Association Between Chromium and Lead Exposure and Liver Injury Biomarkers Among Adults in Addis Ababa,Ethiopia

Abstract

The objective of this study was to investigate the association between chromium (Cr) and lead (Pb) exposure and liver injury biomarkers in the adult population of Addis Ababa, Ethiopia. The study used cross-sectional design and collected urine and blood samples from 417 adult participants. Chromium (Cr) and Lead (Pb) levels in the urine were analyzed using Microwave Plasma-Atomic Emission Spectroscopy (MP-AES) while aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP) in the serum were analyzed using COBAS 6000 automated clinical chemistry analyzer. The association between liver injury biomarkers and exposure to heavy metals was anlysed using multivariable binary logistic regression model. The findings indicate that Cr levels ranged from 12.8 to 476.7 µg/g creatinine (25th-95th percentile), while Pb levels varied from 9.5 to 80.4 µg/g creatinine (25th-95th percentile). Elevated levels of ALP, ALT and AST above the reference range were observed in 14.9%, 4.6% and 8.9% of the study participants, respectively. In addition, elevated level of AST showed a significant positive association with Cr exposure in the third and fourth quartiles showing higher odds ratio (of 5.12 and 8.83 respectively. Similarly, the elevated ALT levels were significantly associated with Pb in the third and fourth quartile (P < .05). However, there was no significant association between elevated levels of ALP and heavy metal exposure. This study observed a strong association between Cr and AST as well as between Pb and ALT. These findings highlight the potential health risks associated with heavy metal exposure and suggest the need for further investigation into the public health impacts and clinical implications of heavy metal risks in Addis Ababa city.

Keywords

heavy metals liver biomarkers liver injury tests Addis Ababa residents

Introduction

Heavy metals represent a group of naturally occurring metals and metalloids with specific gravities 5 times that of water.¹ Heavy metals such as arsenic (As), cadmium (Cd), Cr, (Pb) and mercury (Hg) are widely distributed in the environment.^2,3 Various sources of pollution are influenced by human activities such as metallurgy, electroplating, tanning industry, mining operations, leaching processes, and smelting release large volume of waste containing toxic heavy metals into the environment.^3
-10 Agricultural activities also contribute to heavy metal pollution through the use of agrochemicals such as fertilizers and pesticides.^11,12 Additionally, improper handling and disposal of batteries, electronic wastes and other metal – containing wastes significantly contribute to heavy metal pollution in the environment.¹³

Nowadays, heavy metal pollution of water bodies and soil has become a major global concern because they are non-biodegradable, bio-accumulative, persist in the environment for several decades and have long biological half-life.^3,14,15 Some metals such as iron (Fe), manganese (Mn) and zinc (Zn) are essential for biological functioning in small amount.¹⁶ On the other hand, other metals including Pb, Cd, Cr, Ni, Hg are non-essential and toxic to living organisms.⁶ Exposure to these metals can cause both acute and chronic health problems in human and animals.^3,14 Heavy metals accumulation in human tissues occurs through metabolized deposition leading to toxicity. This toxicity can disrupt metabolic processes, deactivate enzymes, weaken defense mechanisms, oxidative stress and damage to the kidneys, liver, brain and other organs^11,17,18 Pb primarily damage – nervous system, leading to neurological problems in adults, as well as development delays and cognitive impairment in children^19,20 impaired physiological functions and oxidative stress from excessive ATP synthesis.^21,22 It also leads to hematological effects due to enzyme inhibition and disrupts calcium-dependent processes.²² Furthermore, Pb exposure is associated with the pathophysiology of multiple sclerosis, as it interferes with heme synthesis in myelin, potentially triggering autoimmune reactions.²³ A study on Pb poisoning in Nigeria revealed that 40% of affected children experience neurological disorders due to contact with Pb-based paints and batteries, leading to anemia, with nearly 400 deaths annually.^22,24 Additionally, research conducted in cattle demonstrated that reactive oxygen species generated by Pb-induced oxidative stress damage cells and cause apoptosis, impairing brain growth and function.²⁵

