tube feeding, gastroesophageal reflux, and prolonged hospitalization (Scannapieco et al., 2003, Limeback, 1998, Russell et al., 1999, Scannapieco and Mylotte, 1996, Paju and Scannapieco, 2007, Fourrier et al., 1998), see also Figure 12. Oropharyngeal colonization by gram-negative bacteria is quite common in elderly nursing home residents (Scannapieco et al., 1992) and debilitated hospitalized patients, in particular intensive care unit (ICU) patients (DeRiso et al., 1996, Yoneyama et al., 1999, Fourrier et al., 2000). These patients have more dental plaque accumulation than their community-dwelling counterparts, which can cause colonization of the oral flora by bacteria such as respiratory pathogens (Scannapieco et al., 1992, Pietrokovski et al., 1995, Abe et al., 2001). Several trials demonstrated that better oral hygiene, either by the

63 use of mechanical cleansing or oral antiseptic rinses (e.g., betadine, chlorhexidine gluconate), significantly reduces the rate of lower respiratory tract infection in institutionalized subjects (Russell et al., 1999, Scannapieco, 1999, Mojon, 2002, Fourrier et al., 1998, Yoneyama et al., 2002, DeRiso et al., 1996). Diminished salivation and lower salivary pH are side effects of the intake of various medications (e.g., non-steroidal anti- inflammatory drugs, diuretics) and may promote colonization by pathogens (Scannapieco, 1999). Moreover, Mojon and Bourbeau (Mojon and Bourbeau, 2003) reported that oral infections can cause upper respiratory tract infections even in young adults.

Fig. 12: Transcolonization of the oropharynx with contiguous structures. Adapted from (Estes and Meduri, 1995).

The resident oral bacteria are likely aspirated along with respiratory pathogens and may facilitate the adhesion and colonization of respiratory pathogens to mucosal surfaces by altering the environment of the upper airways (Scannapieco and Mylotte, 1996). In addition, spread of infection from contiguous sites (Toews, 1986), inhalation of infectious

64 aerosols, or haematogenous spread from extrapulmonary sources of infection (e.g., translocation from the gastrointestinal tract, inevitable adverse effect of some dental treatments) (Mandell and Wunderink, 2008) may also be possible pathways for microorganisms to contaminate the lower respiratory tract (Azarpazhooh and Leake, 2006, Scannapieco, 1999). Furthermore, oral bacteria stimulate the inflamed periodontal tissues to release cytokines via the sulcus fluid into the saliva, which promotes the adhesion and colonization of respiratory pathogens to oropharyngeal surfaces (Scannapieco, 1999) (see Figure 13). In subjects with periodontal disease salivary biomarker load is increased (Saygun et al., 2011, Ramseier et al., 2009). Subsequently, respiratory epithelial cells may release cytokines, which can cause a recruitment of inflammatory cells and increase its susceptibility to infection (Mojon, 2002, Svanborg et al., 1996, Hedges et al., 1995, Scannapieco, 1999).

Fig. 13: Oral bacteria are likely aspirated along with respiratory pathogens and may facilitate the adhesion and colonization of respiratory pathogens to mucosal surfaces by altering the environment of the upper airways. Furthermore, cytokines from periodontally diseased tissues may be aspirated to stimulate local inflammatory processes increasing the susceptibility to infection in the lung. Adapted from (Scannapieco, 1999).

Pulmonary diseases by respiratory pathogens can decrease pulmonary compliance, which affects air intake (Hamalainen et al., 2004). Meurman et al. (Meurman et al., 1995)

65 suggesting that an increased bacterial load can cause respiratory tract infections and is a

risk factor for the decline of forced expiratory volume in one second (FEVR1R). The longitudinal study of Wilkinson et al. (Wilkinson et al., 2003) demonstrated an association

between a decrease in FEVR1R and an increase of the bacterial load in the lungs. However, even though bacteria are necessary for respiratory infection, alone, they are insufficient to cause disease (Gomes-Filho et al., 2010). Other factors such as the wide variety of confounding factors are involved and need to be taken into consideration.

