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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article">
<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Endocrinol.</journal-id>
<journal-title>Frontiers in Endocrinology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Endocrinol.</abbrev-journal-title>
<issn pub-type="epub">1664-2392</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fendo.2016.00147</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Endocrinology</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Serum Zn/Cu Ratio Is Associated with Renal Function, Glycemic Control, and Metabolic Parameters in Japanese Patients with and without Type 2 Diabetes: A Cross-sectional Study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name><surname>Hamasaki</surname> <given-names>Hidetaka</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="cor1">&#x0002A;</xref>
<uri xlink:href="http://frontiersin.org/people/u/378463"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Kawashima</surname> <given-names>Yu</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<uri xlink:href="http://frontiersin.org/people/u/390292"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Yanai</surname> <given-names>Hidekatsu</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="http://frontiersin.org/people/u/386013"/>
</contrib>
</contrib-group>
<aff id="aff1"><sup>1</sup><institution>Department of Internal Medicine, National Center for Global Health and Medicine, Kohnodai Hospital</institution>, <addr-line>Chiba</addr-line>, <country>Japan</country></aff>
<aff id="aff2"><sup>2</sup><institution>Department of Radiology, Chiba University Hospital</institution>, <addr-line>Chiba</addr-line>, <country>Japan</country></aff>
<author-notes>
<fn fn-type="edited-by"><p>Edited by: Jan Pol&#x000E1;k, Charles University in Prague, Czech Republic</p></fn>
<fn fn-type="edited-by"><p>Reviewed by: Alberto Granzotto, CeSI-MeT &#x02013; Centro Scienze dell&#x02019;Invecchiamento e Medicina Traslazionale, Italy; Esra Hatipoglu, Liv Hospital, Turkey</p></fn>
<corresp content-type="corresp" id="cor1">&#x0002A;Correspondence: Hidetaka Hamasaki, <email>hhamasaki78&#x00040;gmail.com</email></corresp>
<fn fn-type="other" id="fn001"><p>Specialty section: This article was submitted to Diabetes, a section of the journal Frontiers in Endocrinology</p></fn>
</author-notes>
<pub-date pub-type="epub">
<day>15</day>
<month>11</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection">
<year>2016</year>
</pub-date>
<volume>7</volume>
<elocation-id>147</elocation-id>
<history>
<date date-type="received">
<day>16</day>
<month>09</month>
<year>2016</year>
</date>
<date date-type="accepted">
<day>01</day>
<month>11</month>
<year>2016</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x000A9; 2016 Hamasaki, Kawashima and Yanai.</copyright-statement>
<copyright-year>2016</copyright-year>
<copyright-holder>Hamasaki, Kawashima and Yanai</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/"><p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p></license>
</permissions>
<abstract abstract-type="executive-summary">
<sec id="ST1">
<title>Background</title>
<p>Zinc (Zn) and copper (Cu) may play a pivotal role in the pathogenesis of diabetes and diabetic complications by mediating oxidative stress. Both Zn deficiency and excess of Cu are associated with an increased risk of type 2 diabetes and cardiovascular disease. We aimed to investigate the relationships between serum Zn/Cu ratio and glycemic status, renal function, and metabolic parameters in patients with and without type 2 diabetes.</p>
</sec>
<sec id="ST2">
<title>Methods</title>
<p>We conducted a cross-sectional study on 355 subjects (149 type 2 diabetic and 206 non-diabetic) in whom serum Zn and Cu levels were measured at the same time. Associations between serum Zn/Cu ratio and clinical data were evaluated using multiple regression analysis. We also evaluated associations between serum Zn/Cu ratio and the prevalence of type 2 diabetes and glycemic control by multivariate logistic regression analysis.</p>
</sec>
<sec id="ST3">
<title>Results</title>
<p>Serum Zn/Cu ratio was positively associated with estimated glomerular filtration rate after adjustment for body mass index (BMI) (&#x003B2;&#x02009;&#x0003D;&#x02009;0.137, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.014). Plasma B-type natriuretic peptide levels were negatively associated with serum Zn/Cu ratio after adjustment for age, sex, and BMI (&#x003B2;&#x02009;&#x0003D;&#x02009;&#x02212;0.258, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.032). In patients with type 2 diabetes, serum Zn/Cu ratio was negatively associated with plasma HbA1c levels after adjustment for age, sex, and BMI (&#x003B2;&#x02009;&#x0003D;&#x02009;&#x02212;0.239, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.003). In addition, multivariate logistic regression analysis revealed that the highest quartile of serum Zn/Cu ratio was associated with a reduced risk of poor (HbA1c&#x02009;&#x02265;&#x02009;7%) glycemic control (odds ratio&#x02009;&#x0003D;&#x02009;0.382; 95% confidence interval, 0.165&#x02013;0.884; <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.025) in patients with type 2 diabetes.</p>
</sec>
<sec id="ST4">
<title>Conclusion</title>
<p>Serum Zn/Cu ratio was favorably associated with renal function in all subjects and glycemic control in patients with type 2 diabetes. The Zn/Cu ratio, in addition to the individual serum levels of trace elements, is important for metabolism in humans.</p>
</sec>
</abstract>
<kwd-group>
<kwd>type 2 diabetes</kwd>
<kwd>zinc</kwd>
<kwd>copper</kwd>
<kwd>eGFR</kwd>
<kwd>B-type natriuretic peptide</kwd>
</kwd-group>
<counts>
<fig-count count="1"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="61"/>
<page-count count="8"/>
<word-count count="6320"/>
</counts>
</article-meta>
</front>
<body>
<sec id="S1" sec-type="introduction">
<title>Introduction</title>
<p>Diabetes is a metabolic disease characterized by defects in insulin secretion, insulin sensitivity, or both. Trace elements, such as zinc (Zn) and copper (Cu), may play a pivotal role in the pathogenesis of diabetes and diabetic vascular complications by mediating oxidative stress (<xref ref-type="bibr" rid="B1">1</xref>&#x02013;<xref ref-type="bibr" rid="B4">4</xref>). Several studies have shown that both Zn deficiency and excess of Cu are associated with an increased risk of type 2 diabetes and cardiovascular disease (CVD) (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B5">5</xref>&#x02013;<xref ref-type="bibr" rid="B9">9</xref>).</p>
