This study investigated the association of copper and zinc levels in the serum or urine of patients living in northeast China, with either prediabetes or diabetes. From January 2010 to October 2011, patients with type 1 diabetes (T1D,
Diabetes has become a pandemic disease. According to the International Diabetes Federation, diabetes affects at least 285 million people worldwide, and this number is expected to reach 438 million by the year 2030 [
Excessive caloric intake and high-energy diet quality are major driving forces behind escalating diabetes and the appearance of epidemics worldwide [
Cu is a prooxidant, participating in metal-catalyzed formation of free radicals. Cu and Zn also act as structural and catalytic components of some metalloenzymes [
Most previous studies have focused on the comparison of the serum Cu levels of diabetic subjects with nondiabetic healthy subjects. There is relatively little information about the effect of serum Cu level on the prevalence of prediabetes and diabetic patients with and without complications, focusing on Chinese populations. Therefore, we have examined Cu and Zn levels in the serum and urine of populations residing in northeast China, analyzing different subgroups of study subjects defined by insulin sensitivity and presence of diabetic complications.
This study was approved by the institutional ethics committee of the First Hospital of Jilin University. Written informed consent was obtained from all subjects. For patients younger than 18 years, their parents provided written informed consent and when possible, child subjects provided written assent.
Description of this study population has been reported in a separate publication [
Demographic data for these patients, including age, sex, body mass index (BMI), presence or absence of diabetes, hypertension, dyslipidemia, and medication (simvastatin), were obtained from the patients’ medical records. BMI was calculated as body weight (kg) divided by height (m) squared. Hypertension was defined as systolic blood pressure ≥140 mm Hg and/or diastolic blood pressure ≥90 mm Hg; IFG was defined as a fasting glucose concentration 6.1–6.9 mmol/L or nonfasting glucose concentration >7.8 mmol/L; impaired glucose tolerance (IGT) was defined as a fasting glucose <7.0 mmol/L but nonfasting glucose level in 7.8–11.0 mmol/L; diabetes was defined as a fasting glucose concentration ≥7.0 mmol/L, nonfasting glucose concentration ≥11.1 mmol/L, hemoglobin A1c (HbA1c) value ≥6.5%, and/or a previous diagnosis of diabetes; dyslipidemia was defined as a triglyceride level ≥1.7 mmol/L, total cholesterol ≥5.18 mmol/L, low density lipoprotein (LDL) ≥3.37 mmol/L, and/or previous diagnosis of dyslipidemia; estimating glomerular filtration rate (eGFR) was used as measure of kidney function.
Laboratory data including blood glucose, HbA1c, red blood cell (RBC), hemoglobin, urea nitrogen (BUN), creatinine, total cholesterol (CHO), triglyceride (TG), high-density lipoprotein (HLDL), and low-density lipoprotein cholesterol (LDL) were measured from the first blood samples after admission. The total serum and urinal Cu and Zn, including free/active and conjugated components, were determined using inductively coupled plasma spectrometer (ICP-MS, PerkinElmer Life and Analytical Sciences, Inc., CT, USA).
Blood samples from subjects were taken after overnight fasting into commercial tubes for analysis of laboratory parameters and into special metal-free tubes for analysis of Cu and Zn. After blood centrifugation, serum was aliquoted into metal-free Eppendorf test tubes, frozen, and stored at −80°C until further analysis.
A 24 h urine sample was obtained after admission to measure eGFR. The eGFR was calculated based on the Cockroft-Gault equation for Chinese individual: Creatinine Clearance (Ccr) = ((140 − age) × body weight)/(serum creatinine × 72) × (0.85, if female) [
Twenty-four patients with T2D who were not receiving any lipid-lowering drugs were recruited. Inclusion criteria were CHO > 6.22 mmol/L and LDL > 4.14 mmol/L. Exclusion criteria included receiving any lipid-lowering therapy including fish oil, probucol, vitamin E, steroid hormones, immunosuppressants, aluminum-containing antacids and erythromycin, ketoconazole or analogues, or p-aminoacetic acid. Patients were assigned to treatment for 1 month with 10 mg/day of simvastatin as clinically indicated. Blood samples were taken from fasting patients at the beginning and the end of the 1-month therapy.
