Associations between Dietary and Plasma Copper and Liver Function in a Chinese Population

doi:10.21203/rs.3.rs-1731313/v1

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Associations between Dietary and Plasma Copper and Liver Function in a Chinese Population

https://doi.org/10.21203/rs.3.rs-1731313/v1

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The potential mechanisms underlying the association between copper exposure and liver function are unclear. This study aimed to investigate the potential association of dietary copper (Cu) intake and plasma Cu with liver function parameters. A cross-sectional study was performed to determine liver function parameters—namely, total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), alanine transaminase (ALT), and aspartate transaminase (AST)—in 2376 subjects in Guangxi, China. Dietary Cu was obtained from a food frequency questionnaire containing 109 common foods. Plasma Cu concentrations were determined by inductively coupled plasma mass spectrometry. Multiple linear regression combined with the multivariate restricted cubic spline function (RCS) was used to evaluate the correlation of plasma and dietary Cu with liver function parameters. The covariate adjustment results of the linear regression analysis showed that plasma Cu levels were significantly negatively correlated with serum IBIL (β = −0.37), DBIL (β = −0.22), and TBIL (β = −0.32) (all p < 0.05), and dietary Cu was negatively correlated with serum AST (β = −0.12, p < 0.05). The spline analysis further elucidated the negative linear relationship between dietary Cu and AST in the total population. In summary, our results suggested that plasma Cu is associated with serum bilirubin and dietary Cu is associated with serum AST. Further studies are needed to validate these associations and reveal the underlying mechanisms.

liver function

dietary copper

plasma copper

The burden of liver disease is becoming a global concern. Over the past few decades, liver diseases have relentlessly risen to become one of the leading causes of death and disease worldwide [1]. Worldwide, approximately 2 million people die from liver disease per year [2]. In China, more than one-fifth of the population was reported to suffer from liver disease in 2017 [1]. Liver disease causes serious public health problems due to its high prevalence worldwide and poor long-term clinical outcome, including premature death due to liver decompensation and cirrhosis.

Clinical investigations that measure levels of various biomarkers (proteins or enzymes) in the blood can be used to assess liver function and liver disease [3]. For instance, aspartate transaminase (AST) and alanine transaminase (ALT) are common biomarkers of liver injury [4]. ALT and AST levels reveal whether hepatocytes are intact. When hepatocytes suffer damage or die, AST and ALT are released into the bloodstream, thus increasing the serum levels of these biomarkers. The presence of high levels of bilirubin in the blood also indicates hepatocellular injury, and total serum bilirubin is routinely detected to identify hepatobiliary and hemolytic disorders. Liver function testing (LFT) is one of the most commonly used and primary clinical investigations for the assessment of liver function. In addition to viruses, bacteria, and nutritional factors, an association between metal exposure and biomarkers of liver function has been reported [5].

The effects of metal exposure on liver function are receiving increasing attention. However, few studies have clearly identified a possible link between metal exposure and liver function, and the potential mechanisms by which metals affect liver function are not yet clear. There is therefore an urgent need to investigate the relationship between metal exposure and liver function and to elucidate the underlying mechanisms. The liver plays a central role in the dynamic balance of essential trace metals and in the deposition of toxic heavy metals [6] and is one of the main target organs for metal metabolism and detoxification. Almost all the substances that are absorbed in the small intestine are first delivered to the liver. Therefore, any type of metal exposure can be first detected in the liver, and this is considered to be crucial [7]. Copper (Cu) is an essential nutrient in the human body and is required for the vital metabolic functions of living organisms. As a trace element, copper mainly originates from daily dietary intake, while, as an environmental pollutant, it is usually generated in the process of discharge, mining and smelting activities, and calcium oxide combustion, and it enters the human body through multiple pathways, including ingestion, inhalation, and dermal contact. Previous studies indicate that optimal levels of essential metals are important for metabolism, growth, and immunological and neurological function [8], but that insufficient or excessive essential metal exposure also causes or exacerbates adverse health effects in humans [9]. Dietary Cu is absorbed through the intestinal epithelium in the small intestine and stored in the liver. Inadequate dietary Cu supply may increase lipid accumulation in the liver and may be involved in the development of nonalcoholic fatty liver disease (NAFLD) [10]. Copper is also currently considered as a therapeutic approach to improve liver injury and in the treatment of various cancers [11]. In recent years, evidence for an association between metal exposure and biomarkers of liver function has also continued to grow. Experimental animal studies revealed that exposure to high Cu concentrations could promote oxidative stress liver injury by elevating the hepatic function index (ALT, AST) and decreasing the activity of antioxidant enzymes associated with oxidative stress (T-SOD, GSH-Px, and CAT) [12]. Urinary Cu was positively correlated with serum ALT levels in the general population [13]. In Cd-contaminated areas, blood Cu levels were positively correlated with elevated ALT and AST levels [14]. In addition, plasma Cu was found to be negatively associated with TBIL, AST, and ALT in a health cohort study of manganese-exposed workers [15]. However, an important limitation of these studies on hepatotoxicity associated with metal exposure is that there are fewer studies that also consider dietary exposure, ignoring the fact that metal exposure enters the body through multiple pathways, especially that essential trace elements enter the body mainly through the digestive tract. Another knowledge gap includes the identification of linear or nonlinear dose–response relationships between metal exposure and liver function parameters.