Cr can exist in the environment in different oxidative states ranging from −2 to +6. Trivalent (Cr(III)) and hexavalent chromium (Cr(VI)) are the most stable ones. Cr(III) is an essential micronutrient that support metabolism of energy, glucose, lipids and maintains the integrity of nucleic acid structure.^26
-28 However, Cr(VI) is toxic, recognized as carcinogenic and is associated with adverse health effects on the human body.^17,29 Cr(III) is poorly absorbed (0.5%) in the body, while Cr(VI) has strong oxidizing properties and can bind to oxygen to form chromate and dichromate.¹⁸ Cr(VI) easily penetrates cell membranes and is reduced to lower oxidation state,³⁰ which initiates oxidative stress and damages protein and genetic material.³¹ Additionally, Cr(VI) is identified to induce respiratory diseases such as asthma, bronchitis, pneumonitis, lung cancer and hepatotoxic causing parenchymatous degeneration, steatosis of hepatocytes and necrosis.^32
-35 The liver is an essential organ that detoxifies various chemicals including heavy metals that enter to the body. In the detoxification process, liver cells are exposed to different toxic chemicals potentially leading to liver cell damage, dysfunction and organ failure.^36
-38 Liver disease associated with heavy metal exposure includes cirrhosis, liver cancer and fatty liver disease.³⁹ When hepatocytes are damaged, they release enzymes such as – ALT, AST and gamma-glutamyl transferase GCT in the blood.^40,41 These enzymes are commonly used as biomarkers to study the effect of heavy metals on liver.^42,43 Various researchers have studied the associations between heavy metal exposure and liver injury using these biomarkers.⁴⁴ Different studies^44,45 reported significantly increased AST and ALT levels in mice exposed to Pb and Cd. In addition, elevated AST and ALT levels were found in animals exposed to Cr⁴⁶ and Pb.^47,48 Recent epidemiological studies in humans have shown an association between heavy metal exposure and liver injury biomarkers. As Kim et al⁴³ suggested, they found a correlation between exposure to Pb and mercury (Hg) and levels of AST), ALT), and GGT in a national study of the Korean adult population. As Zhao et al⁴² reported, a strong association between Cr and total phosphorus (TP), as well as between Cd and AST levels among residents of Northern China. A follow-up study of 305 participants exposed to Cr in their occupations revealed an associated increase in total bilirubin, AST and ALT levels.⁴⁹ Concerning the health effects of heavy metals, different studies witnessed the exposure of human beings with different types these metals.^50
-52 Other studies tried to investigate the association of some heavy metal with health with cancer,^53
-55 kidney disease^56
-58 and hematological effects.^59
-61 There are few studies that examine the association between heavy metals exposure and liver injury biomarkers in some countries.^43,62
-64 In Ethiopia, many studies have focused on environmental contamination where,^65
-67 assessed the level of heavy metals from surface water including different rivers. Other studies found higher concentration from vegetable and soil samples.^68,69 Exposure assessment various studies have was conducted in the central rift valley region as well as among residents and garage workers in Addis Ababa.^70
-72 While these studies provide valuable information regarding the levels of heavy metals and their potential toxic effects, they do not show the individual variation in toxicokinetic and subsequent health effects. People with similar environmental exposure levels may show variations in biological responses due to differences in socioeconomic factors.⁷³ The integration of biomarker analysis with exposure assessment is crucial for understanding the link between exposure levels of these 2 metals and physiological responses in the body.^74,75 Evidence concerning health implications of heavy metal exposure in Addis Ababa city is very essential for developing effective public health intervention programs and safety regulation. The aim of this study was to investigate the toxicological risks associated with Cr and Pb exposure among the residence of Addis Ababa city by using liver injury biomarkers. The study focused on Cr and Pb only because these 2 metals are the potential environmental pollutants in Addis Ababa. They abundantly found in drinking water,⁷⁶ vegetables⁷⁷ and human urine and blood.^71,78 The specific objectives were to: (1) determine the concentration of Cr and Pb in the urine of the residents; (2) measure liver injury biomarkers mainly serum ALT, AST and ALP levels of the residents; (3) evaluate the relationship between exposure to Cr and Pb, and the levels of liver injury biomarkers and (4) identify potential demographic, occupational and substance use factors that may contribute to elevated heavy metal exposure. The results provide valuable evidence to help develop effective public health strategies and interventions programs aimed at reducing exposure risks and improving the overall well-being of affected community.

