Arsenic exposure, diabetes-related genes and diabetes prevalence in a general population from Spain

Abstract

Brief summary of the main result of the work:

Epidemiological studies support that people with higher inorganic arsenic exposure levels are more likely to have diabetes (Navas-Acien et al. 2006) but the evidence at low to moderate exposure levels is not sufficient. Moreover, most studies have been conducted in Asia and the Americas, with very few studies on arsenic and diabetes being conducted in Europe. Epidemiological studies in diverse populations are needed given the elevated burden of diabetes, a world-wide rapidly growing disease (World Health Organization 2016).

The sum of urine concentrations of inorganic arsenic (arsenite (AsIII) plus arsenate (AsV)) and its methylated species (monomethylarsonate (MMA) and dimethylarsinate (DMA)) is an established biomarker of inorganic arsenic exposure in populations with low seafood intake (Navas-Acien et al. 2011). It is well known that inorganic arsenic (iAs) is highly toxic to humans (World Health Organization 2001) and is associated with a wide range of adverse health effects, including cancer (Bates, Smith, and Hopenhayn-Rich 1992), cardiovascular disease (Moon, Guallar, and Navas-Acien 2012) and others (D’Ippoliti et al. 2015). People are exposed to inorganic arsenic mainly through drinking contaminated water (National Research Council 1999) and through food intake (Wei, Zhu, and Nguyen 2014). Organic arsenic species, such as arsenobetaine (Asb), arsenolipids and arsenosugars, are mostly found in saltwater finfish and shellfish and are considered not harmful to human health (Mozaffarian and Rimm 2006). Among the seafood arsenicals, arsenobetaine is the most common compound, which is rapidly cleared from the bloodstream and excreted unchanged in the urine (Molin et al. 2014), contributing to the total urinary arsenic concentrations. Arsenosugars and arsenolipids are metabolized in the body resulting in multiple metabolites in the urine, including DMA, and contributing to the total amount of inorganic arsenic species. As a result, in the presence of seafood intake, it is necessary to account for the potential residual confounding of organic arsenic in the interpretation of urinary arsenic concentrations as a biomarker of inorganic arsenic exposure (Navas-Acien et al. 2011).

The objective of this study was to evaluate the association of inorganic arsenic exposure, assessed as total urinary arsenic concentrations, with the prevalence of type 2 diabetes in a population from Spain with relatively high seafood consumption. This research was complicated by the presence of missing completely at random urine arsenobetaine concentrations for the ~80% of the study sample. We thus developed and implemented a multiple imputation model based in Markov Chain Monte Carlo (MCMC) methods using Gibbs sampling to account for the potential residual confounding by organic arsenicals in the whole study population. Given the well-established role of genetic variation on diabetes traits (Mahajan et al. 2014) and the paucity of gene-environmental interaction studies, we also evaluated the interaction of arsenic and diabetes-related candidate polymorphisms as a potential determinant of diabetes.

Methods

Results

Total Urine Arsenic and Diabetes Prevalence

We found a positive significant association of total urine arsenic with diabetes prevalence before and after adjustment for arsenobetaine concentrations (Table 2). The fully adjusted odds ratio (95% CI) for diabetes comparing the second and the third tertiles with the first one of total urine arsenic were 2.03 (1.63–2.53) and 2.14 (1.47, 3.11), respectively (Table 2, model 3). In model 2, which was not adjusted for Asb, this relation was weaker and not strictly increasing (odds ratio (95% CI) comparing the second and third tertiles to the lowest were 1.85 (1.04, 3.30) and 1.76 (1.01, 3.09), respectively). The analyses repeated in the random subsample with originally measured arsenic species showed consistent findings (e-Table 4). Flexible models based on splines confirmed that multivariable adjusted odds ratio were higher after adjusting for Asb (model 2 versus model 3) (Figure 1). No significant differences for the odds ratio of diabetes were found for gender, ever-smoking status and abnormal albuminuria subgroups (Table 3). For reduced glomerular filtration rate status, however, the association between arsenic and diabetes was stronger among participants without kidney disease (P of interaction = 0.02).

