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American Journal of Epidemiology Advance Access originally published online on September 18, 2008
American Journal of Epidemiology 2008 168(9):1065-1072; doi:10.1093/aje/kwn218
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American Journal of Epidemiology © The Author 2008. Published by the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

ORIGINAL CONTRIBUTIONS

The Association Between Water Supply and Inflammatory Bowel Disease Based on a 1990–1993 Cohort Study in Southeastern Norway

Geir Aamodt, Geir Bukholm, Jørgen Jahnsen, Bjørn Moum, Morten H. Vatn and the IBSEN Study Group

Correspondence to Dr. Geir Aamodt, Department of Epidemiology, Norwegian Institute of Public Health, Postbox 4404 Nydalen, 0403 Oslo, Norway (e-mail: geir.aamodt{at}fhi.no).

Received for publication April 3, 2008. Accepted for publication June 23, 2008.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Inflammatory bowel disease refers to a group of chronic diseases of unknown etiology related to both genetic and environmental factors. In this 1990–1993 study, the authors investigated associations between the content and quality of drinking water and the incidence of inflammatory bowel disease. They used data from a population-based cohort recruited in southeastern Norway and a registry of water quality derived from Norwegian waterworks that contained measurements of iron, aluminum, acidity (pH), color, turbidity, and coliform bacteria. The authors found that risk of developing inflammatory bowel disease, including ulcerative colitis and Crohn's disease, was associated with high iron content. The relative risk of developing inflammatory bowel disease increased by 21% (95% confidence interval: 9, 34) when the iron content in the drinking water increased by 0.1 mg/L. They found no association between the diseases and aluminum in the water, color of the water, and turbidity of the water. The authors suggest that the observations can be explained by 2 mechanisms. First, high iron concentration works as a catalyst for oxidative stress, which will cause inflammation and/or increase the rate of cell mutations. Second, iron content stimulates the growth of bacteria and increases the likelihood of inappropriate immune responses in genetically predisposed individuals.

cohort studies; colitis, ulcerative; Crohn disease; inflammatory bowel diseases; Norway; water supply


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Ulcerative colitis and Crohn's disease are the 2 main diseases in the group of inflammatory bowel diseases, and both are idiopathic inflammatory bowel disorders. Whereas ulcerative colitis is restricted to the colon, Crohn's disease might affect the gastrointestinal tract from the mouth to the anus (1, 2). They are most prevalent in the northern hemisphere, and the highest incidence rates are observed in Manitoba, Canada: approximately 20 per 100,000 for both diseases (3). The incidence rates of ulcerative colitis seem to have flattened out in the northern regions, whereas the incidence of Crohn's disease is still rising (4).

The etiology of the diseases is unknown, but family studies show that the diseases may cluster in families (2). Evidence exists that several genes are involved in the development of the diseases, such as mutations in the nucleotide-binding oligomerization domain 2 (caspase recruitment domain 15) gene NOD2 (CARD15) in Crohn's disease. The function of this gene concerns initial defense against commensal and pathogenic bacteria and mechanisms associated with the intestinal immune system (5). Several environmental factors have also been studied, and a negative association has been found between smoking and ulcerative colitis (2). Furthermore, the diseases have been shown to be related to socioeconomic factors, hygiene, and exposure to microorganisms. The exact role of each of the factors is so far inconclusive (2). Several studies indicate a variable geographic distribution within countries (6–9).

A recent theory regarding development of Crohn's disease and ulcerative colitis is a dysregulated and inappropriate immune response to normal gut flora in genetically predisposed individuals (10, 11). In particular, it is assumed that toll-like receptors, which are important in protecting the intestinal epithelial barrier in healthy individuals, stimulate diverse inflammatory responses in affected individuals. The environment in the gut is an essential component of this theory because the interaction between the gut flora and its chemical composition is complex. One part of this picture is the quality and content of the drinking water, including both organic and inorganic components. Different metals such as iron and aluminum as well as acidity govern the ecology of microorganisms and their interaction with the host, including their expression of virulence (12–15). The gut's content of different metals is also assumed to govern the potency of molecular biologic changes (16). Studies have also shown different levels of copper, iron, and zinc in biopsies from Crohn's and ulcerative colitis patients compared with controls (17).