Continuous variables were expressed as median (interquartile range) and categorical variables as number (percent). Mann-Whitney
Baseline characteristics of these subjects were summarized in Table
Baseline characteristics of subjects.
CON ( |
IFG ( |
|
IGT ( |
|
T1D ( |
|
T2D ( |
| |
---|---|---|---|---|---|---|---|---|---|
Age (years) | 55.0 (45.0–63.0) | 50.5 (43.0–51.8) | 0.038* | 53.0 (49.0–55.0) | 0.285 | 25.0 (19.0–30.5) |
|
56.0 (46.0–63.0) | 0.521 |
Sex, male (%) | 31 (62.0) | 8 (66.7) | 0.764 | 9 (60.0) | 0.889 | 8 (32.0) | 0.014* | 85 (62.0) | 0.996 |
BMI (kg/m2) | 21.6 (19.5–22.9) | 22.4 (20.5–27.6) | 0.110 | 22.1 (20.4–24.0) | 0.055 | 22.9 (20.3–27.5) | 0.002* | 25.4 (23.3–27.6) |
|
Hypertension (%) | 0 | 3 (25.0) |
|
4 (26.7) |
|
15 (60.0) |
|
58 (42.3) |
|
Dyslipidemia (%) | 0 | 4 (33.3) | <0.001* | 5 (33.3) |
|
22 (88.0) | <0.001* | 77 (56.2) |
|
Glu (mmol/L) | 4.8 (4.1–5.6) | 6.4 (6.3–6.7) | <0.001* | 5.9 (5.6–6.1) | 0.019* | 11.9 (8.5–14.8) | <0.001* | 8.8 (7.5–11.5) |
|
HbA1c (%) | 5.1 (4.2–6.0) | 6.1 (5.9–6.2) | 0.017* | 5.5 (5.4–5.7) | 0.278 | 11.8 (10.3–14.2) | <0.001* | 8.4 (7.7–8.9) |
|
RBC (×1012) | 4.8 (4.3–5.0) | 4.9 (4.3–5.0) | 0.830 | 4.8 (4.4–5.0) | 0.604 | 5.0 (4.6–5.2) | 0.229 | 4.7 (4.3–5.1) | 0.344 |
Hb (g/L) | 133.2 (128.1–153.7) | 143.5 (131.3–148.0) | 0.837 | 142.0 (132.0–148.0) | 0.578 | 146.0 (135.0–155.0) | 0.324 | 141.0 (129.0–154.0) | 0.791 |
BUN (mmol/L) | 5.5 (4.2–6.4) | 6.5 (5.4–7.2) | 0.389 | 4.5 (3.6–5.4) | 0.400 | 5.7 (4.3–6.6) | 0.063 | 6.2 (5.2–8.3) | 0.938 |
Cre ( |
73.9 (63.1–80.9) | 66.7 (60.3–70.2) | 0.951 | 64.5 (56.9–74.1) | 0.138 | 71.5 (61.6–88.1) | 0.286 | 77.5 (64.6–113.6) | 0.657 |
eGFR (mL/min) | 24.6 (22.7–26.4) | 21.9 (20.2–25.2) | 0.009* | 25.4 (22.8–27.0) | 0.487 | 115.7 (83.8–143.5) | <0.001* | 87.4 (54.0–112.7) |
|
Cu (mg/L) | 0.80 (0.70–0.99) | 1.13 (1.02–1.29) | 0.001* | 1.15 (1.03–1.24) |
|
0.94 (0.75–1.19) | 0.352 | 1.07 (0.95–1.33) |
|
UCu ( |
25.0 (22.0–40.0) | 32.0 (28.3–42.5) | 0.293 | 26.0 (24.0–28.0) | 0.306 | 25.0 (23.0–27.5) | 0.187 | 30.0 (25.5–40.5) | 0.408 |
Zn (mg/L) | 0.81 (0.67–0.93) | 0.75 (0.70–0.84) | 0.462 | 0.77 (0.67–0.87) | 0.834 | 0.59 (0.53–0.75) | 0.056 | 0.61 (0.51–0.75) |
|
UZn (mg/L) | 0.20 (0.14–0.32) | 0.32 (0.26–0.37) | 0.745 | 0.27 (0.19–0.41) | 0.836 | 0.86 (0.67–0.91) | <0.001* | 0.48 (0.38–0.57) |
|
Zn/Cu | 0.92 (0.84–1.07) | 0.68 (0.62–0.73) | 0.009* | 0.65 (0.62–0.77) | 0.002* | 0.64 (0.57–0.77) | 0.018* | 0.56 (0.43–0.68) |
|
CHO (mmol/L) | 4.5 (3.2–5.1) | 4.8 (4.5–5.1) | 0.228 | 4.7 (4.2–5.0) | 0.169 | 5.4 (4.4–5.7) | 0.548 | 5.0 (4.4–6.0) | 0.594 |