Material and Methods

Description of the Study Area

This study was conducted in Addis Ababa city, located on the western edge of the Rift Valley escarpment at latitude 9°2′N and longitude 36°45′E. The city sits at the foot of the Entoto range, which has an altitude of 2900 m and slopes down to 2300 m in the southern periphery toward the Akaki Plains. Addis Ababa is home to over 2000 industries, accounting for 65% of all industries in the country.⁷⁹ Most of these industries, including tanneries, textiles, woodworks, pharmaceuticals, paint, rubber, plastic and metal producers, are located along the riverbanks, primarily in the western and southern parts of the city⁸⁰ Alarmingly, the majority of these industries (90%) lack any form of treatment plant and discharge their untreated solid, liquid and gaseous wastes directly into the environment.⁷⁹ Besides, Addis Ababa produces around 0.45 kg per capita per day solid waste and about 30% of the waste is scattered into the streets, ditches and rivers.⁸¹ Even waste collected by the municipality is dumped into an open landfill, which is located in the center of Addis Ababa.⁸² Such uncontrolled waste disposal practices have led to the contamination of air, surface water, soil, sediment and vegetables. As reported previously,^80,83
-85 on heavy metals in rivers, the surrounding soil, the sediment and vegetables grown around the Akaki rivers had elevated levels of Cd, As, Hg, Pb, Cr in water; As, Cr, Ni, Zn, in soil; As, Cr, Ni, Pb in sediment and As, Pb and Zn in vegetables. Open burning of trash, emissions from landfills and vehicles exhaust pollute the ambient air.⁸⁶ Specifically, the air quality of Addis Ababa is significantly deteriorating due to poor efficiency of the engines of old vehicles.⁸⁷ In addition, about 75% of household energy needs are fulfilled by using kerosene, wood, wood charcoal and cow dung, which contribute to air pollution largely.

Study Design

The study employed a cross-sectional design involving a sample of 417 households selected from 17 districts across 11 sub-cities. It focused on men and women aged 18 years and older who had lived in the city for at least 6 months.

A systematic random sampling method was employed Figure 1, excluding individuals under 18 and those who were severely ill. The research was conducted from May 2022 to June 2023.

Figure 1.

Sampling districts.

Data and Specimen Collection

Demographic characteristics and personal habits of the participants were collected through a face-to-face interview. Demographic data gathered included age, sex, height, weight, years of residence in the city, educational background, occupation and monthly income. In addition, personal habits such as cigarette smoking, alcohol consumption and khat chewing were recorded. For this study, urine was used as a biomarker for detecting Pb and Cr. Measurement of Cr in urine indicates total Cr exposure,⁸⁸ while urinary Pb serve as an alternative method to blood Pb and reflecting filterable Pb diffused from plasma and excreted through the kidneys.⁸⁹ This method is non-invasive and reduces the need for repetitive blood draws from individuals.⁹⁰ However, measuring Pb in urine has limitations as it can vary due to urine dilution.⁶² To address the impact of dilution, a creatinine adjustment was applied. Among several heavy metals Pb and Cr were prioritized for this study because Cr and Pb are primary pollutants found in the environment specifically in Akaki river, industrial wastes, vegetables and soil.^77,91,92 Additionally human exposure assessment results indicated that Pb and Cr levels were higher in the clinical samples of Addis Ababa residents.^71,78,93 Spot urine samples were collected from 417 study participants using sealed plastic cups that had been pre-rinsed with nitric acid and distilled water. The collected samples were stored in a cold box and transported to the Ethiopian Public Health Institute (EPHI). Samples were refrigerated at temperature of −20°C until the analysis was performed. Moreover, trained phlebotomist collected 6 ml of whole venous blood from each participants using serum-separating tubes for clinical chemistry tests. The collected samples were stored in a cold box and transported to EPHI, where the serum was separated using a centrifuge immediately upon arrival in the laboratory. Subsequently, the serum was stored at −80°C until analysis.

Sample Preparation and Analysis

The urine samples were prepared for heavy metal analysis using the wet digestion methods as described by.⁹⁴ The concentration of Pb and Cr in the urine samples were measured using Microwave Plasma-Atomic Emission Spectroscopy (Agilent 4210 MP-AES, USA) at the Food and Drug Authority laboratory. Urinary creatinine levels were assessed in the Clinical Chemistry Department at EPHI following the enzymatic method.⁹⁵ In addition, liver injury biomarkers, including AST, ALT and ALP were analyzed in the serum samples using COBAS 6000 automated clinical chemistry analyzer.

Covariates

Potential covariates were chosen based on previous similar epidemiological studies and considered in the model.⁶² Age was categorized into 4 groups: 18 to 27, 28 to 34, 35 to 43 and 44 to 95 years. Sex was also classified as male or female. Education level were categorized as no formal schooling, primary, secondary and college or above. Alcohol consumption was recorded as either yes or no. Body mass index (BMI) was calculated by dividing weight in kilograms by height squared in meters (kg/m²) and was classified into 3 groups based on WHO recommendations: underweight (less than 18.5), normal (18.5-24.9) and overweight (⩾25). The number of years residents have lived in Addis Ababa city was grouped into categories: less than 1 year, 1 to 5 years, 5 to 10 years and more than 10 years. Occupation was classified into the following categories: students and housewives, industry workers, solid waste collectors and street vendors and other.