4.3.3. Aspiration of Salivary Secretions The most common route by which the oral cavity may influence pulmonary function is aspiration of oropharyngeal contents including oral pathogens. Aspiration of small amounts of salvia during sleep is quite common, even in healthy subjects (Sinclair and Evans, 1994). However, individuals with altered consciousness (e.g., alcoholics, drug abusers, epileptics, stroke patients, patients with Parkinson’s disease) (Huxley et al., 1978), mechanically ventilated patients (Kollef, 1999), or those who are fed by others (Elpern et al., 1994, Russell et al., 1999) aspirate secretions of oropharyngeal flora more frequently and in larger amounts yielding greater incidences of bacterial pneumonia. Subjects who have gastroesophageal reflux may even aspirate gastric contents. In all these neurological conditions the coordinated breathing and swallowing mechanism is often disordered. There is evidence in the literature indicating that oral bacteria, poor oral hygiene, and periodontal disease may influence respiratory pathogen colonization and subsequently cause aspiration pneumonia, especially in high-risk subjects (Scannapieco and Mylotte, 1996, Scannapieco, 1999). The majority of pulmonary infections and emphysema cases are the result of aerobic bacteria found in the oral cavity including various Streptococcus species, various gram-negative bacilli (e.g., Enterobacteriaceae such as Escherichia coli, Klebsiella pneumoniae, Serratia species, and Enterobacter species), Pseudomonas aeruginosa, and Staphylococcus aureus (Scannapieco, 1999). Several studies (Bartlett and Gorbach, 1975, Bartlett and Finegold, 1974) indicated that periodontal organisms such as Porphyromonas gingivalis, Staphylococcus aureus, and Aggregatibacter actinomycetemomitans are involved in aspiration pneumonia, independent of the presence or absence of teeth (Terpenning, 2001). Nevertheless, the quantity of aspirated bacteria seems to be more important than the type (Inglis et al., 1993). Several studies found that typical respiratory pathogens colonize the dental plaque of hospitalized intensive care and nursing home patients, which can then be aspirated and cause infections (Yoneyama et al., 1999, Scannapieco et al., 1992, Fourrier et al., 1998). Anaerobic bacteria from periodontal pockets have been found in infected lungs (Fiddian-

Green and Baker, 1991) and pharyngeal microflora (Mojon, 2002, Terpenning et al., 2001, Russell et al., 1999). Furthermore, several interventional trials have suggested that improved oral hygiene by the use of mechanical cleansing or antimicrobial agents such as chlorhexidine or betadine can reduce the risk for respiratory disease (Mojon, 2002, Russell et al., 1999, DeRiso et al., 1996, Yoneyama et al., 2002).

4.3.4. Failure of Host Defence Mechanisms The lung is able to protect itself from aspirated material and infection by various defence mechanisms (DeRiso et al., 1996, Yoneyama et al., 1999, Fourrier et al., 2000). Intact cough reflexes that depend on the diaphragm and the stiffness or compliance of the pleura, the action of tracheobronchial secretions, and mucociliary transport of inhaled microorganisms and particulate material from the lower airways to the oropharynx along with humoral and cellular immune mechanisms maintain the sterility of the lower respiratory tract (Thurlbeck, 1990, Burrows et al., 1988, Burrows, 1990). Furthermore, the airways of the lung, even the smallest ones, have the ability to spring back helping to clear the lung (Burrows, 1990, Burrows et al., 1988). However, these instruments have to be neurologically intact and not suppressed by medication or other noxious agents. Factors like smoking, COPD, diabetes, malnutrition, corticosteroid use, and endotracheal or nasogastric intubation can affect the efficiency of these different defence mechanisms (Donowitz and Mandell, 2000, Mandell and Wunderink, 2008, Marik, 2001). The failure of the host defence mechanisms to eliminate pathogens from the mucosal surface results in proliferation of these pathogens, which are subsequently aspirated into the lung causing infection and tissue destruction (American Thoracic Society, 1996, Azarpazhooh and Leake, 2006).

