Association of urinary cadmium with renal injury biomarkers and optimal cut-off value of urinary cadmium in preschool children from mining area of northwestern China

Association of urinary cadmium with renal injury biomarkers and optimal cut-off value of urinary cadmium in preschool children from mining area of northwestern China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association of urinary cadmium with renal injury biomarkers and optimal cut-off value of urinary cadmium in preschool children from mining area of northwestern China Gulipiyan Balajiang, Yue Du, Wenzheng Yuan, Jingru Xie, Wenting Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5770154/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Children demonstrate increased sensitivity and vulnerability to cadmium exposure compared to adults. Current research predominantly focuses on adults residing in cadmium-contaminated areas, while studies involving children remain relatively scarce. This study aimed to explore the relationship between urinary cadmium (U-Cd) and biomarkers of renal injury, identify sensitive biomarkers associated with cadmium-related renal injury, and evaluate the optimal cut-off value for U-Cd in preschool children. Morning urine samples were collected to detect urinalysis, U-Cd, and renal injury biomarkers, including urinary N-acetyl-β-D-glucosidase (UNAG), urinary β2-microglobulin (Uβ2-MG), and urinary retinol-binding protein (URBP). Pearson correlation, quantile regression, and logistic regression models were utilized to explore the relationships between U-Cd and the renal injury biomarkers. Receiver operating characteristic (ROC) curves were employed to determine the optimal cut-off value of U-Cd for inducing abnormalities in renal injury biomarkers. U-Cd demonstrated positive associations with UNAG, Uβ2-MG, and URBP. The optimal cut-off values of U-Cd for inducing abnormalities in UNAG, Uβ2-MG, URBP, and combined biomarker were 7.78, 14.74, 12.75, and 10.42 µg/g cr, respectively. When the sensitivity was set at 95%, the cut-off values were adjusted to 4.70, 10.42, 11.07, and 5.18 µg/g cr, respectively. U-Cd was significantly associated with renal injury biomarkers. Our findings suggest that the appropriate cut-off value for U-Cd should be established based on the sensitivity and specificity of various renal injury biomarkers. Preschool children Urinary cadmium Renal injury Cut-off value Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Cadmium (Cd) is a non-essential element for the human body, characterized by its strong mobility and toxicity [1], and is widely present in the ecological environment. Activities such as mining, smelting, industrial manufacturing, and agricultural production contribute to Cd pollution in soil, water, and air. Cd can cause health damage upon entering the human body through oral, dermal, and respiratory routes [2, 3]. The International Agency for Research on Cancer has classified Cd as the confirmed carcinogen associated with human lung and prostate cancer [4]. Furthermore, Cd exposure may lead to multiple organ damage, as well as cardiovascular and cerebrovascular diseases, with significant effects on the kidneys and bones [5]. The kidneys are primarily affected, serving as the first target organ to exhibit changes following Cd exposure. Renal damage caused by Cd predominantly impacts renal tubular function and glomerular function [6]. Numerous previous studies on the health hazards of Cd have identified its biomarkers, which primarily include exposure markers such as blood-Cd and urinary-Cd (U-Cd), as well as effect markers like urinary N-acetyl-β-D-glucosidase (UNAG), urinary β 2 -microglobulin (Uβ 2 -MG), and urinary retinol-binding protein (URBP) [7, 8]. Recent studies conducted in Japan, Belgium, Sweden, China, and other regions over the past few years have led scholars to generally agree that Uβ 2 -MG and UNAG can serve as early sensitive biomarkers of renal injury [9, 10]. Prior research by Chinese scholars has involved health surveys and investigations into environmental Cd pollution caused by various factories and mines established since the 1960s, including a smelter in Zhejiang, a lead-zinc mine in Guizhou, and a tungsten mine in Jiangxi. These surveys indicated the strong correlation between biomarkers such as U-Cd, UNAG, and Uβ 2 -MG [11]. Furthermore, the BMDL for Uβ 2 -MG derived from previous U-Cd benchmark dose study involving the adult population in five Cd-polluted areas in China were found to be 2.00 µg/g cr for men and 1.69 µg/g cr for women [12]. Additionally, the results of calculating the cut-off value using the minimum P value method in another typical Cd-contaminated area revealed that the cut-off values for abnormal urinary cadmium in adults were 2.51 µg/g cr for UNAG, 3.07 µg/g cr for Uβ 2 -MG, and 4.34 µg/g cr for URBP [13]. These findings highlight the variability in safety limits for U-Cd across different regions, research groups, effect biomarkers, and statistical methods, suggesting that the U-Cd threshold is not universally applicable and that its results cannot be simply extrapolated to other areas or specific populations. In summary, research on U-Cd primarily focuses on adults residing in Cd-contaminated areas, while studies involving children are comparatively scarce. Children exhibit greater sensitivity and vulnerability to Cd exposure than adults. A birth cohort study conducted in Bangladesh indicated that U-Cd significantly impact early childhood growth and development, with early low-dose Cd exposure correlating with children's IQ [14]. The metabolic pathways in preschool children differ from those in adults, and their blood-brain barrier is not fully developed, making them more susceptible to environmental toxicants that can adversely affect their growth and development [15]. Even Cd exposure at doses well below those considered harmful to adults may cause damage to preschool children during critical developmental windows [16]. Therefore, this study selected a mining city in northwest China as the representative sample area to explore the relationship between Cd exposure in preschool children and early biomarkers of renal injury. Additionally, the receiver operating characteristic (ROC) curve was employed to assess the U-Cd cut-off value associated with abnormal biomarkers of renal injury in preschool children living in heavy metal-polluted regions, thereby providing reference for the prevention and treatment of health hazards related to Cd exposure in this vulnerable population. Methods Study area and participants The study area