Pablo Rojas

Avril M Monahan

Simone Schuller

Ian S Miller

Bryan K Markey

Dr Jarlath E Nally
email: jarlath.nally@ucd.ie

; Jarlath

E Nally

Detection and quantification of leptospires in

Keywords: Leptospira, Zoonosis, Canine

come

and shed leptospires into the environment via urine. In contrast, accidental hosts such as 5 humans can suffer severe acute forms of the disease. Infection results from direct contact 6 with infected urine or indirectly, through contaminated water sources. In this study, a 7 quantitative Real-Time PCR specific for lipL32 was designed to detect the urinary shedding 8 of leptospires from dogs. The sensitivity and specificity of the assay was evaluated using 9 both a panel of pathogenic Leptospira species and clinical microbial isolates, and samples 10 of urine collected from experimentally infected rats and non-infected controls. The lower 11 limit of detection was approximately 3 genome equivalents per reaction. The assay was 12 applied to canine urine samples collected from local dog sanctuaries and the University 13 Veterinary Hospital (UVH) at University College Dublin. Of 525 canine urine samples 14 assayed, 37 were positive, indicating a prevalence of urinary shedding of leptospires of 15 7.05%. These results highlight the need to provide effective canine vaccination strategies 16 and raise public health awareness. 17

Text 1

Leptospires colonize the renal tubules of maintenance host species such as dogs, rats and 2 cattle, from which they are excreted via urine into the environment, where they can survive 3 in suitable moist conditions. Maintenance hosts are typically asymptomatic whilst 4 accidental hosts can suffer a wide range of clinical manifestations including hepatic and 5 renal failure, and severe pulmonary haemorrhage [START_REF] Ko | Leptospira: the dawn of the molecular 2 genetics era for an emerging zoonotic pathogen[END_REF]. Contact with infected urine or 6 contaminated water sources may result in infection since leptospires can enter the body 7 through broken skin or mucosal surfaces such as the conjunctival tissue of the eye [START_REF] Monahan | Leptospirosis: risks during recreational 4 activities[END_REF]. 8 Specific serovars of Leptospira are associated with asymptomatic carriage and 9 persistent shedding in urine in particular mammalian host species [START_REF] Faine | Leptospira and Leptospirosis[END_REF][START_REF] Thiermann | The Norway rat as a selective chronic carrier of Leptospira 8 icterohaemorrhagiae[END_REF][START_REF] Monahan | Review paper: Host-pathogen 10 interactions in the kidney during chronic leptospirosis[END_REF]. Leptospiruria in 10 maintenance hosts is of high intensity, constant and of long duration compared to accidental 11 hosts where it is of low intensity, intermittent and of short duration [START_REF] Chernukcha | Pathogenicity of Leptospires 13 of various serological types for some species of wild rodents[END_REF]. Such host 12 adaptation was first described by Babudieri who observed 'biological equilibrium' between 13 rodent hosts and certain Leptospira serovars [START_REF] Babudieri | Animal reservoirs of leptospires[END_REF]. 14 Dogs can act as a maintenance host for pathogenic Leptospira serovars. Historically, 15 canine leptospirosis has been associated with serovars Canicola and Icterohaemorrhagiae, 16 but canine serum may contain antibodies specific for a wide range of serovars including 17 Autumnalis, Bratislava, Grippotyphosa, Hardjo, Pomona, and Zanoni [START_REF] Zwijnenberg | Clinical and epidemiological features of canine leptospirosis 18 in North Queensland[END_REF][START_REF] Alton | Increase in 20 seroprevalence of canine leptospirosis and its risk factors, Ontario 1998-2006[END_REF][START_REF]Canine leptospirosis--do we have a problem?[END_REF][START_REF] Iwamoto | Nationwide survey of Leptospira antibodies in dogs in Japan: 26 results from microscopic agglutination test and enzyme-linked immunosorbent assay[END_REF][START_REF] Stokes | Prevalence of serum antibodies against six Leptospira serovars in healthy dogs[END_REF][START_REF] Timoney | Leptospira and infectious canine 32 hepatitis (ICH) virus antibodies and nephritis in Dublin dogs[END_REF]. Clinical 18 presentation of acute disease depends in part on the infecting serovar and can range from 19 mild to severe [START_REF] Goldstein | 34 Influence of infecting serogroup on clinical features of leptospirosis in dogs[END_REF]. Clinical resolution of acute infection may lead to asymptomatic 20 shedding. In addition, there is evidence to suggest that clinically normal dogs can be 21 chronic carriers of infection, and thus maintenance hosts, shedding leptospires via urine 22 into the environment [START_REF] Harkin | 38 Comparison of polymerase chain reaction assay, bacteriologic culture, and serologic testing 39 in assessment of prevalence of urinary shedding of leptospires in dogs[END_REF][START_REF] Suepaul | Serovars 42 of Leptospira isolated from dogs and rodents[END_REF][START_REF] Zakeri | Djadid ND 44 Leptospira wolffii, a potential new pathogenic Leptospira species detected in human, sheep 45 and dog[END_REF]. Serological surveys often report more than 20% of examined 23 canine sera contain antibodies specific for pathogenic Leptospira serovars. However, it is 4 difficult to correlate serological findings with the prevalence of chronic infection and 1 leptospiruria, which has been reported in the presence of relatively low or negative 2 antibody titres [START_REF] Faine | Leptospira and Leptospirosis[END_REF]18]. 3

