Several studies have indicated an association between serum copper and zinc and chronic kidney disease, while there was antagonism between serum copper and zinc, and the serum copper/zinc ratio was more accurate than detecting the two elements alone. This study aims to evaluate the associations of serum copper, zinc and copper/zinc rate with the risk of CKD using the 2011–2016 National Health and Nutritional Examination Surveys (NHANES). A total of 7764 participants were included in the analyses. Weighted multivariable logistic regression was applied to investigate the association between serum zinc and copper level and CKD after adjusting for potential confounders. A restricted cubic spline (RCS) to explore the relationships and trend between copper/zinc ratio and the risk of developing CKD. Machine learning of the XGBoost algorithm model was used to assess each variable’s contribution to the risk of having CKD. In this study, when the serum copper/zinc level decreased to less than 1.2, the risk of having CKD was reduced to 0.57 (95%CI, 0.39–0.84, p = 0.006). The RCS model showed a negative liner correlation between serum copper/zinc ratio and CKD risk (p < 0.001 and the value of nonlinear equal to 0.06), and when the ratio was lower than 1.49, the OR of having CKD begins to be lower than 1 and shows a rapidly increasing trend. In the XGBoost model, besides the glomerular filtration rate, the Copper/Zinc ratio was the most relative variable in our study. Therefore, the copper/zinc ratio would be a relatively important risk factor for predicting having CKD, and further researches are needed to explore the potential mechanism between copper/zinc and CKD.
Research Article
Associations Between Serum Copper, Zinc and Copper/Zinc Ratio and Risk of Chronic Kidney Disease: A cross-sectional analysis of the NHANES 2011-2016
https://doi.org/10.21203/rs.3.rs-1952772/v1
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Chronic kidney disease (CKD) was a serious public health problem worldwide[1], ranked 16th leading cause of life lost worldwide and affected 8–16% of the global population, which was often unrecognized by patients and clinicians[2]. According to WHO global health estimates, there were 1.2 million deaths in 2017 and the global all-age mortality rate increased 41.5% from CKD[3]. CKD patients often have complications such as diabetes, hypertension, dyslipidemia and others, which would lead to cardiovascular disease and even death, in which oxidative stress plays an essential role[4, 5]. The pivotal to the occurrence of oxidative stress was the reactive of oxygen species (ROS), decreasing of cellular antioxidant capacity, and destroy of physiological defense mechanisms, when mitochondria was overloaded due to various reasons[6]. Excessive accumulation of ROS in the body damages a variety of normal proteins, lipids, nucleic acids and other substances in the body, aggravates the inflammatory, oxidant response and accelerates the progression of kidney damage[6].
Trace elements such as copper (Cu) and zinc (Zn) are essential minerals involved in many enzymatic processes and oxidant/antioxidant balance and have been implicated in the pathogenesis of CKD[7, 8]. Zinc participates in the formation of copper-zinc superoxide dismutase (SOD), which was the most important antioxidant enzyme, and plays a key role in scavenging free radicals in body[9]. In addition to scavenging free radicals, zinc also determines the activity of angiotensin-I converting enzyme (ACE), carbonic anhydrase and endothelin converting enzyme[9]. Copper was considered a potentially toxic element because of its redox potential, which could catalyze the generation of aggressive free radicals through the Fenton reaction and disrupt protein structures[9]. Copper interferes with zinc homeostasis and disrupts oxidative defense mechanisms, thereby increasing oxidative stress[9]. The copper/zinc ratio as a biomarker has essential implications in different disease states[10, 11]. For example, the serum copper/zinc ratio was higher than normal in cystic fibrosis patients[12]. In malignant tumors, such as gallbladder, non-small cell lung cancer or gynecological malignancies, the ratio of copper to zinc was also higher in patients[13–16]. On the other hand, in metabolic-related diseases, patients with diabetes had a high level of copper/zinc ratio than normal[17, 18]. In addition, the Cu/Zn ratio may be considered as an important clinical inflammatory-nutritional biomarker as well as a significant predictor of all-cause mortality in over 70-year-olds[19].