is located in the central region of Gansu along the upper reaches of the Yellow River. It serves as a significant non-ferrous heavy metal processing and smelting hub in northwest China [17]. For an extended period, heavy metal pollution has posed a pressing issue during the processes of mineral exploitation, processing, and industrialization. Notably, the Cd concentration in farmland soil within the sewage irrigation area of the study region was found to be 18.4 times higher than the national secondary standard for soil environmental quality. Additionally, the Cd content in wheat grains cultivated in this area exceeded the national limit by 6 times [18]. Research indicated that children had a higher risk of Cd exposure; specifically, the Cd intake among children aged 1 to 10 years was 1.52 times greater than that of adults, primarily due to their higher food consumption relative to body weight [19]. Our study participants comprised preschool children who had resided in the area for nearly two years. Basic demographic information, along with height, weight, and routine urine tests of all preschool children, was provided by the Maternal and Child Health Hospital in the study area. Prior to participation, all children and their guardians received detailed explanations of the study and provided written informed consent. The study received approval from the Ethics Committee of Lanzhou University School of Public Health (Ethical Approval Number: IRB 23061001). In this study, a total of 482 preschoolers were surveyed, with 420 participants included in the analysis after excluding those with missing information (Fig. 1 ). The study participants were classified based on their community and kindergarten location, with children attending 28 different kindergartens and residing in 52 distinct communities (Fig. 2 ). Sample collection and processing Fresh mid-stream morning urine samples were collected from preschool children in the field using 50ml sterile cups. The urine samples were then transferred into 5ml frozen centrifuge tubes. Samples intended for urinalysis, urine creatinine, UNAG, Uβ 2 -MG, and URBP were stored at temperatures between 2 and 6°C, while samples for U-Cd were stored at -20°C. Urine samples were digested using HNO 3 (PreeKem, TOPEX+, China), and U-Cd was determined by inductively coupled plasma mass spectrometry (Agilent 7500cs, USA). For urinalysis, the ULIT1600 automatic urine analyzer was employed for qualitative or semi-quantitative detection of urine composition. Urinary creatinine was assessed using spectrophotometry. UNAG, Uβ 2 -MG, and URBP were quantified using enzyme-linked immunosorbent assay kits manufactured by BioTek (Model: Eonc). The test kits were provided by Nanjing Jiancheng Biotechnology Co., LTD. All samples underwent repeated analysis, maintaining the coefficient of variation of less than 10%. Finally, urinary creatinine was utilized to correct the U-Cd and the three biomarkers of renal injury. Criteria for abnormal examination items Body mass index (BMI): BMI (kg/m²) = weight / height². According to the Growth standard for children under 7 years of age (WS/T 423–2022) issued by the National Health Commission of China, preschool children were divided into four groups: underweight (-3 SD ≤ • < -2 SD ), normal (-2 SD ≤ • < +1 SD ), overweight (+ 1 SD ≤ • < +2 SD ), obesity (+ 2 SD ≤ • < +3 SD ). Abnormal urinalysis was defined as the presence of urine specific gravity values outside the normal range (urine specific gravity (SG): 1.003–1.030), or positive findings for urine white blood cells (WBC), blood in urine (BLD), urine protein (PRO), urine occult blood (URO), urine bilirubin (BIL), urine ketone bodies (KET), urine nitrite (NIT), urine vitamin c (Vc). Renal injury was defined as UNAG ≥ 17 U/g cr or Uβ 2 -MG ≥ 1000 µg/g cr or URBP ≥ 1000 µg/g cr according to the criteria of the 1998 Health Risk Area for Cd pollution [20]. Statistical analysis In this study, we assessed the normality of the quantitative data from the participants. Variables that demonstrated a normal distribution were reported as mean ( x ) ± standard deviation ( SD ), while those that did not conform to the normal distribution were represented as geometric means ( GM ). Urinary creatinine-corrected U-Cd was characterized using the P 25 , P 50 , P 75 , and GM . For group comparisons, we employed the t -test or analysis of variance (ANOVA) when the assumption of normality was satisfied; conversely, the Wilcoxon rank sum test or the Kruskal-Wallis H test was utilized when the normality condition was not met. Qualitative data were expressed as frequency and percentage (%), with the χ² test applied for group comparisons. Spatial interpolation analysis of U-Cd among the study participants was conducted using the Kriging interpolation tool in ArcGIS. All statistical tests were two-sided, and P -value < 0.05 was considered statistically significant. U-Cd and three renal injury biomarkers were logarithmically transformed to achieve normal distribution, after which they were utilized for correlation and quantile regression analyses. Given that UNAG yielded negative values following logarithmic transformation, the transformation lg(x + 1) was employed in both Pearson correlation and quantile regression analyses to prevent reverse correlation. The relationship between U-Cd and the renal injury biomarkers were examined using Pearson correlation analysis. Quantile regression was applied to explore the concentration relationship between U-Cd and biomarkers, with log -transformed U-Cd as the independent variable and the log -transformed renal injury biomarkers as dependent variables. According to the criteria for assessing renal injury, the concentrations of the three early renal injury biomarkers were classified into two groups: normal (equal to or below the critical value) and abnormal (above the critical value). U-Cd was stratified into three groups based on tertiles: low concentration (≤ P 33.3 ), medium concentration ( P 33.3 , P 66.7 ) and high concentration (≥ P 66.7 ). Linear χ² trend analysis was conducted to explore the linear trend in the abnormal rates of U-Cd-induced renal injury biomarkers. Multivariate logistic regression model was used to analyze the association between U-Cd and the risk of abnormalities in biomarkers, with low concentration group serving as the reference for calculating the 95% confidence intervals ( CIs ) and odds ratios ( ORs ) for the renal injury biomarkers. The R language “pROC” package was used to create ROC curve to assess the cut-off values of U-Cd for inducing abnormalities in the