In this study, a Real-Time PCR assay was designed to target a 57bp amplicon within 4 lipL32, a gene that encodes an outer membrane lipoprotein present in pathogenic 5 leptospires but absent in saprophytic species [19,20]. The lipL32 gene sequence is highly 6 conserved amongst pathogenic leptospires, and displays 96.9% average pairwise DNA 7 sequence identity [21]. The nucleotide sequences for lipL32 from 36 submissions 8 (accession numbers listed below) to the National Center for Biotechnology Information 9 (www.ncbi.nlm.nih.gov), comprising 6 species and more than 17 serovars of leptospires, 10 were aligned using Mega 4 [22]. Primer Express software (Perkin Elmer Applied 11 Biosystems) was used to design primers which were subsequently modified to include 12 degenerate bases, and accommodate all sequence alignments (forward primer; 5'-13 TCGCTGAAATRGGWGTTCGT-3', reverse primer 5'-CGCCTGGYTCMCCGATT-3'). 14 A fluorescent probe (FAM 5'-ATTTCCCCAACAGGCG-3' NFQ) was designed which 15 was specific for a consensus region of the resulting 57bp amplicon. Specificity of primers 16 and probe was confirmed by BLAST (http://blast.ncbi.nlm.nih.gov/). Quantitative Real-17 Time PCR was performed using an Applied Bioscience 7500 Thermo cycler, with a single 18 starting cycle of 50°C for 2 minutes, then a 95°C step for 10 minutes followed by 40 cycles 19 of 95°C for 15 seconds and 60°C for 60 seconds. Each reaction comprised 25µL final 20 volume; 12.5µL of Taqman mastermix (Applied Biosystems), 1µL of labelled probe 21 (250µM), 3µL of each primer (600nM) (Sigma Aldrich), 1µL of ultra pure water and 4.5µL 22 of template. Each unknown sample was tested in triplicate and each dilution of the standard 23 curve in duplicate. Each run included a non-template control, a positive control urine 1 sample from experimentally infected rats and a negative control. 2

The use of Taqman chemistry provided additional sensitivity and specificity for 3 Time PCR assay (Table 1). In contrast, DNA derived the saprophytic serovar Patoc and 13 seventeen additional clinical microbial isolates obtained from the Bacteriology Diagnostic 14 Laboratory at the University College Dublin Veterinary Hospital were negative. [START_REF] Harkin | 38 Comparison of polymerase chain reaction assay, bacteriologic culture, and serologic testing 39 in assessment of prevalence of urinary shedding of leptospires in dogs[END_REF] In order to emulate as closely as possible a typical clinical urine sample, and the 16 effects of typical inhibitors of PCR associated with urine, a standard curve to determine the 17 efficiency of the lipL32 Real-Time PCR was generated by spiking non-infected rat urine 18 with known amounts of a virulent strain of L. interrogans serovar Copenhageni prior to 19 DNA extraction. A standard curve was generated as follows: 10mL of L. interrogans 20 serovar Copenhageni (4.3 x 10 8 leptospires/mL) was centrifuged for 10 minutes at 10,000 x 21 g at 4 o C. The resulting pellet was resuspended in 1mL of urine from non-infected rats and 22 10 fold serial dilutions performed in negative urine. Each dilution was centrifuged at 23 10,000 x g at 4 o C, followed by DNA extraction. DNA was extracted using a Qiagen Mini 6 Kit with a Qiagen Vacuum Manifold, as per manufacturer's instructions with slight 1 modification. Samples were digested with proteinase K for 2 hours. A positive (urine 2 collected from an experimentally infected rat) and negative control were included for 3 extraction in each processed batch of 22 samples. The number of leptospires (assuming one 4 genome equivalent per leptospire) was calculated based on the extracted DNA 5 concentration obtained with a fluorospectrometer (Nanodrop ND 1000, Coleman 6 Technologies, V3.5.2). The efficiency of the standard curve was 98.5% (Figure 1). The 7 lowest detectable number of genomes equivalents was 2.59 per reaction, equivalent to 8 172.6 leptospires/mL of urine, assuming one genome equivalent/leptospire (Figure 1 and remained positive up to 6 weeks post-infection. In contrast, urine samples collected at 22 day 0, week 1 and from all negative control rats remained negative. The sensitivity and 23 specificity of the lipL32 Real-Time PCR assay is therefore validated by the lack of any 24 positive amplification in non-infected control rats, and perhaps more importantly, by the 1 lack of any positive amplification in urine samples from experimentally infected rats at day 2 0 or at week 1 post-infection. This suggests that these rats were infected but leptospires 3

were not yet being excreted in urine from colonized renal tubules. 4

The lipL32 Real-Time PCR was used to assess canine urine samples submitted to a 5 diagnostic laboratory (UVH) for routine urinalysis. It was also used for samples collected 6 by free catch from local dog sanctuaries. A 2mL aliquot of urine was collected from 498 7 canine urine samples, routinely submitted for urinalysis to the University Veterinary 8 hospital (UVH) in University College Dublin (UCD). A further 27 canine urine samples 9 were collected from dogs in sanctuaries by free catch, with sample volumes ranging from 10 0.35 to 35mL. All collected samples were stored at 4 o C for no more than 24 hours, 11 centrifuged at 10,000 x g for 10 minutes and the resulting pellets stored at -20 o C prior to 12 analysis. Canine urine samples were considered positive when at least 2/3 of the Ct values 13 were lower than that of the Ct value of the lowest dilution of the standard curve. In 14 addition, all canine urine pellets from dog sanctuaries were examined by dark-field 15 microscopy. Of 498 samples collected from the UVH and tested for the presence of lipL32, 16 34 (6.83%) were positive (Table 2). Of the 34 positive samples, the average number of 17 genome equivalents of leptospires shed per ml of urine was 6.22x10 4 , and ranged from as 18 few as 35.5 leptospires per ml to 1.33x10 6 leptospires per mL of urine collected. Out of 27 19 samples collected from local dog sanctuaries, 3 (11.11%) were positive. The average 20 number of genome equivalents of leptospires shed per mL of urine was 2.3x10 4 , and ranged 21 from as few as 76.1 leptospires per mL to 4.46x10 4 leptospires per mL of urine collected. In 22 addition, one of the three samples was positive for the presence of spirochaetes by dark 23 field microscopy (Table 2). None of the canine urine samples from sanctuaries cultured 1 positive. 2

Whilst a larger number of samples collected from dog sanctuaries were positive 3 (11.1%) compared to samples from UVH (6.82%), insufficient numbers of samples were 4 collected from dog sanctuaries to determine if this was a significant difference. Further, 5 larger volumes of urine samples were collected by free catch from sanctuaries (up to 35ml) 6 compared to UVH samples (limited to 2ml). Clinical examination of dogs in local dog 7 sanctuaries indicated that all dogs were clinically normal (data not shown). Vaccination 8 status was not available in order to determine whether any of the positive urine samples 9 came from dogs vaccinated against leptospirosis. 10 An internal amplification control was not included in this assay. Rather, for every 11 batch of clinical urine samples from which DNA was extracted, a positive urine sample 12 from an experimentally infected rat was extracted, as well as a negative control. Each 13 lipL32 Real-Time assay plate contained each of these controls, as well as each dilution of 14 the standard curve, which were performed in duplicate, whilst unknowns were performed in 15 triplicate. [START_REF] Suepaul | Serovars 42 of Leptospira isolated from dogs and rodents[END_REF] Results suggest that 7.05% of domestic dogs in Ireland shed leptospires in urine. 17

