Arsenic exposure, arsenic metabolism, and incident diabetes in the Strong Heart Study
Chin-Chi Kuo, MD, PhD1-4, Barbara V Howard, PhD5,6, Jason G Umans, MD,PhD5,6, Matthew O Gribble,PhD7, Lyle G. Best, PhD 8, Kevin A Francesconi, PhD9, Walter Goessler, PhD9, Elisa Lee, PhD10, Eliseo Guallar, MD, DrPH1,3,11, and Ana Navas-Acien, MD, PhD1-3,12
1Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
2Department of Environmental Health Sciences, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
3Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, Maryland, USA
4 Kidney Institute and Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and College of Medicine, China Medical University, Taichung, Taiwan
5MedStar Health Research Institute, Hyattsville, MD, USA
6Georgetown-Howard Universities Center for Clinical and Translational Science, Washington DC, USA 7University of Southern California, Los Angeles, CA, USA
8Missouri Breaks Industries Research, Inc., Timber Lake, South Dakota
9Institute of Chemistry – Analytical Chemistry, Karl-Franzens University Graz, Graz, Austria
10Center for American Indian Health Research, College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA
11Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA 12Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Short Title: Arsenic metabolism and diabetes
Index Words: arsenic, arsenic metabolism, arsenic methylation, diabetes
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
Potential conflicts of interest: All authors: no conflicts. Word count: Abstract, 251; Text, 4,291; Tables, 3; Figures, 3
Funding: This study was supported by grants from the National Heart, Lung and Blood Institute
(R01HL090863, HL41642, HL41652, HL41654, and HL65521) and the National Institute of Environmental Health Sciences (R01ES021367 and P30ES03819).
Author contributions:
CCK, MOG, EG and ANA : Prepared research data, conducted statistical analysis, and manuscript writing
BVH, JGU, LGB, and EL: Primary investigators of Strong Heart Study and prepared research data
KAF and WG: Arsenic measurements for Strong Heart Study participants
Acknowledgments
Dr. Ana Navas-Acien is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
Abstract Objective
Little is known regarding arsenic metabolism in diabetes development. We investigated the prospective associations of low-moderate arsenic exposure and arsenic metabolism with diabetes incidence in the Strong Heart Study.
Research Design and Methods
A total of 1,694 diabetes-free participants aged 45-75 years were recruited in 1989-1991 and followed through 1998-1999. We used the proportions of urine inorganic arsenic(iAs), monomethylated(MMA), and dimethylated(DMA) over their sum (expressed as iAs%, MMA%, and DMA%) as the biomarkers of arsenic metabolism. Diabetes was defined as fasting glucose ≥126 mg/dL, 2–h glucose ≥200 mg/dL, self-reported diabetes history, or self-reported use of anti-diabetic medications.
Results
Over 11,263.2 person-years of follow-up, 396 participants developed diabetes. Using the leave-one-out approach to model the dynamics of arsenic metabolism, we found lower MMA% was associated with higher diabetes incidence. The hazard ratios (95% CI) of diabetes incidence for a 5% increase in MMA% were 0.69 (0.52, 0.90) and 0.76 (0.65, 0.89) when iAs% and DMA% were, respectively, left-out of the model. DMA% was associated with higher diabetes incidence only when MMA% decreased (left-out from the model), but not when iAs% decreased. iAs% was also associated with higher diabetes incidence when MMA% decreased. The association between MMA% and diabetes incidence was similar by age, sex, study site, obesity, and urine inorganic arsenic concentrations.
Conclusions
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
Arsenic metabolism, in particular lower MMA%, was prospectively associated with increased incidence of diabetes. Research is needed to evaluate whether arsenic metabolism is related to diabetes incidence
per se, or through its close connections with one-carbon metabolism.
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
Background
Humans are exposed to inorganic arsenic through drinking water, food, dust, and ambient air. Increasing epidemiologic and experimental evidence supports a role for inorganic arsenic in the development of diabetes mellitus. At high arsenic levels (>150 µg/L in drinking water), evidence from Taiwan and Bangladesh supports an association with diabetes, although most studies are cross-sectional and there are concerns about measures of arsenic exposure and the definition of diabetes in some studies. At low-moderate arsenic levels, recent evidence from Mexico and the United states, including cross-sectional and prospective studies support the role of arsenic in diabetes development.
Little is known, however, about the association between arsenic metabolism and diabetes. After absorption, inorganic arsenic (iAs; arsenate and arsenite) is methylated, primarily in the liver, to form monomethylated (MMA) and dimethylated (DMA) arsenic compounds, which are excreted into the urine together with iAs. Higher MMA% and lower DMA% in urine have been related to increased risk of cancer and cardiovascular disease in studies from Taiwan and Bangladesh. The increased risk of cancer and cardiovascular disease associated with higher MMA% in urine may be related to the high toxicity of MMA (III), the trivalent form that is rapidly oxidized to MMA in urine and thus difficult to measure in epidemiologic studies. DMA is regarded as a less toxic arsenic species, as DMA is more rapidly excreted through the urine compared to inorganic arsenic. DMA (III), however, has been recently linked to the prevalence of diabetes in cross-sectional studies from Mexico and Bangladesh, although it is also an unstable species in urine. Higher DMA% and lower MMA% has also been related to obesity in studies from Mexico and the US, although the temporality of these associations is unclear. In addition, arsenic metabolism is tightly connected with one-carbon metabolism, which has been implicated in both cancer development and cardiovascular disease, and may also play a role in diabetes. These findings highlight the need to properly evaluate the role of arsenic methylation profiles in diabetes development.