URBP, Uβ 2 -MG, and UNAG. All statistical descriptions, analyses, and plots were conducted using SPSS 26.0, R 4.3.2 softwares. Results Demographic characteristics In this study, 231 participants (55%) were boys and 189 (45%) were girls. The GM of U-Cd for all participants was 7.58 µg/g cr. Notably, U-Cd was higher in girls ( GM : 8.45 µg/g cr) compared to boys ( GM : 6.70 µg/g cr), with a statistically significant difference observed between the genders ( P < 0.05) (Table 1 ). Furthermore, spatial distribution maps of U-Cd among preschool children in community and kindergarten revealed similar trends (Fig. 3 ). Specifically, U-Cd was higher in the eastern region compared to the western region, with the lowest level observed in the southwest and the highest in the northeast, indicating a decreasing trend from northeast to southwest. Table 1 U-Cd (µg/g cr) in preschool children with different characteristics. Characteristics n (%) U-Cd (µg/g cr) Z/H P -value P 25 P 50 P 75 GM Total 420 (100.00) 3.39 6.79 15.17 7.58 Gender -2.24 0.025 Boy 231 (55.00) 3.02 6.02 13.87 6.94 Girl 189 (45.00) 3.99 7.64 16.95 8.45 Age (years) 1.16 0.763 3~ 65 (15.48) 3.66 6.39 14.93 7.48 4~ 170 (40.48) 3.26 7.31 15.04 7.52 5~ 98 (23.33) 3.11 6.18 12.99 7.13 6~ 87 (20.71) 3.55 7.16 19.97 8.33 BMI (kg/m 2 ) 3.29 0.349 Underweight 6 (1.43) 3.07 4.06 10.28 5.03 Normal 360 (85.71) 3.22 6.79 14.89 7.41 Overweight 31 (7.38) 3.99 6.30 17.85 8.94 Obesity 23 (5.48) 5.44 8.42 18.19 9.60 Urinalysis -0.19 0.799 Abnormal 166 (39.52) 3.30 7.17 14.39 7.57 Normal 254 (60.48) 3.40 6.41 15.91 7.58 The concentrations of UNAG, Uβ 2 -MG, and URBP were 3.76 (1.18, 9.95) U/g cr, 322.40 (164.76, 613.99) µg/g cr, and 343.87 (184.86, 657.40) µg/g cr, respectively. No significant differences were observed between boys and girls concerning the three renal injury biomarkers ( P > 0.05) (Table S1 ). Correlation analysis between U-Cd and renal injury biomarkers Pearson correlation analysis was conducted to examine the relationship between U-Cd and biomarkers of renal injury in preschool children. The log -transformed U-Cd exhibited positive correlations with UNAG, Uβ 2 -MG, and URBP ( P < 0.01). The correlation coefficients for UNAG, Uβ 2 -MG, and URBP in relation to U-Cd were 0.41, 0.80, and 0.72, respectively. Additionally, significant correlation was observed among UNAG, Uβ 2 -MG, and URBP, with URBP showing the strong correlation with Uβ 2 -MG, evidenced by correlation coefficient of 0.79 ( P < 0.01) (Fig. 4 d). The findings indicated that U-Cd was positively correlated with UNAG, Uβ 2 -MG, and URBP, with R² values of 0.17, 0.64, and 0.52, respectively. Notably, Uβ 2 -MG and URBP demonstrated stronger correlations with U-Cd compared to UNAG (Fig. 4 a, 4 b, 4 c). Quantile regression was used to analyze the regression of three renal injury biomarkers across ten quantile points: P 5 、 P 15 、 P 25 、 P 35 、 P 45 、P 55 、 P 65 、 P 75 、 P 85 , and P 95 (Table S2 ). Generally, the βs for UNAG and U-Cd were positive, with statistically significant differences observed among the βs at each quantile. Notably, the differences between boys and girls after P 25 were also statistically significant ( P < 0.05). Overall, the βs exhibited a pronounced increasing trend, which was particularly evident in boys, suggesting that the impact of UNAG intensifies with increasing quantiles. Furthermore, the βs for Uβ 2 -MG, URBP, and U-Cd were positive, with statistically significant differences noted across the entire participants, as well as between boys and girls at each quantile ( P < 0.05). The trend in the βs for these biomarkers displayed slight fluctuations alongside gradual growth pattern, indicating that U-Cd positively influences both Uβ 2 -MG and URBP (Figs. 5 and S1). Association analysis between U-Cd and abnormal renal injury biomarkers The tertiles of U-Cd among preschool children were classified into T 1 (≤ 4.12), T 2 (4.13 ~ 11.24), and T 3 (≥ 11.25) groups, allowing for the comparative analysis of UNAG, Uβ 2 -MG, and URBP across participants in different U-Cd groups. Trend analysis results indicated that the abnormal rates of UNAG, Uβ 2 -MG, URBP, and the combined rates across the three groups significantly increased with rising U-Cd concentrations in both boys and girls. Notably, the T 3 group exhibited the highest abnormal rate, with statistically significant differences observed in abnormal rates among the different U-Cd groups ( P < 0.05) (Fig. 6 ). Subgroup analysis of urinalysis and BMI revealed that the highest rates were observed in the SG and normal weight groups. However, the difference was statistically significant only in the BLD group ( P < 0.05) (Fig. S2 ). The multivariate logistic regression model analysis demonstrated that UNAG, Uβ 2 -MG, URBP, and combined biomarker were all positively correlated with U-Cd. However, different levels of U-Cd were not associated with UNAG in girls. In comparison to the T 1 group, the risk of UNAG abnormalities in preschool children from the T 3 group increased by 2% and 3% in the overall participants and in boys, respectively, with OR (95% CI ) values of 1.02 (1.01, 1.04) and 1.03 (1.01, 1.05) ( P < 0.05). When compared to the T 1 group, the risk of abnormalities in Uβ 2 -MG, URBP, and combined biomarker in preschool children from the T 3 group rose by 5%, 6%, and 5% in the overall participants, respectively, with OR (95% CI ) values of 1.05 (1.02, 1.07), 1.06 (1.02, 1.09), and 1.05 (1.02, 1.08) ( P < 0.05). In boys, the risk increased by 4%, 4%, and 3%, respectively, with OR (95% CI ) values of 1.04 (1.01, 1.06), 1.04 (1.01, 1.07), and 1.03 (1.01, 1.06) ( P < 0.05). In girls, the risk increased by 8%, 12%, and 10%, respectively, with OR (95% CI ) values of 1.08 (1.03, 1.13), 1.12 (1.05, 1.19), and 1.10 (1.03, 1.17) ( P < 0.05) (Fig. 7 ). Due to the low abnormal rates of BIL and NIT in urinalysis, as well as the underweight group in the BMI of preschool children, these variables were excluded from the logistic regression model analysis. Overweight and obesity were combined into a single variable for this analysis. Subgroup analysis revealed that varying levels of U-Cd were not associated with the risk of abnormal urinalysis or BMI ( P > 0.05) (Fig. S3). Analysis of U-Cd cut-off value for abnormal biomarkers of induced renal injury The results of the ROC curve (Fig. 8 ) showed that the optimal cut-off values of overall U-Cd for inducing abnormalities in UNAG, Uβ 2 -MG, URBP, and combined biomarker were 7.78, 14.74, 12.75, and 10.42 µg/g cr, respectively, with corresponding sensitivities of 81%, 89%, 94%, and 81%. The