<p>Zn is a critical trace element in human health. Zn has a potential to be utilized for the treatment of type 2 diabetes; however, the epidemiologic evidence suggests that the effect of Zn on type 2 diabetes remains unclear (<xref ref-type="bibr" rid="B10">10</xref>). Up to 85% of the whole body Zn content is found in muscle and bones, with 11% in the skin and liver (<xref ref-type="bibr" rid="B11">11</xref>). Zn is an indispensable cofactor for more than 300 enzymes involved in metabolism and also reportedly plays a role in aging, immune system, apoptosis, and oxidative stress (<xref ref-type="bibr" rid="B11">11</xref>). Although the effect of zinc supplementation in the improvement of oxidative stress is controversial, one of the causes that the oxidative stress is present in patients with type 2 diabetes is the change in zinc metabolism (<xref ref-type="bibr" rid="B4">4</xref>). Moreover, a number of studies have suggested that matrix metalloproteinases which include a Zn ion-binding site are associated with the progression of diabetic microvascular complications and diabetic tendon disorders (<xref ref-type="bibr" rid="B12">12</xref>). Recent studies have demonstrated that the islet-restricted zinc transporter, ZnT8 (<italic>SLC30A8</italic>), regulates insulin secretion (<xref ref-type="bibr" rid="B13">13</xref>) and hepatic insulin clearance (<xref ref-type="bibr" rid="B14">14</xref>), suggesting that Zn is a key biological factor in glucose homeostasis and the risk of developing type 2 diabetes (<xref ref-type="bibr" rid="B15">15</xref>).</p>
<p>Cu has an integral role in many enzymatic activities involved in modifying oxidative stress. Free Cu ions have catalytic activity in the generation of highly reactive hydroxyl radicals (<xref ref-type="bibr" rid="B16">16</xref>). Disruption of Cu homeostasis induces oxidative damage by free radicals; such Cu toxicity is associated with disrupted lipid metabolism, hepatic disorders, neurodegenerative disorders, and atherogenesis (<xref ref-type="bibr" rid="B17">17</xref>, <xref ref-type="bibr" rid="B18">18</xref>). Cu ion may also play a protective role in the accumulation of human islet amyloid peptide, which is the major component of amyloid deposits in pancreatic &#x003B2;-cells of type 2 diabetic patients; however, whether or not Cu have a protective role in the etiology of type 2 diabetes is not clarified (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>). Excess of Cu under inflammatory conditions trigger oxidative stress which are present in chronic diseases (<xref ref-type="bibr" rid="B21">21</xref>). On the other hand, increased Zn ion levels may provide a protective effect against Cu toxicity by competing for Cu binding sites (<xref ref-type="bibr" rid="B16">16</xref>).</p>
<p>Epidemiological and biological studies have indicated that an imbalance between serum Zn and Cu levels is a causative factor for various diseases, particularly diabetes and CVD (<xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B3">3</xref>, <xref ref-type="bibr" rid="B5">5</xref>&#x02013;<xref ref-type="bibr" rid="B9">9</xref>). However, there is a lack of evidence regarding the association between serum Zn/Cu ratio and metabolic parameters in humans. In this study, we aimed to investigate the relationships between serum Zn/Cu ratio and various parameters, such as hematological parameters, glycemic status, lipid profile, renal function, and body composition, in patients with and without type 2 diabetes.</p>
</sec>
<sec id="S2" sec-type="materials|methods">
<title>Materials and Methods</title>
<sec id="S2-1">
<title>Subjects</title>
<p>Between April 2010 and November 2014, a total of 355 individuals (149 type 2 diabetic patients and 206 non-diabetic patients) who measured both serum Zn and Cu at our hospital were investigated retrospectively. Exclusion criteria were type 1 diabetes, malnutrition [serum albumin (Alb)&#x02009;&#x0003C;&#x02009;3.0&#x02009;g/dl], and anemia [plasma hemoglobin (Hb) levels&#x02009;&#x0003C;&#x02009;10&#x02009;g/dl]. Chronic kidney disease was defined as an estimated glomerular filtration rate (eGFR) less than 60&#x02009;mL/min/1.73&#x02009;m<sup>2</sup>. Patients were diagnosed as having type 2 diabetes according to the Japanese diagnostic criteria for type 2 diabetes (<xref ref-type="bibr" rid="B22">22</xref>). Briefly, diabetic type was diagnosed if subjects met the following criteria: fasting plasma glucose (PG) level of &#x02265;126&#x02009;mg/dl or casual PG level of &#x02265;200&#x02009;mg/dl or HbA1c level &#x02265;6.5%. Patients with type 1 diabetes were excluded. If such conditions were confirmed more than once in the past or the presence of typical symptoms of diabetes and definite diabetic retinopathy were detected, patients were diagnosed as having type 2 diabetes. However, HbA1c level &#x02265;6.5% alone cannot be defined as diabetes (<xref ref-type="bibr" rid="B22">22</xref>).</p>
<p>The patients were anonymized to protect their personal information. The study protocol was approved by the Medical Ethics Committee of the National Center for Global Health and Medicine Kohnodai Hospital (Reference No. NCGM-G-001912), and the study was performed in accordance with the Declaration of Helsinki.</p>
</sec>
<sec id="S2-2">
<title>Anthropometric Measurement</title>
<p>Height and weight were measured using a rigid stadiometer and calibrated scales (seca 764, seca Co., Ltd., Birmingham, UK). Body mass index (BMI) was calculated as body weight in kilograms divided by the square of body height in meters.</p>
</sec>
<sec id="S2-3">
<title>Blood Examination</title>
<p>We measured blood cell count, Alb, blood urea nitrogen, creatinine, eGFR, Zn, and Cu. Serum Zn and Cu levels were measured by atomic absorption spectrophotometry (Z6100, Hitachi Power Solutions Co., Ltd., Ibaraki, Japan) (<xref ref-type="bibr" rid="B23">23</xref>, <xref ref-type="bibr" rid="B24">24</xref>). eGFR was calculated using the revised equation adjusted for the Japanese population (<xref ref-type="bibr" rid="B25">25</xref>). We also measured PG, hemoglobin A1c (HbA1c), B-type natriuretic peptide (BNP), serum total cholesterol, triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and insulin levels. Low-density lipoprotein cholesterol (LDL-C) levels were calculated using the Friedewald formula (<xref ref-type="bibr" rid="B26">26</xref>). Plasma BNP levels were measured using a specific immunoradiometric assay for human BNP (ARCHITECT BNP-JP<sup>&#x000AE;</sup>, ABBOTT JAPAN Co., Ltd., Tokyo, Japan).</p>