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
In this study, we investigated the associations of low-moderate arsenic exposure and arsenic metabolism with diabetes in the Strong Heart Study (SHS). The SHS is a population-based prospective cohort study of cardiometabolic diseases among 3 American Indian communities in rural Arizona, Oklahoma, and North and South Dakota (“the Dakotas”). In participants from Arizona and the Dakotas, drinking water was probably the major source of inorganic arsenic while in participants from Oklahoma, diet, including rice, flour and other grains, was probably the main source. Urine arsenic concentrations and measures of arsenic metabolism were stable in SHS participants during the time of follow-up, supporting the use of urine arsenic as a suitable surrogate for chronic arsenic exposure and metabolism. In the SHS, we recently found that higher inorganic arsenic exposure was associated with higher diabetes prevalence, supporting the need to further investigate the prospective associations between arsenic exposure and metabolism with diabetes incidence.
Method
Study population
In 1989-1991, the Strong Heart Study examined 4,549 American Indian men and women aged 45 to 74 years at baseline enrollment from 13 tribes and communities. All community members were invited to participate in Arizona and Oklahoma, whereas a cluster sampling procedure was used in the Dakotas. The overall participation rate was 62%. Compared with nonparticipants, participants were similar in age, body mass index, and prevalence of self-reported diabetes but were more likely to be female and to have self-reported hypertension. Participants were invited to subsequent clinical visits between 1993 and 1995, and between 1998 and 1999. The SHS population is very stable, with low migration rates due to strong cultural and social links in the community. The Indian Health Service, institutional review boards, and participating communities approved the study protocol. All participants provided informed consent.
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
The prevalence of diabetes in the Strong Heart Study in 1989-1991 was 50%. For this study, we used data from participants free of diabetes and with sufficient urine available for arsenic measurements at the baseline visit (N=1,986) (Supplementary figure 1). We further excluded 117 participants lost during follow up or missing both fasting glucose and 2-hour plasma glucose data during follow-up, 105 participants with inorganic or methylated arsenic species below the limit of detection as it is difficult to estimate arsenic methylation in these participants, and 70 participants missing other variables of interest leaving 1,694 participants for this analysis. Socidemographic and diabetes risk factors were similar between our study population and the overall Strong Heart Study population free of diabetes at baseline (data not shown).
Data Collection
Baseline clinical information consisted of a personal interview, physical examination, fasting blood sample, and spot urine sample. Sociodemographic (age, sex, and education) and lifestyle (smoking and alcohol status) information was collected by trained and certified interviewers using standardized questionnaires. Physical examination measurements (height, weight, waist and hip circumferences, and systolic and diastolic pressures) and bio-specimen collection (blood and urine) were conducted by centrally trained nurses and medical assistants following a standardized protocol. Detailed procedures of clinical and laboratory examinations have been described. Estimated glomerular filtration rate at baseline was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula. Participants were asked to fast for 12 hours before blood samples were collected in the morning, at baseline and in the two subsequent visits. Spot urine samples were also collected in the morning and were frozen with 1 to 2 hours of collection. The biospecimens were stored at -70°C or lower before analyses. Diabetes measurements
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
Fasting plasma glucose level was determined by a hexokinase method at MedStar Health Research Institute, Washington, DC. A 2-hour, 75g oral glucose tolerance test was performed on all participants except those who were under insulin therapy, remained with poor glycemic control on oral medication, or had a fasting glucose level greater than 225 mg/dL determined by Accu-Chek II (Baxter Healthcare, Grand Prairie, Texas). Glycated hemoglobin was measured at the laboratory of the National Institute of Diabetes and Digestive and Kidney Diseases Epidemiology and Clinical Research Branch, Phoenix, Arizona, by a high-performance liquid-chromatographic (HPLC) method. Diabetes was defined as a fasting plasma glucose ≥126 mg/dL, plasma glucose ≥200 mg/dL 2–h after ingestion of 75 g oral glucose load, self-reported diabetes history, or self-reported use of insulin or oral hypoglycemic medications.
Urine arsenic
To assess long-term arsenic exposure, we measured urine arsenic species after confirmation that concentrations were stable over a 10-year period. To assess arsenic methylation profiles, we estimated the relative proportions of each urine arsenic species (iAs%, MMA%, and DMA%) standardized by the urine total arsenic concentration were utilized to approximate individual’s arsenic methylation capacity. For example, MMA% is determined as urine MMA [(MMA(V) +MMA(III)] concentration divided by urine total inorganic arsenic concentration(iAs + MMA +DMA). In a subsample of diabetes-free participants with urine arsenic measures repeated over the 3 study visits (n=207), the individual (single) intra-class correlation coefficient for arsenic measures over a 10-year period was 0.60 for the sum of inorganic and methylated species and 0.55, 0.59, and 0.69 for iAs%, MMA%, and DMA%, respectively, confirming the moderate long-term stability of arsenic exposure and arsenic metabolism in this cohort. Detailed analytic methods and associated quality control procedures for arsenic analysis have been published. Arsenic species concentrations were determined by high-performance liquid chromatography (HPLC)
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
coupled to inductively coupled plasma mass spectrometry (ICP-MS) that served as the arsenic selective detector (Agilent 1100 HPLC and Agilent 7700x ICP-MS, Agilent Technologies, Santa Clara, California). Arsenic speciation can discriminate species directly related to iAs exposure (arsenite, arsenate,
monomethylarsonate [MMA], and dimethylarsinate [DMA]) from those related to organic arsenicals in seafood (arsenobetaine as an overall marker of seafood arsenicals), which are generally considered nontoxic. Urine concentrations of arsenobetaine and other arsenic cations were very low (median, 0.71; interquartile range, 0.41 to 1.69 μg/g creatinine), confirming that seafood intake was low in this sample, and indicating that DMA mainly came from inorganic arsenic exposure. The limit of detection (LOD) for total arsenic and for iAs (arsenite plus arsenate), MMA, DMA, and arsenobetaine plus other arsenic cations was 0.1 μg/L. In the original cohort, for participants with arsenic species concentrations below LOD (5.2% for inorganic arsenic, 0.8% for MMA, 0.03% for DMA), levels were imputed as the LOD/√2 . Based on previous simulation studies, this “fill-in” approach can be unbiased if the percentage of measurements below detection limits is small (5–10%). Because a major goal of the study was to evaluate the role of arsenic metabolism in diabetes development, we excluded participants with iAs (5.2%), MMA (0.8%) and DMA (0.03%) below the limit of detection from the original cohort. An in-house reference urine and the Japanese National Institute for Environmental Studies No. 18 Human Urine were analyzed together with the samples. Interassay coefficients of variation for iAs, MMA, DMA and
arsenobetaine for the in-house reference urine were 6.0%, 6.5%, 5.9%, and 6.5%, respectively.