4.3.5. Modification of Respiratory Mucosal Surfaces by Salivary Enzymes It has been suggested that periodontitis associated enzymes in the salvia (e.g., mannosidase, fucosidase, hexosaminidase, sialidase) may modify the oral and respiratory epithelium leading to enhanced adhesion and colonization of respiratory pathogens. A possible further cause could be destruction of macromolecules on the mucosal surface, exposing receptors that permit the adhesion and colonization of respiratory pathogens and release of cytokines (Estes and Meduri, 1995, Scannapieco, 1999, Fagon and Chastre, 1996). Indeed, it is well known that this salivary enzymatic activity is increased in subjects with periodontal disease and in subjects with poor oral hygiene (Gomes-Filho et al., 2010, Scannapieco, 1999, Estes and Meduri, 1995, Gibbons et al., 1990). In addition, the presence of Streptococcus gordonii, a key bacterium in dental plaque formation, facilitates

67 adhesion of pathogens such as Haemophilus influenzae to respiratory mucosal surfaces via specific adhesion-receptor interactions (Loesche and Lopatin, 1998, Scannapieco et al., 2001, Scannapieco and Rethman, 2003). The infection of the respiratory epithelium is initiated through a transmitted signal to the epithelial cell, either direct via the adhesion receptor (Svanborg et al., 1996) or through the release of biologically active molecules such as lipopolysaccharides (Scannapieco et al., 2001).

4.3.6. Destruction of Salivary Film by Enzymes It has been proposed that hydrolytic enzymes of periodontal disease associated pathogens may destroy the salivary film that protects against pathogenic bacteria (Travis et al., 1994). The destruction of the protective salivary molecules (e.g., mucin) makes it difficult to clear bacteria from the mucosal surface by reducing the ability of mucins to adhere to pathogens such as Haemophilus influenzae, which leaves them free to adhere to mucosal receptors in the respiratory tract (Gomes-Filho et al., 2010). As a consequence, non-specific host defence mechanisms against respiratory pathogens are diminished in high-risk individuals, thus promoting the risk of aspiration of these pathogens into the lungs (Nakamura and Slots, 1983, Gibbons and Etherden, 1986, Gibbons et al., 1990).

It has been shown that high concentrations of 0TPorphyromonas0T gingivalis in the saliva increases the risk for pulmonary disease with an odds ratio of 4.2 (Scannapieco, 1999). Salivary Staphylococcus aureus, which can be found in the mouths of debilitated or institutionalized patients, increased the risk for aspiration pneumonia by a factor of 7.4 (Page, 2001).

4.4. Strengths and Limitations There are several strengths of our analysis of the effects of periodontal diseases on lung function. The major strength was the population-based approach representing a general German adult population with a broad age range (25-85 years). Most studies were limited to narrow age ranges limiting generalizability of results to the general population. Secondly, a highly standardized method in obtaining comprehensive lung function parameters using standard spirometry, body plethysmography, helium dilution for measurement of alveolar volume, and diffusing capacity measurement for carbon monoxide was available. Thirdly, various clinical periodontal definitions were used covering both current (probing depth) and cumulative disease extent (attachment loss, tooth count). Data on bleeding on probing, which reflects gingival inflammation, and plaque, which reflects quality of oral hygiene, complete the analyses. All definitions presented a consistent impression of the effect of periodontitis on lung function.

Fourth, considering not only smoking status, but also the number of cigarettes smoked and the number of years of smoking (pack years) might have considerably reduced residual confounding. Evidence in the literature indicated that cigarette smoking is the leading risk factor not only for periodontal disease (Tomar and Asma, 2000, Albandar, 2002) but also for emphysema, chronic bronchitis, and lung infections resulting in the decline of pulmonary function (Garcia et al., 2001b, Hyman and Reid, 2004, Katancik et al., 2005). We additionally restricted analyses to never smokers excluding effects on dynamic lung volumes and parameters of airway limitation implied by recent smoking (Terpenning, 2001). Restricting analyses to never smokers, significant associations of

mean CAL with FVC, FEVR1R, and RV/TLC were confirmed. Last but not least, substituting waist circumference with weight did not change the results. Some limitations of our study merit comment. One of these is its cross-sectional design, known to preclude the establishment of a definite cause and effect relationship. Furthermore, selection bias due to non-participation in pulmonary function tests occurred. Predominantly, younger and healthier subjects were selected and no subjects with advanced COPD were identified. To counter this, multiple imputations of missing lung function data via chained equations were performed to reduce potential bias due to missing values in complete case analyses (van Buuren et al., 1999). After imputation, associations between periodontal disease and lung function were confirmed. Secondly, since periodontal measurements were taken half-mouth, extent of the disease might be underestimated and observed associations with lung function might be biased towards the null (Beck et al., 1990, Beck et al., 1999). However, use of mean values of PD and CAL limits underestimation and bias of risk estimates.