</sec>
<sec id="S2-4">
<title>Body Composition Analysis</title>
<p>Body composition was analyzed using a bioelectrical impedance analysis device (InBody720/S10, Biospace Co., Ltd., Tokyo, Japan). This method is based on the principle that lean body mass contains higher water and electrolyte content than fat tissue; hence, these tissues can be distinguished by electrical impedance. Segmental body composition was estimated using a patented 8-point tactile electrode system. The device uses six frequencies (1, 5, 50, 250, 500, and 1000&#x02009;kHz) and produces 30 impedance values for five body segments: the right and left upper extremities, trunk, and right and left lower extremities (<xref ref-type="bibr" rid="B27">27</xref>). A previous validation study demonstrated that both fat mass and lean body mass content measured using this device were highly correlated with measurements using dual-energy X-ray absorptiometry (<xref ref-type="bibr" rid="B28">28</xref>).</p>
</sec>
<sec id="S2-5">
<title>Statistical Analysis</title>
<p>Statistical analyses were performed using SPSS version 23 (IBM Co., Ltd., Chicago, IL, USA). All values are expressed as mean&#x02009;&#x000B1;&#x02009;SD. Pearson&#x02019;s correlation coefficient was calculated to analyze the association of serum Zn/Cu ratio with physical, biochemical, and physiological data. Multiple regression analysis was performed to test independent correlations between serum Zn/Cu ratio and clinical data. Differences in clinical data between patients with and without type 2 diabetes were analyzed using the paired <italic>t</italic>-test. Furthermore, multivariate logistic regression analyses were performed to analyze the association between the quartile of serum Zn/Cu ratio and glycemic control (good: HbA1c&#x02009;&#x0003C;&#x02009;7.0%; poor: HbA1c&#x02009;&#x02265;&#x02009;7.0%) in patients with type 2 diabetes and to determine odds ratios and 95% confidence intervals (CI). <italic>p</italic> values&#x02009;&#x0003C;&#x02009;0.05 were considered statistically significant.</p>
</sec>
</sec>
<sec id="S3">
<title>Results</title>
<sec id="S3-1">
<title>Characteristics of Subjects</title>
<p>A total of 355 subjects (151 men and 204 women) were included in this study. The mean age was 61.1&#x02009;&#x000B1;&#x02009;17.6&#x02009;years. The number of patients with type 2 diabetes was 149 (42.0%). The number of patients with dyslipidemia, hypertension, CKD, and liver diseases such as fatty liver were 117 (33.0%), 94 (26.5%), 87 (24.5%), and 35 (9.9%), respectively. Seventy two patients (20.3%) suffered from autoimmune diseases such as Hashimoto disease, Sj&#x000F6;gren&#x02019;s Syndrome, and rheumatoid arthritis. Thirty-three patients (9.3%) suffered from endocrine disorders such as adrenal deficiency and hyper/hypothyroidism. Eighteen patients (5.1%) had malignant diseases, such as lung cancer, prostate cancer, breast cancer, gastric cancer, and colon cancer. Eleven patients (3.1%) had neurodegenerative diseases, such as Parkinson disease and Alzheimer dementia. The number of patients who had a history of CVD was 29 (8.2%). Patient characteristics are summarized in Table <xref ref-type="table" rid="T1">1</xref>. The prevalence of dyslipidemia (<italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001), hypertension (<italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001), and CVD (<italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001) was higher and the prevalence of endocrine disorders (<italic>p</italic>&#x02009;&#x0003D;&#x02009;0.005) and autoimmune diseases (<italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001) was lower in patients with type 2 diabetes than in those without (by chi-square test).</p>
<table-wrap position="float" id="T1">
<label>Table 1</label>
<caption><p><bold>Patient clinical characteristics</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left">Demographics</th>
<th valign="top" align="left"/>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top"><italic>n</italic></td>
<td align="center" valign="top">355</td>
</tr>
<tr>
<td align="left" valign="top">Age, years</td>
<td align="center" valign="top">61.1 (17.6)</td>
</tr>
<tr>
<td align="left" valign="top">Sex (male/female)</td>
<td align="center" valign="top">151/204</td>
</tr>
<tr>
<td align="left" valign="top">Height, cm</td>
<td align="center" valign="top">159.4 (9.4)</td>
</tr>
<tr>
<td align="left" valign="top">Weight, kg</td>
<td align="center" valign="top">61.6 (18.1)</td>
</tr>
<tr>
<td align="left" valign="top">BMI, kg/m<sup>2</sup></td>
<td align="center" valign="top">24.1 (5.7)</td>
</tr>
<tr>
<td align="left" valign="top"><bold>Blood data</bold></td>
<td align="left" valign="top"/>
</tr>
<tr>
<td align="left" valign="top">Red blood cell, &#x000D7;10<sup>4</sup>/&#x003BC;l</td>
<td align="center" valign="top">430 (55.3)</td>
</tr>
<tr>
<td align="left" valign="top">Hemoglobin, g/dl</td>
<td align="center" valign="top">13.1 (1.5)</td>
</tr>
<tr>
<td align="left" valign="top">Hematocrit, %</td>
<td align="center" valign="top">38.8 (5.2)</td>
</tr>
<tr>
<td align="left" valign="top"><bold>Biochemical data</bold></td>
<td align="left" valign="top"/>
</tr>
<tr>
<td align="left" valign="top">Albumin, g/dl</td>
<td align="center" valign="top">4.2 (0.5)</td>
</tr>
<tr>
<td align="left" valign="top">Blood urea nitrogen, mg/dl</td>
<td align="center" valign="top">15.1 (7.2)</td>
</tr>
<tr>
<td align="left" valign="top">Creatinine, mg/dl</td>
<td align="center" valign="top">0.8 (0.3)</td>
</tr>
<tr>
<td align="left" valign="top">eGFR, ml/min/1.73&#x02009;m<sup>2</sup></td>
<td align="center" valign="top">75.4 (26.9)</td>
</tr>
<tr>
<td align="left" valign="top">Total cholesterol, mg/dl</td>
<td align="center" valign="top">187.4 (39.9)</td>
</tr>
<tr>
<td align="left" valign="top">Triglycerides, mg/dl</td>
<td align="center" valign="top">135.1 (91.5)</td>
</tr>
<tr>
<td align="left" valign="top">HDL cholesterol, mg/dl</td>
<td align="center" valign="top">54.5 (15.8)</td>
</tr>
<tr>
<td align="left" valign="top">LDL cholesterol, mg/dl</td>
<td align="center" valign="top">105.5 (31.5)</td>
</tr>
<tr>
<td align="left" valign="top">Plasma glucose, mg/dl</td>
<td align="center" valign="top">127 (60.6)</td>
</tr>
<tr>
<td align="left" valign="top">HbA1c, %</td>
<td align="center" valign="top">6.8 (1.8)</td>
</tr>
<tr>
<td align="left" valign="top">Serum insulin, &#x003BC;U/ml (<italic>n</italic>&#x02009;&#x0003D;&#x02009;42)</td>
<td align="center" valign="top">11.6 (7.7)</td>
</tr>
<tr>