From the risk assessment perspective, the sum of inorganic and methylated arsenic species in the urine is used to estimate arsenic exposure from multiple sources and exposure routes. The estimated risk based on urine concentrations of arsenic metabolites can inform and help evaluate dose-response relationships related to exposure levels and inform risk assessment. The relative proportions of arsenic metabolites (iAs%, MMA% and DMA%) are utilized to estimate the extent to which inorganic arsenic is metabolized in the human body and inform on different arsenic metabolic profiles across people. This
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
information is also useful as part of the susceptibility evaluation in risk assessment. The assessment of arsenic metabolism in addition to concentrations levels has been recommended by the 2013 National Research Council Report on arsenic.
Statistical methods
We used the sum of urine inorganic (iAS; arsenite and arsenate) and methylated arsenic species (MMA and DMA) as the biomarker of inorganic arsenic exposure from multiple sources. We used the proportions of urine iAs, MMA and DMA over the sum of inorganic and methylated species, expressed as iAs%, MMA%, and DMA%, to evaluate arsenic metabolism. We graphically described the distribution of arsenic metabolism in people with and without diabetes using a triplot, a diagram with 3 axes that is well-suited to represent arsenic metabolism (Figure 1).
The prospective associations between arsenic exposure and arsenic metabolism with incident diabetes were evaluated by Cox proportional hazards models. Arsenic exposure was evaluated based on the urinary concentration of the sum of inorganic and methylated arsenic species. We also evaluated the urinary concentration of iAs, MMA and DMA in separate models. Arsenic metabolism was evaluated as iAs%, MMA% and DMA%. Similar to previous studies, we first entered each arsenic metabolism
biomarker alone in the regression model together with the sum of inorganic and methylated arsenic species to adjust for arsenic exposure. Entering each biomarker alone is difficult to interpret, as the increase in iAs, for instance, could be related to a decrease in either MMA or DMA. To address this problem, we used a “leave-one-out” approach. In this method, two biomarkers are entered at a time, e.g. iAs% and MMA%, leaving out the third one, DMA%, while holding constant urine arsenic
concentrations. In the example, the regression coefficients for iAs% and MMA% estimate the hazard ratio associated with an increase in %iAs by decreasing DMA%, and with an increase in MMA% by decreasing DMA%, respectively. This method is used in the nutrition literature to estimate the specific
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
contribution of different macronutrients beyond their contribution to total energy intake as well as in the hematology literature to estimate the specific contribution of different blood cell types beyond total cell count.
All arsenic variables were modeled per interquartile range increment (in the log scale for urine arsenic species concentrations and in the original scale for % species) and using restricted cubic splines. We also modeled them using quartiles with similar findings (data not shown). The time scale for survival analysis was age. To handle left-truncation induced at time of enrollment and appropriately aligning risk sets on the age scale, the late entry method was conducted using age at baseline as the individual entry time. The exit time for participants with newly diagnosed diabetes (N=218) was the date of second or third visits. For participants with known diabetes status and data on diabetes duration, the exit date was the self-reported duration subtracted from the visit date (N=178). To account for differences across geographical areas, we added the regions (Arizona, Oklahoma and North/South Dakota) as strata to the Cox proportional hazards models 1 to 4. This method allows the form of the underlying hazard function to vary across levels of study regions. Models were adjusted progressively. Initially, we adjusted for sex and education (no high school, some high school, and high school or more). We then adjusted further for smoking status (current, former, never) and alcohol drinking status (current, former, never). Finally, we further adjusted for body mass index and waist–hip ratio as continuous variables. All models were adjusted for urine creatinine to account for urine dilution. In an alternative analysis we adjusted for specific gravity instead of urine creatinine. Both models yielded similar results (models for specific gravity are not shown). We confirmed that the proportional hazards assumption was fulfilled based on Schoenfeld residuals.
We conducted additional sensitivity analyses to evaluate the robustness of our primary findings. First, we evaluated the prospective associations of arsenic exposure and arsenic metabolism with
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
incident diabetes by fitting generalized gamma distributions to survival times. Model selection was based on the Akaike Information Criterion (AIC) and estimates for the shape parameter indicated that log-normal distributions were appropriate. This approach yielded consistent findings as the Cox proportional hazards model (data not shown). Second, as the diabetes onset date is not exact, we also conducted multiple logistic regression and Poisson regression to examine the robustness of the associations. The conclusions were consistent with our Cox proportional hazards model (not shown). Third, because mortality rate was high in the SHS population and there is a link between cancer and arsenic metabolism, we conducted competing risk analysis using death and cancer mortality as competing events, respectively, based on Fine and Gray’s method, with similar results. We also used generalized gamma modeling to describe the competing relationship between mortality and incident diabetes comparing the highest and the lowest quartiles of urine inorganic arsenic concentrations (supplementary figure 2). Forth, we repeated the analysis for arsenic exposure including participants who had iAs, MMA or DMA below the limit of detection (LOD) by replacing levels below the LOD by the LOD divided by the square root of 2, also with similar findings (not shown). Fifth, we applied two additional urine dilution correction methods by adjusting urine creatinine in the model and by adjusting specific gravity using Levine’s approach, with consistent results (data not shown).