4.5. Conclusions To conclude, periodontal disease was significantly associated with reduced lung volumes, hyperinflation, and airflow limitation in a general adult population independently of potential confounders. Systemic inflammation did not provide a mechanism linking both diseases. However, large cohort studies with long observation periods evaluating comprehensive lung function parameters are needed to validate the observed associations and to assess causality more closely. Further to this, it needs to be investigated, with the help of randomized clinical trials, whether prevention or treatment of periodontitis might have a beneficial impact on lung function.

5. Summary Chronic infections, including periodontal infections, may reduce lung function. To date, there are hardly any population-based studies evaluating the association between periodontitis and lung function. However, there are some studies that used variables

associated with obstructive pulmonary diseases (FEVR1R, FEVR1R/FVC). Thus, we aimed to assess the potential association of periodontal diseases with lung volumes and airflow limitation in the population-based Study of Health in Pomerania (SHIP). Of 3300 participants aged 25-85 years of the 5-year follow-up (SHIP-1), 1809 subjects participated in lung function examinations. 1465 subjects were included in the analyses. Lung function was measured using spirometry, body plethysmography, helium dilution, and diffusing capacity for carbon monoxide. Periodontal status was assessed by clinical attachment loss, probing depth, and number of missing teeth. Linear regression models using fractional polynomials were used to assess linear and non-linear associations between periodontal disease and lung function adjusting for confounders. Adjusting for age, sex, waist circumference, physical activity, diabetes, asthma, and time between core and pulmonary examination, mean attachment loss was significantly associated with variables of dynamic and static lung volumes, airflow limitation and hyperinflation. Total lung capacity and diffusing capacity for carbon monoxide were not associated with mean attachment loss. Adjustment for smoking and height considerably changed coefficients indicating profound confounding. Including fibrinogen and high sensitive CRP into fully adjusted models did not change coefficients of mean attachment loss. Restricted to never smokers, mean attachment loss was significantly associated with

FEVR1R, FVC, and RV/TLC. Relations with lung function were confirmed for mean probing depth, extent measures of attachment loss/probing depth, and number of missing teeth. Periodontal disease was significantly associated with decreased lung function. Systemic inflammation did not provide a mechanism linking both diseases. However, cohort studies evaluating lung function in the current manner are needed to confirm results from this study and to assess a causal relationship. Furthermore, it needs to be investigated with the help of randomized clinical trials whether prevention or treatment of periodontitis might have a beneficial impact on lung function.

6. Zusammenfassung Parodontitis wird als Risikofaktor für eine verminderte pulmonale Leistungsfähigkeit angesehen. Leider gibt es derzeit kaum populationsbasierte Studien, welche den Zusammenhang zwischen Parodontitis und pulmonaler Leistungskapazität untersucht haben. Lediglich bzgl. der mit einer obstruktiven Lungenstörung assoziierten Werte