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Figure 1.

Odds ratio of type 2 diabetes by total urine arsenic concentrations, before and after adjustment for arsenobetaine (Asb).

Curves represent the odds ratios for type 2 diabetes based on restricted quadratic splines with knots at the 10^th, 50^th, and 90^th percentiles (10.4, 61.1 and 450.1 μg/g, respectively) of the total urine arsenic distribution. The reference was set at the 10^th percentile of the total urine arsenic distribution. Dotted line and light gray area represent the odds ratio and the 95% confidence interval before adjustment for arsenobetaine levels, i.e., adjusted for age, gender, education (<secondary education, ≥secondary education), urine cotinine levels (<34, 34–500 and >500 ng/mL), smoking status (non-smoker, former, current), alcohol intake, fish intake, and residence place (rural, urban). Solid line and dark gray area represent the odds ratio and the 95% confidence interval after further adjustment for log-transformed urine arsenobetaine (μg/g). Bars represent the weighted distribution of creatinine corrected total urine arsenic concentrations (μg/g). Tails were truncated by excluding 1% of the study sample with total urine arsenic concentrations below 2.6 μg/g and 1% of the study sample with total urine arsenic concentratiions above 2400 μg/g.

Table 2.

Odds ratio (95% confidence interval) for type 2 diabetes by total urine arsenic concentrations (N=1,451)

		Model 1a		Model 2b		Model 3c
Total Urine Arsenic, μg/g	Cases / Non cases	OR	95% CI	OR	95% CI	OR	95% CI
Tertile 1 (0 – 33.77)	25 / 437	1 Ref.		1 Ref.		1 Ref.
Tertile 2 (33.77 – 123.90)	41 / 432	1.85	1.04, 3.28	1.85	1.04, 3.30	2.03	1.63, 2.53
Tertile 3 (> 123.90)	54 / 462	1.75	1.01, 3.03	1.76	1.01, 3.09	2.14	1.47, 3.11
p80th vs. p20th	120 / 1331	1.30	0.94, 1.80	1.31	0.94, 1.81	1.76	1.54, 2.02

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Abbreviations: OR, odds ratio; CI, confidence interval.

p80^th and p20^th are 229.93 and 19.25 μg/g of creatinine, respectively.

^aModel 1: Adjusted for age (years), gender (male, female) and education (<secondary education, >= secondary education).

^bModel 2: Model 1 further adjusted for urine cotinine levels (<34, 34–500 and >500 ng/mL), smoking status (non-smoker, former and current), alcohol consumption (mg/day), fish consumption (g/month), and residence place (rural, urban).

^cModel 3: Model 2 further adjusted for log-transformed urine arsenobetaine levels (μg/g).

Table 3.

Odds ratio (95% confidence interval) for type 2 diabetes comparing the 80^th and the 20^th percentiles of total urine arsenic by participant subgroups (N=1,451).

	N	Cases / Non cases	OR	95% CI	Interaction P
Overall	1451	120 / 1331	1.76	1.54, 2.02
Gender
Female	722	46 / 676	1.68	1.45, 1.94
Male	729	74 / 655	1.85	1.57, 2.19	0.77
Smoking
Never	681	59 / 622	2.08	1.78, 2.42
Ever	770	61 / 709	1.52	1.30, 1.78	0.35
Abnormal Albuminuria
No	1421	108 / 1313	1.92	1.67, 2.21
Yes	26	14 / 12	2.79	1.67, 4.66	0.61
Reduced GFR
No	1331	94 / 1237	2.19	1.89, 2.54
Yes	120	26 / 94	0.99	0.85, 1.15	0.02

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Abbreviations: OR, odds ratio; CI, confidence interval; GFR, glomerular filtration rate. p80^th and p20^th are 229.93 and 19.25 μg/g of creatinine, respectively. Models were adjusted for further adjusted for age (years), gender (male, female), educational level (<secondary education, >= secondary education), urine cotinine (<34, 34–500 and >500 ng/mL), smoking status (non-smoker, former and current), alcohol consumption (mg/day), fish consumption (g/month), residence place (rural, urban), and log-transformed urine arsenobetaine levels (μg/g).

