Geochemical Correlates to Type 1 Diabetes Incidence in Southeast Sweden: An Environmental Impact?

By Samuelsson, Ulf; Lofman, Owe | Journal of Environmental Health, January-February 2014 | Go to article overview

Geochemical Correlates to Type 1 Diabetes Incidence in Southeast Sweden: An Environmental Impact?


Samuelsson, Ulf, Lofman, Owe, Journal of Environmental Health


Introduction

Type 1 diabetes is a heterogeneous disease involving genetic, environmental, and autoimmune mechanisms (Akerblom, Vaarala, Hyoty, Ilonen, & Knip, 2002). That environmental factors play a central role is supported by the clear seasonal variation in the diagnosis of the disease (Padaiga et al., 1999) and its increasing incidence in many countries (Patterson et al., 2009). Several studies have also indicated different incidence rates and geographical variation between, and within, countries (Rytkonen et al., 2001; Samuelsson & Lofman, 2004).

Moreover, the offspring of an Asian transmigratory population showed a rising incidence of type 1 diabetes approaching that of the indigenous English population (Bodansky, Staines, Stephensen, Haigh, & Cartwright, 1992).

Several studies have found spatial clustering at the time of diagnosis (Cherubini et al., 1999; Staines et al., 1997). We and others have also reported time-space clustering at diagnosis (Law et al., 1997; Samuelsson, Carstensen, Johansson, & Ludvigsson, 1994). Furthermore, evidence has been produced of time-space clustering of date of birth in children who later develop diabetes (Dahlquist & Kallen, 1996).

The studies of spatial clustering have shown that the incidence is higher in rural than in urban areas (Rytkonen et al., 2003; Staines et al., 1997) and higher in highlands than in lowlands (Patterson, Smith, & Webb, 1988). Socioeconomic factors may also influence the risk of developing diabetes, with a lower incidence in deprived areas and in areas with a high population density and overcrowding (Staines et al., 1997).

Several studies have examined the relationship between the risk of disease and compounds in drinking water (Zhao et al., 2001), as well as the acidity of the water (Stene, Hongve, Magnus, Ronningen, & Joner, 2002). It has been suggested that zinc, and to some extent copper and magnesium, have a protective effect against developing diabetes (Haglund, Ryckenberg, Selinus, & Dahlquist, 1996; Zhao et al., 2001), whereas high levels of nitrate/ nitrite, mercury, and arsenic in drinking water may increase the risk (Lai et al., 1994; Parslow et al., 1997; Zhao et al., 2001). In contrast, however, some studies have found that no obvious associations exist between nitrate/nitrite, vanadium, and chromium and the risk of diabetes (Casu, Carlini, Contu, Bottazzo, & Songini, 2000; Haglund et al., 1996).

A previous study from southeast Sweden showed a clear geographical variation in incidence of type 1 diabetes (Samuelsson & Lofman, 2004). During a period of 25 years (1977-2001) the community with the highest incidence in this region had an incidence of 52.4/100,000 children 0-15 years with type 1 diabetes compared with an incidence of 14.3/100,000 in the community with the lowest incidence. A recent follow-up study from the same region found that relative socioeconomic deprivation influences the incidence of type 1 diabetes, with the lowest incidence in the most deprived areas (Holmqvist, Lofman, & Samuelsson, 2008). The aim of our study was to explore whether geological factors may contribute to the geographical variation of incidence of type 1 diabetes in the southeast region of Sweden.

As we had no specific hypothesis regarding physiological mechanisms or the geological factor(s) that might determine the risk of diabetes, we simply began a mapping exercise using analyzed data from a survey of the study area conducted by the Geological Survey of Sweden (SGU). The density of the number of analyzed sites varied depending on which compound was analyzed. Using this data-mining approach, our study would be able to clarify any spatial associations and generate further hypotheses on exposure mechanisms and causality.

Methods

Setting and Population at Risk

In the Swedish health care system, all children up to 16-18 years of age with diabetes are diagnosed and treated at pediatric clinics. …

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