Statistical Analysis

Descriptive statistics, including median values and proportions, were employed to evaluate the demographic characteristics of the participants. The distribution of urinary heavy metals (Pb and Cr) were adjusted for creatinine and liver injury biomarkers (AST, ALT and ALP) was were presented as median values, interquartile ranges and 95th percentiles. The association between liver injury biomarkers and demographic characteristics was analyzed using chi-square test. In this study, the outcome variables were AST, ALT and ALP. The level of Cr and Pb exposure (μg/g creatinine) in the urine sample were categorized into quartiles. A binary logistic regression analysis was conducted to examine the association between individual urinary metal concentrations and liver injury biomarkers. In this model, the second, third and fourth quartiles of heavy metals concentrations were compared against the first quartile. The odds ratio (OR) with 95% confidence interval (CL) was calculated for model 1 (unadjusted), model 2 (adjusted for age in quartiles and BMI as categorical variables) as well as model 3 (model 2 plus education, occupation and year of residence all as categorical variables. All statistical analysis were performed using IBM SPSS statistical software version 20.

Result and Discussion

Demographic Characteristics

The overall demographic characteristics of the participants indicated that approximately 67.6% were women and the remaining were men with age level 18 to 95 years old (Table 1). The largest proportion consisted of students or individuals with unspecified occupation were 45.1% while 36.2% were working at home. The distribution of BMI showed that 9.6% of the participants were underweight (BMI < 18.5 kg/m²) while the majority had normal range of BMI (18.5-24.9 kg/m²). The remaining 14.8% were overweight (BMI > 25 kg/m²). Most participants had attended formal education, ranging from primary to college. Only 16% of those living in the city had not received any formal education. The participants’ substance use behaviors were low, only 17% consuming alcohol, 1.7% chewing khat and 3.3% smokings. The majority of participants (38.1%) have lived more than 10 years in the city and only 18.7% lived for less than 1 year.

Table 1.

Demographic Characteristics of the Study Participants.

Demographic parameters	N	%
Sex
Male	135	32.4
Female	282	67.6
Age (year) quartiles
18-26	108	25.9
27-33	107	25.7
34-43	102	25.4
44-95	100	24
BMI (kg/m²)
<18.5	40	9.6
18.5-24.9	315	75.5
⩾25	62	14.9
Education
No formal schooling	66	15.8
Primary school	134	32.1
Secondary school	129	30.9
College and above	88	21.1
Alcohol drinking
Yes	71	17
No	346	83
Smoking
Yes	12	2.9
No	405	97.1
Khat chewing
Yes	14	3.4
No	403	96.6
Occupation
Student & other	188	45.1
Work at home	151	36.2
Construction	33	7.9
Solid waste & street vendors	27	6.5
Industry	18	4.3
Residence (year)
<1	78	18.7
1-5	122	29.3
5.1-10	58	13.9
>10	159	38.1

Distribution of Heavy Metals and Liver Injury Biomarkers

The concentrations of creatinine-adjusted urinary Cr and Pb found in this study along with liver injury biomarkers are shown in Table 2. The concentration of Cr ranged from 12.8 µg/g creatinine at the 25th percentile to 476.7 µg/g creatinine at the 95th percentile. Likewise, Pb ranged from 9.5 µg/g creatinine in the 25th percentile to 80.4 µg/g creatinine in the 95th percentile. The exposure of Adddis Ababa residents may be from environment such as air, water, food and occupation. For instance, the Akaki River which crosses Addis Ababa city is highly contaminated by heavy metals⁷¹ because it receives large volume of waste produced from industries, commercial and residential areas.⁹⁶ The soils bordering the Akaki river and vegetables produced also demonstrated high concentration of heavy metals.^96,97 Study performed by Endale et al⁹⁸ have got heavy metal contamination in 50% tap water of drinking purposes. Liver injury biomarkers showed the highest value at 95th percentile. For example, the level of AST at the 95th was 47.0 IU/L, exceeding the normal reference range of 0 to 32 IU/L. In addition, ALP levels varied from 62.5 to 132 IU/L across the different percentiles, with most values exceeding the normal reference range of 0 to 105 IU/L. Increased levels of liver injury biomarkers especially ALT and AST may indicate liver damage or stress. The correlation between elevated levels of liver enzyme activity with higher concentrations of heavy metals raises concerns about the effects of environmental toxins on liver function.

Table 2.

Percentile Distribution of Creatinine Adjusted Heavy Metals and Liver Injury Biomarkers.