All statistical analyses were performed in Stata/IC, version 12 (StataCorp, College Station, Texas) and R, version 3.0.2 (R Foundation for Statistical Computing, Vienna, Austria [www.r-project.org]).
Results
The median urine concentration of inorganic plus methylated arsenic species was 10.2 μg/L (interquartile range, 6.1 to 17.7 μg/L). Urine arsenic concentrations were higher in participants from Arizona (median 14.3 μg/L), followed by the Dakotas (11.9 μg/L) and Oklahoma (median 7.0 μg/L ). The median (interquartile range) for iAs%, MMA% and DMA% was 8.3 (5.7 to 11.3)%, 15.2 (11.7 to 18.8)%
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
and 76.4 (70.3 to 81.4)%, respectively. Men, participants from the Dakotas, current smokers and participants with lower body mass index had higher MMA%, and correspondingly lower DMA% (Figure 2).
Over 11,263.2 person-years of follow-up, 396 participants developed diabetes. Diabetes incidence was 35.2 per 1000 person-years. Participants with incident diabetes were more likely to be female, from Arizona, and obese at baseline (Table 1). Younger age was borderline associated with incident diabetes (p-value 0.05). Urine concentrations of inorganic plus methylated arsenic were similar in participants with and without incident diabetes. Participants with incident diabetes had lower MMA% and higher DMA% compared to those without incident diabetes (Table 1, Figure 1). Arsenic exposure, assessed as the summed concentrations in urine of inorganic and methylated arsenic species or as each of the individual arsenic species, was not associated with incident diabetes in any of the multivariable adjusted models (Table 2 and Supplement Figure 3).
For arsenic metabolism, the multi-adjusted hazard ratio (95% CI) of diabetes incidence per 5% increase in arsenic metabolism biomarkers entered one-by-one in the model (conventional approach) was 1.00 (0.87-1.14) for iAs%, 0.79 (0.68-0.92) for MMA% and 1.17 (1.00-1.36) for DMA% (Table 3, model 4). Using the leave-one-out approach, we confirmed that higher MMA% was associated with lower diabetes incidence. The hazard ratios (95% CI) of diabetes incidence for an 5% increase in MMA% were 0.69 (0.52, 0.90) and 0.76 (0.65, 0.89) when iAs% and DMA% were, respectively, left-out of the model (Table 3, model 4). In other words, when MMA% and iAs% were in the model, the hazard ratio of incident diabetes of MMA% was the effect of “replacing” DMA% with MMA% while holding iAs% constant. The same interpretation applied when DMA% and iAs% and when MMA% and DMA% were in the model simultaneously. Consistently, higher MMA% was related to lower diabetes incidence, showing a linear relationship in flexible dose-response analyses when either iAs% or DMA% were left-out of the
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
model (Figure 3). DMA% was associated with higher diabetes incidence only when substituted for MMA % and iAs% was associated with higher diabetes incidence only when substituted for MMA% (Table 3, Figure 3).
The association between MMA% and diabetes incidence was similar by age, sex, study site, obesity, and the sum of inorganic and methylated arsenic concentrations (Supplementary table 1).
Discussion
Arsenic metabolism, but not inorganic arsenic exposure, was prospectively associated with diabetes incidence in American Indians from Arizona, Oklahoma and North/South Dakota. Higher iAs% and DMA% in urine, because of lower MMA%, was associated with higher diabetes incidence.
Consistently, higher MMA% was associated with lower risk of diabetes. The associations persisted after adjustment for sociodemographic factors, smoking, alcohol, kidney function, and measures of adiposity. These novel findings support that arsenic metabolism patterns, in particular lower MMA%, may be a predisposing factor for diabetes. Arsenic exposure, measured by the concentration of inorganic plus methylated arsenic species in urine, however, was not associated with diabetes incidence in our study population. The study was conducted in a population with a high burden of obesity and diabetes and characterized by low-to-moderate arsenic exposure levels.
Non-genetic determinants of arsenic metabolism include sex (women have higher DMA% than men), smoking (never smokers generally have higher DMA% than current smokers), nutritional status (dietary folate and vitamin deficiencies are associated with lower DMA%), and BMI (obese participants have higher DMA%). In women, MMA% decreases and DMA% increases during pregnancy. While the risk of gestational diabetes is also increased, a connection with changes in arsenic metabolic patterns during pregnancy is unknown. Interestingly, in our study the arsenic metabolic pattern associated with
increased diabetes risk paralleled that observed during pregnancy, i.e., lower MMA% and higher DMA%.
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
Genetic determinants, especially variants in arsenic (III) methyltransferase (AS3MT), have also been related with arsenic methylation patterns in urine. Additional research is needed to evaluate whether genetic variants play a role in the connection between arsenic metabolism profile and diabetes.
Little is known about arsenic metabolism and diabetes as compared to its role in cancer and cardiovascular disease. In those studies, conducted mostly in Taiwan and Bangladesh, higher MMA% was associated with the development of lung, bladder and skin cancers and with cardiovascular disease including atherosclerosis and peripheral vascular disease. In one small case-control study from Bangladesh, higher DMA% was associated with increased prevalence of diabetes, although the association was not statistically significant. High BMI has also been significantly associated with low MMA% and high DMA% in urine in adults from Mexico and the Strong Heart Study. In our study, adjusting for baseline BMI and waist-hip ratio slightly attenuated the association between arsenic metabolism and incident diabetes, although the association remained. How this specific pattern (low MMA% with either high iAs% or DMA%) may affect individual susceptibility to endocrine and metabolic diseases remains unclear.