(FEVR1R, FEVR1R/FVC) gibt es einige wenige publizierte Studien. Ziel war es daher, den Zusammenhang zwischen Parodontitis und Lungenfunktion anhand der populationsbasierten Study of Health in Pomerania (SHIP) zu untersuchen. Als Grundlage für diese Studie diente das 5-Jahres-Follow-Up. Von den insgesamt 3300 Teilnehmern im Alter von 25-88 Jahren nahmen 1809 freiwillig an der Lungenfunktionsdiagnostik teil. Es wurden 1465 Probanden in diese Studie eingeschlossen. Die Lungenfunktionsdiagnostik beinhaltete eine Spirometrie, eine Ganzkörperplethysmographie, eine Heliumverdünnungsmethode und eine Messung der Diffusionskapazität für Kohlenmonoxid. Der parodontale Gesundheitszustand wurde anhand des klinischen Attachmentverlustes, der Sondierungstiefen und der Anzahl fehlender Zähne erfasst. Lineare Regressionsmodelle wurden genutzt, um den Zusammenhang zwischen parodontalen Variablen und Lungenfunktionsvariablen unter Adjustierung für potentielle Confounder zu bestimmen. Die Anwendung fraktioneller Polynome ermöglichte eine adäquate Beschreibung nicht-linearer Zusammenhänge. Unabhängig von Alter, Geschlecht, Bauchumfang, sportlicher Bewegung, Diabetes, Asthma und Zeit zwischen Basis- und Lungenfunktionstest konnte ein signifikanter Zusammenhang zwischen Attachmentverlust und Variablen für das dynamische und statische Lungenvolumen sowie für Obstruktion nachgewiesen werden. Für die totale Lungenkapazität und die Diffusionskapazität wurde kein Zusammenhang nachgewiesen. Rauchverhalten und Körpergröße stellten sich als wichtige Confounder dar. Durch das Hinzufügen von Fibrinogen und hochsensitivem CRP kam es nicht zu einer Änderung der Koeffizienten für den mittleren Attachmentverlust. Unter Nichtrauchern war der mittlere

Attachmentverlust signifikant mit FEVR1R, FVC und RV/TLC assoziiert. Der Zusammenhang zur Lungenfunktion wurde für die mittlere Sondierungstiefe, Extentmaße für Attachmentverlust und Sondierungstiefe und die Anzahl fehlender Zähne bestätigt. Die vorliegende Untersuchung zeigte, dass Parodontitis signifikant mit einer verringerten Lungenfunktion assoziiert war. Entzündung wurde nicht als Mediator beider Erkrankungen bestätigt. Um einen möglichen kausalen Zusammenhang zu evaluieren, sind weitere Studien mit größerem Stichprobenumfang und longitudinalem Design erforderlich. Anhand randomisierter klinischer Studien ließe sich ein positiver Einfluss von Prävention oder parodontalen Behandlungen auf die Lungenfunktion untersuchen.

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Appendix I List of Abbreviations ABL alveolar bone loss AGE advanced glycation end products AL attachment loss ATC Anatomic Therapeutic Classification of drugs ATS American Thoracic Society BMI body mass index BOP bleeding on probing CAL chronic airflow limitation CAL clinical attachment loss CAP community acquired pneumonia CEJ cemento-enamel junction

CRIR initial helium concentration CI confidence interval

CRFR final helium concentration CMR Community Medicine Research Net CO carbon monoxide COAD chronic obstructive airway disease COLD chronic obstructive lung disease COPD chronic obstructive pulmonary disease CORD chronic obstructive respiratory disease CRP C-reactive protein DMFS/T number of decayed (D), missing (M), and filled (F) surfaces (S)/ teeth (T) DMS Deutsche Mundgesundheitsstudie (German Dental Health Survey) ERS European Respiratory Society ERV expiratory reserve volume F/M female/male FEF25/50/75 forced expiratory flow at 25%, 50%, and 75% of FVC

FEVR1R forced expiratory volume in one second

FEVR1R/FVC forced expiratory volume in one second to forced vital capacity ratio FGM free gingival margin FPs fractional polynomials FRC functional residual capacity

FRCRplethR functional residual capacity derived body plethysmography

FVC forced vital capacity GB gingival bleeding GH gingiva height GI gingival index GOLD Global Initiative for Chronic Obstructive Lung Disease HAP hospital (nosocomial) acquired pneumonia HCLD hospitalized chronic lung-diseased He helium hs-CRP high sensitivity C-reactive protein ICU intensive care unit IL-1α, IL-1β, IL-6, IL-8interleukin 1α, 1β, 6, 8 LLL left lower lobe bronchus LM left mainstem bronchus LUL left upper lobe bronchus MEF75/50/25 maximal expiratory flow at 75%, 50%, and 25% of FVC n number n.a. not annotated NoMT number of missing teeth NoT number of teeth

OR2 Roxygen OH oral hygiene OHI Oral Hygiene Index OR odds ratio

PRAR pressure of the lungs, also called intrathoracic pressure PAL proximal attachment loss