Leptospiruria has also been demonstrated in 8.8% of dogs in the U.S. and 22% of dogs in 18

Iran by routine PCR and nested PCR of 23S and 16S gene sequence respectively [START_REF] Harkin | 38 Comparison of polymerase chain reaction assay, bacteriologic culture, and serologic testing 39 in assessment of prevalence of urinary shedding of leptospires in dogs[END_REF][START_REF] Zakeri | Djadid ND 44 Leptospira wolffii, a potential new pathogenic Leptospira species detected in human, sheep 45 and dog[END_REF]. 19

Results highlight the global significance of canine leptospirosis and its zoonotic potential. 20

The lower prevalence of Leptospiruria in our study may reflect the smaller volume tested 21 (2ml compared to 6-20ml) or the specificity for lipL32 compared to 23S and 16S encoding 22 genes. Quantification of numbers of leptospires in each positive sample indicated that 23 several dogs excreted more than 10 4 leptospires/mL urine. Despite repeated attempts, 24 culture of leptospires from canine urine was unsuccessful and thus typing of excreted 1 leptospires was not possible. It will be important in future studies to determine the species 2 and serovar identity of leptospires excreted in such samples, to assess zoonotic risk, and to 3 further understand canine leptospirosis. Detection of pathogenic leptospires in canine urine samples in Ireland. [START_REF] Timoney | Leptospira and infectious canine 32 hepatitis (ICH) virus antibodies and nephritis in Dublin dogs[END_REF] Numbers of samples tested are provided, as are numbers of samples that were positive by 14 the lipL32 Real-Time PCR. *A single canine urine sample from a dog sanctuary was also 15 positive by dark-field microscopy for the presence of spirochaetes. Standard curve of the lipL32 Real-Time PCR assay using DNA extracted 3 from 10 fold serial dilutions of in vitro cultivated Leptospira diluted in negative rat urine. [START_REF] Thiermann | The Norway rat as a selective chronic carrier of Leptospira 8 icterohaemorrhagiae[END_REF] 5

Figure 2

Detection and quantification of Leptospira by the lipL32 Real-Time PCR 6 assay in urine samples collected from experimentally infected rats (n=8, square) and non-7 infected controls (n=4, diamond). 8

1 1 2 the zoonotic disease leptospirosis 3 4 5 7 P 15 UCD 17 College

 12471517 Detection and quantification of leptospires in urine of dogs; a maintenance host forRunning title: Detection of pathogenic Leptospira in canine urine 6 . Rojas, A. M.Monahan, S. Schuller, I.S. Miller, B.K. Markey and J.E. Nally*. School of Agriculture Food Science & Veterinary Medicine, 16 UCD Conway Institute of Biomolecular and Biomedical Research,

  PCR amplicons by inclusion of a 16bp lipL32 gene specific fluorescent probe which was 4 conserved amongst all aligned sequences, and representative of 6 different species of 5 pathogenic leptospires. The incorporation of two degenerate bases into forward and reverse 6 primers permitted consensus amongst all aligned sequences, and successful amplification 7 from all serovars tested. For example, lipL32 gene sequence derived from L. interrogans 8 serovar Icterohaemorrhagiae (AY423075.1), Leptospira interrogans serovar Canicola 9 (AY763509.1), Leptospira interrogans serovar Tarassovi (AY609330.1) and L. interrogans 10 serovar Pomona (AY223718.1) differ at the base pair positions replaced with degenerate 11 bases. DNA from all pathogenic serovars of Leptospira tested positive by the lipL32 Real-12

  ), thus 9 demonstrating the sensitivity of the assay with clinically relevant criteria. 10 Urine samples collected from experimentally infected rats were used to further 11 demonstrate the sensitivity and specificity of the lipL32 Real-Time PCR assay. 12 Experimentally infected rats remain clinically asymptomatic but shed large numbers of 13 leptospires in urine approximately 10-14 days post-infection [23-25]. Wistar male Rattus 14 norvegicus (University College Dublin, Biomedical Facility), 150-210 g, 6 weeks of age, 15 were experimentally infected as previously described [23]. Rats were housed in metabolism 16 cages once weekly and 200µL urine collected for enumeration of leptospires by 17 quantitative Real-Time PCR. All study protocols were approved by the University College 18 Dublin Animal Research Ethics Committee and conducted under license from the 19 Department of Health and Children. All urine samples collected from experimentally 20 infected rats were positive by the lipL32 Real-Time PCR assay by 2 weeks post-infection, 21

4 5Accession

 4 Numbers of lipL32 gene sequences used for alignments 6 AF366366, AM937000.1, AY461905.1, AY763509.1, AY609322.1, AY423075.1, 7 AY609330.1, AY461901.1, EU871716.1, AY609326.1, AY609327.1, DQ149595.1, 8 AY776294.1, AY609333.1, AY609329.1, U89708.1, AF181553.1, AF121192.1, 9 AF181556.1, AF181555.1, AY461910.1, AY776293.1, AY223718.1, AY461904.1, 10 EU871720.1, EU871719.1, EU871718.1, AY461908.1, AY461907.1, AY461906.1, 11 EU871723.1, AY461899.1, AY461903.1, AY609328.1, AY609331.