Currently, findings on the relationship between Cu exposure and liver function are inconsistent, and the mechanisms underlying the association between Cu exposure and liver function remain unclear. Therefore, in the present work, we conducted a cross-sectional study to assess the relationship between dietary Cu and plasma Cu and liver function parameters.

2.1. Study Population

The study population was drawn from a rural population participating in a health survey during 2018–2019 in Guangxi Yao Autonomous County, a southwestern region of China. Study subjects who met the following criteria were selected: (a) residing in the study area; and (b) aged ≥ 30 years. Subjects were excluded based on the following criteria: (a) those who did not complete the questionnaire or had incomplete information on the questionnaire; (b) those with a cardiovascular disease or liver disease, such as liver cancer, liver fibrosis, or hepatitis virus; (c) those who had taken herbal medicine or antibiotics in the past two weeks prior to the physical examination; and (d) participants with abnormal metal values (defined as three times the 99th percentile of copper). A final total of 2376 subjects were included for analysis. The study was approved by the Medical Ethics Committee of Guilin Medical University (No: 20180702-3). Written informed consent was provided by each participant.

2.2. Detection of Liver Function

Fasting blood samples were collected from the elbow vein in the morning after at least 12 h of fasting and transported to the Department of Laboratory of Gongcheng Yao Autonomous County People’s Hospital by the cold chain transport method on the same day. Serum total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), alanine transaminase (ALT), and aspartate transaminase (AST) levels were measured by a chemical autoanalyzer (Hitachi 7600-020, Kyoto, Japan) to assess liver function. We set normal reference ranges for 5 liver function parameters according to local hospitals: AST, 5.00–34.00 U/L; ALT, 0.00–40.00 U/L; TBIL, 2.00–30.00 μmol/L; DBIL, 0.00–12.00 μmol/L; IBIL, 0.00–18.00 μmol/L.

2.3. Dietary Cu Assessment

Diet was assessed using the Food Frequency Questionnaire (FFQ), with reference to the questionnaire designed by Liu [16] and Wang [17] et al. The FFQ is a reasonable, valid, and reliable method for assessing long-term average dietary intake [18, 19]. The FFQ contains 109 common foods. Subjects were asked to recall and report their average frequency of consumption and the amount consumed per serving over the past year. The FFQ came with a food album that included diet items and measurement tools, and the weight of each portion size was determined by a standardized portable electronic kitchen scale, which could measure food in g and drinks in mL. The intake of each food item was derived by multiplying the serving size by the weight of each serving. The energy and nutrient content of foods were calculated in reference to the Chinese Food Composition Table (2019) [20], and we used daily intake = intake/frequency per intake to calculate the total daily energy intake and dietary copper intake.