However, there was lack of large cross-sectional studies on the relationship between serum copper and zinc levels and CKD, in addition, previous studies have focused on the relationship between single metal concentration and CKD[20], while ignoring the antagonism between copper and zinc, so this study included the ratio of copper/zinc as a risk factor for the risk of CKD. Therefore, we performed this large, case-control study to examine the associations between serum level of Zn, Cu, and copper/zinc levels and CKD with potential effect modification by other factors.
2.1. Study population
The data on the research subjects came from the National Health and Nutrition Examination Survey (NHANES) database, an organization of The National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC), which was an ongoing project that conducts surveys of unique people every year. A total of three survey cycles from 2011 to 2016 were included in this study. The statistics of this project was consisted by socio-demographic characteristics, physiological indicators, medical indicators, laboratory tests data, health status etc. The subjects were a representative sample of 7764 participants, with weight information, in this database from 2011 to 2016, included in complex sampling analysis. In the complex sampling analysis, 1/3*WTSA2YR was set as weighted value, the NHANES stratification variable (SDMVSTRA) and primary sampling unit variable (SDMVPSU) were incorporated according to the survey design to appropriately adjust the variance estimates. Then Extracted a subset of age larger and equal to 20 for analysis in complex sampling analysis, with a total of 5296.
2.2.1 Assessment of CKD and Estimate glomerular filtration rate (eGFR)
Diabetes was defined as a history of diabetes or fasting glucose >7 mmol/L or 2-h post-challenged plasma glucose concentration ≥ 200 mg/dL (11.1 mmol/L) or glycated hemoglobin A1c >6.5 % (48 mmol/mol) or use of hypoglycemic medication. Impaired Fasting Glucose (IFG) was defined as fasting glucose higher than normal but not reaching the diagnostic criteria for diabetes. Impaired glucose tolerance (IGT) refers to an increase in blood glucose level after 2 hours and the OGTT test, which exceeds the normal 7.8 mmol/L, but does not reaching the diagnostic criteria for diabetes of 11.1mmol/L.
EGFR for male was equal to (140-age) *weight*1.23/serum creatinine, and equal to (140-age) *weight*1.03/serum creatinine for female. CKD was defined as participants with an estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2 or urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g, and CKD patients were classified into stages according to GFR categories and albuminuria categories by the Kidney Disease: Improving Global Outcomes (KDIGO) organization.
2.2.3 Socio Demographic and Lifestyle characteristics
Extracted from demographic data, height, age, weight, ethnicity (non-Hispanic white, non-Hispanic black, Mexican American or others), education level (less than high school, high school or equivalent and college or above), household income classificated by "Under $20,000", "$20,000 to 55,000", "Over $55,000". Body mass index (BMI), calculated as weight divided by height squared, were included in this study. On the other hand, smoking status from the questionnaire were referred. Details of all variable acquisition procedures can be found at http://www.cdc.gov/nchs/nhanes/.
2.2.4 Laboratory data
The level of blood glucose, glycosylated hemoglobin, serum creatinine measured by standard biochemistry assays. Electrochemical detection on serum ascorbic acid measured by isocratic high-performance liquid chromatography at 650 mV1, and converted from mg/dL to μmol/L.
2.3 Statistical analyses
Survey package for R was used to account for the complex sampling design, and all the result were weighted. Mean and standard deviation for continuous and normally distributed variables. Interquartile range (IQR) was used to describe variables non-normal distribution variables. Categorical variables were presented as the number (percentage). Continuous variables in baseline characteristics were analyzed by Student's t-test and nonparametric Mann-Whitney U test for variables (did not obey normal distribution), as well as Pearson’s Chi-square test was used for qualitative variables. Weighted multivariable logistic regression was applied to investigate the association between serum zinc and copper level and CKD after adjusting for potential confounders. And the distribution on serum zinc and copper was divided into Q1-4 according to tertiles. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) was calculated with Q1 as reference, using binary logistic regression. A restricted cubic spline (RCS) to explore the relationships and trend between copper/zinc ratio and the risk of CKD. Machine learning of the XGBoost algorithm model was used to assess each variable’s contribution to the risk of CKD. Data were analyzed using R software version 3.6.0. A two-sided P < 0.05 was considered statistically significant.