16

 16

17 Figure Legends 1 2 Figure 1

 1721 Figure Legends 1

Panel of clinical microbial isolates tested for specificity 14

	1 1	[18] Ellis WA, O'Brien JJ, Cassells J (1981) Role of cattle in the maintenance of
	2 2	Leptospira interrogans serotype hardjo infection in Northern Ireland. Vet Rec 108 (26):555-
	3	557
	4	[19] Haake DA, Chao G, Zuerner RL, Barnett JK, Barnett D, Mazel M, Matsunaga J, Serogroup Serovar Strain
	5	Levett PN, Bolin CA (2000) The leptospiral major outer membrane protein LipL32 is a
	6	lipoprotein expressed during mammalian infection. Infect Immun 68 (4):2276-2285 Autumnalis Autumnalis Akyami
	7	[20] Stoddard RA, Gee JE, Wilkins PP, McCaustland K, Hoffmaster AR (2009)
	8	Detection of pathogenic Leptospira spp. through TaqMan polymerase chain reaction Ballum Ballum Mus 127
	9	targeting the LipL32 gene. Diagnostic microbiology and infectious disease 64 (3):247-255
	10	[21] Haake DA, Suchard MA, Kelley MM, Dundoo M, Alt DP, Zuerner RL (2004) Bataviae Bataviae Kariadi-Satu
	11	Molecular evolution and mosaicism of leptospiral outer membrane proteins involves
	12	horizontal DNA transfer. Journal of bacteriology 186 (9):2818-2828 Bataviae Bataviae Swart
	13	[22] Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: Molecular Evolutionary
	14 15	Genetics Analysis (MEGA) software version 4.0. Molecular biology and evolution 24 (8):1596-1599 *Semaranga *Patoc *Patoc 1
	16 17	[23] Monahan AM, Callanan JJ, Nally JE (2008) Proteomic analysis of Leptospira interrogans shed in urine of chronically infected hosts. Infect Immun 76 (11):4952-4958 Australis Bratislava B2a
	18 19	[24] Nally JE, Chow E, Fishbein MC, Blanco DR, Lovett MA (2005) Changes in lipopolysaccharide O antigen distinguish acute versus chronic Leptospira interrogans Canicola Canicola Hond Utrecht IV
	20 21	infections. Infect Immun 73 (6):3251-3260 [25] Athanazio DA, Silva EF, Santos CS, Rocha GM, Vannier-Santos MA, McBride AJ, Icterohaemorrhagiae Copenhageni RJ16441
	12 22 23	1, AE016823.1. Ko AI, Reis MG (2008) Rattus norvegicus as a model for persistent renal colonization by pathogenic Leptospira interrogans. Acta tropica 105 (2):176-180 Icterohaemorrhagiae Icterohaemorrhagiae Lai
	13 24 25	Icterohaemorrhagiae Lai Type Langkawi Langkawi
		Icterohaemorrhagiae Icterohaemorrhagiae RGA
	15	Bacillus cereus, Bordetella bronchiseptica, Candida albicans, Citrobacter freundii, Pomona Pomona Pomona
	16	Enterobacter aerogenes, Enterococcus faecalis, Escherichia coli, Hyphomicrobium Sejroe Hardjo Type Bovis Hardjobovis
	17	species, Klebsiella pneumoniae, Leptospira biflexa serovar Patoc, Listeria monocytogenes, Tarassovi Tarassovi Mitis Johnson
	18 3	Pasteurella multicocida, Proteus vulgaris, Pseudomonas aeruginosa, Salmonella enterica
	19 4	serovar Dublin, Staphylococcus aureus, Staphylococcus epidermidis, Yersinia
	20 5	enterocolitica.
	21 6
		13

Table 1

 1 List of serovars of Leptospira tested by the lipL32 Real-Time PCR assay. 7 *All strains are pathogenic except for the saprophytic serovar Patoc.

	8
	9

Table 2

 2

Acknowledgments


Sabine Hertel
email: sabine.hertel@uk-essen.de

Anja Viehmann

Susanne Moebus

Klaus Mann

Martina Bröcker-Preuss

Stefan Möhlenkamp

Michael Nonnemacher

Raimund Erbel

Hermann Jakobs

Michael Memmesheimer

Karl-Heinz Jöckel

Barbara Hoffmann

Influence of short-term exposure to ultrafine and fine particles on systemic inflammation

Keywords: air pollution, particulate matter, inflammation, cardiovascular disease

Daily to monthly variations in fine particulate matter have been linked to systemic inflammatory responses. It has been hypothesized that smaller particles resulting from combustion processes confer higher toxicity. We aim to analyze the association between short-term exposure to ultrafine and fine particles and systemic inflammation. We use baseline data (2000)(2001)(2002)(2003) of the Heinz Nixdorf Recall Study, a population-based cohort study of 4814 participants in the Ruhr Area in Germany. A chemistry transport model was applied to model daily surface concentrations of particulate air pollutants on a grid of 1 km 2 .

Exposure included particle number (PN) and particulate matter mass concentration with an aerodynamic diameter ≤2.5µm (PM 2.5 ) and ≤10µm (PM 10 ). Generalized additive models were used to explore the relation of air pollutants using single day lags and averaging times of up to 28 days with high-sensitivity C-reactive protein (hs-CRP). We adjusted for meteorology, season, time trend, and personal characteristics. Median hs-CRP level in the 3999 included participants was 1.5 mg/L. Median daily concentration of PN was 8,414*10 4 /ml (IQR 4,580*10 4 /ml), of PM 2.5 14.5µg/m³ (IQR 11.5µg/m³) and of PM 10 18.5µg/m³ (IQR 13.9µg/m³). A positive association between PN and hs-CRP could be observed only for single day lags and for averaged PN concentrations with higher estimates for longer averaging times.

The highest hs-CRP-increase of 7.1% (95%-CI: 1.9%,12.6%) was found for the 21-day average. These results support the hypothesis that short-term exposure to traffic-related particles might lead to detrimental cardiovascular health effects via an inflammatory mechanism.

Introduction

Epidemiological studies on health effects of air pollution have shown adverse cardiovascular effects. Increased cardiovascular morbidity and mortality can be observed after short-and long-term exposure to particulate air pollution [START_REF] Brook | Cardiovascular effects of air pollution[END_REF]. The biological mechanisms linking air pollution to cardiovascular events still remain mostly unclear. One possible pathway is that oxidative stress in the lungs from inhaled particulate matter (PM) leads to a systemic inflammatory cascade that can increase cardiovascular risk among susceptible individuals.