Parameters	25th	50th	75th	95th
Cr (μg/g creatinine)	12.8	55.5	126.4	476.7
Pb (μg/g creatinine)	9.5	19.5	38.8	80.4
AST (IU/L)	15.9	18.1	22.1	47.0
ALT (IU/L)	9.9	12.6	16.7	31.9
ALP (IU/L)	62.5	75.0	95.0	132.0

Demographic Characteristics and Abnormal Liver Enzyme Levels

Effect of Sex, Age and BMI on Liver Enzyme Profile

The analysis of serum levels of AST, ALT and ALP across various socio-demographic categories shows significant differences. As shown in Table 3, the percentage of study participants with elevated levels were 8.9% for AST, 4.6% for ALT and 14.9% for ALP. The proportion of participants with normal and elevated levels of ALT, AST and ALP did not significantly vary between male and female participants (ALT, P = .35; AST, P = .99; ALP, P = .98. This shows a uniform distribution of liver health among sex differences. In contrast, significant difference were observed among BMI groups. A significant proportion of underweight individuals (22.5% for BMI < 18.5 kg/m²) and overweight individuals (22.6% for BMI > 22.5 kg/m²) showed elevated ALP levels. The analysis in age groups among the study participants did not show a significant difference in the levels of ALT (P = .77) and ALP (P = .99). However, AST levels were significantly associated with age group variation (P < .05 = .04). Elevated AST levels were found in participants aged group 34-43 and those over 43 years while ALP levels (>105) remained consistent across all age groups. This indicates that change in age may not significantly affect ALP levels.

Table 3.

Association of Sex, BMI and Age with Liver Injury Biomarkers (ALT, AST and ALP).

Demographic variables	Total no of participant	ALT			AST			ALP
		⩽33	>33	P-value	⩽32	>32	P-value	⩽105	>105	P-value
		(%)	(%)	P-value	(%)	(%)	P-value	(%)	(%)	P-value
Sex
Female	282	96.1	3.9		91.1	8.9		85.1	14.9
Male	135	94.1	5.9	.35	91.1	8.9	.99	85.2	14.8	.98
BMI (kg/m²)
<18.5	40	95.7	2.5		92.5	7.5		77.5	22.5
18.5-24.9	315	96.2	3.8		92.4	7.4		87.6	12.4
⩾25	62	90.9	9.7	.1	83.9	16.1	.09	77.4	22.6	.04
Age( years)
⩽26	108	97.2	2.8		94.5	4.5		84.3	15.7
27-33	107	94.4	5.6		93.5	6.5		86	14
34-43	102	95.1	4.9		90.2	9.8		85.3	14.7
>43	100	95	5.0	.77	85	15	.04	85	15	.99

Statistically significance P-value (< 0.05) are highlighted in bold.

Impact of Education, Occupation and Long-Term Residence on Liver Enzyme Profile

The effects of participant’s educational level, occupation type and duration of residence on liver injury biomarkers are presented in Table 4. Education status of participants was not significantly associated with serum levels of ALT (P = .81) and ALP (P = .84). However, participants without formal education showed the highest proportion of elevated serum AST levels compared to those with formal education (P < .05). In addition, participants from different occupational groups showed significant differences in the elevated levels of ALT and AST (P < .05). The highest percentage of participants with elevated ALT and AST levels above the reference range were observed among those working in waste collection and street vending. This aligns with the findings of a recent study⁹⁹ from Nigeria that investigated toxic-inflammatory and hepato-renal biomarkers in workers involved in waste picking. The study found that elevated levels of AST, ALT and ALP were associated with over 5 years of experience in waste picking. Solid waste contains various substances that can pose health risks to waste handlers, including volatile organic compounds, heavy metals, radioactive substances, microbial organisms and dust.¹⁰⁰ These findings suggest that occupational exposure to environmental toxins may adversely affect liver health. In contrast, smaller proportion of housewives and students (2.7%) had elevated ALT levels.

Table 4.

Association of Occupation, Education Level and Years of Residence with Liver Enzymes.