2.4. Plasma Cu Assessment

Blood samples were delivered to our laboratory by cold chain and stored in an ultra-low-temperature refrigerator at −80 °C until analysis. The Cu concentration in blood was measured using inductively coupled plasma mass spectrometry (Thermo Fisher Scientifc iCAPRQ01408, USA). The detailed method for measuring metal exposure levels has been described elsewhere [21]. The experiment was performed in accordance with the instruction manual. We diluted 0.1 mL of plasma to 2.0 mL with an acidic solution containing 0.01% Triton (Triton X-100, Inc.), 0.5% n-butanol (Across, Denmark), and 1% nitric acid (ultrapure). The standard solutions consisted of 43 and 6 elements in mixed standard solutions and single standard solutions of Cu, Zn, Fe, Mg, and Ca and 1% nitric acid solution. The accuracy of ICP-MS was checked by analyzing certified reference materials in every 25 samples (ClinChek human plasma controls for trace metals Level 1 [No.8883] and Level 2 [No.8884]; Recipe Chemicals). Quality control of plasma metal content determination was achieved by three replicate measurements. We used mixed plasma sample spiked recoveries (200 randomly selected samples) to assess the precision and accuracy of the method. In addition, the standard recovery of plasma Cu was controlled at 80–120%. The detection limit for Cu metal elements was 0.004 μg/L, and values lower than the detection limit were filled with the value (detection limit/√2). The intra-assay and inter-assay coefficients of variations in plasma Cu metal content were below 10%.

2.5. Assessment of Covariates

In order to collect information including sociodemographic characteristics and lifestyle habits, trained interviewers administrated a face-to-face interview questionnaire, including gender (male or female), age (30–59 years or ≥60 years), ethnicity (Han, Yao, or other), occupation (farmer or other), education level (≤6 years or >6 years), smoking (yes or no), and alcohol consumption (yes or no). Body mass index (BMI) was calculated from weight and height data = weight (kg)/height²(m²). Smoking was defined as currently smoking at least one cigarette per day. Alcohol consumption was defined as consuming at least 50 g or more of alcohol per month. Hypertension was defined as a systolic blood pressure ≥140 mmHg or diastolic blood pressure ≥90 mmHg or indicated by the current use of anti-hypertensive medication. Hyperlipidemia was defined as total cholesterol >5.72 mmol/L or triglycerides >1.70 mmol/L or current use of lipid-lowering medication. Type 2 diabetes was defined as a fasting blood glucose level ≥7.0 mmol/L or self-reported treatment for diabetes.

2.6. Statistical Analysis

Descriptive statistics were calculated for all demographic and clinical characteristics of the study population. Continuous variables were expressed as mean (SD) or median (IQR). Categorical variables were expressed as numbers (percentages). The levels of metals were subjected to log10 (lg) transformation to reduce their skewness. Spearman correlation analysis was used to evaluate the correlation between dietary Cu and plasma Cu and liver function parameters, and multiple linear regression models combined with multivariate RCS functions were used to investigate the dose–response relationships between dietary and plasma Cu and liver function parameters. Adjustments were made for covariates that may affect liver function parameters or metallic copper, including sex, age, race, education, occupation, BMI, smoking, alcohol consumption, hypertension, dyslipidemia, and type 2 diabetes. Five sections of RCS were located at 5%, 27.5%, 50%, 72.5%, and 95% of the elemental concentration range, respectively. Different demographic characteristics such as gender influence the distribution of Cu metals in the body [22]. Therefore, we stratified the analysis by gender (male/female). In addition, alcohol is a major risk factor for liver disease, and we performed sensitivity analyses on participants who did not drink alcohol to exclude potential effects of alcohol consumption on diet and plasma Cu. All statistical analyses were performed using SPSS 22.0 and R 4.0.3, and a two-sided p-value of <0.05 was considered to indicate a statistically significant difference.

3.1. Characteristics of Participants

Table 1 shows the clinical and sociodemographic characteristics of the 2376 participants (932 males and 1444 females), 50.51% of whom were aged 60 years or older. The majority of participants were from the Yao population, accounting for 74.62% of the total population; the majority of the subjects also had education levels below junior high school. The clinical median values (25^th and 75^th) of serum ALT, AST, IBIL, DBIL, and TBIL were 17.00 U/L (14.00 and 23.00), 21.00 U/L (19.00 and 25.00), 9.30 umol/L (7.50 and 11.60), 3.50 umol/L (2.80 and 4.60), and 12.80 umol/L (10.50 and 16.10), all of which were higher in the male population than in the female population. More participants self-reported to be non-smokers than non-drinkers.

