International Journal of Epidemiology, 2015, 240–248
doi: 10.1093/ije/dyu200
Advance Access Publication Date: 15 October 2014
Original article
Miscellaneous
Nickel exposure is associated with the
prevalence of type 2 diabetes in Chinese adults
Gang Liu,1 Liang Sun,1 An Pan,2 Mingjiang Zhu,1 Zi Li,1
Zhenzhen Wang,1 Xin Liu,1 Xingwang Ye,1 Huaixing Li,1 He Zheng,1
Choon Nam Ong,2,3 Huiyong Yin,1 Xu Lin1* and Yan Chen1*
1
Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for
Biological Sciences, Chinese Academy of Sciences, University of Chinese Academy of Sciences,
Shanghai, China, 2Saw Swee Hock School of Public Health and Yong Loo Lin School of Medicine,
National University of Singapore and National University Health System, Singapore and 3NUS
Environmental Research Institute, National University of Singapore, Singapore
*Corresponding authors. Institute for Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese
Academy of Sciences, 320 Yue-Yang Rd. Shanghai, 200031, China. E-mail: [email protected] or [email protected]
Accepted 16 September 2014
Abstract
Background: Nickel exposure can induce hyperglycaemia in rodents, but little is known
about its association with abnormal glucose metabolism in humans. We aimed to investigate the association of nickel exposure with the prevalence of type 2 diabetes in
Chinese adults.
Methods: A total of 2115 non-institutionalized men and women aged 55 to 76 years from
Beijing and Shanghai were included, and urinary nickel concentration was assessed
by inductively coupled plasma mass spectroscopy. The prevalence of type 2 diabetes
was compared across urinary nickel quartiles. Fasting plasma glucose, insulin, lipids,
C-reactive protein and glycated haemoglobin A1c, as well as urinary albumin and creatinine were measured.
Results: The median concentration of urinary nickel was 3.63 mg/l (interquartile range:
2.29–5.89 mg/l), and the prevalence of diabetes was 35.3% (747 cases/2115 persons). Elevated levels of urinary nickel were associated with higher fasting glucose, glycated haemoglobin A1c, insulin and homeostatic model assessment of insulin resistance (all P < 0.01).
The odds ratios (95% confidence interval) for diabetes across the increasing urinary nickel
quartiles were 1.27 (0.97–1.67), 1.78 (1.36–2.32) and 1.68 (1.29–2.20), respectively (referencing to 1.00), after multivariate adjustment including lifestyle factors, body mass index and
family history of diabetes (P for trend <0.001). The association remained unchanged after
further controlling for urinary creatinine and C-reactive protein (P for trend <0.001).
Conclusions: Increased urinary nickel concentration is associated with elevated prevalence of type 2 diabetes in humans.
Key words: Nickel exposure, epidemiology, type 2 diabetes
C The Author 2014; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
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International Journal of Epidemiology, 2015, Vol. 44, No. 1
241
Key Messages
• This is the first population-based study linking environmental nickel exposure, measured as urinary nickel concentra-
tion by ICP-MS, with risk of type 2 diabetes.
• The nickel-diabetes association was independent of traditional risk factors including lifestyle, BMI, family history of
diabetes and inflammatory biomarkers.
• This study pinpoints potential harmfulness of nickel exposure in the pathogenesis of type 2 diabetes.
Introduction
The type 2 diabetes (T2D) epidemic has been evidenced
worldwide, especially in many developing countries.1
Among the top 10 countries with high T2D prevalence,
five are Asian countries including China, India, Pakistan,
Indonesia and Bangladesh.2 For instance, the prevalence of
T2D in the adults in China was less than 1% in 1980 and
reached 11.6% in 2010.1,3,4 Although excess energy intake
and sedentary lifestyle are well-recognized risk factors
for diabetes, growing evidence has suggested that environmental exposures such as heavy metals may contribute
to the pathogenesis of T2D.5–7
As a heavy metal, nickel is widely distributed in the environment and can be released into ambient air and soil
when burning coal, fuel oil and waste or discharging sewage.8–10 Nickel and its compounds are also commonly used
in many industries8 such as electroplating, alloy production and the production of nickel-cadmium batteries.11
Therefore, the general public are exposed to nickel from
air, foods and drinking water.12 Other sources of nickel
exposure may come from use of tobacco, dental or orthopaedic implants, stainless steel kitchen utensils, inexpensive jewellery and nickel-releasing coins.3,4,8,10 Most
absorbed nickel is excreted in the urine regardless of its exposure route13 and urinary nickel concentration, hence,
is commonly used to assess nickel exposure levels.14,15
To date, it remains largely unclear whether nickel exposure is associated with diabetes risk in humans. Several animal
studies have indicated that nickel exposure can induce hyperglycaemia,7,16–18 likely due to its effects in promoting hepatic
glycolysis and pancreatic glucagon release and decreasing
peripheral utilization of glucose.19 However, evidence from
human studies is limited regarding whether nickel exposure
is associated with dysregulation of glucose homeostasis. In
this study, we investigated the levels of nickel exposure in a
Chinese population and analysed its association with T2D.