Substantial experimental research supports the role of arsenic exposure in diabetes
development. Experimental studies, in general, have not focused on differences by arsenic metabolism. High MMA% may be considered as a marker of insufficient methylation capacity to DMA. Recent
experimental studies have shown that methylation could be a bio-activation process, with DMA(III) being a potent and highly toxic dimethylated arsenic species. In adipocytes, DMA(III) impairs
insulin-stimulated glucose uptake. In addition, DMA(III) may induce pancreatic -cell apoptosis and inhibit glucose-stimulated insulin secretion in murine pancreatic islet cells. These experimental findings were consistent with a cross-sectional study from Mexico, where the concentrations of DMA(III) in urine were associated with diabetes prevalence. In our study, similar to other large epidemiologic studies, we
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
measured total MMA and DMA, as MMA(III) and DMA(III) are unstable in urine and quickly oxidized to their pentavalent forms. The rapid oxidation of MMA(III) and DMA(III) in urine to their pentavalent counterparts makes estimation of the trivalent methylated species very difficult in epidemiological studies. However, participants with higher DMA% may be exposed to more DMA(III) given the same amount of total inorganic arsenic exposure.
The association of arsenic metabolism with diabetes could also be related to one carbon metabolism, as S-adenosylmethionine (SAM) is the methyl donor for arsenic metabolism. Recent experimental evidence has shown that SAM plays an important role in lipogenesis and in the
development of diabetes. An in vitro study in Caenorhabditis elegans, an experimental model for human diseases and metabolic pathways, found that the synthesis of SAM regulated the expression of genes required for adequate lipid metabolism. In HepG2 human hepatocytes, the optimal balance between SAM and S-adenosylhomocystine (SAH) was critical to maintain appropriate expression of gluconeogenic enzymes. In addition, in a cross-sectional study of 50-75 year old adults from the Netherlands (N=648), plasma SAM was positively associated with fat mass and truncal adiposity, although reverse causation could not be excluded. We cannot discount the possibility that arsenic metabolism acts as a marker of one carbon metabolism. In our study we had no serological measures of one carbon metabolism and dietary estimations were only available in 20% of the sample.In any case, our findings indicate that more research is needed to understand the impact of arsenic methylation and other methylation processes related to one-carbon metabolism on the development of diabetes.
In our study, we found no association between arsenic exposure and incident diabetes, although cross-sectionally we had found an association. Inorganic arsenic and its methylated metabolites may induce diabetes by impairing insulin production by pancreatic ß cells or inhibiting basal or insulin-stimulated glucose uptake by peripheral tissues. Relevant mechanisms by which arsenic could affect
ß-338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
cell function and insulin sensitivity include oxidative stress, glucose uptake and transport,
gluconeogenesis, adipocyte differentiation, calcium signaling, and epigenetic effects. A number of recent studies have reported a prospective association between arsenic exposure and diabetes development It is possible that arsenic exposure is not a risk factor for diabetes in our population. At the same time, the presence of an association between arsenic exposure and diabetes cross-sectionally but not
prospectively could be related to competing risk of premature death and differential survival bias that may mask the true association in our population. Because arsenic was strongly associated with diabetes at baseline and the prevalence of diabetes at baseline was 50%, another possible explanation for the lack of association is that the pool of susceptible participants is too small for the association to be seen prospectively. In support of this possibility, age was not positively associated with diabetes incidence either (Table 1). BMI, however, remained a strong risk factor.
Strengths of our study include standardized protocol to collect data over the follow-up, high-quality laboratory methods for measuring concentrations of urine arsenic species at baseline and careful modeling of the dynamic of arsenic metabolism including the leave-one-out approach. Another
advantage is that we use the sum of inorganic arsenic and methylated arsenic metabolites in the urine to represent inorganic arsenic exposure that integrates different sources and routes of exposure and avoids the measurement error problems associated with seafood exposure. This study had several limitations. First, the urine arsenic concentrations and metabolism were measured in a single sample at baseline to represent internal doses and individual metabolism profiles. However, we have confirmed that arsenic levels in urine and arsenic metabolism were constant over 10-years in this population. Second,
adjustment for adiposity could induce over-adjustment as obesity may be in the causal pathway between arsenic metabolism and diabetes. Third, our population was between 40 and 74 years of age and the burden of diabetes at baseline was already 50%. It is thus possible that participants susceptible of developing diabetes at baseline were different from the source population. Studies in younger
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
populations with a lower prevalence of diabetes at baseline are needed. Forth, the study was observational and we cannot exclude the possibility of residual or unmeasured confounding, for example, by accurate smoking and drinking information, or by other measures of one carbon
metabolism. However, our results were consistent after further adjustment for folic acid, vitamin B6, and cobalamin based on food frequency questionnaire in the 20% subsample with this information available (data not shown).
In conclusion, arsenic metabolism, in particular low MMA%, was associated with increased incidence of diabetes and could reflect individual susceptibility for diabetes development. Arsenic metabolism is related to one-carbon metabolism, and could be functioning as a surrogate measure of one-carbon metabolism. Research is needed to assess the relationship between arsenic metabolism and diabetes in different populations, evaluate the diabetogenic role of arsenic metabolism in experimental settings, and clarify whether the development of diabetes is related to arsenic metabolism specifically or to one-carbon metabolism in general.
References
1. World Health Organization(WHO). Exposure to Arsenic: A major public health concern. Geneva, Switzerland: World Health Organization(WHO); 2010
2. Maull EA, Ahsan H, Edwards J, Longnecker MP, Navas-Acien A, Pi J, et al. Evaluation of the association between arsenic and diabetes: a National Toxicology Program workshop review. Environ Health Perspect. 2012;120(12):1658-70.