specificities for these values were 61%, 85%, 83%, and 84%, respectively. For boys, the optimal U-Cd cut-off values were 4.66, 15.66, 10.42, and 7.90 µg/g cr, with sensitivities of 97%, 81%, 97%, and 81%, and specificities of 49%, 88%, 80%, and 75%, respectively. In contrast, the optimal U-Cd cut-off values for girls were 11.07, 14.59, 12.75, and 11.07 µg/g cr, with corresponding sensitivities of 84%, 97%, 94%, and 93%, and specificities of 67%, 84%, 83%, and 79%, respectively. When the sensitivity was set at 95%, the cut-off values of U-Cd for inducing abnormalities in UNAG, Uβ 2 -MG, URBP, and combined biomarker were 4.70, 10.42, 11.07, and 5.18 µg/g cr, respectively, with corresponding specificities of 42%, 76%, 78%, and 52%. For boys, the cut-off values of U-Cd were 4.69, 6.81, 10.48, and 4.69 µg/g cr, yielding specificities of 49%, 63%, 80%, and 55%, respectively. In contrast, the cut-off values of U-Cd for girls were 5.70, 11.19, 13.03, and 7.77 µg/g cr, with corresponding specificities of 41%, 74%, 84%, and 65%, respectively (Table S3). Discussion This study found that GM of U-Cd in preschool children was 7.58 µg/g cr. the GM for boys and girls were 6.94 µg/g cr and 8.45 µg/g cr, respectively. Although these values were lower than the threshold of 15 µg/g cr set by the Determination Standard for Environmental Cadmium Pollution Health Hazard Area (GB/T17221-1998) [20], they exceeded the threshold of 5 µg/g cr established for the ‘potential high-risk population’ in the Technical Guidelines for Diagnosis and Treatment of Heavy Metal Pollution (trial) [21, 22]. Notably, the GM in girls was higher than that in boys. Previous studies have indicated gender differences in U-Cd in China, with women typically exhibiting higher level than men, which aligns with the findings of this study [23]. Furthermore, research conducted in the United States and Canada has similarly demonstrated that U-Cd concentrations are higher in the normal adult female population compared to their male counterparts [24]. Suggesting that Cd might accumulated more readily in women than in men, particularly in polluted areas. This phenomenon may be related to decreased iron stores in women due to physiological factors, which could enhance Cd absorption [25, 26]. The GM of U-Cd in preschool children within the study area was approximately 5.3 times greater than the upper limit of average U-Cd levels reported in foreign literature for children aged 3 to 14 years (0.07–1.43 µg/g cr) [27]. Furthermore, the highest U-Cd level recorded among preschool children in Wuxi and Shanghai (4.86 µg/g cr), along with the median level in preschool children from the Dachang mining area of Nandan County in Guangxi (1.19 µg/g cr), were both lower than the findings of this study [28, 29]. Also, the spatial interpolation analysis indicated that Cd in the northeastern part of the study area was higher than in other regions. Furthermore, the data revealed that the total number of mines in the study area reached 51 by the end of 2021, with the majority of these mines located in the northeastern part of the area [30]. This correlation suggests that U-Cd levels are associated with the distribution of mines. These results indicate that preschool children in the study area are exposed to high level of environmental Cd exposure. The results of the correlation analysis in this study showed that U-Cd was positively correlated with UNAG, Uβ 2 -MG, and URBP. Sun Hong et al. reported a strong linear correlation between UNAG, Uβ 2 -MG, and U-Cd, which align with the findings of the present study [21, 31]. A 35-year cohort study involving 2,213 adults in Japan demonstrated a positive and significant dose-response relationship between U-Cd and both Uβ 2 -MG and URBP [32]. Quantile regression analysis revealed that U-Cd positively influenced the three renal injury biomarkers; specifically, as U-Cd concentration increased, each quantile of renal injury biomarkers exhibited an upward trend. This indicated that U-Cd has a significant positive effect on early renal injury within the study participants. Furthermore, the relationship between U-Cd and renal injury biomarkers were assessed using tertiles. It was observed that high U-Cd significantly increased the abnormal rates of UNAG, Uβ 2 -MG, URBP, and the combined biomarker, with the increasing trend being statistically significant. These findings suggested that UNAG, Uβ 2 -MG, URBP, and the combined biomarker serve as indicators of early renal injury. Du Yu et al. conducted a long-term dynamic observation of the population in Cd-contaminated area in Jiangxi, concluding that urinary protein did not significantly increase in the early stages when glomerular and renal function remained intact. During this initial period, the primary indicators were elevated levels of U-Cd and urinary enzymes. As Cd exposure increased, both U-Cd and urinary enzymes rose concurrently, followed by an increase in urinary protein, indicating that organic lesions in the renal system had already developed. In the later stages of Cd poisoning, the rise in U-Cd and urinary protein became predominant concerns [33]. However, variability in pollution levels, population characteristics, methodologies, and inconsistent exposure assessment indicators and endpoints across population-based studies limits the comparability of the results. The relationship between U-Cd and biomarkers in the progression of early renal injury among preschool children remains unclear, necessitating a substantial number of population-based epidemiological studies to clarify this relationship. Furthermore, it was observed that U-Cd was significantly higher in individuals with lower BMI [14], while this study found no correlation between U-Cd and BMI, this discrepancy may be attributed to the age and exposure of the participants selected for the study. In ROC curve analysis, selecting the optimal cut-off value is crucial for maximizing both sensitivity and specificity [34]. We utilized the coordinates of the ROC curves to identify the optimal cut-off values for U-Cd that yielded the highest sensitivity and specificity. In this study, the optimal U-Cd cut-off values for inducing abnormal UNAG, Uβ 2 -MG, URBP, and combined biomarker in preschool children, both overall and for boys and girls, were found to be lower than the established standard of 15 µg/g cr for populations in Cd-contaminated areas in China [20]. With the exception of the optimal U-Cd cut-off values for inducing abnormal UNAG in boys, the other cut-off values exceeded the Cd poisoning standard for occupational populations in