PRCR pressure of the chamber PD probing depth PI plaque index RAW airway resistance RLL right lower lobe bronchus RM right mainstem bronchus RML right middle lobe bronchus RR risk ratio RSV respiratory syncytial virus

RRtot R total airway resistance RUL right upper lobe bronchus RV residual volume

RV/TLC residual volume to total lung capacity ratio SD standard deviation SHIP Study of Health in Pomerania

RslowRVC inspiratory vital capacity TLC total lung capacity

TLCOc-VRA R diffusing capacity for carbon monoxide (single breath), corrected for haemoglobin level and ventilated area (assessed by helium dilution) TNF-α tumour necrosis factor-alpha Tr trachea

VRAR alveolar volume

VRAR inthrathoracic volume (ITGV), also called FRCRpleth VAP ventilator acquired pneumonia

VRCR air volume in the chamber

VRIR volume of gas in spirometer

VRtotR total gas volume WC waist circumference WHO World Health Organization

II Overview on Lung Function Parameters in SHIP

Expiratory reserve volume (ERV) is defined as the maximal volume of air that is exhaled from the end-expiratory level (see Figure 14) (American College of Chest Physicians and American Thoracic Society, 1975).

Inspiratory vital capacity (Rslow RVC) is defined as the maximal volume of air that is inhaled from the end-inspiratory level (see Figure 14) (American College of Chest Physicians and American Thoracic Society, 1975).

Vital capacity (VC) is the maximum volume of air that a person can exhale after maximum inhalation (see Figure 14) (American College of Chest Physicians and American Thoracic Society, 1975).

Fig. 14: Dynamic lung volumes as measured by spirometry. Adapted from (Sultan Qaboos University, 2000).

Forced vital capacity (FVC) is measured in ml. It is the maximum amount of air one can breathe out with force after maximum inhalation (American College of Chest Physicians and American Thoracic Society, 1975). Its value is lowered in restrictive lung disease and remains constant in obstructive disease. Age, height, sex, and physical activity have an impact on the outcome volume.

Forced expiratory volume in one second (FEVR1R) is measured in ml. This measures the amount of air that can be exhaled as fast and strong as possible within the first second (American College of Chest Physicians and American Thoracic Society, 1975). It is

dependent on age, height, sex, and physical activity. FEVR1R diminishes both in obstructive

100 and restrictive lung disease and is as a guiding parameter of great importance, especially in chronic lung diseases.

FEVR1R divided by FVC can also be determined (FEVR1R/FVC ratio in %). Its value decreases in obstructive conditions and increases or remains steady respectively in restrictive conditions (American College of Chest Physicians and American Thoracic Society, 1975).

The maximum expiratory flow rate at 75%, 50%, and 25% of vital capacity (MEF75/50/25) is measured in l/s. It describes the expiratory flow rate at the moment that 75%, 50%, or 25% of air still has to be exhaled (American College of Chest Physicians and American Thoracic Society, 1975). An isolated MEF25 value that describes the narrowing of small airways is typical for a smoker. Patients with bronchial asthma have a lowered MEF50 value.

RRtotR (in kPa/s * l) characterizes total airway resistance, a sensitive parameter for airway obstruction (American College of Chest Physicians and American Thoracic Society, 1975). Resistance is greatest at the bronchi of intermediate size, because of soaring

numbers of bronchi with every bifurcation. RRtotR decreases with increasing lung volume: patients with increased R often breathe with increased lung volume to minimize R.

Functional residual capacity (FRCRplethR in ml) measures the amount of air that remains in the lungs at the end of a normal expiration (see Figure 14) (American College of Chest Physicians and American Thoracic Society, 1975). It is the sum of expiratory reserve volume (ERV) and residual volume (RV). Asthma patients can have a very high value because air cannot be breathed out.

The amount of air in the lungs after inhaling as deeply as possible is described as total lung capacity (TLC in ml) (American College of Chest Physicians and American Thoracic Society, 1975). It is the sum of the volume of air in the lungs after maximal inspiration (vital capacity (VC) plus residual volume (RV)) or the sum of all volume compartments (see Figure 14).