TG (mmol/L) | 1.2 (1.0–1.4) | 1.4 (1.3–1.6) | 0.893 | 1.3 (1.2–1.4) | 0.079 | 1.8 (1.1–2.8) | 0.346 | 1.6 (1.1–2.7) | 0.490 |
HDL (mmol/L) | 1.2 (1.1–1.3) | 1.1 (0.9–1.2) | 0.981 | 1.1 (1.0–1.2) | 0.619 | 1.1 (1.0–1.5) | 0.095 | 1.2 (1.0–1.5) | 0.665 |
LDL (mmol/L) | 2.4 (2.0–2.9) | 3.0 (2.5–3.2) | 0.464 | 2.9 (2.6–3.1) | 0.002* | 3.5 (2.8–4.2) | 0.763 | 3.4 (2.7–3.8) | 0.044* |
Data are presented as number (%) or median (interquartile range). Baseline characteristics were adjusted for age, sex, BMI, hypertension and dyslipidemia by analysis of covariance using general linear models. *
Compared to control subjects, the serum Cu level was significantly higher in IFG, IGT, and T2D but not different in T1D (Table
There were no significant differences between groups with disease and the healthy controls for the following laboratory parameters: RBC, Hb, BUN, Cre, CHO, TG, and HDL. The serum LDL level was significantly higher in IGT and T2D subjects compared to the control group.
Among the patients with T2D, we further compared the baseline characteristics between those who had been diagnosed with DN, DR, or DPN versus those who did not have any of these complications (Table
Baseline characteristics of T2D subjects.
T2D Con ( |
DN ( |
|
DR ( |
|
DPN ( |
| |
---|---|---|---|---|---|---|---|
Age (years) | 46.0 (39.0–60.0) | 60.0 (46.8–65.0) | 0.007* | 59.5 (46.8–67.3) | 0.003* | 55.5 (46.8–62.3) | 0.007* |
Sex, male (%) | 22 (75.9) | 19 (79.2) | 0.775 | 15 (44.1) | 0.011* | 29 (58.0) | 0.110 |
BMI (kg/m2) | 25.4 (23.0–27.3) | 27.1 (24.9–30.4) | 0.026* | 24.8 (22.4–27.4) | 0.539 | 25.1 (23.0–26.5) | 0.618 |
Hypertension (%) | 0 | 21 (87.5) |
|
17 (50.0) |
|
20 (40.0) |
|
Dyslipidemia (%) | 20 (69.0) | 11 (45.8) | 0.089 | 16 (47.1) | 0.080 | 27 (54.0) | 0.192 |
Glu (mmol/L) | 9.8 (8.3–12.8) | 8.2 (7.5–9.5) | 0.248 | 9.2 (7.7–11.4) | 0.182 | 8.8 (7.6–11.1) | 0.413 |
HbA1c (%) | 8.5 (7.7–8.9) | 8.5 (8.0–8.8) | 0.380 | 8.6 (8.0–8.9) | 0.849 | 8.2 (7.2–8.8) | 0.421 |
RBC ( |
5.1 (4.6–5.4) | 4.8 (4.0–5.1) | 0.224 | 4.6 (4.2–4.9) | 0.395 | 4.7 (4.3–4.9) | 0.027* |
Hb (g/L) | 154.0 (137.0–161.5) | 144 (129.8–159.5) | 0.076 | 138.0 (125.8–151.0) | 0.760 | 140.0 (128.5–150.0) | 0.067 |
BUN (mmol/L) | 5.6 (4.7–6.5) | 17.5 (13.2–19.1) |
|
5.9 (5.2–7.4) | 0.430 | 5.9 (4.2–6.9) | 0.525 |
Cre ( |
70.2 (58.6–85.6) | 287.0 (233.9–386.3) |
|
79.0 (65.0–107.0) | 0.095 | 71.0 (61.4–87.6) | 0.824 |
eGFR (mL/min) | 102.8 (87.4–166.2) | 25.7 (16.8–34.9) |
|
85.5 (58.2–120.0) | 0.182 | 98.0 (72.8–112.6) | 0.530 |