Methods
Study population
A population-based sample was obtained from the
Nutrition and Health of Aging Population in China study
(NHAPC) to examine environmental and genetic factors
related to chronic diseases.20 A total of 3289 Chinese
(1458 men and 1831 women) aged 50 to 70 years were
recruited in 2005 from Beijing and Shanghai, which were
selected as representative cities in northern and southern
China. In each city, one rural district and two urban districts were sampled to represent populations of low, middle or high socioeconomic level.20 In 2011, a total of 2529
(76.9%) eligible participants who attended the baseline
survey were successfully followed up, details were previously reported.21 The present study is a cross-sectional
analysis of the 2011 follow-up visits when urine samples
were collected. After excluding those without urine samples (n ¼ 335), or with missing information on covariates
(n ¼ 79), a total of 2115 individuals were eligible for
the present analyses. The study was approved by the
Institutional Review Board of the Institute for Nutritional
Sciences, and all participants provided informed consent.
Data collection
A face-to-face interview was conducted by trained health
workers with standard questionnaires in 2011 to obtain
data on demographics, health status, lifestyle and physical
activities. Education attainment (0–6 years, 7–9 years
or10 years), current smoking status, alcohol drinking
(yes or no), physical activity (low, moderate or high) and
family history of chronic diseases (yes or no) were previously defined.21 After overnight fasting, all participants
were invited to undergo physical examination, and their
height, weight, waist circumference and blood pressure
were measured by trained medical professionals according
to standard protocols. Body mass index (BMI) was calculated as weight in kilograms divided by the square
of height in metres and categorized as normal weight
(<24.0 kg/m2), overweight (24.0 to 27.9 kg/m2) or obesity
(28.0 kg/m2), according to the criteria for Chinese
individuals.22
Laboratory measurements
Fasting peripheral venous blood samples were collected by
EDTA-contained tubes and centrifuged to separate plasma,
242
buffy coat and erythrocytes and then stored at 80 C until
analysis. Plasma glucose, triglyceride, total cholesterol,
low-density lipoprotein (LDL) cholesterol and high-density
lipoprotein (HDL) cholesterol were measured using an
automatic analyser (Hitachi 7080, Tokyo, Japan) with reagents purchased from Wako Pure Chemical Industries
(Osaka, Japan). Urinary albumin and creatinine were also
measured by the automatic analyser with reagents from
Roche Diagnostics (Mannheim, Germany). Fasting insulin
was assessed by radioimmunoassay with kits from Merck
Millipore (Billerica, MA). The insulin resistance index
[homeostasis model assessment of insulin resistance
(HOMA-IR)] was calculated using the original homeostasis model assessment method of Matthews et al.23 Plasma
high-sensitive C-reactive protein (CRP) was measured
by a particle-enhanced immunoturbidimetric assay
(Ultrasensitive CRP kit; Orion Diagnostica, Espoo,
Finland). Glycated haemoglobin A1c (HbA1c) from
resolved erythrocytes was measured with an automated
immunoassay (Tina-Quant Hemoglobin A1C II; Roche
Diagnostics, Indianapolis, IN).