Demographic variables			ALT			AST			ALP
Demographic variables	No of respondants	⩽33 (%)	>33 (%)	P-value	⩽32 (%)	>32 (%)	P-value	⩽105 (%)	>105 (%)	P-value
Education
No education	66	97	3		81.8	18.2		86.4	13.6
Primary school	134	94.8	5.2		91.8	8.2		82.2	17.2
Secondary school	129	94.6	5.4			6.2		86.0	14.0
College/ university	88	96.6	3.4	.81	93.2	6.8	.03	86.4	13.6	.84
Occupation
Students & housewives	185	97.3	2.7		91.9	8.1		84.9	15.1
Industry	51	92.2	7.8		84.3	15.7		84.3	15.7
Solid waste & street workers	27	85.2	14.8		77.8	22.2		77.8	22.2
Other	154	96.1	3.9	.02	94.8	5.2	.01	87.0	13.0	.66
Residence year
>0.5	22	90.9	9.1		95.5	4.5		72.7	27.3
0.6-2	59	89.8	10.2		91.5	8.5		84.7	15.3
2.1-5	121	99.2	0.8		96.7	3.3		88.4	11.6
5.1-10	58	96.6	3.4		96.6	3.4		86.2	13.8
>10	156	94.9	5.1	.05	84.1	15.9	.02	84.1	15.9	.43

Statistically significance P-value (<0.05) are highlighted in bold.

Effect of Substance Use Behavior on Liver Enzyme Profile

Table 5 shows the association between liver injury biomarkers and substance use behavior specifically khat chewers, smoking and alcohol consumptions. ALT levels do no differ significantly between khat chewers and non-chewers (P = .64). Both groups have high proportions of participants with ALT levels ⩽ 33 (95.5% for non-chewers and 92.9% for chewers). On the other hand, khat chewers showed a higher percentage of elevated AST levels (14.3%) compared to non-chewers (8.7%); however this difference is not statistically significant (P = .47). Unlike the other 2 biomarkers, ALP level showed a significant variation, with 37% of khat chewers showing elevated levels compared to 14.1% of non-chewers (P < .05). Additionally, smoking habit significantly affect AST levels (P ⩽ .05), as smokers had a higher proportion of elevated AST levels (25.0%) compared to non-smokers (8.4%). This finding reveals that smoking may contribute to liver enzyme elevation, potentially linked to the harmful effects of tobacco on liver health. In contrast, smoking status did not show significant differences in both ALT levels (P = .52) and ALP levels (P = .86), with both smoking and nonsmoking groups having similar proportions. Moreover, participants who consumed alcohol did not show significant differences in ALT (P = .43) and AST (P = .12). Both alcohol drinkers and non-drinkers had ALT levels of 33 or lower and their AST followed similar trends. However, 11.1% of alcohol consumers had elevated ALP levels compared to 15.7% of non- consumers (P = .32), indicating that alcohol consumption may not be directly linked to ALP levels.

Table 5.

Association of substance use behavior with liver enzymes.

Demographic variables			ALT			AST			ALP	P-value
Demographic variables	No of respondants	⩽33 (%)	>33 (%)	P-value	⩽32 (%)	>32 (%)	P-value	⩽105 (%)	>105 (%)	P-value
Chewing khat
Yes	14	92.9	7.1		85.7	14.3		64.3	35.7
No	403	95.5	4.5	.64	91.3	8.7	.47	85.9	14.1	.02
Smoking
Yes	12	91.7	8.3		75	25		83.3	16.7
No		95.6	4.4	.52	91.6	8.4	.04	85.2	14.8	.86
Alcohols
Yes	72	97.2	2.8		95.8	4.2		88.9	11.1
No	345	95.1	4.9	.43	90.1	99	.12	84.3	15.7	.32

This finding show significant association between demographic characteristics and liver enzyme levels; however, several limitations must be acknowledged. The cross-sectional design of the study prevents from establishing causality. The observed associations may be influenced by underlying factors not included in the analysis and cannot rule out the possibility of reverse causality. In addition, the regression models did not account for several important confounders including chronic liver disease, dietary habits and medication use that can significantly impact liver enzyme levels. These unmeasured confounders can affect the observed associations and their absence may limit the interpretation of the results. Future studies should include a wider array of clinical factors and dietary habits to more accurately identify the determinants of liver injury biomarkers. Moreover, longitudinal studies incorporating a broader array of clinical, lifestyle and environmental factors are essential for a more accurate evaluation of causality and the direction of associations. These studies would provide valuable insights into the temporal dynamics and potential causal pathways linked to liver injury biomarkers