Gene-environment interaction

In gene-environment interaction analyses, while no polymorphism showed significant interactions with total urine arsenic after correcting for multiple comparisons, several genotypes showed marginally significant differential associations with diabetes (Figure 2 and Table 4). The top 5 genes showing the highest statistical interactions were IL8RA (rs1008562 [P=0.004]), TXN (rs4135168 [P=0.004]), NR3C2 (rs13117325 [P=0.007]), COX5A (rs1133322 [P =0.01]) and GCLC (rs11415624 [P =0.01]).

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Figure 2.

Candidate genes-arsenic interaction –log₁₀ P-values.

P-values for 354 SNPs derived from linear regression models (dominant, recessive and additive model) adjusted for age, sex, education, urine cotinine levels, smoking status, alcohol consumption, fish consumption, residence place and log-transformed arsenobetaine, are presented on the left Y axis on the logarithmic scale according to the position of the SNPs on chromosome (X axis). The horizontal solid line corresponds to a nominal p-value of significance equal to 0.05. Horizontal dashed line corresponds the effective SNP number corrected p-value equal to 0.0002.

Table 4.

Odds ratio (95% CI) of type 2 diabetes comparing the 80^th vs the 20^th percentiles of total urine arsenic distribution by genotypes among the 5 genes with top interaction p-values.

Chr.	Gene	SNP	Model	Cases / Non cases	Genotype	OR	95% CI	P Interaction
2	IL8RA	rs1008563	ADD	40/329	T/T	2.26	2.18, 2.34	0.004
				46/608	T/C	3.38	3.29, 3.46
				27/313	C/C	5.04	4.69, 5.42
		rs1008562	ADD	49/451	C/C	2.48	2.40, 2.56	0.01
				44/602	C/G	3.48	3.39, 3.58
				20/207	G/G	4.89	4.48, 5.33
9	TXN	rs4135168	DOM	71/732	A/A	4.19	4.01, 4.38	0.004
				47/566	A/G+G/G	2.48	2.38, 2.58
4	NR3C2	rs13117325	ADD	48/572	G/G	3.87	3.73, 4.02	0.007
				46/554	G/A	2.82	2.76, 2.88
				18/138	A/A	2.06	1.93, 2.19
		rs2137335	DOM	82/779	T/T	2.70	2.63, 2.76	0.01
				33/492	T/C+C/C	4.65	4.30, 5.03
6	GCLC	rs11415624	ADD	58/585	D/D	3.80	3.67, 3.93	0.01
				54/574	D/A	2.73	2.67, 2.79
				8/164	A/A	1.96	1.84, 2.09
15	COX5A	rs1133322	REC	96/978	T/C+T/T	3.44	3.36, 3.53	0.01
				21/323	C/C	2.03	1.90, 2.17

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Abbreviations: OR, odds ratio; CI, confidence interval; ADD, DOM, and REC, additive, dominant and recessive model, respectively.

The Bonferroni-corrected significance level was 0.0002.

Odds ratio for diabetes comparing the 80th and 20th percentiles of urine arsenic distributions (229.93 and 19.24 μg/g respectively) and associated test for interaction were obtained from linear regression models with log-transformed arsenic as a continuous variable. Models were adjusted for age (years), sex (male, female), education (<secondary education, >=secondary education), urine cotinine levels (<34, 34–500 and >500 ng/mL), smoking status (non-smoker, former and current), alcohol consumption (mg/day), fish consumption (g/month), residence place (rural, urban) and log-transformed arsenobetaine (μg/g).