International Journal of Epidemiology, 2015, Vol. 44, No. 1
First morning urine samples were collected with clean
containers and stored at -80 C until analysis. Each 1 ml of
the urine samples was mixed with 1 ml 2% HNO3, and
then centrifuged at 4000 rpm for 10 min. The supernatants
were injected into an Agilent 7700x inductively coupled
plasma mass spectroscopy system (ICP-MS, Agilent
Technologies, Tokyo, Japan). All the containers or tubes
were pre-cleaned by overnight soaking in ultrapure grade
2% HNO3 solution.8 Quality control was performed
(1 out of 20 samples), and the inter- and intra-assay coefficients of variation were <10% and <8%, respectively.
All participants had urinary nickel levels above the detection limit (0.084 mg/l).
confidence intervals (CIs) of T2D for each urinary nickel
quartile compared with the lowest quartile, with adjustment for age (continuous), sex, region (Beijing, Shanghai),
residence (urban, rural), education (6, 7–9 or 10 years),
current smoking status (yes, no), alcohol drinking (yes,
no), physical activity (low, moderate, high), family history
of diabetes (yes, no), BMI (continuous) and urinary creatinine concentration (log-transformed continuous variable). Tests of linear trend across increasing nickel
quartiles were conducted by assigning the median value to
each quartile and treating it as a continuous variable.
Plasma CRP was further adjusted to test influence of
inflammatory status on the association. The log-linear
dose-response relationship was estimated by applying a
restricted cubic spline regression model with 3 knots at the
5th (1.27 mg/l), 50th (3.64 mg/l) and 95th (22.9 mg/l) percentiles.24 Stratified analyses were performed according to
age (<65, 65), sex, region, residence, current smoking
status, BMI category (normal weight, overweight and
obesity) and physical activity. Likelihood ratio tests were
conducted to examine interactions.
Because duration of diabetes may influence nickel metabolism, and diabetes may be accompanied with deteriorated renal function leading to increased nickel leak into
urine, we have conducted sensitivity analyses by including
diabetes duration (0–6, 6 years) and albuminuria
(defined as albumin to creatinine ratio 30 mg/g as indicator of renal damage)25 in stratified analyses. In addition,
nickel may coexist or interact with other elements, such as
arsenic (As) and cadmium (Cd),8,11 which were reported to
be associated with T2D.26,27 Therefore, these elements
were additionally adjusted as continuous variables (log10transformed) on the basis of the final model aforementioned. Finally, HbA1c 6.5% was also employed as a diabetes diagnosis criterion in sensitivity testing. All analyses
were performed with SAS (version 9.3; SAS Institute Inc.,
Cary, NC).
Definition of type 2 diabetes
Results
T2D was defined as fasting plasma glucose concentration
of 7.0 mmol/l or higher, self-reported physician-diagnosed
diabetes or receipt of antidiabetic medications.
As shown in Table 1, the median concentration of urinary
nickel was 3.63 mg/l (interquartile range: 2.29–5.89 mg/l),
and 35.3% (747/2115) of the participants had T2D.
Participants with higher urinary nickel concentrations
were more likely to be men and Shanghai residents.
Furthermore, participants with increased urinary nickel
concentration tended to have elevated levels of glucose,
HbA1c, insulin and HOMA-IR, as well as urinary albumin
and creatinine (all P for trend <0.05).