China, as well as the 'potential high-risk population' cut-off value of 5 µg/g cr outlined in the Technical Guidelines for the Diagnosis and Treatment of Heavy Metal Pollution (trial) [17, 22]. Notably, research has shown that even U-Cd below 0.5 µg/g cr can lead to adverse renal effects [35]. Therefore, to better safeguard the health of preschool children, it is recommended that lower U-Cd thresholds be considered, as UNAG demonstrated greater sensitivity for the early identification of renal injury, followed by the combined biomarker. We also set a fixed sensitivity (95%, which meant 5% false negative rate), and identified the U-Cd values that achieved this sensitivity on the ROC curves. In comparison to the U-Cd thresholds previously estimated using the benchmark dose (BMD) method in the study area, when UNAG as the outcome biomarker, the optimal cut-off value of U-Cd and the cut-off value obtained under 95% sensitivity were similar to those of BMD/BMDL 10 (8.87/6.14 µg/g cr) and BMD/BMDL 05 (4.76/2.76 µg/g cr), respectively. When Uβ 2 -MG or URBP as the outcome biomarker, the cut-off value of U-Cd obtained under 95% sensitivity was also similar to that of BMD/BMDL 05 (Uβ 2 -MG: 9.08/7.59 µg/g cr, URBP: 8.31/6.82 µg/g cr) [36]. However, the choice of higher sensitivity comes at the expense of lower specificity. In the current study, we observed that as sensitivity increased, the concentration of U-Cd required to induce abnormal biomarkers of renal injury decreased. While utilizing high-sensitivity determination thresholds may have identified more individuals with abnormalities in renal injury biomarkers, it would also have resulted in a higher rate of false positives. This situation suggests that children who are unlikely to develop renal injury may require more frequent examinations and visits, thereby inflating the perceived prevalence of Cd-induced renal injury among preschool children. Consequently, in ROC curve analysis, selecting cut-off value should take into account the clinical and practical objectives of screening or diagnosis [34, 37, 38]. Conclusion U-Cd levels among preschool children in this study were generally high, revealing the correlation between U-Cd and biomarkers of renal injury. Our findings suggest that appropriate cut-off value for U-Cd should be established based on the sensitivity and specificity of various renal injury biomarkers. This approach will enhance diagnostic accuracy and prevent misdiagnosis, thereby alleviating unnecessary distress for families of preschool children and society at large. Declarations Funding The present study was supported by the Natural Science Foundation of Gansu (23JRRA1073) and the Science and Technology Plan of Baiyin (2023-1-53Y). CRediT authorship contribution statement Gulipiyan Balajiang: Research design, Sample collection and testing, Data processing, Writing-Original draft preparation, Formal analysis, Methodology. Yue Du: Sample collection, Formal analysis. Wenzheng Yuan: Sample collection, Formal analysis. Jingru Xie: Sample collection, Formal analysis. Wenting Zhao: Sample collection, Formal analysis. Shiwei Ai: Research design, Conceptualization, Writing—review & editing, Funding acquisition. 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Ciesielski T, Weuve J, Bellinger DC, Schwartz J, Lanphear B, Wright RO. Cadmium Exposure and Neurodevelopmental Outcomes in US Children. Environmental Health Perspectives. 2012;120(5):758–763. Li Y, Wang Y-b, Gou X, Su Y-b, Wang G. Risk assessment of heavy metals in soils and vegetables around non-ferrous metals mining and smelting sites, Baiyin, China. Journal of Environmental Sciences. 2006;18(6):1124–1134. Wei C, Ting W. Occurrence characteristics and health risk assessment of cadmium in soilwheat system in polluted irrigation area of Baiyin City. Journal of Nuclear Agronomy. 2020;34(04):878–886. (in Chinese) Ferrari P, Arcella D, Heraud F, Cappe S, Fabiansson S. Impact of refining the assessment of dietary exposure to cadmium in the European adult population. Food Additives and Contaminants Part a-Chemistry Analysis Control Exposure & Risk Assessment. 1 2013;30(4):687–697. Institute of Environmental Health and Sanitary Engineering, Chinese Academy of Preventive Medicine. 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Cadmium exposure induced itai-itai-like syndrome in male rats. Central European Journal of Medicine. 2011;6(4):425–434. Kippler M, Ekstrom E-C, Lonnerdal B, et al. Influence of iron and zinc status on cadmium accumulation in Bangladeshi women. Toxicology and Applied Pharmacology. 2007;222(2):221–226. Aguilera I, Daponte A, Gil F, et al. Urinary levels of arsenic and heavy metals in children and adolescents living in the industrialised area of Ria of Huelva (SW Spain). Environment International. 2010;36(6):563–569. Jun Y, Ping H. Investigation of urinary cadmium level in preschool children. Contemporary Medicine. 2015;21(23):161–162. (in Chinese) Zhixian T, Yongjin S, Xinying Z. Urinary cadmium level and its correlation with physical growth among children aged 3–6 years in Dachang mining area of Nandan County, Guangxi. Guangxi Medicine. 2020;42(13):1687–1690. (in Chinese) Shaohua X. Study on Local Government's Performance of Duty in Environmental Governance of Mining Area [Master]. 2022. (in Chinese) Wang X, Wang Y, Feng L, et al. Application of the Benchmark Dose (BMD) Method to Identify Thresholds of Cadmium-Induced Renal Effects in Non-Polluted Areas in China. Plos One. 2016;11(8). Suwazono Y, Nogawa K, Morikawa Y, et al. Renal tubular dysfunction increases mortality in the Japanese general population living in cadmium non-polluted areas. Journal of Exposure Science and Environmental Epidemiology. 2015;25(4):399–404. Du Y, Shang Q. Review of effect on human health for environmental cadmium pollution. Journal of hygiene research. 2006;35(2):241–243. Habibzadeh F, Habibzadeh P, Yadollahie M. On determining the most appropriate test cut-off value: the case of tests with continuous results. Biochemia Medica. 2016;26(3):297–307. Chen X, Chen X, Wang X, et al. The association between estimated glomerular filtration rate and cadmium exposure: An 8-year follow-up study. International Journal of Hygiene and Environmental Health. 2021:235. Du Y, Chen Y, Cao A, et al. Estimation of urinary cadmium benchmark dose thresholds for preschool children in a cadmium-polluted area based on Bayesian model averaging. Environmental Geochemistry and Health. 2024;46(7). Tripepi G, Jager KJ, Dekker FW, Zoccali C. Diagnostic methods 2: receiver operating characteristic (ROC) curves. Kidney International. 