Table 1. General characteristics of the study population

Variables	Overall (n = 2376)	Men (n = 932)	Women (n = 1444)
Age, years, n (%)
30–59	1176 (49.49)	405 (43.45)	771 (53.39)
≥60	1200 (50.51)	527 (56.54)	673 (46.61)
Ethnicity, n (%)
Han	487 (20.50)	175 (18.78)	312 (21.61)
Yao	1773 (74.62)	719 (77.15)	1054 (72.99)
Others	116 (4.88)	38 (4.08)	78 (5.40)
BMI, m/kg², n (%)
<18.5	196 (8.25)	52 (5.58)	144 (9.97)
18.5–23.9	1437 (60.48)	589 (63.20)	848 (58.73)
≥24	743 (31.27）	291 (31.22)	452 (31.30)
Education, n (%)
≤6	1547 (65.11)	493 (52.90)	1054 (72.99)
>6	829 (34.89)	439 (47.10)	390 (27.01)
Occupation, n (%)
Farmer	2169 (91.29)	835 (89.59)	1334 (92.38)
Other	207 (8.71)	97 (10.41)	110 (7.62)
Type 2 diabetes, yes, n (%)	90 (3.79)	44 (4.72)	46 (3.19)
Hyperlipidemia, yes, n (%)	1158 (48.74)	445 (47.75)	713 (49.38)
Hypertension, yes, n (%)	1091 (45.92)	406 (43.56)	685 (47.44)
Smoking, yes, n (%)	458 (19.28)	451 (48.39)	7 (0.48)
Alcohol consumption, yes, n (%)	813 (34.22)	532 (57.08)	281 (19.46)
ALT (U/L)	17.00 (14.00, 23.00)	19.00 (15.00, 25.00)	16.00 (13.00, 21.00)
AST (U/L)	21.00 (19.00, 25.00)	23.00 (19.25, 27.00)	21.00 (18.00, 25.00)
IBIL (μmol/L)	9.30 (7.50, 11.60)	9.90 (7.93, 12.90)	9.00 (7.30, 11.00)
DBIL (μmol/L)	3.50 (2.80, 4.60)	4.10 (3.20, 5.40)	3.30 (2.70, 4.10)
TBIL (μmol/L)	12.80 (10.50, 16.10)	14.00 (11.20,1 8.00)	12.30 (10.10, 14.90)
Energy (kcal/day)	1468.05 (1055.15, 2041.67)	1688.56 (1215.95, 2304.64)	1315.71 (966.03, 1855.00)
Dietary Cu (mg/day)	0.99 (0.65, 1.52)	1.07 (0.74, 1.65)	0.91 (0.59, 1.42)
Plasma Cu (mg/L)	0.92 (0.80, 1.04)	0.89 (0.77, 0.99)	0.94 (0.83, 1.06)

Abbreviations: BMI, body mass index; ALT, alanine transaminase; AST, aspartate transaminase; IBIL, indirect bilirubin; DBIL, direct bilirubin; TBIL, total bilirubin; Cu, copper.

3.2. Correlation Analysis between Liver Function Parameters and Trace Element Copper

The correlation between dietary Cu, plasma Cu, and liver function parameters was analyzed via Spearman’s correlation analysis (Figure 1). The results showed that dietary Cu was positively correlated with serum DBIL (r = 0.051, p < 0.05) and negatively correlated with serum AST (r= −0.048, p < 0.05). Plasma Cu was negatively correlated with serum IBIL, TBIL, and DBIL, with correlation coefficients (r) ranging from −0.15 to −0.0096 (all p < 0.001). In the gender-based stratified analysis, in the male population, plasma Cu was positively correlated with serum AST (r = 0.066, p < 0.05). In the female population, dietary Cu was negatively correlated with serum AST (r= −0.11, p < 0.001), while plasma Cu was positively correlated with serum AST (r = 0.078, p < 0.01) and negatively correlated with bilirubin, with

Table 2. Relationship between log₁₀-transformed dietary copper levels and ln-transformed liver function parameters