Several studies have linked drinking water to inflammatory bowel disease, but most focused on the content of different microorganisms such as Mycobacterium paratuberculosis (18). The aim of this investigation was to study the relation between important metals and other qualities indirectly related to organic components of the drinking water and incidence rates of inflammatory bowel diseases. Results from such epidemiologic studies will help us to understand recent etiologic theories.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Participants
Participants were recruited from a population-based incidence study in 4 counties in southeastern Norway during 1990–1993. The study was conducted to investigate the incidence of inflammatory bowel diseases in this part of Norway and to describe and analyze the diseases’ clinical course. The counties were Østfold, Oslo, Telemark, and Aust-Agder (Figure 1), with a population living in a mixture of urban and rural districts in Norway. The design of and clinical results from the study cohort are described elsewhere (19, 20). Patients suspected to have inflammatory bowel disease were recruited by their general practitioners, and diagnosis was based on internationally accepted criteria (21). We used the diagnoses confirmed at the 5-year follow-up as the final and correct one (22). By this time, the patients had been through 2 systematic, comprehensive screenings for quality assurance of their diagnosis.


Figure 1
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  		Figure 1. The study area, consisting of the four counties Østfold, Oslo, Telemark, and Aust-Agder in the southeastern part of Norway. Patients were recruited during 1990–1993. Shown are the incidence rates of inflammatory bowel disease per 100,000 for the different municipalities.

 
A total of 843 patients were recruited into the study, and 762 were still participating in the study at the 5-year follow-up. Eighty-one patients were dead, were lost to follow-up, or were classified as having non–inflammatory bowel disease. In Østfold County, 186 patients with inflammatory bowel disease were identified (111 with ulcerative colitis, 46 with Crohn's disease, and 29 with unclassified inflammatory bowel disease). In Aust-Agder and Telemark counties, 254 patients were identified (149 with ulcerative colitis, 71 with Crohn's disease, and 34 with unclassified inflammatory bowel disease). We identified 265 persons with inflammatory bowel disease in Oslo (175 with ulcerative colitis, 68 with Crohn's disease, and 22 with unclassified inflammatory bowel disease). The remaining participants were living in other municipalities in Norway (n = 57) because they had moved from or lived outside the catchment area.

In 1992, Telemark and Aust-Agder counties consisted of 33 municipalities with a population of 260,705 persons, whereas Østfold consisted of 18 municipalities and 238,311 inhabitants. The population size in the municipalities ranged from 653 to 64,706 inhabitants, with a median of 4,296. The population in Oslo was 467,441 in 1992.

Figure 1 shows a disease map of inflammatory bowel disease in Norway. The incidence rates in the study area were 13.6 and 5.8 per 100,000 for ulcerative colitis and Crohn's disease, respectively. For the different municipalities, the incidence rates ranged from 0 to 34.4 for ulcerative colitis and 0 to 19.3 for Crohn's disease.

The Norwegian waterworks registry
The responsibility for the water supply in Norway belongs to the municipalities and is regulated by the Drinking Water Act (Drikkevannsforskriften, 2001, www.lovdata.no (http://www.lovdata.no/cgi-wift/ldles?ltdoc=/for/ff-20011204-1372.html [in Norwegian])). Guidelines for drinking water quality are also provided by the World Health Organization (23). The Drinking Water Act addresses the service of delivering the drinking water and its standards of quality to ensure safe and healthy drinking water for the population. The waterworks must report the quality of their raw water and purified water to the Norwegian Institute of Public Health. This registry is called the Norwegian waterworks registry, and the number of laboratory tests for each waterwork per year is governed by the number of people being supplied by the waterwork. In our investigations, we used the registrations of the mean values for purified water from 1994, which was the point of time closest to the interval when the patient data were collected.

The sources of the drinking water are lakes, rivers, and reservoirs. The study area is situated in districts with agriculture and some larger-industry plants (heavy and process industry, paper mills) in the coastal parts of the area. Smaller industry is scattered throughout the study area.

The variables sampled in 1994 were iron, aluminum, pH (acidity), coliform bacteria, color, and turbidity, and we included all these variables in the analysis. Iron and aluminum are the most abundant metals in the soil and bedrock, but corrosion of water pipes is also an important source of iron. The pH level is an indirect measure of the quality of the drinking water and is related to the content of other metals such as calcium. If the pH level is too high, chlorine disinfection will be ineffective, but a low pH will lead to corrosion of the water pipes. Coliform bacteria are a heterogeneous group of bacteria including species from different genera such as Escherichia and Enterobacter. Coliform bacteria most likely develop in the gut of animals and humans (23). Color is associated with humic and fulvic acids as well as iron and other metals and is measured in true color units. Turbidity, associated with clay and other small particles that may be present in the source water but are not removed in the filtration process, is measured in nephelometric turbidity units. High levels of turbidity will stimulate bacteria growth and reduce effectiveness of the disinfection process (23).