<td align="left" valign="top">Plasma BNP, pg/ml (<italic>n</italic>&#x02009;&#x0003D;&#x02009;80)</td>
<td align="center" valign="top">53.9 (71.1)</td>
</tr>
<tr>
<td align="left" valign="top">Serum Zn, &#x003BC;g/dl</td>
<td align="center" valign="top">72.6 (15.4)</td>
</tr>
<tr>
<td align="left" valign="top">Serum Cu, &#x003BC;g/dl</td>
<td align="center" valign="top">110.8 (26.4)</td>
</tr>
<tr>
<td align="left" valign="top">Serum Zn/Cu ratio</td>
<td align="center" valign="top">0.69 (0.21)</td>
</tr>
<tr>
<td align="left" valign="top"><bold>Body composition</bold> (<bold><italic>n</italic>&#x02009;&#x0003D;&#x02009;119</bold>)</td>
<td align="center" valign="top"/>
</tr>
<tr>
<td align="left" valign="top">Skeletal muscle mass (whole body), kg</td>
<td align="center" valign="top">26.1 (6.7)</td>
</tr>
<tr>
<td align="left" valign="top">Right upper extremity muscle mass, kg</td>
<td align="center" valign="top">2.5 (0.8)</td>
</tr>
<tr>
<td align="left" valign="top">Left upper extremity muscle mass, kg</td>
<td align="center" valign="top">2.5 (0.8)</td>
</tr>
<tr>
<td align="left" valign="top">Right lower extremity muscle mass, kg</td>
<td align="center" valign="top">7.3 (1.9)</td>
</tr>
<tr>
<td align="left" valign="top">Left lower extremity muscle mass, kg</td>
<td align="center" valign="top">7.2 (1.9)</td>
</tr>
<tr>
<td align="left" valign="top">Body fat mass, kg</td>
<td align="center" valign="top">22.8 (12.5)</td>
</tr>
<tr>
<td align="left" valign="top">Body fat percentage, %</td>
<td align="center" valign="top">30.8 (10.7)</td>
</tr>
</tbody>
</table>
<table-wrap-foot><p><italic>Values are expressed as means (SD) except for number of subjects and sex</italic>.</p>
<p><italic>BMI, body mass index; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, hemoglobin A1c; BNP, B-type natriuretic peptide</italic>.</p></table-wrap-foot></table-wrap>
</sec>
<sec id="S3-2">
<title>Associations between Serum Zn/Cu Ratio and Clinical Parameters in All Subjects</title>
<p>Serum Zn/Cu ratio was inversely correlated with age and plasma BNP levels, whereas it was positively correlated with height, weight, serum Alb levels, and eGFR. Serum Zn/Cu ratio was also positively correlated with red blood cell count, Hb, Ht, and skeletal muscle mass (Table <xref ref-type="table" rid="T2">2</xref>).</p>
<table-wrap position="float" id="T2">
<label>Table 2</label>
<caption><p><bold>Correlations between serum Zn/Cu ratio and clinical parameters in all subjects</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="center">Correlation coefficient</th>
<th valign="top" align="center"><italic>p</italic>-Value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top" colspan="3"><bold>Demographics</bold></td>
</tr>
<tr>
<td align="left" valign="top">Age</td>
<td align="center" valign="top">&#x02212;0.284</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Height</td>
<td align="center" valign="top">0.219</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Weight</td>
<td align="center" valign="top">0.152</td>
<td align="center" valign="top">0.005</td>
</tr>
<tr>
<td align="left" valign="top">BMI</td>
<td align="center" valign="top">0.07</td>
<td align="center" valign="top">0.2</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3"><bold>Blood data</bold></td>
</tr>
<tr>
<td align="left" valign="top">Red blood cell</td>
<td align="center" valign="top">0.192</td>
<td align="center" valign="top">0.003</td>
</tr>
<tr>
<td align="left" valign="top">Hemoglobin</td>
<td align="center" valign="top">0.302</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Hematocrit</td>
<td align="center" valign="top">0.197</td>
<td align="center" valign="top">0.003</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3"><bold>Biochemical data</bold></td>
</tr>
<tr>
<td align="left" valign="top">Albumin</td>
<td align="center" valign="top">0.357</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">eGFR</td>
<td align="center" valign="top">0.144</td>
<td align="center" valign="top">0.008</td>
</tr>
<tr>
<td align="left" valign="top">Total cholesterol</td>
<td align="center" valign="top">&#x02212;0.046</td>
<td align="center" valign="top">0.44</td>
</tr>
<tr>
<td align="left" valign="top">Triglycerides</td>
<td align="center" valign="top">0.108</td>
<td align="center" valign="top">0.062</td>
</tr>
<tr>
<td align="left" valign="top">HDL cholesterol</td>
<td align="center" valign="top">&#x02212;0.063</td>
<td align="center" valign="top">0.3</td>
</tr>
<tr>
<td align="left" valign="top">LDL cholesterol</td>
<td align="center" valign="top">&#x02212;0.039</td>
<td align="center" valign="top">0.5</td>
</tr>
<tr>
<td align="left" valign="top">Plasma glucose</td>
<td align="center" valign="top">&#x02212;0.047</td>
<td align="center" valign="top">0.42</td>
</tr>
<tr>
<td align="left" valign="top">HbA1c</td>
<td align="center" valign="top">&#x02212;0.085</td>
<td align="center" valign="top">0.16</td>
</tr>
<tr>
<td align="left" valign="top">Serum insulin</td>
<td align="center" valign="top">0.123</td>
<td align="center" valign="top">0.44</td>
</tr>
<tr>
<td align="left" valign="top">Plasma BNP</td>
<td align="center" valign="top">&#x02212;0.383</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top" colspan="3"><bold>Body composition</bold></td>
</tr>
<tr>
<td align="left" valign="top">Skeletal muscle mass (whole body)</td>
<td align="center" valign="top">0.189</td>
<td align="center" valign="top">0.039</td>
</tr>
<tr>
<td align="left" valign="top">Right upper extremity muscle mass</td>
<td align="center" valign="top">0.183</td>
<td align="center" valign="top">0.046</td>
</tr>
<tr>
<td align="left" valign="top">Left upper extremity muscle mass</td>
<td align="center" valign="top">0.193</td>
<td align="center" valign="top">0.036</td>
</tr>
<tr>
<td align="left" valign="top">Right lower extremity muscle mass</td>
<td align="center" valign="top">0.214</td>
<td align="center" valign="top">0.019</td>
</tr>
<tr>
<td align="left" valign="top">Left lower extremity muscle mass</td>
<td align="center" valign="top">0.224</td>
<td align="center" valign="top">0.014</td>
</tr>
<tr>
<td align="left" valign="top">Body fat mass</td>
<td align="center" valign="top">&#x02212;0.064</td>
<td align="center" valign="top">0.49</td>
</tr>
<tr>
<td align="left" valign="top">Body fat percentage</td>
<td align="center" valign="top">&#x02212;0.152</td>
<td align="center" valign="top">0.099</td>
</tr>
</tbody>
</table>
<table-wrap-foot><p><italic>eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, hemoglobin A1c; BNP, B-type natriuretic peptide</italic>.</p></table-wrap-foot></table-wrap>