	Model 1		Model 2
	β (95% CI)	p	β (95% CI)	p
ALT
Total	0.05 (−0.02, 0.12)	0.139	−0.04 (−0.15, 0.07)	0.514
Men	0.07 (−0.05, 0.19)	0.234	−0.001 (−0.21, 0.21)	0.990
Women	−0.02 (−0.10, 0.06)	0.670	−0.04 (−0.17, 0.09)	0.520
AST
Total	−0.04 (−0.09, 0.01)	0.081	−0.12 (−0.19, −0.04)	0.002
Men	−0.002 (−0.09, 0.08)	0.966	−0.12 (−0.27, 0.04)	0.149
Women	−0.11 (−0.16, −0.06)	<0.001	−0.12 (−0.21, −0.04)	0.004
IBIL
Total	0.01 (−0.04, 0.07)	0.587	−0.06 (−0.15, 0.03)	0.172
Men	0.07 (−0.03, 0.17)	0.158	−0.03 (−0.21, 0.15)	0.754
Women	−0.06 (−0.12, 0.00)	0.051	−0.07 (−0.17, 0.03)	0.161
DBIL
Total	0.07 (0.01, 0.12)	0.024	−0.04 (−0.13, 0.06)	0.436
Men	−0.12 (−0.12, 0.09)	0.780	−0.11 (−0.30, 0.07)	0.221
Women	0.02 (−0.05, 0.09)	0.533	−0.01 (−0.12, 0.09)	0.806
TBIL
Total	0.03 (−0.03, 0.08)	0.319	−0.06 (−0.14, 0.03)	0.201
Men	0.04 (−0.05, 0.14)	0.396	−0.06 (−0.23, 0.12)	0.507
Women	−0.04 (−0.10, 0.02)	0.186	−0.06 (−0.16, 0.04)	0.263

Notes: Model 1 was crude model. Model 2 was adjusted for age and/or sex, ethnicity, BMI, culture, occupation, diabetes, hyperlipidemia, hypertension, smoking and alcohol consumption, and total energy intake.

3.4. Liver Function Parameters and Plasma Copper in Multiple Linear Regression Analysis

The results of the linear regression analysis of liver function parameters and plasma Cu are shown in Table 3. Plasma Cu was significantly associated with ALT (β = −0.20), IBIL (β = −0.41), DBIL (β = −0.53), and TBIL (β = −0.44) in all participants (all p < 0.05). After adjusting for covariates, the association of plasma Cu with bilirubin remained in the total population, while the association with ALT disappeared. In the gender-stratified analysis, plasma Cu was associated with IBIL in both men (β = −0.27) and women (β = −0.46) (all p < 0.05). In females, DBIL (β = −0.32) and TBIL (β = −0.42) were significantly associated with plasma Cu (all p < 0.05).

Table 3.Relationship between log₁₀-transformed plasma copper levels and ln-transformed liver function parameters

	Model 1			Model 2
	β (95% CI)	p		β (95% CI)	p
ALT
Total	−0.20 (−0.39, −0.01)	0.043	−0.13 (−0.32, 0.06)		0.181
Men	−0.25 (−0.55, 0.04)	0.091	−0.19 (−0.48, 0.10)		0.204
Women	0.078 (−0.17, 0.33)	0.539	−0.05 (−0.30, 0.20)		0.723
AST
Total	0.09 (−0.04, 0.22)	0.179	0.04 (−0.09, 0.17)		0.519
Men	0.21 (−0.01, 0.42)	0.061	0.02 (−0.20, 0.24)		0.857
Women	0.17 (0.02, 0.33)	0.032	0.05 (−0.11, 0.21)		0.534
IBIL
Total	−0.41 (−0.56, −0.26)	<0.001	−0.37 (−0.52, −0.22)		<0.001
Men	−0.24 (−0.48, 0.001)	0.051	−0.27 (−0.52, −0.02)		0.032
Women	−0.39 (−0.58, −0.20)	<0.001	−0.46 (−0.65, −0.27)		<0.001
DBIL
Total	−0.53 (−0.69, −0.36)	<0.001	−0.22 (−0.37, −0.06)		0.007
Men	−0.16 (−0.42, 0.10)	0.231	−0.13 (−0.38, 0.13)		0.323
Women	−0.49 (−0.70, −0.29)	<0.001	−0.32 (−0.52, −0.12)		0.002
TBIL
Total	−0.44 (−0.58, −0.29)	<0.001	−0.32 (−0.47, −0.17)		<0.001
Men	−0.20 (−0.44, 0.03)	0.091	−0.22 (−0.46, 0.02)		0.075
Women	−0.41 (−0.60, −0.23)	<0.001	−0.42 (−0.61, −0.23)		<0.001

3.5. Dose–Response Relationship between Liver Function Parameters and Dietary Copper

Dose–response relationships between liver function parameters and dietary Cu were analyzed using a restricted cubic spline (RCS) (Figure 2). In Figure 2, we have kept only the plots where the statistical analysis is meaningful. We found a negative linear relationship between dietary Cu and serum AST in the total population (all P_{overall association}<0.05, all P_{non-linearity} >0.05) (Figure 2a), with consistent results observed in females (all P_{overall association} <0.05, all P_{non -linearity}>0.05) (Figure 2c). In men, there was a nonlinear relationship between dietary Cu and serum AST, IBIL, DBIL, and TBIL (all P_{overall association} <0.05, all P_{non-linearity} <0.05) (Figure 2b). However, we did not find a relationship between dietary Cu and serum ALT.