Multivariable Analysis of Associations Between AST/Cr and ALT/Pb

The association between individual heavy metals specifically Cr and Pb and liver injury biomarkers were analyzed through binary logistic regression model, as shown in Table 6. The findings from the crude models indicated a significant association between urinary Cr and serum AST in the third and fourth quartiles compared to reference group (P < .05). An odds ratio (Exp (B)) of 6.58 (P < .05) for the third quartile indicates that individuals in this category had over six fold elevated AST levels compared to those in the reference group. Even the odds ratio (9.97) was higher in the fourth quartiles, indicating a tenfold increased likelihood of elevated AST levels compared to the reference group. After adjusting for age and body mass index (BMI), the odds ratios of elevated AST levels remained significantly higher in the third and fourth quartiles, with values of 6.22 (P ⩽ 05) and 8.79 (P < .05), respectively. Moreover, there was a significant association with urinary Cr levels, with an odds ratio of 5.12 (P = .04) for the third quartile and 8.83 (P = .01) for the fourth quartiles in the fully adjusted model. These findings revealed that increased Cr exposure was strongly associated with increased AST levels, which reflect liver injury. However, no significant association between Cr and ALT was found in both the crude and adjusted models. . Different researchers have also confirmed the association between elevated serum AST levels and chromium. For examples, a study⁴⁹ involved 305 occupationally exposed workers in China reported increased ALT and AST levels in relation to a unit increase in Cr concentration. In addition, another study¹⁰¹ showed a positive dose-response relationship between Cr levels and ALT. Furthermore, animal experiments have shown that exposure to chromium⁴⁶ and lead¹⁰² has resulted in increased levels of AST and ALT in the serum.

Table 6.

Quartile-dependent Associations of Liver Injury Biomarkers with Cr and Pb (Creatinine Adjusted).

Types of enzymes	Metals (μg/g creatinine)	Quartile	Model 1	Model 2	Model 3
Types of enzymes	Metals (μg/g creatinine)	Quartile	Exp(B) (95% CL), P-value	Exp(B) (95% CL), P-value	Exp(B) (95% CL), P-value
AST (IU/L)	Cr	Q1(Reference)	-	-	-
		Q2	3.64 (0.74-17.97), P = .11	3.72 (0.74-18.55), P = .11	2.77 (0.53-14.56), P = .23
		Q3	6.58 (1.44-30.18), P = .02	6.22 (1.33-29.08), P = .02	5.12 (1.04-25.19), P = .04
		Q4	9.97 (2.24-44.34), P = .00	8.79 (1.93-39.94), P = .01	8.83 (1.85-42.23), P = .01
ALT (IU/L)	Pb	Q1(Reference)	-	-	-
		Q2	.97 (0.06-15.74), P = .98	1.01 (0.06-16.56), P = .99	0.83 (0.04-16.52), P = .9
		Q3	9.66 (1.20-77.72), P = .03	10.90 (1.32-89.82), P = .03	9.72 (0.99-94.7), P = .05
		Q4	9.56 (1.19-76.90), P = .03	9.59 (1.15-79.89), P = .04	9.81 (1.03-92.93), P = .04

Model 1 = unadjusted, Model 2 = adjusted for age (quartiles) + BMI (categorical), Model 3 = Model 2 + education status (categorical) + occupation (categorical) + year of residence.

Statisitcally significance P-value (<0.05) are highlighed in bold.

In additional, a significant association was found between Pb level and ALT in the third and fourth quartiles when compared to the first quartile, in both the unadjusted and adjusted models. In the fully adjusted model, the odds ratios were 9.72 (P = .05) for the third quartile and 9.81 (P ⩽ .05) for the fourth quartile. Individuals exposed to higher levels of Pb are more likely to have ALT values exceeding 22 IU/L compared to those with lower ALT values. This finding aligns with the work of different researchers, including those who reported an association between Pb exposure and ALT levels in a national cohort study involving 4582 American adults.¹⁰³ Similarly, a cohort study conducted in Wuhan and Zhuhai cities from 2011 to 2012 found an association between Pb and ALT levels.¹⁰¹ As Betanzos-Robledo et al¹⁰⁴ demonstrated that the highest quartile of blood Pb levels was associated with the highest serum ALT levels in a cohort study examining childhood exposure in Mexico. In the United States, data extracted from the National Health and Nutrition Examination Survey (NHANES) covering the years 2005 to 2018 indicated that participants with abnormal levels of GGT and ALT had higher concentrations of Pb.¹⁰⁵ Conversely, a study³⁶ conducted in Northern China reported divergent results, arguing that there is no significant association between urinary Pb levels and ALT based on a cross-sectional study.