The Drinking Water Act specifies limits for chemical components and sensory measurements as well as which action should be taken if the limits are exceeded. The limit values for the different components listed above are reported in Table 1.


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  		Table 1. Summary Statistics Regarding Water Quality for 6 Drinking Water Variables in the Norwegian Waterworks Registry, Southeastern Norway, 1994

 
Five municipalities in Østfold County had a common waterwork, and the same measurements were used for each of these municipalities. For some of the remaining municipalities, there was more than 1 waterwork in each municipality. Most of these municipalities had 1 large public waterwork and 1 or more smaller waterworks supplying a small number of inhabitants. For these municipalities, except Oslo, we used only the larger waterworks because we had no information linking study participants' address to the waterworks. The city of Oslo has 4 waterworks, but only 2 are in regular service. The larger of these 2 (Oset) supplies water to 90% of the population, and the smaller waterwork (Skullerud) serves 10% of the population. We used participants' street addresses to allocate the participants to either of these 2 waterworks. During this process, we lost 1 patient who had temporarily moved to a health institution. Some of the measurements were recorded as zero and, because all variables except pH were log-transformed in the prediction model, we added a minimum detectable value to these values. Linear associations between the variables were studied by using Pearson's correlation coefficients.

Prediction model
We based our analyses on the values reported to the Norwegian waterworks registry (www.fhi.no (http://www.fhi.no/eway/default.aspx?pid=233&trg=MainArea_5661&MainArea_5661=5631:0:15,2873:1:0:0:::0:0 [in Norwegian])). The registry contained data from 35 of the 43 waterworks, and the challenge was to predict the values of the components for the remaining waterworks and their sites. We used multivariate kriging to predict the components for those waterworks that had not reported values to the registry. Kriging is a standard geostatistical tool to predict a value at a new site based on the observed values and the underlying spatial process (24). The underlying spatial process is modeled with the variogram function. The multivariate extension also accommodates the spatial covariances between the different components included in cross-variogram functions. We used exponential variogram and cross-variogram functions in our analysis. The geostatistical computer routines gstat (www.gstat.org) were used to perform the Kriging procedure, which are implemented in R software (www.r-project.org).

Statistical methods for modeling incidence rates
To model the incidence rates, we used Poisson regression models. These models estimate the site-specific incidence rates of developing the diseases in each waterwork district and should not be confused with individual risk of developing the diseases. The number of cases was assumed to follow a Poisson distribution and was modeled as a function of the 6 variables described above. We also included (log-transformed) person-years as an offset term in the models. First, we fitted univariate models with only 1 variable in the model and then multivariate models by using a forward selection procedure to find the optimal set of explanatory variables. We included degree of urbanization as an explanatory variable, defined as the percentage of the population living in a neighborhood with more than 200 persons and where the distance between houses is less than 50 m. To investigate whether age at onset was dependent on iron content or any other water quality variable, we fitted a linear regression model. P values of <0.05 were considered statistically significant.

Ethics
This study was approved by the regional ethics committee.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
Summary statistics regarding water quality for the 6 drinking water variables are given in Table 1. The table shows key statistics for the observed variables, the predictions, and the reference or limit values from the Drinking Water Act. For all components, there were waterworks whose values exceeded the limit.

Scatter diagrams of the 6 variables are shown in Figure 2. Statistically significant correlation coefficients were found between color and turbidity (r = 0.75, P < 0.001), coliform bacteria and turbidity (r = 0.50, P < 0.001), and iron and color (r = 0.36, P = 0.032). To fit variogram and cross-variogram models as optimally as possible, we used registrations from all available waterworks in Norway, not only the 35 waterworks in the study area.


Figure 2
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  		Figure 2. Scatter diagram of the 6 different components in the study area's drinking water based on 1994 registrations of the mean values for purified water, Norwegian waterworks registry. Only those registrations from the 35 waterworks included in the study were considered.