3.6. Sensitivity Analysis

We excluded 813 participants with alcohol consumption habits, and the results after adjusting for covariates showed that a negative association between dietary Cu and serum AST remained in the general population (β = −0.09) and in the female population (β = −0.11) (all p < 0.05). A negative linear relationship between plasma Cu and serum IBIL (β = −0.32), DBIL (β = −0.19), and TBIL (β = −0.28) remained in all participants (all p < 0.05), and this relationship did not change significantly in women, as shown in Table 4.

Table 4. Sensitivity analysis of dietary copper, plasma copper, and liver function parameters

ALT

AST

IBIL

DBIL

TBIL

β (95% CI)

Dietary copper

Model 1

Total

−0.06 (−0.19, 0.08)

0.398

−0.09 (−0.17, −0.002)

0.044

−0.04 (−0.10, 0.02)

0.170

−0.01 (−0.07, 0.06)

0.876

−0.04 (−0.10, 0.02)

0.240

Men

0.06 (−0.12, 0.24)

0.504

0.03 (−0.09, 0.14)

0.656

−0.01 (−0.15, 0.13)

0.854

−0.15 (−0.30, −0.001)

0.049

−0.06 (−0.20, 0.08)

0.380

Women

−0.01 (−0.10, 0.08)

0.871

−0.10 (−0.16, −0.05)

<0.001

−0.06 (−0.13, 0.01)

0.080

0.02 (−0.06, 0.09)

0.689

−0.04 (−0.11, 0.02)

0.217

Model 2

Total

−0.06 (−0.19, 0.08)

0.398

−0.09 (−0.17, −0.002)

0.044

−0.05 (−0.15, 0.06)

0.383

−0.03 (−0.14, 0.08)

0.586

−0.05 (−0.15, 0.06)

0.388

Men

−0.15 (−0.48, 0.18)

0.361

−0.06 (−0.29, 0.16)

0.577

−0.02 (−0.29, 0.25)

0.892

−0.18 (−0.46, 0.10)

0.200

−0.08 (−0.34, 0.18)

0.548

Women

−0.04 (−0.18, 0.11)

0.610

−0.11 (−0.20, −0.01)

0.025

−0.05 (−0.17, 0.06)

0.345

0.004 (−0.12, 0.11)

0.947

−0.04 (−0.15, 0.07)

0.462

Plasma copper

Model 1

Total

−0.15 (−0.38, 0.08)

0.213

0.11 (−0.04, 0.26)

0.162

−0.35 (−0.52, −0.17)

<0.001

−0.51 (−0.70, −0.31)

<0.001

−0.38 (−0.56, −0.21)

<0.001

Men

−0.47 (−0.90, −0.04)

0.032

0.11 (−0.18, 0.40)

0.453

−0.19 (−0.53, 0.16)

0.288

−0.10 (−0.46, 0.28)

0.633

−0.13 (−0.47, 0.20)

0.440

Women

0.15 (−0.14, 0.43)

0.308

0.17 (−0.01, 0.35)

0.057

−0.35 (−0.56, −0.14)

0.001

−0.48 (−0.71, −0.25)

<0.001

−0.39 (−0.59, −0.18)

<0.001

Model 2

Total

−0.15 (−0.38, 0.09)

0.221

0.03 (−0.12, 0.18)

0.709

−0.32 (−0.50, −0.14)

0.001

−0.19 (−0.38, −0.003)

0.046

−0.28 (−0.46, −0.10)

0.002

Men

−0.39 (−0.82, 0.04)

0.075

−0.04 (−0.34, 0.25)

0.781

−0.09 (−0.44, 0.26)

0.607

−0.01 (−0.38, 0.35)

0.947

−0.05 (−0.39, 0.30)

0.785

Women

0.01 (−0.27, 0.30)

0.928

0.06 (−0.12, 0.23)

0.545

−0.41 (−0.63, −0.19)

<0.001

−0.29 (−0.52, −0.07)

0.012

−0.38 (−0.59, −0.16)

0.001

Notes: Model 1 was crude model. Model 2 was adjusted for age and/or sex, ethnicity, BMI, culture, occupation, diabetes, hyperlipidemia, hypertension, smoking, alcohol consumption, and total energy intake.