3. Kuo CC, Moon K, Thayer KA, Navas-Acien A. Environmental chemicals and type 2 diabetes: an updated systematic review of the epidemiologic evidence. Curr Diab Rep. 2013;13(6):831-49. 4. Navas-Acien A, Silbergeld EK, Streeter RA, Clark JM, Burke TA, Guallar E. Arsenic exposure and
type 2 diabetes: a systematic review of the experimental and epidemiological evidence. Environ Health Perspect. 2006;114(5):641-8.
5. Del Razo LM, Garcia-Vargas GG, Valenzuela OL, Castellanos EH, Sanchez-Pena LC, Currier JM, et al. Exposure to arsenic in drinking water is associated with increased prevalence of diabetes: a cross-sectional study in the Zimapan and Lagunera regions in Mexico. Environ Health.
2011;10:73.
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
6. Gribble MO, Howard BV, Umans JG, Shara NM, Francesconi KA, Goessler W, et al. Arsenic exposure, diabetes prevalence, and diabetes control in the Strong Heart Study. Am J Epidemiol. 2012;176(10):865-74.
7. James KA, Marshall JA, Hokanson JE, Meliker JR, Zerbe GO, Byers TE. A case-cohort study examining lifetime exposure to inorganic arsenic in drinking water and diabetes mellitus. Environ Res. 2013;123:33-8.
8. Kim NH, Mason CC, Nelson RG, Afton SE, Essader AS, Medlin JE, et al. Arsenic Exposure and Incidence of Type 2 Diabetes in Southwestern American Indians. Am J Epidemiol. 2013.
9. Tseng CH. A review on environmental factors regulating arsenic methylation in humans. Toxicol Appl Pharmacol. 2009;235(3):338-50.
10. National Research Council (NRC). Critical Aspects of EPA's IRIS Assessment of Inorganic Arsenic: Interim Report (2013). Washington, DC: The National Academies Press; 2013.
11. Chen Y-C, Su H-JJ, Guo Y-LL, Hsueh Y-M, Smith TJ, Ryan LM, et al. Arsenic methylation and bladder cancer risk in Taiwan. Cancer causes & control : CCC. 2003;14(4):303-10.
12. Steinmaus C, Bates MN, Yuan Y, Kalman D, Atallah R, Rey OA, et al. Arsenic methylation and bladder cancer risk in case-control studies in Argentina and the United States. Journal of occupational and environmental medicine / American College of Occupational and Environmental Medicine. 2006;48(5):478-88.
13. Yu RC, Hsu KH, Chen CJ, Froines JR. Arsenic methylation capacity and skin cancer. Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology. 2000;9(11):1259-62. 14. Huang YL, Hsueh YM, Huang YK, Yip PK, Yang MH, Chen CJ. Urinary arsenic methylation capability
and carotid atherosclerosis risk in subjects living in arsenicosis-hyperendemic areas in southwestern Taiwan. Sci Total Environ. 2009;407(8):2608-14.
15. Wu M-M, Chiou H-Y, Hsueh Y-M, Hong C-T, Su C-L, Chang S-F, et al. Effect of plasma homocysteine level and urinary monomethylarsonic acid on the risk of arsenic-associated carotid
atherosclerosis. Toxicology and applied pharmacology. 2006;216(1):168-75.
16. Walton FS, Harmon AW, Paul DS, Drobná Z, Patel YM, Styblo M. Inhibition of insulin-dependent glucose uptake by trivalent arsenicals: possible mechanism of arsenic-induced diabetes. Toxicology and applied pharmacology. 2004;198(3):424-33.
17. Paul DS, Harmon AW, Devesa V, Thomas DJ, Styblo M. Molecular mechanisms of the
diabetogenic effects of arsenic: inhibition of insulin signaling by arsenite and methylarsonous acid. Environ Health Perspect. 2007;115(5):734-42.
18. Vahter M. Mechanisms of arsenic biotransformation. Toxicology. 2002;181-182:211-7.
19. Petrick JS, Ayala-Fierro F, Cullen WR, Carter DE, Vasken Aposhian H. Monomethylarsonous acid (MMA(III)) is more toxic than arsenite in Chang human hepatocytes. Toxicol Appl Pharmacol. 2000;163(2):203-7.
20. Nizam S, Kato M, Yatsuya H, Khalequzzaman M, Ohnuma S, Naito H, et al. Differences in urinary arsenic metabolites between diabetic and non-diabetic subjects in Bangladesh. Int J Environ Res Public Health. 2013;10(3):1006-19.
21. Gomez-Rubio P, Roberge J, Arendell L, Harris RB, O'Rourke MK, Chen Z, et al. Association between body mass index and arsenic methylation efficiency in adult women from southwest U.S. and northwest Mexico. Toxicol Appl Pharmacol. 2011;252(2):176-82.
22. Gribble MO, Crainiceanu CM, Howard BV, Umans JG, Francesconi KA, Goessler W, et al. Body composition and arsenic metabolism: a cross-sectional analysis in the Strong Heart Study. Environ Health. 2013;12(1):107.
23. Hall MN, Gamble MV. Nutritional manipulation of one-carbon metabolism: effects on arsenic methylation and toxicity. J Toxicol. 2012;2012:595307.
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
24. Locasale JW. Serine, glycine and one-carbon units: cancer metabolism in full circle. Nat Rev Cancer. 2013;13(8):572-83.
25. Baccarelli A, Rienstra M, Benjamin EJ. Cardiovascular epigenetics: basic concepts and results from animal and human studies. Circ Cardiovasc Genet. 2010;3(6):567-73.