<p>Positive associations of serum Zn/Cu ratio with serum Alb levels and Hb remained after adjustment for age, sex, and BMI (&#x003B2;&#x02009;&#x0003D;&#x02009;0.335, <italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001 and &#x003B2;&#x02009;&#x0003D;&#x02009;0.237, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.002, respectively). Plasma BNP levels were also negatively associated with serum Zn/Cu ratio (&#x003B2;&#x02009;&#x0003D;&#x02009;&#x02212;0.258, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.032). In addition, eGFR was positively associated with serum Zn/Cu ratio after adjustment for BMI (&#x003B2;&#x02009;&#x0003D;&#x02009;0.137, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.014). However, independent associations between serum Zn/Cu ratio and other biochemical parameters were not detected.</p>
<p>To investigate differences in clinical parameters between patients with and without type 2 diabetes, patients with cancers, autoimmune diseases, endocrine disorders, and liver dysfunction, which were known to affect serum levels of Zn and Cu, were excluded from the analysis. Height, weight, BMI, red blood cell count, and Hb were significantly higher in patients with type 2 diabetes than those without type 2 diabetes. Serum levels of HDL-C were significantly lower in patients with type 2 diabetes than those without type 2 diabetes (Table <xref ref-type="table" rid="T3">3</xref>).</p>
<table-wrap position="float" id="T3">
<label>Table 3</label>
<caption><p><bold>Comparison of clinical data between patients with and without type 2 diabetes</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="center">Type 2 diabetic patients (<italic>n</italic>&#x02009;&#x0003D;&#x02009;113)</th>
<th valign="top" align="center">Non-diabetic subjects (<italic>n</italic>&#x02009;&#x0003D;&#x02009;110)</th>
<th valign="top" align="center"><italic>p</italic>-Value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Age, years</td>
<td align="center" valign="top">64.4 (14.7)</td>
<td align="center" valign="top">60.7 (20.4)</td>
<td align="center" valign="top">0.12</td>
</tr>
<tr>
<td align="left" valign="top">Duration of diabetes, years</td>
<td align="center" valign="top">10.5 (10)</td>
<td align="center" valign="top">&#x02013;</td>
<td align="center" valign="top">&#x02013;</td>
</tr>
<tr>
<td align="left" valign="top">Sex (male/female)</td>
<td align="center" valign="top">64/49</td>
<td align="center" valign="top">36/74</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Height, cm</td>
<td align="center" valign="top">161 (8.8)</td>
<td align="center" valign="top">157.5 (10.2)</td>
<td align="center" valign="top">0.009</td>
</tr>
<tr>
<td align="left" valign="top">Weight, kg</td>
<td align="center" valign="top">68.8 (17.9)</td>
<td align="center" valign="top">57.9 (17.7)</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">BMI, kg/m<sup>2</sup></td>
<td align="center" valign="top">26.4 (5.6)</td>
<td align="center" valign="top">23.2 (5.6)</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr><tr><td align="left" valign="top" colspan="4"><hr/></td></tr>
<tr>
<td align="left" valign="top">Red blood cell, &#x000D7;10<sup>4</sup>/&#x003BC;l</td>
<td align="center" valign="top">445.6 (61.2)</td>
<td align="center" valign="top">423.5 (50.2)</td>
<td align="center" valign="top">0.02</td>
</tr>
<tr>
<td align="left" valign="top">Hemoglobin, g/dl</td>
<td align="center" valign="top">13.7 (1.7)</td>
<td align="center" valign="top">13 (1.4)</td>
<td align="center" valign="top">0.013</td>
</tr>
<tr>
<td align="left" valign="top">Hematocrit, %</td>
<td align="center" valign="top">40.1 (5.8)</td>
<td align="center" valign="top">38.2 (4.8)</td>
<td align="center" valign="top">0.2</td>
</tr><tr><td align="left" valign="top" colspan="4"><hr/></td></tr>
<tr>
<td align="left" valign="top">Albumin, g/dl</td>
<td align="center" valign="top">4.1 (0.5)</td>
<td align="center" valign="top">4.3 (0.5)</td>
<td align="center" valign="top">0.028</td>
</tr>
<tr>
<td align="left" valign="top">eGFR, ml/min/1.73&#x02009;m<sup>2</sup></td>
<td align="center" valign="top">71.2 (25.1)</td>
<td align="center" valign="top">79 (32.9)</td>
<td align="center" valign="top">0.054</td>
</tr>
<tr>
<td align="left" valign="top">Total cholesterol, mg/dl</td>
<td align="center" valign="top">184.9 (37.4)</td>
<td align="center" valign="top">192.7 (41.5)</td>
<td align="center" valign="top">0.18</td>
</tr>
<tr>
<td align="left" valign="top">Triglycerides, mg/dl</td>
<td align="center" valign="top">138.6 (88.7)</td>
<td align="center" valign="top">130.6 (85.2)</td>
<td align="center" valign="top">0.53</td>
</tr>
<tr>
<td align="left" valign="top">HDL cholesterol, mg/dl</td>
<td align="center" valign="top">51.5 (16.3)</td>
<td align="center" valign="top">58.1 (15.2)</td>
<td align="center" valign="top">0.007</td>
</tr>
<tr>
<td align="left" valign="top">LDL cholesterol, mg/dl</td>
<td align="center" valign="top">104.1 (30.8)</td>
<td align="center" valign="top">108.3 (31.5)</td>
<td align="center" valign="top">0.35</td>
</tr>
<tr>
<td align="left" valign="top">Plasma glucose, mg/dl</td>
<td align="center" valign="top">151.5 (62.2)</td>
<td align="center" valign="top">99.5 (21)</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">HbA1c, %</td>
<td align="center" valign="top">7.7 (1.9)</td>
<td align="center" valign="top">5.7 (0.4)</td>
<td align="center" valign="top">&#x0003C;0.001</td>
</tr>
<tr>
<td align="left" valign="top">Serum insulin, &#x003BC;U/ml</td>
<td align="center" valign="top">10.9 (7.1)</td>
<td align="center" valign="top">13.1 (10)</td>
<td align="center" valign="top">0.56</td>
</tr>
<tr>
<td align="left" valign="top">Plasma BNP, pg/ml</td>
<td align="center" valign="top">55.5 (97.3)</td>
<td align="center" valign="top">62.5 (59.6)</td>
<td align="center" valign="top">0.76</td>
</tr>
<tr>
<td align="left" valign="top">Serum Zn, &#x003BC;g/dl</td>
<td align="center" valign="top">75.8 (13.7)</td>
<td align="center" valign="top">72.7 (18.7)</td>
<td align="center" valign="top">0.15</td>
</tr>
<tr>
<td align="left" valign="top">Serum Cu, &#x003BC;g/dl</td>
<td align="center" valign="top">109.9 (21)</td>
<td align="center" valign="top">109.4 (23)</td>
<td align="center" valign="top">0.42</td>
</tr>
<tr>
<td align="left" valign="top">Serum Zn/Cu ratio</td>
<td align="center" valign="top">0.72 (0.19)</td>
<td align="center" valign="top">0.69 (0.23)</td>
<td align="center" valign="top">0.87</td>
</tr><tr><td align="left" valign="top" colspan="4"><hr/></td></tr>
<tr>