Compared with those without T2D, participants with
T2D had elevated urinary nickel concentration (median:
4.03 mg/l in T2D vs 3.40 mg/l in non-T2D subjects,
Measurement of urinary nickel concentration
Statistical analysis
Analysis of covariance for continuous variables and multivariate logistic regression analysis for categorical variables
were applied for the comparison across urinary nickel
quartiles. Whenever appropriate, log10 transformations
of skewed variables were used in analyses. A logistic
regression model was used to test odds ratios (ORs) and
International Journal of Epidemiology, 2015, Vol. 44, No. 1
243
Table 1. Characteristics of participants according to urinary nickel quartilesa
Characteristics
Q1: <2.29 mg/l
Q2: 2.29-3.62 mg/l
Q3: 3.63-5.89 mg/l
Q4: >5.89 mg/l
N
Age (years)b
Male sex, n (%)b
Urban residents, n (%)b
Residents of Beijing, n (%)b
Education (years), n (%)
0–6
7–9
10
Smoking, yes, n (%)
Alcohol drinking, yes, n (%)
Physical activity, n (%)
Low
Moderate
High
Family history of diabetes, n (%)
BMI (kg/m2)
SBP (mmHg)
DBP (mmHg)
Glucose (mmol/l)
HbA1c (%)
Insulin (lU/ml)c
HOMA-IRc
Total cholesterol (mmol/l)
HDL cholesterol (mmol/l)
LDL cholesterol (mmol/l)
Triglycerides (mmol/l)
CRP (mg/l)
Albumin (mg/l)
Urinary creatinine (mg/dl)
528
64.3 6 5.9
201 (38.1)
241 (45.6)
276 (52.3)
529
65.4 6 6.1
236 (44.6)
209 (39.5)
273 (51.6)
529
65.1 6 6.0
213 (40.3)
234 (44.2)
253 (47.8)
529
65.0 6 5.8
246 (46.5)
240 (45.4)
245 (46.3)
208 (39.4)
209 (39.6)
111 (21.0)
98 (18.6)
129 (24.4)
260 (49.2)
171 (32.3)
98 (18.5)
138 (26.1)
131 (24.8)
249 (47.1)
172 (32.5)
108 (20.4)
108 (19.9)
123 (23.3)
225 (42.5)
195 (36.9)
109 (20.6)
140 (26.5)
147 (27.8)
349 (66.1)
98 (18.6)
81 (15.3)
78 (14.8)
24.5 6 3.4
136.1 6 19.9
80.6 6 10.7
6.75 6 2.04
6.59 6 1.03
12.7 (9.67–17.0)
3.62 (2.72–5.32)
5.56 6 1.25
1.45 6 0.43
3.78 6 1.13
1.49 (1.06–2.04)
1.34 (0.53–3.35)
8.10 (3.05–18.0)
65.8 (46.5–87.6)
337 (63.7)
83 (15.7)
109 (20.6)
87 (16.5)
24.5 6 3.4
137.7 6 20.1
81.0 6 11.1
7.00 6 2.14
6.78 6 1.18
12.7 (9.24–17.6)
3.78 (2.65–5.39)
5.52 6 1.17
1.43 6 0.42
3.78 6 1.08
1.40 (1.03–1.98)
1.50 (0.61–3.50)
12.4 (5.30–22.7)
88.3 (64.4–123.7)
345 (65.2)
83 (15.7)
101 (19.1)
79 (14.9)
24.8 6 3.8
136.3 6 18.8
80.9 6 10.0
7.11 6 2.20
6.86 6 1.30
13.2 (9.80–19.1)
4.01 (2.82–6.19)
5.54 6 1.23
1.45 6 0.42
3.80 6 1.12
1.40 (1.03–1.99)
1.56 (0.62–3.55)
16.1 (7.70–30.1)
104.8 (76.5–154.4)
336 (63.5)
102 (19.3)
91 (17.2)
80 (15.1)
24.8 6 3.6
135.3 6 19.5
80.2 6 10.0
7.13 6 2.25
6.83 6 1.26
13.1 (9.70–18.5)
3.87 (2.72–5.91)
5.50 6 1.22
1.41 6 0.40
3.77 6 1.13
1.43 (1.07–2.07)
1.30 (0.50–3.47)
17.1 (8.00–31.1)
111.8 (81.6–175.7)
P for trendb
0.21
0.03
0.70
0.03
0.38
0.11
0.69
0.45
0.81
0.05
0.17
0.47
<0.001
<0.001
0.004
<0.001
0.89
0.29
0.67
0.93
0.64
<0.001
<0.001
Q, quartile; BMI, body mass index; CRP, C-reactive protein; DBP, diastolic blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; SBP,
systolic blood pressure; UA, uric acid; UN, urea nitrogen.
a
Data are means 6 SD, n (%), or median (interquartile range).
b
P-value was calculated after adjustment for age, sex, region (Beijing or Shanghai) and residence (urban or rural), except for itself.
c
Data are missing for 5 participants.