26. Ngo S, Li X, O'Neill R, Bhoothpur C, Gluckman P, Sheppard A. Elevated s-adenosylhomocysteine alters adipocyte functionality with corresponding changes in gene expression and associated epigenetic marks. Diabetes. 2014;63(7):2273-83.
27. Krishnaveni GV, Veena SR, Karat SC, Yajnik CS, Fall CH. Association between maternal folate concentrations during pregnancy and insulin resistance in Indian children. Diabetologia. 2014;57(1):110-21.
28. Howard BV, Welty TK, Fabsitz RR, Cowan LD, Oopik AJ, Le NA, et al. Risk factors for coronary heart disease in diabetic and nondiabetic Native Americans. The Strong Heart Study. Diabetes. 1992;41 Suppl 2:4-11.
29. Navas-Acien A, Umans JG, Howard BV, Goessler W, Francesconi KA, Crainiceanu CM, et al. Urine arsenic concentrations and species excretion patterns in American Indian communities over a 10-year period: the Strong Heart Study. Environ Health Perspect. 2009;117(9):1428-33.
30. Lee ET, Howard BV, Wang W, Welty TK, Galloway JM, Best LG, et al. Prediction of coronary heart disease in a population with high prevalence of diabetes and albuminuria: the Strong Heart Study. Circulation. 2006;113(25):2897-905.
31. Lee ET, Welty TK, Fabsitz R, Cowan LD, Le NA, Oopik AJ, et al. The Strong Heart Study. A study of cardiovascular disease in American Indians: design and methods. Am J Epidemiol.
1990;132(6):1141-55.
32. Stoddart ML, Jarvis B, Blake B, Fabsitz RR, Howard BV, Lee ET, et al. Recruitment of American Indians in epidemiologic research: the Strong Heart Study. Am Indian Alsk Native Ment Health Res. 2000;9(3):20-37.
33. Howard BV, Lee ET, Fabsitz RR, Robbins DC, Yeh JL, Cowan LD, et al. Diabetes and coronary heart disease in American Indians: The Strong Heart Study. Diabetes. 1996;45 Suppl 3:S6-13.
34. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150(9):604-12.
35. Scheer J, Findenig S, Goessler W, Francesconi KA, Howard B, Umans JG, et al. Arsenic species and selected metals in human urine: validation of HPLC/ICPMS and ICPMS procedures for a long-term population-based epidemiological study. Anal Methods. 2012;4(2):406-13.
36. National Research Conuncil. Arsenic in drinking water. Washington D.C. : National Academy Press; 1999.
37. Navas-Acien A, Francesconi KA, Silbergeld EK, Guallar E. Seafood intake and urine concentrations of total arsenic, dimethylarsinate and arsenobetaine in the US population. Environ Res.
2011;111(1):110-8.
38. Hornung RW, Reed LD. Estimation of average concentration in the presence of nondetectable values. Appl. Occup. Environ. Hyg. . 1990;5(1):46-51.
39. Lubin JH, Colt JS, Camann D, Davis S, Cerhan JR, Severson RK, et al. Epidemiologic evaluation of measurement data in the presence of detection limits. Environ Health Perspect.
2004;112(17):1691-6.
40. National Research Council (NRC). Critical Aspects of EPA's IRIS Assessment of Inorganic Arsenic: Interim Report (2014). In: Studies DoEaL, ed. Washington DC: The National Academies Press; 2014.
41. Gilbert-Diamond D, Li Z, Perry AE, Spencer SK, Gandolfi AJ, Karagas MR. A population-based case-control study of urinary arsenic species and squamous cell carcinoma in New Hampshire, USA. Environ Health Perspect. 2013;121(10):1154-60.
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
42. Lindberg AL, Rahman M, Persson LA, Vahter M. The risk of arsenic induced skin lesions in Bangladeshi men and women is affected by arsenic metabolism and the age at first exposure. Toxicol Appl Pharmacol. 2008;230(1):9-16.
43. Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65(4 Suppl):1220S-8S; discussion 9S-31S.
44. Donahue JG, Nelson KE, Munoz A, McAllister HA, Yawn DH, Ness PM, et al. Transmission of HIV by transfusion of screened blood. N Engl J Med. 1990;323(24):1709.
45. Barr DB, Wilder LC, Caudill SP, Gonzalez AJ, Needham LL, Pirkle JL. Urinary creatinine concentrations in the U.S. population: implications for urinary biologic monitoring measurements. Environ Health Perspect. 2005;113(2):192-200.
46. Nwaneri C., Cooper H., Bowen-Jones D. Mortality in type 2 diabetes mellitus: magnitude of the evidence from a systematic review and meta-analysis. The British Journal of Diabetes & Vascular Disease. 2013;13:192-207.
47. Martinez VD, Vucic EA, Adonis M, Gil L, Lam WL. Arsenic biotransformation as a cancer promoting factor by inducing DNA damage and disruption of repair mechanisms. Mol Biol Int. 2011;2011:718974.
48. Fine JP, Gray RJ. A proportional hazards model for the subdistribution of a competing risk. Journal of the American Statistical Association. 1999;94(446):496-509.
49. Cox C, Chu H, Schneider MF, Munoz A. Parametric survival analysis and taxonomy of hazard functions for the generalized gamma distribution. Stat Med. 2007;26(23):4352-74.
50. Levine L, Fahy J. Evaluation of urinary lead determination: I. The significance of the specific gravity. The Journal of industrial hygiene and toxicology 1945;27:217-23.
51. Thompson FC. Vital Statistics of the United States 2012: Births, Life Expectancy, Deaths, and
Selected Health Data. Fifth ed: Bernan Press; 2012.