<td align="left" valign="top">Skeletal muscle mass (whole body), kg</td>
<td align="center" valign="top">26.9 (6.4)</td>
<td align="center" valign="top">25.3 (7.2)</td>
<td align="center" valign="top">0.28</td>
</tr>
<tr>
<td align="left" valign="top">Right upper extremity muscle mass, kg</td>
<td align="center" valign="top">2.7 (0.8)</td>
<td align="center" valign="top">2.4 (0.9)</td>
<td align="center" valign="top">0.13</td>
</tr>
<tr>
<td align="left" valign="top">Left upper extremity muscle mass, kg</td>
<td align="center" valign="top">2.6 (0.8)</td>
<td align="center" valign="top">2.4 (0.9)</td>
<td align="center" valign="top">0.16</td>
</tr>
<tr>
<td align="left" valign="top">Right lower extremity muscle mass, kg</td>
<td align="center" valign="top">7.6 (1.9)</td>
<td align="center" valign="top">7.2 (2.1)</td>
<td align="center" valign="top">0.36</td>
</tr>
<tr>
<td align="left" valign="top">Left lower extremity muscle mass, kg</td>
<td align="center" valign="top">7.5 (1.9)</td>
<td align="center" valign="top">7.1 (1.9)</td>
<td align="center" valign="top">0.33</td>
</tr>
<tr>
<td align="left" valign="top">Body fat mass, kg</td>
<td align="center" valign="top">23.9 (13.2)</td>
<td align="center" valign="top">23 (11.8)</td>
<td align="center" valign="top">0.76</td>
</tr>
<tr>
<td align="left" valign="top">Body fat percentage, %</td>
<td align="center" valign="top">31.2 (10.1)</td>
<td align="center" valign="top">31.9 (10.2)</td>
<td align="center" valign="top">0.75</td>
</tr>
</tbody>
</table>
<table-wrap-foot><p><italic>Values are expressed as means (SD) except for sex</italic>.</p>
<p><italic>BMI, body mass index; eGFR, estimated glomerular filtration rate; HDL, high-density lipoprotein; LDL, low-density lipoprotein; HbA1c, hemoglobin A1c; BNP, B-type natriuretic peptide</italic>.</p></table-wrap-foot></table-wrap>
<p>The prevalence of dyslipidemia was higher in patients with type 2 diabetes than that in patients without diabetes (<italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001). However, there were no differences in the prevalence of other diseases, such as hypertension (<italic>p</italic>&#x02009;&#x0003D;&#x02009;0.065) and CKD (<italic>p</italic>&#x02009;&#x0003D;&#x02009;0.23), between patients with and without type 2 diabetes. In patients with type 2 diabetes, serum Zn/Cu ratio was positively correlated with height (<italic>r</italic>&#x02009;&#x0003D;&#x02009;0.178, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.031), serum levels of Alb (<italic>r</italic>&#x02009;&#x0003D;&#x02009;0.46, <italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001), Hb (<italic>r</italic>&#x02009;&#x0003D;&#x02009;0.41, <italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001), and Ht (<italic>r</italic>&#x02009;&#x0003D;&#x02009;0.277, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.016) and negatively correlated with age (<italic>r</italic>&#x02009;&#x0003D;&#x02009;&#x02212;0.223, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.006), plasma HbA1c levels (<italic>r</italic>&#x02009;&#x0003D;&#x02009;&#x02212;0.193, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.019) (Figure <xref ref-type="fig" rid="F1">1</xref>), and plasma BNP levels (<italic>r</italic>&#x02009;&#x0003D;&#x02009;&#x02212;0.392, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.022). Positive associations of serum Zn/Cu ratio with serum Alb levels and Hb were observed after adjustment for age, sex, and BMI (&#x003B2;&#x02009;&#x0003D;&#x02009;0.494, <italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001 and &#x003B2;&#x02009;&#x0003D;&#x02009;0.476, <italic>p</italic>&#x02009;&#x0003C;&#x02009;0.001, respectively), and serum Zn/Cu ratio remained negatively associated with plasma HbA1c levels after the same adjustments (&#x003B2;&#x02009;&#x0003D;&#x02009;&#x02212;0.239, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.003). However, significant associations between serum Zn/Cu ratio and other biochemical data disappeared following statistical adjustments.</p>
<fig id="F1" position="float">
<label>Figure 1</label>
<caption><p><bold>An inverse correlation between serum Zn/Cu ratio and plasma HbA1c levels in patients with type 2 diabetes</bold>.</p></caption>
<graphic xlink:href="fendo-07-00147-g001.tif"/>
</fig>
<p>The multivariate logistic regression analysis revealed that the highest quartile of serum Zn/Cu ratio was significantly associated with a decreased risk of poor glycemic control (odds ratio&#x02009;&#x0003D;&#x02009;0.382; 95% CI, 0.165&#x02013;0.884; <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.025) in patients with type 2 diabetes (Table <xref ref-type="table" rid="T4">4</xref>).</p>
<table-wrap position="float" id="T4">
<label>Table 4</label>
<caption><p><bold>Logistic regression analysis of glycemic control in patients with type 2 diabetes</bold>.</p></caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th valign="top" align="left"/>
<th valign="top" align="center">Adjusted odds ratio</th>
<th valign="top" align="center">95% CI</th>
<th valign="top" align="center">p-Value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Age</td>
<td align="center" valign="top">1.003</td>
<td align="center" valign="top">0.980&#x02013;1.021</td>
<td align="center" valign="top">0.78</td>
</tr>
<tr>
<td align="left" valign="top">Sex</td>
<td align="center" valign="top"/>
<td align="center" valign="top"/>
<td align="center" valign="top"/>
</tr>
<tr>
<td align="left" valign="top">&#x02003;Male</td>
<td align="center" valign="top">0.844</td>
<td align="center" valign="top">0.440&#x02013;1.618</td>
<td align="center" valign="top">0.61</td>
</tr>
<tr>
<td align="left" valign="top">&#x02003;Female</td>
<td align="center" valign="top">(reference)</td>
<td align="center" valign="top"/>
<td align="center" valign="top"/>
</tr>
<tr>
<td align="left" valign="top">BMI</td>
<td align="center" valign="top">1.045</td>
<td align="center" valign="top">0.982&#x02013;1.111</td>
<td align="center" valign="top">0.16</td>
</tr>
<tr>
<td align="left" valign="top"><bold>Serum Zn/Cu ratio</bold></td>
<td align="left" valign="top"/>
<td align="center" valign="top"/>
<td align="center" valign="top"/>
</tr>
<tr>
<td align="left" valign="top">&#x0003C;0.5461</td>
<td align="center" valign="top">(reference)</td>
<td align="center" valign="top"/>
<td align="center" valign="top"/>
</tr>
<tr>
<td align="left" valign="top">0.5462&#x02013;0.6721</td>
<td align="center" valign="top">0.638</td>
<td align="center" valign="top">0.298&#x02013;1.364</td>
<td align="center" valign="top">0.25</td>
</tr>
<tr>