In this study, we observed a negative correlation between plasma Cu and serum bilirubin and a negative correlation between dietary Cu and serum AST after adjustment for covariates. The spline analysis further elucidated the negative linear relationship between dietary Cu and AST. We observed consistent results in sensitivity analyses.

Compared to the levels of plasma Cu reported in other research, the plasma Cu concentrations identified in the current study were close to those reported by Yang et al. [14] in rural residents of Southwest China (910.00 μg/L). Furthermore, the daily dietary Cu intake for both men and women was broadly in line with the recommended intake of the US Food and Nutrition Board (RDI:0.9 mg/d) [23]. These findings suggest that Cu intake in the local population is adequate.

Any damage to the liver will result in elevated ALT and AST in the blood, and elevated ALT found in the serum is considered to be the first sign of cellular and liver injury [24, 25]. In this study, dietary Cu was found to be negatively associated with AST. Copper is known to be an essential mineral element in the diet and has been shown to play a role in antioxidant defense, lipid peroxidation, and mitochondrial function [26]. Drinking water and several foods are typically high in copper, including meat, shellfish, seeds, legumes, and cereals. Poor diet quality or malabsorption may lead to health consequences associated with inadequate dietary Cu intake. Recent studies suggest that inadequate dietary Cu may be important for many common diseases, including obesity, ischemic heart disease, and metabolic syndrome [27]. Specifically, marginal dietary Cu deficiency (CuD) has been reported as a potential cause of diseases characterized by disorders of lipid metabolism, such as NAFLD. There is increasing evidence that Cu levels are low in patients with NAFLD and that liver and Cu intake levels are negatively associated with the severity of NAFLD [28]. Both human and animal studies suggest that reduced dietary Cu utilization or inadequate dietary Cu intake may play an important role in the pathogenesis of NAFLD. Copper-deficient diets can lead to hepatic steatosis in rats [10, 29]. It is thus clear that an adequate dietary Cu intake is an important factor in the protection of liver function, although excessive Cu intake is thought to have the ability to catalyze toxic reactive oxygen species that cause liver injury. However, one study found that rats exhibited only mild liver injury under Cu loading [30]. In a prospective study, healthy adults consumed higher levels of Cu supplementation over a sustained period of 60 days, but the results showed that all enzyme activities were below the corresponding clinical thresholds used to diagnose liver insufficiency, and participants showed no symptoms or positive findings on physical examination suggestive of liver disease [31]. The findings of these studies will help, to some extent, to alleviate concerns about liver injury from high dietary Cu intake. In addition, it is worth mentioning that we found a significant difference between dietary Cu and serum AST in women. This may be related to differences in hepatic gene expression patterns, substance metabolism, and liver enzyme sensitivity among genders [32].

Bilirubin, the end-product of heme catabolism in systemic circulation, has generated considerable interest over the last decade [33]. Compared to the levels reported by Curjuric, the levels of serum total bilirubin in the current study were higher [34]. In the present study, plasma Cu was negatively correlated with serum bilirubin. A 3.2% reduction in TBILI was found as an effect of increased plasma Cu 1-SD in a health cohort study of workers exposed to manganese [15]. Similarly, in a cross-sectional study on the relationship between serum bilirubin levels and essential trace elements in adults, plasma Cu levels were found to be negatively correlated with serum bilirubin [35]. It is suggested that plasma Cu may have a protective effect on liver function. This is consistent with our current findings. Plasma Cu is essential in the body, within a certain concentration range. Deficiency or excess of the trace element Cu in the body leads to various inflammatory changes and/or metabolic abnormalities, such as diabetes, dyslipidemia, and chronic liver injury [36]. One of the mechanisms of liver injury due to plasma copper deficiency is that Cu deficiency causes decreased antioxidant defenses and increased oxidative stress; on the other hand, hepatic iron overload is a common feature of copper deficiency in vivo, and hepatocytes are important cells for iron accumulation. Iron is a catalyst that converts hydrogen peroxide into hydroxyl radicals through the Haber–Weiss and Fenton reactions. Therefore, reactive oxygen species (ROS) produced by the mitochondrial respiratory chain may be further increased by iron overload, which leads to hepatocyte death/liver injury [37]. Although Cu is an essential trace element, excessive amounts may still be harmful to health. Excessive Cu exposure was found to be associated with elevated liver enzyme levels in rat and chicken model studies [38, 39]. In the present study, we did not find a significant association between plasma Cu and ALT and AST. The underlying mechanism of liver injury caused by plasma copper exposure is unclear. Inflammation may be a bridge to explain the relationship between excessive Cu exposure and liver injury in the study population. One study found that the inflammatory biomarker C-reactive protein (CRP) was associated with serum copper [40] and that inflammation is one of the mechanisms that induce acute hepatotoxicity [41]. Therefore, further studies are needed to assess the relationship between plasma Cu levels and liver injury in the population.