The systemic inflammatory response can lead to increased coagulation ability of the blood or development and destabilization of atherosclerotic plaques [2]. Studies investigating the effect of air pollution on systemic inflammation showed inconsistent results. Some studies found associations for short-term exposure up to several days to elevated particulate air pollution levels and increased levels of inflammatory markers [START_REF] Schwartz | Air pollution and blood markers of cardiovascular risk[END_REF][START_REF] Delfino | Air pollution exposures and circulating biomarkers of effect in a susceptible population: clues to potential causal component mixtures and mechanisms[END_REF], whereas other studies found no association of short-term exposure and inflammatory response [START_REF] Roux | Recent exposure to particulate matter and C-reactive protein concentration in the multi-ethnic study of atherosclerosis[END_REF][START_REF] Steinvil | Short-term exposure to air pollution and inflammation-sensitive biomarkers[END_REF] or were limited to prediseased subjects [START_REF] Rückerl | Air pollution and markers of inflammation and coagulation in patients with coronary heart disease[END_REF]. Long-term exposure to particulate air pollution has been associated with an increased inflammatory response [START_REF] Zeka | Inflammatory markers and particulate air pollution: characterizing the pathway to disease[END_REF][START_REF] Roux | Long-term exposure to ambient particulate matter and prevalence of subclinical atherosclerosis in the Multi-Ethnic Study of Atherosclerosis[END_REF][START_REF] Hoffmann | Chronic residential exposure to particulate matter air pollution and systemic inflammatory markers[END_REF], but others did not find a consistent relationship [START_REF] Forbes | Chronic exposure to outdoor air pollution and markers of systemic inflammation[END_REF].

Particulate matter is the component of air pollution which is thought to be responsible for most of the adverse effects on human health, because it contains a broad range of different toxic substances. Particles with aerodynamic diameter ≤10 µm (PM 10 ) and ≤2.5 µm (PM 2.5 ) are the most often investigated particles in the context of inflammatory markers [START_REF] Delfino | Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health[END_REF]. Only few epidemiological studies concentrate also on smaller particle sizes like ultrafine particles that have an aerodynamic diameter ≤0.1µm [START_REF] Delfino | Air pollution exposures and circulating biomarkers of effect in a susceptible population: clues to potential causal component mixtures and mechanisms[END_REF][START_REF] Zeka | Inflammatory markers and particulate air pollution: characterizing the pathway to disease[END_REF][START_REF] Rückerl | Ultrafine particles and platelet activation in patients with coronary heart diseaseresults from a prospective panel study[END_REF]. Toxicological studies showed that ultrafine particles induce the greatest amount of inflammation per unit PM mass, have greater internal doses due to higher lung deposition efficiency, have enhanced oxidant capacity and possibly are able to enter the human systemic circulation [START_REF] Nemmar | Time-course effects of systemically administered diesel exhaust particles in rats[END_REF]. An important source of ultrafine particles is traffic exhaust, therefore ultrafine particles are regarded as a proxy for fresh traffic exposure. Ultrafine particles exhibit a strong temporal and spatial variability, because they accumulate quickly to form larger particles. Because variations in ultrafine particle concentrations are dependent on both a temporal and a spatial component it is important to provide spatially and temporally resolved exposure data. How well both kinds of variability are covered depends of the method of exposure assessment. When investigating short-term effects of ultrafine particles, day-to-day variations can be captured with central site measurements, because daily variations dependent primarily on weather conditions. To capture the spatial variability, either a very tight monitoring network needs to be applied, or the spatial distribution can be modeled with a dispersion and chemistry transport model.

Our aim was to examine short-and medium-term residential exposure to ultrafine and fine particles on systemic inflammation in a large population-based sample from an industrialized area in West Germany. We hypothesize that short-term increases in particulate air pollution at the residence of the participants are positively related to elevated high-sensitive Serum-Creactive protein levels (hs-CRP), which is an independent predictor of cardiovascular disease [START_REF] Ridker | Inflammation, C-reactive protein, and atherothrombosis[END_REF].

Methods

Study design

We used baseline data from the Heinz Nixdorf Recall Study, an ongoing population-based, prospective cardiovascular cohort study that started in 2000 and includes 4,814 randomly selected participants aged 45 to 75 years from three large adjacent German cities (Essen, Mülheim, Bochum) of the densely populated and highly industrialised Ruhr Area. The study design has been described in detail elsewhere [START_REF] Schmermund | Assessment of clinically silent atherosclerotic disease and established and novel risk factors for predicting myocardial infarction and cardiac death in healthy middle-aged subjects: Rationale and design of the Heinz Nixdorf RECALL Study[END_REF]. The study was approved by the relevant institutional ethics committees and follows strict internal and external quality assurance protocols. All study participants gave informed consent prior to the examinations. The baseline assessment included a self-administered questionnaire, face to face interviews for personal risk factor assessment (i. e. family history of cardiovascular disease, hypertension, diabetes, smoking history, medications, socio-economic status), blood pressure measurements, anthropometric measurements, comprehensive laboratory tests according to standard protocols, and a multimodal assessment of subclinical atherosclerosis.

Exposure assessment

The study area covers a region of approximately 600 km 2 . We used a residence-based approach to characterize exposure to urban air pollution. Hourly PM 10 , PM 2.5 , PN surface concentrations for the whole study area were estimated with the EURAD dispersion and chemistry transport model on a spatial scale of 1 km grid. The model has been described elsewhere [START_REF] Ebel | Advanced air pollution models and their application to risk and impact assessment[END_REF]18]. Surface concentrations were calculated by dispersing emissions in horizontal strata, taking chemical reactivity, transport processes between horizontal strata, and measured PM concentrations from state monitoring sites into account via data assimilation.

That means that the primary model output is calibrated using measured PM concentrations to correct exposure concentrations in each grid cell. From the hourly values, mean daily pollutant concentrations were calculated for each 1 km² grid cell of the study area and assigned to the addresses of the participants (ArcView 9.2). Exposure data is available as 24hour averages for each day, so the exposure of the full day was allocated to a participant even if the blood was taken in the morning. We used modelled daily exposure means of the day of each blood withdrawal (lag 0), the prior day (lag 1) up to 3 days prior to the blood withdrawal, six different moving averages ranging from 2 to 7 days and the 14-, 21-and 28day moving average. To evaluate the characteristics of the EURAD model and the contribution of spatial and temporal variability to the distribution of highly varying PN values at the participants addresses we compare temporal and spatial variability. Temporal variability was assessed by calculation of summary statistics for all days of a randomly chosen 1 km² and spatial variability was assessed by a randomly chosen day for all squares.