Residual volume (RV) assesses the amount of air that remains in the lungs after maximal exhalation (see Figure 14) (American College of Chest Physicians and American Thoracic Society, 1975). It can be measured by helium gas method, which is described below. RV can be further determined as the difference of functional residual capacity (FRC) and expiratory reserve volume (ERV). A normal RV value equals one fifth of TLC (20%). Due to a loss of compliance and atrophy of lung tissue with age, RV increases on

101 account of VC, which decreases. TLC is not affected and remains steady. RV is dependent on height, sex, and physical activity. It can be used to calculate RV/TLC ratio in %.

TLCOc-VRA R(in mmol/kPa/min/l) was assessed by gas diffusion tests. It is the diffusing capacity for carbon monoxide (single breath), corrected for hemoglobin level and ventilated area, a parameter for diffusion capacity (American College of Chest Physicians and American Thoracic Society, 1975). Measurements stay normal in asthma patients. A decrease is caused by sarcoidosis, alveolitis, or emphysema due to damaged alveoli membrane.

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III Eidesstattliche Erklärung

Hiermit erkläre ich, dass ich die vorliegende Dissertation selbständig verfasst und keine anderen als die angegebenen Hilfsmittel benutzt habe.

Die Dissertation ist bisher keiner anderen Fakultät, keiner anderen wissenschaftlichen Einrichtung vorgelegt worden.

Ich erkläre, dass ich bisher kein Promotionsverfahren erfolglos beendet habe und dass eine Aberkennung eines bereits erworbenen Doktorgrades nicht vorliegt.

…………………………… ………………………………………. Datum Unterschrift

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IV Curriculum Vitae

Name Stefanie Richter Nationalität Deutsch Geburtsdatum 22. April 1984 Geburtsort Lauchhammer Familienstand ledig

Schulausbildung ...... 1990-1996 Grundschule Guteborn 1996-2000 Emil-Fischer-Gymnasium Schwarzheide 2000-2001 Fayetteville High School, Arkansas, USA 2001-2004 Friedrich-Engels-Gymnasium Senftenberg Juni 2004 Abschluss Abitur

Akademische Ausbildung ...... 2004-2010 Studium der Zahnmedizin an der Ernst-Moritz-Arndt-Universität Greifswald November 2010 Zahnärztliche Approbation Seit Januar 2011 Promotionsstudium an der Abteilung für Parodontologie im Zentrum für Zahn-, Mund- und Kieferheilkunde der Ernst-Moritz-Arndt- Universität Greifswald

Beruflicher Werdegang ...... Seit Februar 2012 Assistenzzahnärztin in Grimmen, Praxis Dr. Birgit Fitsch

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V Acknowledgements

Bei meinem Doktorvater Prof. Dr. Thomas Kocher möchte ich mich für die Vergabe des interessanten Forschungsthemas, für die finanzielle Unterstützung im Rahmen eines fünfzehnmonatigen Forschungsstipendiums, sowie für die Anleitung und stetige Unterstützung bedanken. Ganz besonderen Dank möchte ich meiner Betreuerin Dr. Birte Holtfreter aussprechen. Die unendliche Hilfsbereitschaft, die freundschaftlichen Motivationsschübe, all die konstruktiven Anregungen und Verbesserungsvorschläge, sowie die starke Unterstützung, nicht nur bei der Anfertigung der statistischen Auswertung, haben mich seit Beginn dieser Arbeit begleitet und vorangetrieben. Vielen lieben Dank Birte! Ein großes Dankeschön gilt auch den wissenschaftlichen Mitarbeitern der Klinik für Innere Medizin Prof. Dr. Ralf Ewert, PD Dr. Christoph Schäper und PD Dr. Sven Gläser für die kritischen Anmerkungen und hilfreichen Gedankenanstöße in unseren Diskussionsrunden. Ganz herzlich danke ich den Mitwirkenden der Study of Health in Pomerania, deren umfangreiche Untersuchungen meine Arbeit erst ermöglicht haben. Meiner Familie möchte ich für die liebevolle Unterstützung, für die Ruhe und Gelassenheit und für das fortwährende Verständnis in all den Jahren danken. Ganz großer Dank geht schließlich an meinen Freund Johann. Danke für deine offenen Ohren, für deine Liebe und den Rückhalt in schwierigen Zeiten.

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