<td align="left" valign="top">0.6722&#x02013;0.8</td>
<td align="center" valign="top">0.604</td>
<td align="center" valign="top">0.281&#x02013;1.295</td>
<td align="center" valign="top">0.19</td>
</tr>
<tr>
<td align="left" valign="top">&#x0003E;0.8</td>
<td align="center" valign="top">0.382</td>
<td align="center" valign="top">0.165&#x02013;0.884</td>
<td align="center" valign="top">0.025</td>
</tr>
</tbody>
</table>
<table-wrap-foot><p><italic>The odds ratio of good glycemic control (HbA1c&#x02009;&#x0003C;&#x02009;7.0%) to poor glycemic control (HbA1c&#x02009;&#x02267;&#x02009;7.0%) was calculated</italic>.</p>
<p><italic>HbA1c, hemoglobin A1c; CI, confidence interval; BMI, body mass index</italic>.</p></table-wrap-foot></table-wrap>
</sec>
</sec>
<sec id="S4" sec-type="discussion">
<title>Discussion</title>
<p>To the best of our knowledge, this is to be the first study to demonstrate the associations of serum Zn/Cu ratio with renal function, plasma BNP levels, and glycemic control in a relatively large population.</p>
<p>A positive correlation between serum Zn/Cu ratio and serum Alb and Hb levels is expected. Plasma Zn concentrations are reduced by hypoalbuminemia, as zinc is bound to Alb in the circulation, and Zn deficiency is a known cause of anemia (<xref ref-type="bibr" rid="B29">29</xref>). Although Cu deficiency is a potential cause of hematologic abnormalities (<xref ref-type="bibr" rid="B30">30</xref>), no significant associations between serum Cu levels and blood cell count were observed in this study.</p>
<p>An imbalance between Zn and Cu levels leads to increased oxidative damage in humans, contributing to the pathogenesis of diabetes and diabetic complications (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B31">31</xref>). The dysregulation of matrix metalloproteinases which include a Zn ion-binding site are associated with the progression of diabetic complications <italic>via</italic> the interaction with advanced glycation end products in patients with type 2 diabetes, although the relationship between matrix metalloproteinases and serum Zn levels were not investigated in the present study (<xref ref-type="bibr" rid="B12">12</xref>). Furthermore, these trace elements are necessary for the activity of mitochondrial antioxidant enzymes, such as Cu/Zn superoxide dismutase (SOD), that protect the cell from reactive oxygen species toxicity (<xref ref-type="bibr" rid="B32">32</xref>). Both Zn and Cu are essential for metabolism; however, an imbalance in Zn/Cu (or Cu/Zn) ratio may be a better indicator of metabolic disturbance than Zn or Cu status alone. Indeed, previous studies have demonstrated the utility of Cu/Zn ratio as a biomarker for vascular complications in type 2 diabetes (<xref ref-type="bibr" rid="B33">33</xref>) and a predictor of mortality in elderly individuals (<xref ref-type="bibr" rid="B34">34</xref>).</p>
<p>Renal dysfunction has been shown to be induced by Cu toxicity in both rats (<xref ref-type="bibr" rid="B35">35</xref>) and humans (<xref ref-type="bibr" rid="B36">36</xref>). On the other hand, Zn may contribute to the preservation of renal function. Kurihara et al. reported that Zn deficiency decreased renal blood flow and increased renal vascular resistance, which may be attributable to decreased nitric oxide activity due to the presence of increased concentrations of superoxide anions through low SOD activity in the kidneys of rats (<xref ref-type="bibr" rid="B37">37</xref>). Yanagisawa et al. suggested that Zn deficiency aggravates tubulointerstitial nephropathy in rats due to an increase in the action of angiotensin II and endothelin (<xref ref-type="bibr" rid="B38">38</xref>). Recently, Sun et al. demonstrated that renal function was improved by Zn supplementation in diabetic mice (<xref ref-type="bibr" rid="B39">39</xref>). Zn supplementation has also been shown to reduce Alb excretion in patients with diabetic nephropathy (<xref ref-type="bibr" rid="B40">40</xref>, <xref ref-type="bibr" rid="B41">41</xref>). The role of Zn is of importance in preserving renal function. However, there is a lack of evidence regarding the association between Zn and/or Cu and renal function in humans. It is notable that the results of this study indicate the potential utility of serum Zn/Cu ratio as a biomarker for renal function.</p>
<p>The negative association observed between serum Zn/Cu ratio and plasma BNP levels in this study may be mediated by insulin sensitivity. An inverse association between natriuretic peptides and insulin resistance has been reported by previous studies (<xref ref-type="bibr" rid="B42">42</xref>&#x02013;<xref ref-type="bibr" rid="B44">44</xref>). BNP stimulates lipolysis (<xref ref-type="bibr" rid="B45">45</xref>) and promotes muscle mitochondrial biogenesis and fat oxidation through upregulation of peroxisome proliferator-activated receptor-&#x003B3; coactivator-1&#x003B1; (PGC-1&#x003B1;) (<xref ref-type="bibr" rid="B46">46</xref>) and increases adiponectin secretion (<xref ref-type="bibr" rid="B47">47</xref>), thereby improving insulin resistance. In addition to previous reports of the association of an imbalance in Zn and Cu levels with increased oxidative stress and inflammation (<xref ref-type="bibr" rid="B34">34</xref>, <xref ref-type="bibr" rid="B48">48</xref>), which impair insulin secretion and action (<xref ref-type="bibr" rid="B1">1</xref>), Zn deficiency may lower SOD1 activity in pancreatic islets, which has been shown to increase insulin resistance (<xref ref-type="bibr" rid="B49">49</xref>, <xref ref-type="bibr" rid="B50">50</xref>). Zn has also been shown to be associated with insulin secretion <italic>via</italic> metallothionein synthesis (<xref ref-type="bibr" rid="B51">51</xref>) and the zinc transporter ZnT8 (<xref ref-type="bibr" rid="B15">15</xref>). Recently, Zn has been observed to promote insulin secretion independently on metallothionein synthesis (<xref ref-type="bibr" rid="B52">52</xref>). The inverse association between serum Zn/Cu ratio and plasma BNP levels observed in this study after adjustment for age, sex, and BMI indicates that a higher Zn/Cu ratio may be associated with improved insulin sensitivity. However, we were unable to detect a significant association between serum Zn/Cu ratio and insulin levels. This inconsistency may be because of the following study limitations: the small sample size (<italic>n</italic>&#x02009;&#x0003D;&#x02009;42) for investigating the association between serum Zn/Cu and insulin and confounding effects of oral hypoglycemic agents and diet. We are unable to confidently explain the negative association observed between serum Zn/Cu ratio and BNP in this study. Thus, further studies are required to elucidate the mechanisms underlying the association between serum Zn/Cu ratio and BNP.</p>