Characteristics of Participants
The characteristics of participants in this study were shown in Table 1. Of the 4978 participants, there were 905 patients with CKD. Participants with CKD were significantly older (61.07 vs 45.34, p < 0.01). Men were more likely to developing CKD. Participants with higher education levels, high family income reduced the risk of having CKD. Participants with history of smoking status had a higher proportion of having CKD compared to no-smoker. CKD participants have higher BMI being 29.87(95% CI, 29.30-30.43). On the other hand, participants with impaired glucose tolerance and diabetes had significantly higher risk of CKD. Participants with CKD had lower eGFR being 72.48(95% CI, 70.03-74.93) compared to the normal. Serum zinc were lower in CKD participants compared to others (12.29 vs 12.57, P = 0.028), on the contrary, serum copper was higher in participants with CKD (19.41 vs 18.49, P <0.001). There were no significant differences in race/ethnicity and serum selenium.
Logistic Regression Models
The levels of serum zinc, copper and copper/zinc were categorized Q1-4 by quartiles, in which Q 1 set as referent. Weighted logistic regression model was constructed to explore the associations between serum zinc, copper levels and copper/zinc ratios and the risk of CKD (table 2). In model 1 with adjusted by age and gender, serum copper and zinc level of Q4 was significantly associated with CKD (Table 3). The risk of developing CKD was decreased gradually, with increasing serum zinc levels or decreasing serum copper levels, and this trend was statistically significant (p for trend equal to 0.033 and less than 0.001, respectively). The model 2 was adjusted by age, gender, ethnicity, education status, average household income, BMI, diabetic status and smoke status. In the model 2, serum zinc level of Q2-4 was significantly associated with CKD and the odds ratio (OR) equal to 0.75(95%CI, 0.58-0.96), 0.74(95%CI, 0.56-0.99), 0.63(95%CI, 0.43-0.91) respectively. On the other hand, Q4 level of serum copper was significantly associated with CKD with OR being 1.62(95% CI, 1.02-2.59). Consistent with model 1, the trend of risk in model 2 was statistically significant (p for trend equal to 0.023 and less than 0.001).
For copper/zinc ratio, participants with lower ratio level had lower risk of having CKD in those models. When the copper/zinc level decreased to 1.20-1.46 level in Model 2, the risk of developing CKD decreased to 0.59 (95% CI, 0.43-0.80, p=0.002), and when the copper/zinc level decreased to less than 1.2, the risk of participants had CKD was reduced to 0.57 (95%CI, 0.39-0.84, p=0.006). The forest plot showed the weighted logistic regression (shown in figure 1), in which age, ethnicity, diabetes and smoking status were the risk factors and the higher education level and lower copper/zinc ratio were protective risks.
Dose-Response Relationship between serum copper/zinc ratio and risk of CKD
The restricted cubic spline RCS were used to analyze the relationship between copper/zinc ratio and the risk of CKD (shown in figure 2). Adjusted by age, gender, ethnicity, education status, average household income, BMI, diabetic status and smoke status, the RCS model showed a negative liner correlation between serum copper/zinc ratio and CKD risk (p <0.001 and the value of nonlinear equal to 0.06). In addition, according to the prediction of this model, when the ratio was lower than 1.49, the odds ratio of CKD begins to be lower than 1 and shows a rapidly increasing trend. On the contrary, when the ratio was higher than 1.49 and lower than 4.14, OR was gradually decreasing trend.
Machine Learning Using the XGBoost Algorithm Model
In the phase of model-development and validation, the XGBoost model was applied to determine the relative importance of variables associated with the CKD. Variables included age, education status, average household income, BMI, diabetic status and smoke status. Categorical variables were converted into ordinal factors firstly, and then converted into ordinal numeric variables into the model. According to the results of each variables’ contribution by the XGBoost model, (shown in Figure 3), besides the glomerular filtration rate, the Copper/Zinc ratio, was the most relative variable in our study.