Central site daily meteorological variables (mean temperature, humidity) were obtained from the German National Meteorological Service (Deutscher Wetterdienst, DWD) for the complete study period. Season was defined as winter (1 st December -29 th February), spring (1 st March -31 st May), summer (1 st June -31 st August) and autumn (1 st September -30 th November).

Main outcome

As marker of inflammation we measured high-sensitive Serum-C-reactive protein (hs-CRP), using an automated nephelometer (BN-II, Dade-Behring Inc, Deerfield, USA). All analyses were performed in the central laboratory of the University Hospital of Essen.

Subject-related Covariates

Smoking status was defined as current smoker, ex-smoker and never-smoker. Smoking dose was assessed for current smokers with the number of smoked cigarettes per day. Cumulative smoking exposure was assessed for ex-and current smokers with the packages per day*years of smoking (packyears). Anthropometric measurements (height, weight, waist hip ratio) were conducted according to standardized protocols. Physical activity was assessed by converting daily activities and regular exercise into metabolic equivalents. Regular alcohol intake was defined as any alcohol consumption at least 4-6 days per week. Personal characteristics, that might have an effect modifying role, were assessed as follows:

Diabetes mellitus was defined by self-report or having a blood glucose ≥ 200 mg/dL or having a fasting blood glucose ≥ 126 mg/dL. Coronary heart disease (CHD) was defined as a selfreported history of a myocardial infarction or coronary intervention. Other covariates which are possible effect modifiers were sex, age (≤ 60 years, > 60 years), BMI (≤ 27 kg/m², > 27 kg/m²) and medication with HMG-CoA-reductae inhibitors (statins).

Statistical Analysis

We used a subgroup of participants with complete information on exposure, covariates and inflammatory markers. Furthermore we excluded participants with acute infections of acute exacerbations of inflammatory disease (hs-CRP > 100mg/L) from the study population (n= 4).

The final analysis population consisted of 3,999 participants.

Generalized additive models were used to assess the shape of the relation between each of the exposure estimates and the hs-CRP concentration. These models allow the use of smooth functions for covariates that may not have a linear relationship with the outcome [START_REF] Hastie | Generalized additive models[END_REF].

Penalized cubic regression splines were used for modelling non-linear associations [START_REF] Wood | mgcv: GAMs and Generalized Ridge Regression for R[END_REF].

Penalized splines are a nonparametric method and not sensitive to knot locations presenting an advantage if less is known of the exposure-response shape. The number of knots is chosen with an algorithm resulting in the best model fit. Besides temperature (averaged over lag 1 and 2) and time trend also continuous subject-related covariates were tested for a non-linear relation with hs-CRP. Time trend was modelled with an initial value of 12 degrees of freedom, where we assumed 4 degrees of freedom per year. For all other possible non-linear relationships, we assumed that a maximum of 3 degrees of freedom should suffice. Hs-CRP was log-transformed for two reasons, first, to obtain a symmetric distribution of regression residuals and second, to account for an assumed exponential increase in hs-CRP for increasing exposure.

Because we are looking at short-term effects, we adjusted for temporally changing variables, namely mean temperature averaged over the 2 previous days, season and a time trend variable to represent other seasonal factors not captured by season and temperature. Subject-related covariates were based on a causal diagram which is shown elsewhere [START_REF] Hoffmann | Chronic residential exposure to particulate matter air pollution and systemic inflammatory markers[END_REF]. A sufficient adjustment set including time-varying variables, age, sex, city of residence and a set of lifestyle-related variables (ETS, smoking behaviour, BMI, waist-hip-ratio, alcohol intake, physical activity) was determined by using the causal diagram. In a model without exposure we identified the contributors from the lifestyle-related variable set which are significantly associated with hs-CRP and used this subset for all further models. Effect modification of the association between the particle metrics and hs-CRP was investigated by using interaction terms for age (≤ 60, > 60 years), sex, BMI (≤ median, > median BMI), diabetes, coronary heart disease and medication with HMG-CoA-reductase inhibitors (statins).

Effect estimates from our models and their 95% confidence intervals were transformed into percent change and reported per interquartile range (IQR) of pollutants.

Sensitivity analysis

Instead of mean temperature averaged over the 2 previous days we included the temperature of the day of blood withdrawal. Furthermore, time trend and different combinations or individual-level covariates were excluded to examine the robustness of our results to different model specifications.

Analysis was performed in R 2.9.1 [START_REF] Developement | R: A Language an Environment for Statistical Computing[END_REF].

Results

The analysis population is described in table 1. Table 2 shows the descriptive statistics of the modelled pollutant concentrations. The daily mean values of PM 2.5 and PM 10 were 17.2 µg/m³ (range 183. 6 µg/m³, IQR 11.5) and 21.4 µg/m³ (range 242.2 µg/m³, IQR 13.9), respectively. PM 10 and PM 2.5 were highly correlated (Spearman correlation coefficient = 0.94) whereas PN has a low correlation with PM 10 and PM 2.5 (Spearman correlation coefficients = 0.25 and 0.21, respectively). Daily mean exposure of PN was 9,031*10 4 /ml (range 29,005*10 4 /ml, IQR 4,580*10 4 /ml) for the participants' residence. When comparing whether spatial or day-to-day variability contributes stronger to the variability of the participants' variability in exposure, we found a slightly higher variability in temporal exposure. The IQR of the temporal distribution of PN measured in one single square is 3,801*10 4 /ml (min 3,231*10 4 /ml, max 24,511*10 4 /ml) and the IQR of the spatial distribution of PN measured at a single day is 3,128*10 4 /ml (min 2,104*10 4 /ml, max 106,152*10 4 /ml). Hs-CRP varied between 0.1 and 84.4 mg/L with a median of 1.5 mg/L. The average of the daily mean temperature was 10.56°C (SD 7.74°C) for the whole study period.