<p>Several studies have demonstrated decreased serum/plasma Zn levels in patients with type 2 diabetes compared with healthy individuals (<xref ref-type="bibr" rid="B53">53</xref>&#x02013;<xref ref-type="bibr" rid="B55">55</xref>); on the contrary, serum Zn levels were not lower in patients with type 2 diabetes in this study. As mentioned above, serum Zn levels were found to be strongly affected by nutritional status and Hb levels; thus, no significant difference in serum Zn levels between patients with and without type 2 diabetes was observed.</p>
<p>Low HDL-C levels in patients with type 2 diabetes may have been observed as serum HDL-C levels have been shown to decrease with impaired function in type 2 diabetes (<xref ref-type="bibr" rid="B56">56</xref>). Patients with type 2 diabetes were more obese than those without type 2 diabetes in this study. Although no significant differences in body composition between patients with and without type 2 diabetes were found, obesity may be attributable to lower HDL-C levels in patients with type 2 diabetes.</p>
<p>The observed associations of serum Zn/Cu ratio with lower HbA1c levels and reduced risk of poor glycemic control (HbA1c&#x02009;&#x02265;&#x02009;7.0%) in a population of patients with type 2 diabetes are noteworthy findings of this study. Although excess of intracellular Zn can trigger oxidative stress from mitochondria and lead to neuronal degeneration (<xref ref-type="bibr" rid="B57">57</xref>), Zn has previously been shown to have beneficial effects on glycemic control as well as aging, immunity, and oxidative stress (<xref ref-type="bibr" rid="B58">58</xref>), whereas serum Cu is associated with higher HbA1c levels (<xref ref-type="bibr" rid="B31">31</xref>, <xref ref-type="bibr" rid="B59">59</xref>). Experimental studies have shown that the amino acids residues involved in Cu coordination complexes have a key role in the formation of human islet amyloid peptide aggregation, which alters the autophagy pathway in pancreatic &#x003B2;-cells and leads to the development of diabetes (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>). The actions of Zn and Cu in glucose metabolism appear to be antagonistic; hence, the balance between Zn and Cu ion concentrations is important. In this study, higher Zn levels were not associated with good glycemic control (odds ratio&#x02009;&#x0003D;&#x02009;0.991; 95% CI, 0.968&#x02013;1.014; <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.436), whereas serum Cu levels were associated with glycemic control (odds ratio&#x02009;&#x0003D;&#x02009;1.024; 95% CI, 1.007&#x02013;1.041, <italic>p</italic>&#x02009;&#x0003D;&#x02009;0.006). However, the high serum Zn/Cu ratio was associated with good glycemic control in patients with type 2 diabetes. This result suggests that not only Zn and Cu status alone but also Zn/Cu ratio should be considered when evaluating the relationship between these trace elements in patients with diabetes.</p>
<p>This study had several limitations. First, a causal relationship between serum Zn/Cu ratio and renal function and/or glycemic control could not be evaluated because of the retrospective and observational nature of this study. Second, we were unable to evaluate the utility of other biomarkers in assessing Zn and Cu status, such as urinary and other tissue concentrations of Zn and Cu. Zn and Cu concentrations are known to be affected by other factors, such as inflammation, and fasting or postprandial states (<xref ref-type="bibr" rid="B60">60</xref>, <xref ref-type="bibr" rid="B61">61</xref>). However, blood Zn and Cu levels are currently considered to be useful and reliable biomarkers (<xref ref-type="bibr" rid="B60">60</xref>, <xref ref-type="bibr" rid="B61">61</xref>). Third, we are unable to generalize the results of this study to healthy individuals or other populations. The subjects included in this study had a high prevalence of comorbid diseases, such as hypertension, dyslipidemia, fatty liver, autoimmune disease, CKD, and CVD, besides type 2 diabetes. Neurodegenerative disorders could also undergo changes in serum Zn and Cu levels. Although we excluded subjects with malnutrition and anemia, comorbidities and medications may have affected the observed associations of Zn and Cu status with other parameters. We should also perform a further investigation in healthy controls. Fourth, other confounding factors, such as dietary intake, smoking and drinking habits, and physical activity, should also be considered. Fifth, we were unable to provide a value for the most desirable balance between Zn and Cu from the results of this study. Despite these limitations, we were able to demonstrate significant associations of serum Zn/Cu ratio with renal function, glycemic control, and a number of metabolic parameters, providing new insights in the management of type 2 diabetes.</p>
</sec>
<sec id="S5">
<title>Conclusion</title>
<p>The findings of this study demonstrate that serum Zn/Cu ratio is significantly associated with renal function in all subjects and glycemic control in patients with type 2 diabetes. Serum Zn/Cu ratio was negatively associated with plasma BNP levels. Imbalance between Zn and Cu levels induces oxidative stress and insulin resistance, which may lead to progression of diabetes and diabetic complications. Zn/Cu ratio, in addition to individual levels of each trace element status alone, appears to have an important effect on metabolism, indicating that these trace elements may play a key role in the pathogenesis of metabolic diseases. We, clinicians should note that serum Zn/Cu ratio could be a better indicator for human metabolism as compared with Zn or Cu status alone.</p>
</sec>
<sec id="S6" sec-type="author-contributor">
<title>Author Contributions</title>
<p>HH performed the study, conducted the data analyses, and drafted and revised the manuscript. YK contributed to the data collection and analyses. HY critically reviewed the manuscript and the scientific interpretations of study results. All the authors read and approved the final manuscript.</p>
</sec>
<sec id="S7">
<title>Conflict of Interest Statement</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
</body>
<back>
<ack>
<p>The authors appreciate the supporting of Tomoko Kaga who cooperated to collect the data.</p>
</ack>
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