2009;76(3):252–256. Hajian-Tilaki K. The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. Statistical Methods in Medical Research. 2018;27(8):2374–2383. Supplementary Files SupplementaryMaterial.doc Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5770154","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398434224,"identity":"154c2d45-7e1b-4633-9456-41730733e080","order_by":0,"name":"Gulipiyan Balajiang","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Gulipiyan","middleName":"","lastName":"Balajiang","suffix":""},{"id":398434225,"identity":"43182c83-b3ae-4340-b7e8-6b511a33f39d","order_by":1,"name":"Yue Du","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Du","suffix":""},{"id":398434226,"identity":"f7a8d697-4da0-42b4-a4ef-9e775e8c5b2f","order_by":2,"name":"Wenzheng Yuan","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Wenzheng","middleName":"","lastName":"Yuan","suffix":""},{"id":398434227,"identity":"817f2b6c-2fbe-4148-9353-f55b58912699","order_by":3,"name":"Jingru Xie","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jingru","middleName":"","lastName":"Xie","suffix":""},{"id":398434228,"identity":"f12d628d-3263-4dd7-9588-ab4edcebefcb","order_by":4,"name":"Wenting Zhao","email":"","orcid":"","institution":"Lanzhou 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University","correspondingAuthor":true,"prefix":"","firstName":"Yuhui","middleName":"","lastName":"Dang","suffix":""}],"badges":[],"createdAt":"2025-01-06 03:05:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5770154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5770154/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73315751,"identity":"26d5c125-016d-4fe6-badd-77f2a5a23654","added_by":"auto","created_at":"2025-01-08 19:56:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":217393,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of this study.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/1c39e0e6d2f185e405f8ebb1.png"},{"id":73315746,"identity":"b4d1763e-6294-4663-907f-5f4d218099ca","added_by":"auto","created_at":"2025-01-08 19:56:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1242002,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area preschool children community and kindergarten location map.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/bf11c4c867dd2149abbb10f4.png"},{"id":73315749,"identity":"536d12ae-e54a-4c6b-8e2d-03e9cb6aa289","added_by":"auto","created_at":"2025-01-08 19:56:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71971,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of U-Cd in preschool children (\u003cstrong\u003ea\u003c/strong\u003e The spatial distribution based on community;\u003cstrong\u003e b\u003c/strong\u003e The spatial distribution based on kindergarten).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/ba1a2c86a9f752b4d7d7f538.png"},{"id":73315745,"identity":"69216ee7-5d4a-4ec5-a9e9-6610981a0020","added_by":"auto","created_at":"2025-01-08 19:56:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94046,"visible":true,"origin":"","legend":"\u003cp\u003eLinear fitting plots (a-c) and heatmap (d) of correlation between U-Cd and renal injury biomarkers in preschool children (U-Cd, UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP were transformed by\u003cem\u003e log\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/f70fef9de75f1f79e0018533.png"},{"id":73316181,"identity":"89607166-fd85-49da-8c0d-23f13f7b3a96","added_by":"auto","created_at":"2025-01-08 20:04:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17758,"visible":true,"origin":"","legend":"\u003cp\u003eThe trends of regression coefficients of quantile of U-Cd in renal injury biomarkers in preschool children (The X-axis was the quantiles and the Y-axis was the \u003cem\u003eβ \u003c/em\u003e(95% \u003cem\u003eCI\u003c/em\u003e); U-Cd, UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP were transformed by\u003cem\u003e log\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/06a1b65cf8c874698fdf7f89.png"},{"id":73317649,"identity":"fecb18bc-c22c-4877-96b4-4f8424332042","added_by":"auto","created_at":"2025-01-08 20:20:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":303365,"visible":true,"origin":"","legend":"\u003cp\u003eAbnormal rates of UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG and URBP in different U-Cd groups in preschool children (UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP were used as the outcome indicators, and the combined biomarker (UNAG abnormal or Uβ\u003csub\u003e2\u003c/sub\u003e-MG abnormal or URBP abnormal) were used as the outcome indicators. \u003cstrong\u003ea\u003c/strong\u003e Abnormal rates in total participants; \u003cstrong\u003eb\u003c/strong\u003e Abnormal rates in boys; \u003cstrong\u003ec\u003c/strong\u003e Abnormal rates in girls. \u003cstrong\u003e**\u003c/strong\u003e \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/a39b75b2c123844e5573ef99.png"},{"id":73315755,"identity":"541deb18-f3a5-45f0-9597-db51bf8c8410","added_by":"auto","created_at":"2025-01-08 19:56:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":35016,"visible":true,"origin":"","legend":"\u003cp\u003eLogistic regression analysis of the association between U-Cd and abnormal risk of renal injury biomarkers in preschool children.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/b6b45560031df80ff9c8312b.png"},{"id":73317284,"identity":"b8ceff7f-05a6-4e1e-8e18-5ab966756d60","added_by":"auto","created_at":"2025-01-08 20:12:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":110166,"visible":true,"origin":"","legend":"\u003cp\u003eOptimal cut-off values of U-Cd for abnormal biomarkers of induced renal injury in preschool children (\u003cstrong\u003ea\u003c/strong\u003e Optimal cut-off values in total participants; \u003cstrong\u003eb\u003c/strong\u003eOptimal cut-off values in boys; \u003cstrong\u003ec\u003c/strong\u003e Optimal cut-off values in girls).