52. Hopenhayn C, Huang B, Christian J, Peralta C, Ferreccio C, Atallah R, et al. Profile of urinary arsenic metabolites during pregnancy. Environ Health Perspect. 2003;111(16):1888-91. 53. Gardner RM, Engstrom K, Bottai M, Hoque WA, Raqib R, Broberg K, et al. Pregnancy and the
methyltransferase genotype independently influence the arsenic methylation phenotype. Pharmacogenet Genomics. 2012;22(7):508-16.
54. Butte NF. Carbohydrate and lipid metabolism in pregnancy: normal compared with gestational diabetes mellitus. Am J Clin Nutr. 2000;71(5 Suppl):1256S-61S.
55. Barbour LA, McCurdy CE, Hernandez TL, Kirwan JP, Catalano PM, Friedman JE. Cellular
mechanisms for insulin resistance in normal pregnancy and gestational diabetes. Diabetes Care. 2007;30 Suppl 2:S112-9.
56. Pierce BL, Kibriya MG, Tong L, Jasmine F, Argos M, Roy S, et al. Genome-wide association study identifies chromosome 10q24.32 variants associated with arsenic metabolism and toxicity phenotypes in Bangladesh. PLoS Genet. 2012;8(2):e1002522.
57. Tellez-Plaza M, Gribble MO, Voruganti VS, Francesconi KA, Goessler W, Umans JG, et al. Heritability and preliminary genome-wide linkage analysis of arsenic metabolites in urine. Environ Health Perspect. 2013;121(3):345-51.
58. Chen YC, Su HJ, Guo YL, Hsueh YM, Smith TJ, Ryan LM, et al. Arsenic methylation and bladder cancer risk in Taiwan. Cancer Causes Control. 2003;14(4):303-10.
59. Yu RC, Hsu KH, Chen CJ, Froines JR. Arsenic methylation capacity and skin cancer. Cancer Epidemiol Biomarkers Prev. 2000;9(11):1259-62.
60. Steinmaus C, Yuan Y, Kalman D, Rey OA, Skibola CF, Dauphine D, et al. Individual differences in arsenic metabolism and lung cancer in a case-control study in Cordoba, Argentina. Toxicol Appl Pharmacol. 2010;247(2):138-45.
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
61. Tseng CH, Huang YK, Huang YL, Chung CJ, Yang MH, Chen CJ, et al. Arsenic exposure, urinary arsenic speciation, and peripheral vascular disease in blackfoot disease-hyperendemic villages in Taiwan. Toxicol Appl Pharmacol. 2005;206(3):299-308.
62. Mass MJ, Tennant A, Roop BC, Cullen WR, Styblo M, Thomas DJ, et al. Methylated trivalent arsenic species are genotoxic. Chem Res Toxicol. 2001;14(4):355-61.
63. Agusa T, Fujihara J, Takeshita H, Iwata H. Individual Variations in Inorganic Arsenic Metabolism Associated with AS3MT Genetic Polymorphisms. Int J Mol Sci. 2011;12(4):2351-82.
64. Walton FS, Harmon AW, Paul DS, Drobna Z, Patel YM, Styblo M. Inhibition of insulin-dependent glucose uptake by trivalent arsenicals: possible mechanism of arsenic-induced diabetes. Toxicol Appl Pharmacol. 2004;198(3):424-33.
65. Douillet C, Currier J, Saunders J, Bodnar WM, Matousek T, Styblo M. Methylated trivalent arsenicals are potent inhibitors of glucose stimulated insulin secretion by murine pancreatic islets. Toxicol Appl Pharmacol. 2013;267(1):11-5.
66. Lu TH, Su CC, Chen YW, Yang CY, Wu CC, Hung DZ, et al. Arsenic induces pancreatic beta-cell apoptosis via the oxidative stress-regulated mitochondria-dependent and endoplasmic reticulum stress-triggered signaling pathways. Toxicol Lett. 2011;201(1):15-26.
67. Kalman DA, Dills RL, Steinmaus C, Yunus M, Khan AF, Prodhan MM, et al. Occurrence of trivalent monomethyl arsenic and other urinary arsenic species in a highly exposed juvenile population in Bangladesh. J Expo Sci Environ Epidemiol. 2013.
68. Vahter ME. Interactions between arsenic-induced toxicity and nutrition in early life. J Nutr. 2007;137(12):2798-804.
69. Walker AK, Jacobs RL, Watts JL, Rottiers V, Jiang K, Finnegan DM, et al. A conserved SREBP-1/phosphatidylcholine feedback circuit regulates lipogenesis in metazoans. Cell.
2011;147(4):840-52.
70. Jackson MI, Cao J, Zeng H, Uthus E, Combs GF, Jr. S-adenosylmethionine-dependent protein methylation is required for expression of selenoprotein P and gluconeogenic enzymes in HepG2 human hepatocytes. J Biol Chem. 2012;287(43):36455-64.
71. Leung MC, Williams PL, Benedetto A, Au C, Helmcke KJ, Aschner M, et al. Caenorhabditis elegans: an emerging model in biomedical and environmental toxicology. Toxicol Sci. 2008;106(1):5-28.
72. Hashmi S, Wang Y, Parhar RS, Collison KS, Conca W, Al-Mohanna F, et al. A C. elegans model to study human metabolic regulation. Nutr Metab (Lond). 2013;10(1):31.
73. Elshorbagy AK, Nijpels G, Valdivia-Garcia M, Stehouwer CD, Ocke M, Refsum H, et al. S-adenosylmethionine is associated with fat mass and truncal adiposity in older adults. J Nutr. 2013;143(12):1982-8.
74. Paul DS, Hernandez-Zavala A, Walton FS, Adair BM, Dedina J, Matousek T, et al. Examination of the effects of arsenic on glucose homeostasis in cell culture and animal studies: development of a mouse model for arsenic-induced diabetes. Toxicol Appl Pharmacol. 2007;222(3):305-14.