Cu (mg/L) | 1.00 (0.94–1.15) | 1.26 (1.07–1.42) | 0.015* | 1.05 (0.92–1.48) | 0.717 | 1.08 (0.92–1.38) | 0.298 |
UCu ( |
29.0 (24.5–36.5) | 37.0 (28.3–49.0) | 0.947 | 28.5 (26.8–36.0) | 0.274 | 32.0 (22.8–43.0) | 0.815 |
Zn (mg/L) | 0.73 (0.55–0.79) | 0.59 (0.48–0.76) | 0.157 | 0.58 (0.46–0.63) | 0.002* | 0.63 (0.59–0.75) | 0.080 |
UZn (mg/L) | 0.47 (0.28–0.53) | 0.44 (0.30–0.52) | 0.685 | 0.45 (0.25–0.52) | 0.824 | 0.52 (0.44–0.63) |
|
Zn/Cu | 0.66 (0.55–0.76) | 0.50 (0.36–0.56) | 0.006* | 0.48 (0.40–0.64) | 0.012* | 0.58 (0.46–0.68) | 0.111 |
CHO (mmol/L) | 5.0 (4.4–5.5) | 5.7 (4.6–6.5) | 0.858 | 4.9 (4.5–6.3) | 0.379 | 4.9 (4.3–6.0) | 0.961 |
TG (mmol/L) | 1.7 (1.1–2.5) | 2.0 (1.2–3.3) | 0.457 | 1.4 (1.1–2.8) | 0.734 | 1.3 (1.0–2.6) | 0.726 |
HDL (mmol/L) | 1.2 (0.9–1.9) | 1.1 (1.0–1.5) | 0.014* | 1.2 (1.1–1.6) | 0.616 | 1.1 (1.1–1.4) | 0.700 |
LDL (mmol/L) | 3.3 (2.4–3.7) | 3.5 (2.9–4.1) | 0.919 | 3.4 (2.7–3.8) | 0.335 | 3.3 (2.6–3.9) | 0.703 |
Data presentation and abbreviations spelt out forms are the same as the description for Table
We found that the age of T2D patients with one of complications including DN, DR, or DPN was significant older than that of T2D patients without diabetic complications. Except for T2D with DR for which the percentage of male was significantly lower, there was no significant gender difference in T2D patients with either DN or DPN compared to those without complication. The BMI of T2D with DN was significant higher than that of T2D subjects without complications. There was no significant difference for BMI among other patients. All T2D patients with any of these complications had significant high rates of hypertension than those without complications. There were no significant differences for the proportions of subjects with dyslipidemia or measures of blood glucose, HbA1c and Hb, between the groups defined by presence of diabetic complications. The levels of eGFR were significantly lower in the IFG group and significantly higher in T1D and T2D groups but not significantly different in the IGT group compared to controls. Regarding measures of renal function such as BUN, Cre, and eGFR, T2D patients with DN had significantly higher measures compared to T2D patients without complications.
Serum Cu level was significantly higher only among T2D patients with DN (Table
We analyzed associations of the serum or urinary Cu level with other laboratory parameters in all subjects (Table
Associations between serum Cu or urinary Cu level as a continuous variable and laboratory parameters in subjects.