\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/ad26ca17a87149d6895eb396.png"},{"id":74391690,"identity":"4fe07101-2f49-42e3-a139-d95697891f42","added_by":"auto","created_at":"2025-01-21 23:29:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2892294,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/3c4ef68c-9c1f-41ca-bcda-195cedf91ab9.pdf"},{"id":73315750,"identity":"5d57fa54-0828-4237-8f95-43904f04a84b","added_by":"auto","created_at":"2025-01-08 19:56:25","extension":"doc","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":356864,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.doc","url":"https://assets-eu.researchsquare.com/files/rs-5770154/v1/76aab375c9d8f09499a548a2.doc"}],"financialInterests":"","formattedTitle":"Association of urinary cadmium with renal injury biomarkers and optimal cut-off value of urinary cadmium in preschool children from mining area of northwestern China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCadmium (Cd) is a non-essential element for the human body, characterized by its strong mobility and toxicity [1], and is widely present in the ecological environment. Activities such as mining, smelting, industrial manufacturing, and agricultural production contribute to Cd pollution in soil, water, and air. Cd can cause health damage upon entering the human body through oral, dermal, and respiratory routes [2, 3]. The International Agency for Research on Cancer has classified Cd as the confirmed carcinogen associated with human lung and prostate cancer [4]. Furthermore, Cd exposure may lead to multiple organ damage, as well as cardiovascular and cerebrovascular diseases, with significant effects on the kidneys and bones [5]. The kidneys are primarily affected, serving as the first target organ to exhibit changes following Cd exposure. Renal damage caused by Cd predominantly impacts renal tubular function and glomerular function [6]. Numerous previous studies on the health hazards of Cd have identified its biomarkers, which primarily include exposure markers such as blood-Cd and urinary-Cd (U-Cd), as well as effect markers like urinary N-acetyl-β-D-glucosidase (UNAG), urinary β\u003csub\u003e2\u003c/sub\u003e-microglobulin (Uβ\u003csub\u003e2\u003c/sub\u003e-MG), and urinary retinol-binding protein (URBP) [7, 8].\u003c/p\u003e \u003cp\u003eRecent studies conducted in Japan, Belgium, Sweden, China, and other regions over the past few years have led scholars to generally agree that Uβ\u003csub\u003e2\u003c/sub\u003e-MG and UNAG can serve as early sensitive biomarkers of renal injury [9, 10]. Prior research by Chinese scholars has involved health surveys and investigations into environmental Cd pollution caused by various factories and mines established since the 1960s, including a smelter in Zhejiang, a lead-zinc mine in Guizhou, and a tungsten mine in Jiangxi. These surveys indicated the strong correlation between biomarkers such as U-Cd, UNAG, and Uβ\u003csub\u003e2\u003c/sub\u003e-MG [11]. Furthermore, the BMDL for Uβ\u003csub\u003e2\u003c/sub\u003e-MG derived from previous U-Cd benchmark dose study involving the adult population in five Cd-polluted areas in China were found to be 2.00 \u0026micro;g/g cr for men and 1.69 \u0026micro;g/g cr for women [12]. Additionally, the results of calculating the cut-off value using the minimum \u003cem\u003eP\u003c/em\u003e value method in another typical Cd-contaminated area revealed that the cut-off values for abnormal urinary cadmium in adults were 2.51 \u0026micro;g/g cr for UNAG, 3.07 \u0026micro;g/g cr for Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and 4.34 \u0026micro;g/g cr for URBP [13]. These findings highlight the variability in safety limits for U-Cd across different regions, research groups, effect biomarkers, and statistical methods, suggesting that the U-Cd threshold is not universally applicable and that its results cannot be simply extrapolated to other areas or specific populations.\u003c/p\u003e \u003cp\u003eIn summary, research on U-Cd primarily focuses on adults residing in Cd-contaminated areas, while studies involving children are comparatively scarce. Children exhibit greater sensitivity and vulnerability to Cd exposure than adults. A birth cohort study conducted in Bangladesh indicated that U-Cd significantly impact early childhood growth and development, with early low-dose Cd exposure correlating with children's IQ [14]. The metabolic pathways in preschool children differ from those in adults, and their blood-brain barrier is not fully developed, making them more susceptible to environmental toxicants that can adversely affect their growth and development [15]. Even Cd exposure at doses well below those considered harmful to adults may cause damage to preschool children during critical developmental windows [16].\u003c/p\u003e \u003cp\u003eTherefore, this study selected a mining city in northwest China as the representative sample area to explore the relationship between Cd exposure in preschool children and early biomarkers of renal injury. Additionally, the receiver operating characteristic (ROC) curve was employed to assess the U-Cd cut-off value associated with abnormal biomarkers of renal injury in preschool children living in heavy metal-polluted regions, thereby providing reference for the prevention and treatment of health hazards related to Cd exposure in this vulnerable population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area and participants\u003c/h2\u003e \u003cp\u003eThe study area is located in the central region of Gansu along the upper reaches of the Yellow River. It serves as a significant non-ferrous heavy metal processing and smelting hub in northwest China [17]. For an extended period, heavy metal pollution has posed a pressing issue during the processes of mineral exploitation, processing, and industrialization. Notably, the Cd concentration in farmland soil within the sewage irrigation area of the study region was found to be 18.4 times higher than the national secondary standard for soil environmental quality. Additionally, the Cd content in wheat grains cultivated in this area exceeded the national limit by 6 times [18]. Research indicated that children had a higher risk of Cd exposure; specifically, the Cd intake among children aged 1 to 10 years was 1.52 times greater than that of adults, primarily due to their higher food consumption relative to body weight [19].\u003c/p\u003e \u003cp\u003e Our study participants comprised preschool children who had resided in the area for nearly two years. Basic demographic information, along with height, weight, and routine urine tests of all preschool children, was provided by the Maternal and Child Health Hospital in the study area. Prior to participation, all children and their guardians received detailed explanations of the study and provided written informed consent. The study received approval from the Ethics Committee of Lanzhou University School of Public Health (Ethical Approval Number: IRB 23061001).