Alcohol can also adversely affect liver function, with 90% of ingested alcohol being metabolized and broken down in the liver. Chronic alcoholics often exhibit associated malnutrition, and both ethanol and malnutrition can have profound effects on Cu metabolism [42]. Excessive alcohol drinkers often exhibit a severe Cu deficiency [43], while another study found that Cu levels were elevated or unchanged in patients with alcoholic liver disease (ALD) compared to healthy controls [44]. Impaired intestinal barrier function and oxidative stress damage have long been thought to play a key role in the pathogenesis of ALD, with alcohol leading to liver damage and the disruption of intestinal barrier function, while dietary copper supplementation can reverse these changes, but small amounts of dietary copper can exacerbate them [45]. Thus, copper homeostasis is critical in maintaining intestinal barrier integrity and plays an important role in the prevention of ALD. In the present study, sensitivity analysis of non-drinkers yielded consistent results that a negative correlation between dietary copper and serum AST and between plasma copper and serum bilirubin remained. This finding suggests that the potential association between micronutrient Cu and liver function parameters is not influenced by drinking status. The interaction between trace element Cu and alcohol consumption in the liver needs to be further investigated. In the meantime, relevant public health education is recommended to remind people to reduce their alcohol consumption, especially for heavy drinkers.

The main strength of this study is to demonstrate the effect of Cu on serum liver function biomarkers by considering dietary and blood Cu exposure. Secondly, we used RCS to investigate the dose–response relationship between dietary Cu and serum liver function parameters. Our current study also has some limitations. First, this cross-sectional study was unable to assess the causal relationship between Cu intake and plasma Cu and liver function parameters. Second, the parameters in our research only included serum bilirubin, AST, and ALT. It is necessary to take more liver function parameters into account in future studies, such as alkaline phosphatase, serum total protein, serum albumin, and serum globulin. Third, the effects of other non-persistent and persistent environmental pollutants on liver injury were not considered. Further studies are needed to determine the relationship between Cu intake, plasma Cu, and markers of liver function and the mechanisms underlying this relationship.

Plasma Cu was negatively associated with serum bilirubin levels, and dietary Cu was negatively associated with serum AST levels; this association was gender-specific. Further studies are needed to validate these associations and reveal the underlying mechanisms.

Author Contributions

Writing—original draft preparation, Writing—review and editing, Y.W. and Z.Z.; Investigation, Data curation, M.X. and X.M.; Methodology, Formal analysis, Q.L., J.Z. and S.L., Software, Validation, Y.L.; Data curation, S.H.; Conceptualization, Resources, J.Q.; Investigation, Data Supervision, Project administration, J.C. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by the National Natural Science Foundation of China (Grant Nos. 81960583, 81760577, and 81560523), Major Science and Technology Projects in Guangxi (GKAA22399), the Guangxi Science and Technology Development Project (Grant Nos. AD 17129003 and 18050005), the Guangxi Natural Science Foundation for Innovation Research Team (2019GXNSFGA245002), and the Guangxi Scholarship Fund of Guangxi Education Department of China.

Institutional Review Board Statement

Approval was obtained from the ethics committee of Guilin Medical University (No: 20180702-3). The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

Informed Consent Statement

Written informed consent files were received from all participants before the study.

Acknowledgments

The authors would like to express their gratitude to all the participants and cooperating units who helped in completing this study.

Conflicts of Interest

The authors declare no conflicts of interest.

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Associations between Dietary and Plasma Copper and Liver Function in a Chinese Population

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