Discussion

We found a positive association between increasing levels of total urine arsenic and the prevalence of type 2 diabetes in a representative sample of Spanish adults characterized by exposure to low arsenic levels. Our results support the relevance of adjusting for arsenobetaine levels in populations with high seafood consumption in order to estimate the association with arsenic exposure not derived from seafood. We also observed suggestive statistical interactions of urine arsenic levels with genetic variation in several genes involved in diabetes metabolic pathways, especially genes involved in regulation of oxidative stress response or inflammation.

The arsenobetaine levels in our study population (median: 160.3 μg/L, interquartile range: 15.8–124.5 μg/L) were much higher than those observed in a study of the 2003–2004 National Health and Nutrition Examination Survey (NHANES) (median: 0.9 μg/L, interquartile range: 0.3–3.5 μg/L) (Navas-Acien et al. 2008), reflecting a higher seafood intake. In populations with low seafood consumption, subtracting arsenobetaine concentration from total urine arsenic levels may provide a valid estimate of inorganic arsenic, since it removes the most common part of organic arsenic in seafood (Steinmaus et al. 2009). In populations with high seafood consumption, however, this approach may not be sufficient to remove the contribution of other seafood organic arsenic compounds, such as arsenosugars and arsenolipids and their metabolites, including DMA (Navas-Acien et al. 2011). Arsenobetaine is specific to seafood, it is excreted in the urine untransformed, and it is correlated with other seafood arsenicals. Using arsenobetaine as a covariate in the statistical regression models can thus indirectly control for other seafood arsenic species and their metabolites (Navas-Acien et al. 2009).

In 2003, the maximum admissible arsenic levels in drinking water in Spain decreased from 50 μg/L to 10 μg/L (Ministerio de la Presidencia 2003) as recommended by the World Health Organization. Our study participants were recruited between 1997 and 2003 in Valladolid, in central Spain. Most participants were most likely exposed to low levels of inorganic arsenic in drinking water. It is possible, however, that some participants coming from rural areas could have been exposed to moderate-high arsenic levels from the groundwater. A study evaluating groundwater quality in rural areas of Valladolid found concentrations of arsenic reaching levels as high as 260 μg/L in 2001 (mean=93 μg/L), 187 μg/L in 2003 (mean=48 μg/L) and 51 μg/L in 2007 (mean=15 μg/L) (Mayorga P, Moyano A, and García-Sánchez 2014). In our study population, the sum of inorganic and methylated arsenic concentrations was similar in participants from rural (geometric mean=8.8 μg/g) and urban areas (geometric mean=11.9 μg/g), even after adjustment for Asb and seafood intake (data not shown).

Several epidemiologic studies have found positive associations between arsenic exposure levels and diabetes in adults, both cross-sectionally (Navas-Acien et al. 2008) and prospectively (Brauner et al. 2014; James et al. 2013; Kim et al. 2013). In the US, increasing levels of urine arsenic were associated with increasing odds of diabetes after adjustment for urine arsenobetaine (Navas-Acien et al. 2008). Long-term exposure to low arsenic levels, assessed as arsenic concentrations in drinking water, has been prospectively associated with diabetes in central Italy (D’Ippoliti et al. 2015) and Denmark (Brauner et al. 2014). Studies using biomarkers of arsenic exposure also found positive relationship between arsenic and incident diabetes in Colorado (James et al. 2013) and American Indians from South-West of the United States (Kim et al. 2013), but not in American Indians 45–74 years of age participating in the Strong Heart Study (Kuo et al. 2015).