The assessment of a possible non-linear relationship of the particle metrics with the natural logarithm of hs-CRP regression splines consistently showed curvatures below 2 degrees of freedom. This indicates a linear relationship between the different particle metrics and the natural logarithm of hs-CRP, which led us to include the exposure as a linear term in the regression model for all further analyses. Table 3 shows the effect estimates for the association of lagged and averaged particle metrics and hs-CRP. For lag 1 evidence for a weak increase in hs-CRP of 3.22% (95%-CI: -0.97% to 7.60%) per interquartile increase in PN was observed. Hs-CRP started to increase two days after exposure to increasing levels of PN. For the moving averages this increase in hs-CRP was more pronounced with estimates clearly above 0 and a maximum change of 7.10% (95% CI, 1.85% to 12.62%) per IQR increase in the 21-day mean PN exposure. Figure 1 shows increasing effect estimates for change in hs-CRP for longer averaging times.

For PM 2.5 and PM 10 no association with hs-CRP was seen for single day lags and moving averages up to 21 days. Only for the 28-day mean of both, PM 2.5 and PM 10 , an effect was found (table 3).

Figure 2 shows results for the analysis of effect modification. Participants older than 60 years and overweight participants showed slightly higher increases in hs-CRP than younger or nonoverweight participants for all averaging times. While there is some indication of effect modification due to diabetes, CHD and intake of statins, low sample sizes and wide confidence intervals do not allow definitive statements (figure 3). No effect modification between PN and hs-CRP by sex could be seen. Effect modification with PM 2.5 and with PM 10 and personal characteristics showed the same pattern as for PN, but with much lower estimates (appendix table 4). However, effect modification by statin therapy could be observed for the 28-day mean exposure of PM 2.5 and PM 10 . For statin users effects are reduced to 0 whereas for non-statin users effects were found (appendix table 5).

In the sensitivity analysis the model without adjustment for trend and non-linear relationships showed the same pattern for the three exposures PN, PM 2.5 and PM 10 , but slightly lower effect estimates. Other adjustment sets of personal characteristics resulted in similar exposure estimates.

Discussion

We found that short-and medium-term exposure to ultrafine air-borne particles is positively associated with systemic inflammation in a population-based study. Cumulative elevated levels of particle number during the 28 days before the examination were associated with an 11 increase in hs-CRP concentrations, independent of weather and personal characteristics. No associations of short-term exposure to PM 2.5 and PM 10 with hs-CRP could be seen. However, four weeks cumulative PM 2.5 exposure was positively associated with hs-CRP. These findings support the hypothesis that systemic inflammation is a pathway through which especially particles of smaller size can lead to an acute increase in cardiac risk. This is the first study to use a temporally and spatially resolved exposure to assess short-term effects of ultrafine particles in a population-based study. Others used dispersion modelling to assess the long-term effect of larger particles on systemic inflammation for more highly spatially resolved data [START_REF] Panasevich | Associations of long-and short-term air pollution exposure with markers of inflammation and coagulation in a population sample[END_REF][START_REF] Bellander | Using geographic information systems to assess individual historical exposure to air pollution from traffic and house heating in Stockholm[END_REF]. Higher estimates for longer averaging times indicate perhaps a cumulative effect, but at least a smaller exposure estimation error. CRP increased as early as two days after exposure to elevated levels of PN and associations were positive up to the 28 day moving average of PN. A cross-sectional study by Zeka et al. [START_REF] Zeka | Inflammatory markers and particulate air pollution: characterizing the pathway to disease[END_REF] using central site measurement has shown only suggestive evidence for an association between PN exposure up to 4 weeks and hs-CRP. Different panel studies with coronary heart disease patients show positive associations between centrally measured short-term increases in PN and hs-CRP [START_REF] Rückerl | Air pollution and markers of inflammation and coagulation in patients with coronary heart disease[END_REF][START_REF] Delfino | Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health[END_REF][START_REF] Panasevich | Associations of long-and short-term air pollution exposure with markers of inflammation and coagulation in a population sample[END_REF], however these studies are based on small, selected populations which are thought to be particularly vulnerable.

For PM 2.5 results from prior studies are inconsistent. In cross-sectional analyses of populationbased studies, Diez-Roux et al. [START_REF] Roux | Recent exposure to particulate matter and C-reactive protein concentration in the multi-ethnic study of atherosclerosis[END_REF] and Zeka et al. [START_REF] Zeka | Inflammatory markers and particulate air pollution: characterizing the pathway to disease[END_REF] found no consistent evidence that recent exposure to PM 2.5 is associated with hs-CRP. However, weak associations for longer averaging times were found [START_REF] Roux | Recent exposure to particulate matter and C-reactive protein concentration in the multi-ethnic study of atherosclerosis[END_REF]. Similarly, we found associations of longer averaging times of PM 2.5 with hs-CRP. In this study, the 28 day moving average was associated with CRP and in a prior study we showed that the long-term PM2.5 exposure at the home address was associated with increases in CRP and fibrinogen [START_REF] Hoffmann | Chronic residential exposure to particulate matter air pollution and systemic inflammatory markers[END_REF]. No associations of short-term exposure to PM 2.5 and PM 10 with hs-CRP could be seen. In contrast, in a panel study of older participants, short-term increases in PM 2.5 were associated with hs-CRP levels [START_REF] Dubowsky | Diabetes, obesity, and hypertension may enhance associations between air pollution and markers of systemic inflammation[END_REF]. Chuang et al. [START_REF] Delfino | Circulating biomarkers of inflammation, antioxidant activity, and platelet activation are associated with primary combustion aerosols in subjects with coronary artery disease[END_REF], Rückerl et al. [START_REF] Rückerl | Air pollution and markers of inflammation and coagulation in patients with coronary heart disease[END_REF] and Seaton et al. [START_REF] Seaton | Particulate air pollution and the blood[END_REF] found evidence for a short-term increase of PM 10 and elevated hs-CRP levels. Overall, studies suggest association between air pollution and inflammation. The heterogeneity present in study results can be due to different pollution mixtures, methods of exposure assessment, study design, or caused by different susceptibilities of the populations under study, based on underlying medical conditions or treatments with anti-inflammatory effect.