Table 2. Odds ratio (95% confidence interval) of type 2 diabetes according to quartiles of urinary nickel concentrations
Cases/total
Model 1a
Model 2b
Model 3c
Model 4d
Quartile 1
Quartile 2
Quartile 3
Quartile 4
<2.29 mg/l
2.29-3.62 mg/l
3.63-5.89 mg/l
>5.89 mg/l
149/528
1
1
1
1
177/529
1.28 (0.98–1.67)
1.27 (0.97–1.67)
1.26 (0.96–1.67)
1.26 (0.95–1.67)
215/529
1.81 (1.39–2.36)
1.78 (1.36–2.32)
1.76 (1.33–2.34)
1.77 (1.34–2.36)
206/529
1.69 (1.30–2.20)
1.68 (1.29–2.20)
1.66 (1.25–2.22)
1.69 (1.27–2.26)
P for trend
<0.001
<0.001
<0.001
<0.001
a
Model 1: adjusted for age, sex, region, residence.
Model 2: additionally adjusted for BMI, education, alcohol drinking, current smoking status, physical activity and family history of diabetes.
c
Model 3: additionally adjusted for urinary creatinine level (log-transformed).
d
Model 4: additionally adjusted for C-reactive protein.
b
P < 0.01). The ORs (95% CIs) for T2D from the lowest to
the highest urinary nickel quartiles were 1.28 (0.98–1.67),
1.81 (1.39–2.36) and 1.69 (1.30–2.20), respectively (referencing to 1.00) (P for trend <0.001) (Table 2), after
adjusting for age, sex, region and residence (model 1). The
nickel-diabetes association was not materially changed
(P for trend <0.001) by further controlling for lifestyle
covariates and family history of diabetes (model 2), as well
244
International Journal of Epidemiology, 2015, Vol. 44, No. 1
Figure 1. Odds ratio of diabetes by log-transformed urinary nickel concentrations. Lines represent odds ratios (95% CI) based on restricted cubic
splines for log-transformed nickel concentrations with knots at the 5th, 50th and 95th percentiles. Odds ratios were estimated using a logistic regression model after adjustment for age, sex, region, residence, current smoking status, BMI, education, alcohol drinking, physical activity, family history
of diabetes, C-reactive protein and urinary creatinine level (log-transformed); P for linear <0.01. Bars represent the numbers of participants, 8 equally
sized bins were selected from the 1st to the 99th percentiles of log-transformed nickel distribution.
as additionally adjusting for urinary creatinine (logtransformed) (model 3) and CRP (model 4) (all P for trends
<0.001). The strength of the association was attenuated,
but all P for trends remained <0.01, when using creatinine-corrected nickel concentration in the models (see
Supplementary Table S1, available as Supplementary data
at IJE online). A positive log-linear dose–response relationship was evident in the cubic spline regression model
(Figure 1, P < 0.01 for linearity).
When urinary nickel concentration was considered as a
continuous variable, the overall OR (95% CI) of having
diabetes was 1.33 (1.06–1.67) per unit increment of logtransformed nickel concentration. In the stratified analyses, the nickel-diabetes association was slightly stronger
in men, urban and Beijing residents, current smokers and
individuals with lower physical activity levels as compared
with their counterparts (Figure 2). However, no interaction
was detected with any of the variables (all P for
interaction > 0.10).
The nickel-diabetes association remained when stratified according to diabetes duration (0–6, 6 years) and
having albuminuria (see Supplementary Table S2, available
as Supplementary data at IJE online). Moreover, the association persisted when including As and Cd levels in
the model (OR, 1.71 compared the highest with the
lowest quartile; 95% CI, 1.27–2.29; P for trend ¼0.001).
The association was not materially changed when including HbA1c 6.5% as an additional diagnosis criteria
for T2D (see Supplementary Table S3, available as
Supplementary data at IJE online).
Discussion
To our knowledge, this is the first population-based study
showing that elevated urinary nickel concentrations were
associated with an increased risk of having T2D. The association was independent of traditional diabetes risk factors
including lifestyle, BMI, family history of diabetes and
inflammatory biomarkers.
Nickel exposure
The median concentration of urinary nickel in our population was 3.63 mg/l (interquartile range: 2.29–5.89 mg/l).