 
Results from the Poisson regression analysis are shown in Table 2. In univariate models, several of the 6 variables were significantly associated with Crohn's disease, ulcerative colitis, or both. In multivariate models, only iron and pH were significantly related to inflammatory bowel disease, iron and coliform bacteria were significantly related to ulcerative colitis, and only iron and Crohn's disease were significantly related. All variables except iron showed small effect sizes. When we included degree of urbanization, the results did not change. When iron increased by 0.1 mg per L, the relative risk was 1.21 (21%) for inflammatory bowel disease (95% confidence interval: 9, 34). The same association was present for both ulcerative colitis and Crohn's disease. The corresponding figures were 18% (95% confidence interval: 3, 35) for ulcerative colitis and 22% (95% confidence interval: 0, 48) for Crohn's disease. We found a small, but positive, association between incidence rate of inflammatory bowel disease and pH (2% when pH increased by 1 unit, 95% confidence interval: 1, 3) and a negative association between coliform bacteria and ulcerative colitis (1% reduction in incidence rate, 95% confidence interval: 0, 2), but we do not emphasize the latter result in this paper because only 4 observations for this variable were not zero.


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  		Table 2. Results From Poisson Regression Analysis of Incidence Rates of Crohn's Disease, Ulcerative Colitis, or Both (Inflammatory Bowel Diseases), Southeastern Norway, 1990–1993a

 
To better validate our prediction model and its consequences, we estimated the relative risks by using only the 35 observed measurements and not the predicted values. The resulting estimated values of relative risk associated with iron were 26%, 23%, and 25%, with corresponding P values of <0.001, 0.003, and 0.023, for inflammatory bowel disease, ulcerative colitis, and Crohn's disease, respectively. The estimated confidence intervals were also larger. Therefore, the differences in the estimated relative risks between the full data set including the predictions and the reduced data set were small, and iron was significant in both models.

It is likely that increased iron content could lower age at onset of the diseases for women in particular, and we therefore investigated whether age at onset or gender was related to iron content. The linear regression model showed that iron in drinking water and age at onset were not associated (P = 0.388). Gender did not have any impact on this finding.

We did not find any significant association of aluminum, pH, and turbidity with the different diseases.


   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
In this study, we found that risk of developing inflammatory bowel disease, including ulcerative colitis and Crohn's disease, was associated with iron content in the drinking water. We also found that risk was associated with pH for inflammatory bowel disease and coliform bacteria for ulcerative colitis, but these effects were very small. We did not find corresponding epidemiologic studies of water quality and water supply and the risk of inflammatory bowel disease.

The main strength of this study was the relatively large population-based cohort, including many patients who are living in an area with different water suppliers. This strength enabled us to study water-quality exposures with a relatively large range of observed values (large spatial variability). For the 6 variables, we assessed waterworks with excellent water quality and with low measured values (except pH) and waterworks with measured values exceeding the recommended limits from the national health authorities and the World Health Organization.

To assess drinking water exposure, we linked the values from each waterwork to every person living in that district at the approximate time of diagnosis. Uncertainties are attached to this exposure assessment. One question is the relative importance of drinking water compared with food as a luminal risk factor in the gut. We had no information on individual intake of different foods, and we assumed that the variation in food intake and nutritional content was equally distributed in the study area. It is nevertheless possible that food is a confounder if intake among inhabitants in municipalities with the poorest water quality is different from that among inhabitants in municipalities with better water quality. Nevertheless, no data support this hypothesis.

The second source of uncertainty is the quantity of tap water, compared with bottled water, each person consumes. This contribution is assumed to be negligible: Daily consumption of drinking water per person is approximately 2.0 L (23, p. 90). During the period 1997–1999, mean annual consumption of bottled water was 9.5 L per year (www.ssb.no), and annual consumption was even less during the period we studied (1990–1993). Tap water is therefore the main source of water. The third source of uncertainty regards the number of waterworks with missing information and the prediction model used for the imputation. To better evaluate the impact of the predictions, we fitted a model by using only the registration from the waterworks and no imputations. The results from this sensitivity study were parallel to those using the data from the prediction model, with significant associations between incidence rates and iron content in the drinking water, and the effect sizes were also of the same magnitude. Therefore, the results do not depend on the prediction model.

In our analysis, we also removed smaller waterworks from consideration because we could not link them to the inhabitants. The effect of doing so is unknown, but investigations analyzing spatial clustering of the diseases have found only weak evidence for spatial clustering (3, 9), and it is therefore unlikely that the participants in our study are located in clusters close to these smaller waterworks.

It is possible that iron is a confounder and that other components of the drinking water associated with both the diseases and iron govern the mechanism initiating the diseases. The set of variables in the waterwork registry in 1994 was limited, and we were not able to control for this in our study.