CON ( |
IFG ( |
IGT ( |
T1D ( |
T2D ( |
||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cu | UCu | Cu | UCu | Cu | UCu | Cu | UCu | Cu | UCu | |||||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
Glu | −0.157 | 0.214 | −0.186 | 0.196 | −0.004 | 0.991 | −0.071 | 0.826 | 0.011 | 0.970 | −0.047 | 0.867 | −0.530 | 0.006* | 0.225 | 0.279 | 0.109 | 0.206 | 0.018 | 0.838 |
HbA1c | −0.060 | 0.681 | −0.109 | 0.451 | 0.569 | 0.054 | −0.066 | 0.838 | 0.135 | 0.631 | 0.120 | 0.671 | −0.035 | 0.867 | −0.109 | 0.603 | 0.498 | <0.001* | 0.046 | 0.591 |
RBC | 0.125 | 0.389 | −0.231 | 0.106 | 0.241 | 0.450 | −0.240 | 0.452 | −0.054 | 0.849 | 0.092 | 0.746 | 0.059 | 0.781 | 0.136 | 0.516 | −0.098 | 0.255 | 0.073 | 0.395 |
Hb | −0.007 | 0.959 | −0.151 | 0.295 | 0.308 | 0.330 | −0.131 | 0.685 | −0.011 | 0.970 | 0.085 | 0.763 | 0.228 | 0.273 | 0.256 | 0.216 | −0.036 | 0.673 | 0.039 | 0.650 |
UCu | −0.204 | 0.154 | — | — | 0.237 | 0.459 | — | — | 0.284 | 0.305 | — | — | 0.096 | 0.649 | — | — | 0.184 | 0.032* | — | — |
Zn | 0.298 | 0.036* | −0.121 | 0.402 | 0.116 | 0.720 | 0.057 | 0.861 | 0.118 | 0.676 | −0.186 | 0.506 | 0.403 | 0.046* | 0.140 | 0.506 | 0.158 | 0.065 | −0.048 | 0.581 |
UZn | −0.017 | 0.906 | −0.043 | 0.767 | 0.193 | 0.543 | 0.673 | 0.017* | 0.118 | 0.676 | 0.063 | 0.823 | 0.060 | 0.775 | 0.202 | 0.333 | 0.048 | 0.577 | 0.204 | 0.017* |
BUN | 0.025 | 0.866 | −0.059 | 0.685 | −0.179 | 0.579 | 0.189 | 0.556 | −0.203 | 0.469 | 0.113 | 0.690 | −0.186 | 0.374 | 0.130 | 0.536 | 0.248 | 0.001* | 0.248 | 0.003* |
Cre | −0.090 | 0.533 | 0.040 | 0.781 | −0.427 | 0.166 | −0.347 | 0.269 | 0.143 | 0.611 | 0.302 | 0.273 | 0.092 | 0.663 | 0.329 | 0.109 | 0.125 | 0.145 | 0.357 | <0.001* |
eGFR | 0.108 | 0.457 | −0.003 | 0.983 | 0.329 | 0.296 | 0.366 | 0.241 | 0.077 | 0.785 | −0.332 | 0.226 | −0.025 | 0.904 | −0.034 | 0.873 | −0.146 | 0.090 | −0.337 | <0.001* |
CHO | 0.044 | 0.762 | 0.152 | 0.292 | 0.193 | 0.548 | 0.179 | 0.578 | −0.349 | 0.202 | −0.221 | 0.428 | 0.198 | 0.342 | −0.490 | 0.013* | 0.119 | 0.165 | 0.032 | 0.708 |
TG | 0.092 | 0.526 | 0.151 | 0.295 | 0.199 | 0.536 | 0.522 | 0.082 | −0.116 | 0.681 | −0.270 | 0.330 | −0.023 | 0.911 | 0.268 | 0.196 | 0.076 | 0.379 | −0.046 | 0.597 |
HDL | −0.148 | 0.307 | −0.053 | 0.717 | −0.046 | 0.887 | −0.159 | 0.620 | 0.556 | 0.032 | 0.015 | 0.958 | −0.151 | 0.472 | −0.476 | 0.016* | 0.132 | 0.124 | −0.017 | 0.845 |
LDL | −0.007 | 0.963 | −0.032 | 0.827 | 0.359 | 0.252 | 0.103 | 0.750 | 0.120 | 0.670 | 0.180 | 0.520 | 0.101 | 0.630 | −0.540 | 0.005* | 0.128 | 0.137 | 0.151 | 0.079 |
Data presentation and abbreviations spelt out forms are the same as the description for Table
In T1D patients, serum Cu levels were negatively associated with blood glucose levels and positively associated with serum Zn levels. Urinary Cu levels were negatively associated with serum CHO, HDL, and LDL levels.
For T2D patients, serum Cu levels were positively associated with serum HbA1c, serum BUN, and urinary Cu; urinary Cu levels were positively associated with urinary Zn and serum BUN and Cre levels but negatively associated with eGFR levels.