In most previously published studies, urinary nickel values,
however, varied from 0.6 to 2.4 mg/l among people living
in Italy, Denmark, Germany, Norway, Japan (women
only) and USA,28–33, except in a Finnish study in which
the geometric mean was 4.8 mg/l.34 Although the US Center
for Disease Control Agency for Toxic Substances and
Disease Registry (CDC/ATSDR) has used 1–3.0 mg/l as
a reference value,35 currently there is no internationally
International Journal of Epidemiology, 2015, Vol. 44, No. 1
245
Figure 2. Stratified analyses of the associations [odds ratio (95% confidence interval)] between urinary nickel concentrations and type 2 diabetes.
a
Adjusted for age, sex, region, residence, current smoking status, BMI, education, alcohol drinking, physical activity, family history of diabetes, urinary creatinine level (log-transformed) and C-reactive protein, stratifying factors excepted.
acceptable value or range for urinary nickel concentration
in the general population. Thus, it remains to be elucidated
whether or to what extent the discrepancies regarding urinary nickel concentrations could be explained by its exposure
levels, effects of genetic predisposition and other predisposing factors on its metabolism, between-laboratory
differences in methods (ICP-MS vs electrothermal atomic
absorption spectrometry) and measurement errors, or variations in population characteristics among studies.
The main sources of nickel exposure among the general population are contaminated drinking water and
foods.8,9,36 Some studies found high amounts of nickel
246
in foods like spinach, cocoa, oatmeal, dark chocolate and
dry legumes.10,37 However, we did not observe correlations between urinary nickel concentrations and consumption of rice, wheat or seafood (data not shown).
In addition, tobacco smoking is another small but important source of non-occupational nickel exposure.8 In line
with this idea, we found that both the median concentration of urinary nickel and the OR for having T2D were
higher in smokers than in their non-smoking counterparts
(Figure 2). Certainly, more studies are needed to clarify the
major sources of nickel exposure and its health outcomes
in different populations.
Association between nickel and T2D
In our study, elevated levels of urinary nickel were associated with not only elevated prevalence of T2D, but also
increased levels of fasting plasma glucose, insulin, HbA1C
and HOMA-IR. Although there has been no large-scale
population study designed to investigate the association of
nickel exposure with diabetes, higher blood and urinary
nickel concentrations were reported in Pakistan diabetic
patients (n ¼ 257) compared with non-diabetic controls
(n ¼ 166) (blood nickel concentrations were 2.7 6 0.86 vs
2.2 6 0.56 mg/l in men, and 2.5 6 0.6 vs 2.02 6 0.7 mg/l in
women, respectively, age range 46–60).38 However,
another case-control study by Forte et al. showed that patients with type 2 diabetes (n ¼ 68, mean age 68.4 years)
had lower blood nickel levels than non-diabetic controls
(n ¼ 59, mean age 57.2 years) [blood nickel concentrations
were 0.78 (0.66–0.87) vs 0.89 (0.74–1.15) mg/l, respectively].39 A previous study showed that blood nickel had a
direct correlation with urine nickel (r ¼ 0.3).40 However,
compared with urinary nickel, blood nickel level may
mainly reflect recent exposure due to the short half-life
of nickel in this compartment.8,41 Moreover, it remains
unclear whether or to what degree such discrepancies between these studies could be explained by differences
in participant characteristics (such as disease duration,
medication use or magnitude of renal damage), sample
size, nickel exposure levels, measurement methods (sector
field inductively coupled plasma mass spectrometry vs
atomic absorption spectrometer), or other factors that may
influence blood nickel metabolism. Therefore, more largescale population-based studies are needed to clarify the
role of nickel exposure in the pathogenesis of type 2 diabetes in the future. Evidence from studies in rodent models
demonstrated that nickel exposure was more potent than
other divalent metals in affecting glucose homeostasis and
inducing hyperglycaemia by impairing islet function,
increasing hepatic glycogenolysis and pancreatic release
International Journal of Epidemiology, 2015, Vol. 44, No. 1
of glucagon, reducing peripheral utilization of glucose and
altering gluconeogenesis.7,17–19, 42
The underlying mechanism of nickel exposure in the
pathogenesis of T2D is not yet fully elucidated. Das et al.43
reported that nickel treatment of rats could increase hepatic lipid peroxides and reduce several antioxidant enzymes activities including superoxide dismutase, catalase
and glutathione peroxidase, as well as hepatic glutathione
concentration. It was also reported that nickel might damage insulin function and induce glucose deregulation
through the reactive oxygen species (ROS) pathway.7,11
Moreover, Gupta et al.44 found that nickel could also raise
nitric oxide synthase (NOS) levels in rats, along with an increase in cyclic guanosine monophosphate, which might
lead to hyperglycaemia by stimulating endocrine secretion.