The increases in ALT and AST levels indicate damage to the plasma membrane, mitochondrial dysfunction and disturbances in intracellular calcium homeostasis.¹⁰⁶ The liver is the primary organ responsible for chemical metabolism and detoxification, processes that can result in cellular damage and hepatotoxicity. ALT is predominantly found in the cytoplasm and is more abundant in the liver than in other tissues.^107,108 When the plasma membrane is damaged, ALT can leak into bloodstream, resulting in elevated concentrations.^109,110 In contrast, AST is a mitochondrial enzyme primarily expressed in the mitochondrial regions of the liver and in blood cells.¹¹¹ Exposure to heavy metals inhibits mitochondrial enzyme activity, that promotes mitochondrial permeability and consequently leading to the release of AST.¹¹² However, AST is not specific to the liver; it is also present in the heart, skeletal muscles, kidneys and other organs, which means that its elevation can reflect damage in those areas as well. Nonetheless, high levels of AST are indicative of liver damage and can be utilized for diagnosis of hepatic injury.

The accumulation of heavy metals in the liver leads to disturbances in redox reactions, triggering the excessive formation of reactive oxygen species (ROS).^113,114 These ROS disrupt the permeability and fluidity of hepatocyte membranes, resulting in the leakage of ALP and AST into the bloodstream.^115,116 Additionally, exposure to heavy metals can inactivate the intracellular oxidative defense mechanism by depleting antioxidant stores and free radical scavengers in liver cells.¹¹⁷ The depletion of antioxidants results in the excessive formation of ROS and liver cells suffer from lipid peroxidation consequently causing membrane damage and permeability.¹¹⁸ As Das et al¹¹⁹ observed a dose-dependent production of intracellular reactive oxygen species in human liver cells exposed to hexavalent chromium. Also Omobowale et al¹²⁰ confirmed the production of oxidative stress in an experimental study involving rats exposed to lead acetate, noting an increased production of reactive oxygen species, including malonedialdehyde (MDA) and hydrogen peroxide (H₂O₂).

Limitation of the Study

This study has several limitations. While urine samples were used to detect Pb, the use of blood samples would have been more informative, as blood analysis is a more reliable biomarker for both recent and long-term Pb exposure. Additionally, the study utilized a cross-sectional design, relying on data collected at a single point in time. A longitudinal approach would have allowed for a more comprehensive understanding of the relationship between heavy metal exposure and health outcomes. Expanding the sample to included participants from all age groups would offer a more complete image on the effects of heavy metal exposure on liver. Another significant limitation is that conventional liver biomarkers, such as ALT, AST and ALP lack specificity when used to assess heavy metal-induced liver injury. Elevated levels of these enzymes can result from a wide range of hepatic and extrahepatic conditions. For instance, conditions such as viral hepatitis (types A-E), drug-induced liver injury (DILI), herb-induced liver injury (HILI), autoimmune hepatitis, metabolic disorders, and even systemic infections or muscle damage can all contribute to increased levels of ALT, AST, and ALP. This variability complicates the ability to attribute enzyme elevations solely to heavy metal toxicity.

Conclusion

The findings of this study revealed that there was a significant association between liver injury biomarkers and exposure to heavy metals particularly Cr and Pb. An elevation in liver injury biomarkers AST, ALT and ALP was observed, with levels rising to 8.9%, 4.6% and 14.9%, above the normal range. A significant positive association was found between elevated serum ALT, AST levels and the highest quartiles of urinary Pb and Cr. While these findings offer valuable insights into health risks related to environmental exposures, the cross-sectional design and limited adjustment for confounders necessitate cautious interpretation. However, the results emphasis the urgent need for enhanced environmental health surveillance in urban areas like Addis Ababa. Regular monitoring of Cr and Pb levels in environmental media such as air, water and soil as well as biological samples is essential for identifying exposure sources and protecting vulnerable populations. Public health authorities should consider implementing stricter regulations on industrial emissions, improving waste management practices and raising community awareness about the risks of heavy metal exposure. Furthermore, integrating environmental exposure assessments into routine health surveillance programs can support evidenced-based policy development. Future longitudinal studies are recommended to confirm these associations and guide targeted interventions aimed at reducing heavy metal exposure and mitigating its adverse health effects on the population.

Footnotes

Acknowledgements

We would like to acknowledge thematic research office of Addis Ababa University.

Ethical Considerations

Prior to the commencement of this study, ethical clearance was obtained from the Scientific and Ethical Review Board of the Ethiopian Public Health Institute. Written consent was secured from the study participants before data collection began.

Author Contributions

Tsigereda Assefa Alamayehu designed the study and drafted the manuscript: Andualen Mekonnen Hiruy and Tadesse Alemu contributed supervision of the study from the beginning to the end and reviewed the manuscript. Feyissa Chala support laboratory analysis and draft the manuscript. Dejen Tesfaw supported data analysis and contributed to the review of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study was supported by Addis Ababa University Thematic Research 9th round funding with, Project No. TR/01/2021.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

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