Experimental evidence supports the role of arsenic as a diabetes risk factor. In rodents, exposure to arsenic through contaminated water has shown to induce insulin resistance (Palacios, Roman, and Cifuentes 2012) and beta-cell dysfunction (Liu et al. 2014). The liver is the major target organ of the regulation of arsenic exposure and metabolism levels, and several experimental studies have demonstrated that chronic arsenic exposure induces severe toxic effects in the liver (Santra et al. 1999; Mazumder 2005). The liver also plays a major role in the regulation of glucose metabolism (Cotrozzi et al. 1997). In vivo evidence suggests that arsenic-induced diabetes may be mediated by increased oxidative stress in hepatic and pancreatic tissue (Patel and Kalia 2013). Interestingly, in our gene-environment interaction analysis, we observed differential associations of arsenic with polymorphisms in genes involved in redox-related pathways, including SNPs in TXN and GCLC, genes that are highly expressed in the pancreas and liver (Diaz, Krejsa, and Kavanagh 2002; Tran et al. 2004; Yamamoto et al. 2008; Okuyama et al. 2008), although the evidence was only suggestive. Some studies, however, support a potential biological link between proteins encoded by these genes and arsenic.

A positive correlation was observed between serum thioredoxin1 (TXN1) and total water arsenic intake or urinary arsenic species in humans (Li et al. 2012). Arsenic inhibits thioredoxin reductase, and this may contribute to TXN oxidation (Lin et al. 2001). Another mechanistic study showed that arsenic exposure can disrupt the GCLC expression, since low arsenic exposure increased the expression of GCLC in liver tissues of rats while high arsenic exposure levels reduced its expression (Ren et al. 2015). In addition, the interaction of arsenic with the polymorphism rs1008563 in IL8RA (also known as CXCR2), a chemokine receptor for interleukin-8 (IL8), showed the highest p-value. Several lines of evidence support that IL8RA inhibitors play a key role in the etiopathogenesis of type 1 diabetes (Diana et al. 2013; Valle et al. 2013). Interestingly, an experimental study found that urothelial cells chronically exposed to trivalent monomethylarsonate showed an over-expression of IL8 (Escudero-Lourdes et al. 2012).

Despite the limited power to detect interactions at the Bonferroni-corrected level, we found marginally significant associations for some polymorphisms, which, given the biological relevance on diabetes-related pathways, should be evaluated in larger studies. There are limited studies evaluating the interaction of genetic variation and arsenic on diabetes. A prospective study in Bangladesh (N=957) reported a significant interaction of SNPs in NOTCH2 with arsenic exposure (assessed in drinking water) on the association with type 2 diabetes (Pan et al. 2013). Unfortunately, we did not genotype candidate SNPs in NOTCH2. Additional and larger prospective studies, especially with an extensive panel of candidate genes, are needed.

One of the limitations of our study is the cross-sectional design, which cannot determine the direction of the associations. Elevated arsenic exposure levels could increase diabetes risk by inducing liver damage, as well as liver dysfunction related to diabetes might influence arsenic metabolism and result in higher arsenic excretion in the urine (Ahmadieh and Azar 2014). Another limitation is the use of one single urine sample given the within-subject variability in urinary arsenic excretion. Our study has also some strengths, including the complex sampling design; the adjustment for relevant diabetes risk factors, potential confounders and markers of seafood intake; the use of a multiple imputation model to adjust total urine arsenic for arsenobetaine, which enabled the use of urinary arsenic as a proxy for inorganic arsenic exposure; and the availability of SNPs in 155 candidate genes for gene-environmental evaluation.

In conclusion, arsenic exposure was associated with increased prevalence of type 2 diabetes in a general population from Spain. Our findings support the hypothesis that arsenic plays a biological role as a diabetogenic risk factor. Our analytical strategy also shows the importance of controlling for seafood arsenicals in populations with high seafood consumption. While the evaluated gene-environment interactions were only close to statistical significance after multiple comparisons correction, and should be only considered exploratory, our results provide the basis for gene-environment interaction studies in larger epidemiologic studies.

Supplementary Material

Acknowledgments

Abbreviations:

Footnotes

References