Another important factor for differences between studies could be the study design. Unlike in a panel study where repeated measures on the same individual makes confounding by personal variables unlikely, in a cross-sectional analysis confounding by personal characteristics must be taken in account. Depending on the appropriate choice of confounders in a cross-sectional analysis the association between exposure and outcome could be biased.

We chose the model with the help of causal diagrams which are strongly dependent on assumptions regarding the temporal and causal relations between variables. These relations are based on prior knowledge. However other causal relations could be possible, resulting in another choice of adjustment variables. Therefore, we investigated the robustness of our results to different model specifications, but did not find qualitative changes in our results.

The different sizes and types of inhaled pollutants may determine the importance of different biological pathways. These pathways are not entirely exclusive and may overlap temporally or be activated at different time points after exposure [START_REF] Brook | Cardiovascular effects of air pollution[END_REF]. Ultrafine particles or soluble constituents of PM can directly enter the blood stream and cause primary a systemic inflammation. Larger particles cannot be transported into the circulation, but might induce a secondary pro-inflammatory response by first producing a local inflammation in the lung.

Primary inflammation produces more acute response to particles and an increase in acutephase reactants that can be seen after one to a few days, whereas it is possible that pulmonary tissue oxidative stress induced by larger particles is activated after a longer period of constant enhanced exposure [START_REF] Brook | Cardiovascular effects of air pollution[END_REF].

Particle number and mass display a different spatio-temporal pattern. PN is mainly dominated by ultrafine particles because they occur in a large number. Ultrafine particles are generated by traffic and are very unstable. The concentration of ultrafine particles decreases rapidly perpendicular to highly trafficked roads and reaches background concentrations within 300 m [START_REF] Zhu | Concentration and size distribution of ultrafine particles near a major highway[END_REF]. The 1 km² resolution of the EURAD model, however, estimates only the average PN concentration within this 1 km² grid cell. Pollution peaks close to busy roads will therefore not be captured. PM 2.5 and PM 10 display a more homogeneous spatial distribution. Larger particles are more evenly dispersed because they are more stable and stem from a variety of sources other than motorized traffic in this area (industry, coal combustion, agriculture, sea salt) and long range transport mechanisms [START_REF] Kuhlbusch | Number size distribution, mass concentration, and particle composition of PM1, PM2.5, and PM10 in bag filling areas of carbon black production[END_REF].

Compared to larger particles it is expected that ultrafine particles are controlled to a larger extent by meteorological parameters as wind speed and temperature. This might lead in general to a stronger day-to-day variability of PN compared to PM2.5 and PM10. We examined whether the spatial or temporal variability dominates the short-term exposure pattern of ultrafine particles in our region. With the use of the EURAD model we were able to investigate both spatial and temporal variability of the PN background concentration, because exposure was modeled for each participants' home address and for every day of the study period. We expected that the spatial variability of PN to be lower because the 1km² grid cell average of the PN concentration does not capture the true concentration differences within our study population. On the other hand, day-to-day variability based on meteorological data should be larger, because the temporal variability can be modelled with a greater degree of precision based on hourly meteorological data. When comparing spatial variability of a randomly chosen day and day-to-day variability of a randomly chosen square we found a slightly higher temporal variability as measured with the IQR.

Suggestive evidence for effect modification of the association between PN and hs-CRP by different personal characteristics could be observed. We found that older (> 60 years) and overweight persons (BMI > 27 kg/m³) are more affected by elevated ultrafine pollution. Also participants with CHD showed higher responses to increased air pollution levels suggesting that participants with conditions that increase their cardiovascular risk are more vulnerable to the effect of small particle air pollution. Participants on statin therapy showed no increase in hs-CRP levels with higher air pollution probably due to the anti-inflammatory property of the drug. This was also observed in previous research [START_REF] Rückerl | Ultrafine particles and platelet activation in patients with coronary heart diseaseresults from a prospective panel study[END_REF][START_REF] Delfino | Circulating biomarkers of inflammation, antioxidant activity, and platelet activation are associated with primary combustion aerosols in subjects with coronary artery disease[END_REF]. Sex-specific effects of air pollution with inflammatory markers have been reported by others, but were not observed in our analysis [START_REF] Steinvil | Short-term exposure to air pollution and inflammation-sensitive biomarkers[END_REF][START_REF] Hoffmann | Chronic residential exposure to particulate matter air pollution and systemic inflammatory markers[END_REF]. However, in our study population, sex differences in the association of hs-CRP and coronary artery disease have been documented [START_REF] Erbel | Sex related cardiovascular risk stratification based on quantification of atherosclerosis and inflammation[END_REF].

A limitation of the study is the possibility of exposure misclassification in this residencebased approach of short-term exposure estimation. It is not known, whether the participants actually stayed close to their home address during the days prior to the blood draw. Therefore we are not able to correct for exposures in other environments like the work environment or individual traffic exposure. Strength of the study includes the high degree of spatial resolution within our study area in comparison to central site measurements. This enables a more exact exposure assessment at the home address.

In conclusion, our results indicate an immediate response of hs-CRP to ambient air pollution of ultrafine particles. This supports the hypothesis that short-term exposure to traffic-related particles might lead to detrimental cardiovascular health effects via an inflammatory mechanism.

Fig 1 .Fig 2 .Fig 3 .

 123 Figulla, Jena; C. Hamm, Bad Nauheim; P. Hanrath, Aachen; W. Köpcke, Münster; C.

Acknowledgements

We thank the Heinz Nixdorf Foundation (Chairman: Dr. Jur. G. Schmidt) for their generous support of this study. This study is also supported by the German Ministry of Education and Science. We thank K. Lauterbach (Department of Health Economics and Epidemiology, University of Cologne, Germany) for his valuable contribution to the study. We are indebted to the investigative group and the study personnel of the Heinz Nixdorf Recall study, in particular EM Beck, IMIBE, Dr. M. Bröcker-Preuss from the Department of Clinical Chemistry and Laboratory Medicine.

Appendix tables

Tab 4. Adjusted estimates* for effect modifications of association between PN, PM 2.5 and PM 10 and hs-CRP stratified by personal characteristics (sex, age, BMI) Tab 5. Adjusted estimates* for effect modifications of association between PN, PM 2.5 and PM 10 and hs-CRP stratified by medical conditions (diabetes, CHD, statine intake)