We suggest 2 explanations for our findings, which, in different ways, are associated with oxidative stress and increased bacteria growth. Both explanations can be true at the same time. Reactive oxygen species are products of the metabolic process in the cell. Normally, antioxidants are produced to remove the adverse effects of the reactive oxygen molecules. Oxidative stress occurs when the production of reactive oxygen species exceeds their destruction by antioxidants. Iron works as a catalyst and increases oxidative stress and production of reactive oxygen molecules. The excess of reactive oxygen molecules has been shown to be of importance in the inflammation process initiating or propagating the development of inflammatory bowel disease (25, 26); oxidative stress markers have been found to positively correlate with disease activity for Crohn's disease (17). This explanation is parallel to studies of oral iron intake and inflammatory bowel disease. Erichsen et al. (27–29) showed that oral ferrous fumarate increased disease activity for both Crohn's and ulcerative colitis patients compared with controls and in cross-over studies comparing oral ferrous fumarate with intravenous iron sucrose. Both these and our findings suggest that iron in drinking water is important in the environment-host relation despite the fact that drinking water is of minor importance as an iron source.

Another concept regarding oxidative stress is that metabolic oxidative stress increases the likelihood of genomic instability (refer to the review by Spitz et al. (26)). A corresponding mechanism has been proposed by Delaney et al. (16). They suggest that oxidative stress converts lipids into DNA-damaging agents. Iron and/or copper are important ingredients in this mechanism.

A second explanation is that iron is an important factor influencing the growth of bacteria and their expression of virulence (30, 31). When the level of iron increases, the balance between the quantities of different bacterial species in the gut is altered, depending on their ability to compete for iron. The interaction between bacteria and host tissue will also be altered when iron levels are enhanced, including the ability of the bacteria to express virulence (32). Bacterial virulence can be up- or down-regulated in the presence of increased iron levels, which will consequently lead to a modified immune response, increasing the possibility of triggering the disorders. A higher concentration of mucosal bacteria flora in patients with inflammatory bowel disease compared with controls has been reported and discussed by Swidsinski et al. (33). They suggest that the high concentration is related to a specific host response more than being a secondary characteristic of the disease.

Different studies have linked aluminum and, in particular, Crohn's disease (12–15). As an example, Fogarty et al. (14) found a significant quantity of aluminum in horses with inflammatory bowel disease. The authors’ explanation was that increased levels of aluminum in the gut increase the pathogenicity of the commensal bacteria and trigger a chain reaction in the immune system leading to disease in genetically disposed persons. Our findings, which are based on aluminum in drinking water, do not support this explanation.

We did not find interactions between the drinking water variables and individual risks such as age at onset or gender.

We cannot rule out the possibility that our findings reflect type I errors, and we therefore need independent studies to verify the results. Thus, we will follow up with other epidemiologic studies linking the Norwegian waterworks registry and Norwegian health surveys.

To conclude, our results show a relation between iron content in drinking water and incidence rates of inflammatory bowel disease. We suggest that these findings are explained by increased oxidative stress or increased bacterial growth that increases the likelihood of adverse immune responses in genetically predisposed individuals.


   ACKNOWLEDGMENTS
 
Author affiliations: Institute of Clinical Epidemiology and Molecular Biology, Faculty Division, Akershus University Hospital, University of Oslo, Lørenskog, Norway (Geir Aamodt, Geir Bukholm, Morten H. Vatn); Department of Epidemiology, Norwegian Institute of Public Health, Oslo, Norway (Geir Aamodt); Norwegian Knowledge Centre for the Health Services, Oslo, Norway (Geir Bukholm); Department of Gastroenterology, Aker University Hospital, Oslo, Norway (Jørgen Jahnsen, Bjørn Moum); and Medical Department, Rikshospitalet University Hospital, Oslo, Norway (Morten H. Vatn).

The authors thank Carl-Fredrik Nordheim, Norwegian Institute of Public Health, for providing the data from the Norwegian waterworks registry; and Dr. Trond Peder Flaten, Norwegian University of Science and Technologies, for reading and commenting on an early draft of the manuscript. Also thanked for their contributions to this study are the following members of the Inflammatory Bowel Southeastern Norway (IBSEN) Study Group of Gastroenterologists: Idar Lygren, Ullevål University Hospital; Erik Aubert and Magne Henriksen, Østfold Hospital, Moss and Fredrikstad; Borgar Flaaten, Notodden Hospital; Tom Schultz, Sørlandet Sykehus, Arendal; Jostein Sauar, Telemark Hospital, Skien; Øystein Kjellevold, Blefjell Hospital, Rjukan; and Njål Stray, Diakonhjemmet Sykehus, Oslo.

Conflict of interest: none declared.


   References
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

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