We further analyzed the association of the serum or urinary Cu levels with the laboratory parameters in T2D subjects with or without complications (Table
Associations between serum Cu or urinary Cu level as a continuous variable and laboratory parameters in T2D subjects.
T2D Con ( |
DN ( |
DR ( |
DPN ( |
|||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cu | UCu | Cu | UCu | Cu | UCu | Cu | UCu | |||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| |
Glu | 0.095 | 0.624 | −0.180 | 0.351 | 0.289 | 0.171 | 0.321 | 0.127 | 0.016 | 0.929 | 0.129 | 0.468 | 0.296 | 0.037* | 0.046 | 0.753 |
HbA1c | 0.382 | 0.041* | −0.307 | 0.106 | 0.440 | 0.032* | 0.194 | 0.365 | 0.406 | 0.017* | 0.031 | 0.863 | 0.630 | <0.001* | 0.161 | 0.264 |
RBC | −0.138 | 0.477 | −0.080 | 0.679 | −0.136 | 0.525 | 0.134 | 0.532 | −0.039 | 0.828 | −0.093 | 0.601 | 0.017 | 0.906 | 0.127 | 0.381 |
Hb | 0.093 | 0.63 | −0.133 | 0.491 | −0.145 | 0.500 | 0.084 | 0.697 | 0.125 | 0.483 | −0.006 | 0.971 | −0.031 | 0.833 | 0.073 | 0.612 |
UCu | −0.210 | 0.273 | — | — | −0.067 | 0.755 | — | — | 0.116 | 0.514 | — | — | 0.258 | 0.071 | — | — |
Zn | 0.342 | 0.069 | 0.117 | 0.168 | 0.281 | 0.184 | −0.493 | 0.014* | 0.105 | 0.553 | −0.195 | 0.270 | 0.219 | 0.127 | 0.136 | 0.347 |
UZn | 0.224 | 0.243 | 0.545 | 0.382 | −0.481 | 0.017* | 0.179 | 0.403 | −0.048 | 0.786 | 0.028 | 0.873 | 0.327 | 0.021* | 0.319 | 0.024* |
BUN | 0.042 | 0.828 | 0.350 | 0.062 | 0.087 | 0.687 | 0.152 | 0.479 | 0.033 | 0.854 | −0.146 | 0.411 | 0.200 | 0.164 | 0.148 | 0.305 |
Cre | −0.322 | 0.089 | 0.283 | 0.137 | −0.164 | 0.445 | 0.053 | 0.807 | 0.020 | 0.912 | 0.261 | 0.137 | −0.099 | 0.492 | 0.320 | 0.023* |
eGFR | 0.083 | 0.670 | −0.372 | 0.047* | 0.040 | 0.852 | −0.115 | 0.591 | 0.086 | 0.630 | −0.109 | 0.539 | 0.068 | 0.641 | −0.276 | 0.052 |
CHO | −0.321 | 0.090 | 0.075 | 0.699 | 0.154 | 0.472 | −0.117 | 0.586 | 0.010 | 0.955 | −0.173 | 0.329 | 0.192 | 0.181 | 0.077 | 0.595 |
TG | −0.152 | 0.433 | 0.061 | 0.752 | 0.080 | 0.711 | −0.278 | 0.189 | 0.016 | 0.930 | −0.092 | 0.607 | 0.148 | 0.307 | −0.058 | 0.689 |
HDL | 0.147 | 0.446 | −0.049 | 0.799 | −0.090 | 0.675 | 0.420 | 0.041* | 0.189 | 0.286 | −0.273 | 0.118 | 0.107 | 0.459 | −0.038 | 0.795 |
LDL | −0.397 | 0.033* | 0.049 | 0.800 | 0.198 | 0.353 | 0.197 | 0.357 | 0.132 | 0.457 | 0.003 | 0.986 | 0.198 | 0.168 | 0.156 | 0.281 |
Data presentation and abbreviations spelt out forms are the same as the description for Table
Correlations between Cu with HbA1c in T2D patients. Correlations between serum Cu and HbA1c in T2D patients (
Serum Zn was negatively correlated with urinary Cu in patients with DN. Urinary Zn was negatively correlated with serum Cu in patients with DN and positively correlated with both serum and urinary Cu in patients with DPN.