The target of this study was investigating the association of serum zinc and copper levels with chronic kidney disease in a representative sample of U.S. adults 20 years and older who participated in NHANES 2011–2016. Overall, the findings suggest a significant association between serum copper and zinc and chronic kidney disease. Specifically, higher level of serum zinc was able to reduce the risk of developing CKD, conversely, higher levels of serum copper increased the risk of CKD. In addition, the risk of having CKD decreased gradually with decreasing copper/zinc ratio, which presented as a linear correlation. Maintaining an appropriate copper/zinc ratio of less than 1.49 was essential for avoiding developing CKD. As another point of view, the judgment standard on status of zinc deficiency and copper sufficiency in CKD patients cannot rely solely on serum test of zinc and copper, the Cu/Zn ratio may be another observation indicator.
Zinc plays an important role in promoting kidney development and maintaining normal kidney function[21, 22]. Recently, zinc deficiency has been found to specifically upregulate the expression of nicotinamide adenine dinucleotide phosphate oxidase 2 in renal tubular epithelial cells, promote oxidative stress, and cause kidney damage[23]. In CKD patients, the serum zinc was decreased, which was significantly negatively correlated with the serum phosphorus level, positively correlated with the serum total protein, albumin and serum calcium levels[24]. Correcting the low level serum zinc could help to delay the progression of CKD and prevent its complications[25]. Other studies have shown that under the condition of zinc deficiency, the level of zinc in renal tissue was reduced, and the expression of TGF-β, smad family proteins 2/3, type I collagen in the extracellular matrix up-regulated and the activation of renal interstitial fibroblasts was enhanced, which result in aggravation of renal interstitial damage in diabetic mice[26]. In addition, studies have shown that zinc has a protective effect in the gentamicin-induced AKI model, which can enhance the total antioxidant status, relieve proteinuria, reduce renal tissue necrosis and inflammation, indicating that zinc supplementation can reduce renal tissue degeneration and inflammation caused by AKI[27]. On the other hand, zinc deficiency could lead to dysfunction of plasma membrane proteins and renal anemia[28, 29]. Zinc deficiency may be related to serum copper for the antagonistic effects.
Serum copper, copper transporters and copper-containing enzymes were involved in various physiological processes such as biological oxidation, free radical scavenging, angiogenesis, melanin and neurotransmitter formation. The copper concentration in serum was about 1.05–1.20 µg/ml, of which about 40–70% of copper was bound to ceruloplasmin (CP), which was mainly synthesized in the liver, and its concentration was affected by various factors such as interleukin-6[30]. In inflammation, serum copper and the activity of CP were increased, which was considered to be an acute phase protein[31]. It has been reported that the concentration of serum copper increased in patients with ovarian cancer, and related to disease stage. Serum copper levels decreased After chemotherapy or surgical, so it was believed that copper may be related to the occurrence and development of cancer[32]. At the same time, The biological antagonistic effect between Cu and Zn lead to the increasing of serum copper levels in patients. Normally, the ratio of copper to zinc was basically kept within a certain range. When the ratio fluctuates beyond the normal range, it would cause dysfunction on Zn and Cu, thereby affecting the activation of a variety of enzymes and pathophysiological changes such as decreased energy metabolism [33].
In this study, serum zinc and copper level were abnormal in CKD patients was observed. In addition, the ratio of copper/zinc would be a relatively essential risk factor for predicting CKD, and our results suggest that a value of 1.49 was a potential threshold level for the diagnosis CKD. To justify the validity of the Cu/Zn ratio, it will be necessary to assess the relationships between the serum Cu/Zn ratio and CKD before and after Zn replacement therapy.
Author Contribution
Zheng-Yuan Ma and Sheng-Quan Fang designed the study. Ying-Xia Liu, Wei-Xiang Song and Xu-Bo Shen performed the data collection and statistical analysis. Liu-Bo Li wrote the manuscript. All authors read and approved the final manuscript.
Funding
This work was supported by Youth Innovation Promotion Association of the Chinese Academy of Sciences, 2020275.
Data Availability
The datasets generated and analyzed in the present study are available on the website of NHANES datasets 2011–2016 (https://wwwn.cdc.gov/nchs/nhanes).
Ethics Approval The NHANES study has been approved by the National Center for Health Statistics Research Ethics Review Board, and all participants in the survey signed informed consent.
Consent to Participate The participant data were obtained from the publicly available NHANES database, so no additional consent was obtained.
Conflict of Interest The authors declare no competing interests.
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No competing interests reported.