However, whether these findings in animal models can
explain the association of nickel exposure with diabetes
in humans needs thorough investigation in the future.
Strengths and limitations
To our knowledge, this is the first relatively large-scale
population study that has revealed the association of nickel
exposure with T2D. Comprehensive information regarding
potential confounders were carefully analysed and controlled in our statistical analyses.
There were certain limitations in our study. First, due
to the cross-sectional nature, a causal relationship between
nickel and diabetes cannot be established, and a reverse
causality is also possible in that elevated urinary nickel
could be a consequence of diabetic renal damage. Thus,
it is critical to carry out prospective studies in the future.
Second, a single measurement of urinary nickel may not reflect long-term exposure. However, based on results from
the German Environmental Survey in children, Wilhelm
et al.15 suggested that under steady state conditions, a single urine measurement seems to be acceptable as it can
reflect long-term nickel exposure when continuously consumed nickel-rich foods are presumably consistent with
urine nickel levels. Third, first morning spot urine rather
than 24-h urine samples were used in our study; thus the
results might be influenced by biorhythm or collection
time. However, it is generally not feasible to collect 24-h
urine samples in large-scale epidemiological studies, and
available studies suggest that spot urine is acceptable
for measuring exposure levels of heavy metals such as arsenic.27,45,46 In the current study, we adjusted for urinary
creatinine as a covariate to account for urine dilution.
As an alternative, creatinine-corrected nickel concentration
was also used in the models and the results remained
largely unchanged. Finally, it is noteworthy that other
environmental confounding factors may affect our
International Journal of Epidemiology, 2015, Vol. 44, No. 1
247
conclusions and such factors, if discovered, need to be
taken into account in future analyses.
6.
Conclusions
Our study showed for the first time that elevated urinary
nickel concentrations were associated with increased
prevalence of T2D in a Chinese population. From the perspective of public health, it is interesting and important to
confirm whether there is a causal role of nickel exposure
during the pathogenesis of diabetes in humans. Therefore,
more studies in the general population, particularly with
prospective designs, are warranted. Studies are also needed
to elucidate the potential mechanisms underlying the relation between nickel exposure and diabetes in humans.
7.
8.
9.
10.
11.
12.
Supplementary Data
Supplementary data are available at IJE online.
13.
14.
Funding
This work was supported by Ministry of Science and Technology of
China [2013BAI04B03 to Z.W. and 2012BAK01B00], the National
Natural Science Foundation of China [30930081, 81021002,
81321062, 81200581 and 81390353], the Chinese Academy of
Sciences [KSCX2-EW-R-08 and KSCX2-EW-R-10], the Knowledge
Innovation Program of Shanghai Institutes for Biological Sciences,
Chinese Academy of Sciences [2013KIP107] and the SA-SIBS
Scholarship Program.
Acknowledgements
15.
16.
17.
We thank Yiwei Ma, Qianlu Jin and Yiqin Wang for their contributions at various stages of this study. We are also grateful to all study
participants for their involvement in the study.
The references have been checked by Gang Liu for accuracy and
completeness. Yan Chen and Xu Lin will act as guarantors for the
paper.
18.
Conflict of interest: None declared.
20.
19.
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International Journal of Epidemiology, 2015, 248–250
Commentary:
Environmental chemicals and
diabetes: which ones are we missing?
doi: 10.1093/ije/dyv004
Chin-Chi Kuo1,2,3,4 and Ana Navas-Acien1,2,3*
1
Department of Epidemiology, and 2Department of Environmental Health Sciences, Johns Hopkins
Bloomberg School of Public Health, Baltimore, MD, USA, 3Welch Center for Prevention, Epidemiology
and Clinical Research, Johns Hopkins Medical Institutions, Baltimore, MD, USA and 4Kidney Institute
and Division of Nephrology, Department of Internal Medicine, China Medical University Hospital and
College of Medicine, Taichung, Taiwan
*Corresponding author: 615 N Wolfe Street, Room W7513D, Baltimore, MD 21205. E-mail: [email protected]
C The Author 2015; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association
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