Serum Cu levels were negatively associated with serum LDL in T2D patients without complications. Urinary Cu levels were positively associated with serum Cre in patients with DPN and also with serum HDL in the DN group but negatively associated with eGFR and serum Cu level in T2D patients without complications.
Since T2D patients were often treated with statins to lower their lipid profiles, we examined the effect of 1-month simvastatin treatment on serum and urinary Zn and Cu in T2D patients (Table
Serum parameters in T2D patients treated with simvastatin.
Simvastatin ( |
|||
---|---|---|---|
Pretreatment | Posttreatment |
| |
Cu (mg/L) | 1.18 (0.94–1.62) | 1.11 (0.99–1.64) | 0.765 |
UCu ( |
24.5 (19.0–31.5) | 21.5 (17.3–31.0) | 0.445 |
Zn (mg/L) | 0.65 (0.47–0.94) | 0.53 (0.43–0.77) | 0.108 |
UZn (mg/L) | 0.36 (0.17–0.57) | 0.25 (0.13–0.42) | 0.097 |
Zn/Cu | 0.58 (0.34–0.96) | 0.48 (0.39–0.68) | 0.592 |
Data presentation and abbreviations spelt out forms are the same as the description for Table
The association of serum Cu and Zn levels with diabetes has been extensively studied [
In the present study, we demonstrated for the first time that prediabetic subjects including both IFG and IGT subjects exhibited significantly increased serum Cu levels but no significant change for serum Zn levels, compared to matched control subjects. These results imply that the increased serum Cu may increase oxidative stress and subsequent inflammation, leading to the insulin resistance and eventual development of diabetes [
The urinary Cu levels were not significantly changed in either patients with IFG and IGT or in patients who had progressed to T1D or T2D. Urinary Zn levels were significantly increased only in T1D and T2D patients, which indicate that the low level of serum Zn in T2D may attribute to the high urinary excretion.
It should be noted that the ratio of serum Zn to Cu, as an index of Zn relative deficiency, has been found to be significantly decreased in type 1 diabetic patients [
Several studies have reported significant associations between serum and/or urinary Cu levels and the risk of various diabetic complications [
These contrasting findings among studies comparing serum or urinary Cu levels among patients with diabetic complications may depend on the stage of pathogenesis for diabetes morbidity. It is known that increased Cu may increase prooxidant stress and weaken antioxidant defense, resulting in progressive damage to the blood vessels, heart, kidneys, retina, and nerves [
One possible mechanism by which increased systemic Cu levels can harm diabetic patients may be related to its effect on hemoglobin glycation, shown by the increased level of HbA1c in T2D. Numerous studies have indicated that minimizing hyperglycemia is an effective approach to prevent diabetic complications [
There are few reports regarding the effect of statin therapy on the levels of serum or urinary Cu in patients with diabetes. We found no significant differences in serum or urinary Cu and Zn in the T2D patients before and after treatment with simvastatin (Table
In summary, the present study investigated the association of serum and urinary Cu and Zn levels for northeast Chinese patients with either prediabetes or diabetes with and without complications. Compared to previously published data from other geographic regions, these diabetic patients predominantly exhibited similar profiles for these trace elements but did demonstrate a few inconsistent patterns. We clearly showed the positive association of serum Cu levels with Hb1Ac levels in all T2D patients; however, whether the effect of serum Cu on Hb1Ac is related to the progression of diabetes and the development of diabetic complications remains unclear and in need of further exploration.
Jiancheng Xu, Qi Zhou, and Lu Cai conceived and designed the experiments. Jiancheng Xu and Qi Zhou performed the experiments. Jiancheng Xu, Qi Zhou, and Yi Tan analyzed the data. Jiancheng Xu, Qi Zhou, and Lu Cai contributed reagents/materials/analysis tools. Jiancheng Xu, Yi Tan, Gilbert Liu, and Lu Cai wrote the paper.
This project was supported in part by Grants from National Science Foundation of China (no. 81000330 to J. Xu), Jilin Science and Technology Development Program (no. 20100124, to J. Xu), National Science Foundation of China (no. 81273509, to Y. Tan). The authors are grateful to Zhenxing Chu and Jing Jiang who have assisted them to make statistical analysis.