\u003c/p\u003e \u003cp\u003eIn this study, a total of 482 preschoolers were surveyed, with 420 participants included in the analysis after excluding those with missing information (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The study participants were classified based on their community and kindergarten location, with children attending 28 different kindergartens and residing in 52 distinct communities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection and processing\u003c/h3\u003e\n\u003cp\u003eFresh mid-stream morning urine samples were collected from preschool children in the field using 50ml sterile cups. The urine samples were then transferred into 5ml frozen centrifuge tubes. Samples intended for urinalysis, urine creatinine, UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP were stored at temperatures between 2 and 6\u0026deg;C, while samples for U-Cd were stored at -20\u0026deg;C.\u003c/p\u003e \u003cp\u003eUrine samples were digested using HNO\u003csub\u003e3\u003c/sub\u003e (PreeKem, TOPEX+, China), and U-Cd was determined by inductively coupled plasma mass spectrometry (Agilent 7500cs, USA). For urinalysis, the ULIT1600 automatic urine analyzer was employed for qualitative or semi-quantitative detection of urine composition. Urinary creatinine was assessed using spectrophotometry. UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP were quantified using enzyme-linked immunosorbent assay kits manufactured by BioTek (Model: Eonc). The test kits were provided by Nanjing Jiancheng Biotechnology Co., LTD. All samples underwent repeated analysis, maintaining the coefficient of variation of less than 10%. Finally, urinary creatinine was utilized to correct the U-Cd and the three biomarkers of renal injury.\u003c/p\u003e\n\u003ch3\u003eCriteria for abnormal examination items\u003c/h3\u003e\n\u003cp\u003eBody mass index (BMI): BMI (kg/m\u0026sup2;)\u0026thinsp;=\u0026thinsp;weight / height\u0026sup2;. According to the Growth standard for children under 7 years of age (WS/T 423\u0026ndash;2022) issued by the National Health Commission of China, preschool children were divided into four groups: underweight (-3 \u003cem\u003eSD\u003c/em\u003e \u0026le; \u0026bull; \u0026lt; -2 \u003cem\u003eSD\u003c/em\u003e), normal (-2 \u003cem\u003eSD\u003c/em\u003e \u0026le; \u0026bull; \u0026lt; +1 \u003cem\u003eSD\u003c/em\u003e), overweight (+\u0026thinsp;1 \u003cem\u003eSD\u003c/em\u003e \u0026le; \u0026bull; \u0026lt; +2 \u003cem\u003eSD\u003c/em\u003e), obesity (+\u0026thinsp;2 \u003cem\u003eSD\u003c/em\u003e \u0026le; \u0026bull; \u0026lt; +3 \u003cem\u003eSD\u003c/em\u003e). Abnormal urinalysis was defined as the presence of urine specific gravity values outside the normal range (urine specific gravity (SG): 1.003\u0026ndash;1.030), or positive findings for urine white blood cells (WBC), blood in urine (BLD), urine protein (PRO), urine occult blood (URO), urine bilirubin (BIL), urine ketone bodies (KET), urine nitrite (NIT), urine vitamin c (Vc). Renal injury was defined as UNAG\u0026thinsp;\u0026ge;\u0026thinsp;17 U/g cr or Uβ\u003csub\u003e2\u003c/sub\u003e-MG\u0026thinsp;\u0026ge;\u0026thinsp;1000 \u0026micro;g/g cr or URBP\u0026thinsp;\u0026ge;\u0026thinsp;1000 \u0026micro;g/g cr according to the criteria of the 1998 Health Risk Area for Cd pollution [20].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, we assessed the normality of the quantitative data from the participants. Variables that demonstrated a normal distribution were reported as mean (\u003cem\u003ex\u003c/em\u003e)\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cem\u003eSD\u003c/em\u003e), while those that did not conform to the normal distribution were represented as geometric means (\u003cem\u003eGM\u003c/em\u003e). Urinary creatinine-corrected U-Cd was characterized using the \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e25\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e50\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e75\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eGM\u003c/em\u003e. For group comparisons, we employed the \u003cem\u003et\u003c/em\u003e-test or analysis of variance (ANOVA) when the assumption of normality was satisfied; conversely, the Wilcoxon rank sum test or the Kruskal-Wallis \u003cem\u003eH\u003c/em\u003e test was utilized when the normality condition was not met. Qualitative data were expressed as frequency and percentage (%), with the \u003cem\u003eχ\u0026sup2;\u003c/em\u003e test applied for group comparisons. Spatial interpolation analysis of U-Cd among the study participants was conducted using the Kriging interpolation tool in ArcGIS. All statistical tests were two-sided, and \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003cp\u003eU-Cd and three renal injury biomarkers were logarithmically transformed to achieve normal distribution, after which they were utilized for correlation and quantile regression analyses. Given that UNAG yielded negative values following logarithmic transformation, the transformation \u003cem\u003elg(x\u0026thinsp;+\u0026thinsp;1)\u003c/em\u003e was employed in both Pearson correlation and quantile regression analyses to prevent reverse correlation. The relationship between U-Cd and the renal injury biomarkers were examined using Pearson correlation analysis. Quantile regression was applied to explore the concentration relationship between U-Cd and biomarkers, with \u003cem\u003elog\u003c/em\u003e-transformed U-Cd as the independent variable and the \u003cem\u003elog\u003c/em\u003e-transformed renal injury biomarkers as dependent variables. According to the criteria for assessing renal injury, the concentrations of the three early renal injury biomarkers were classified into two groups: normal (equal to or below the critical value) and abnormal (above the critical value). U-Cd was stratified into three groups based on tertiles: low concentration (\u0026le;\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e33.3\u003c/em\u003e\u003c/sub\u003e), medium concentration (\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e33.3\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e66.7\u003c/em\u003e\u003c/sub\u003e) and high concentration (\u0026ge;\u0026thinsp;\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e66.7\u003c/em\u003e\u003c/sub\u003e). Linear \u003cem\u003eχ\u0026sup2;\u003c/em\u003e trend analysis was conducted to explore the linear trend in the abnormal rates of U-Cd-induced renal injury biomarkers. Multivariate logistic regression model was used to analyze the association between U-Cd and the risk of abnormalities in biomarkers, with low concentration group serving as the reference for calculating the 95% confidence intervals (\u003cem\u003eCIs\u003c/em\u003e) and odds ratios (\u003cem\u003eORs\u003c/em\u003e) for the renal injury biomarkers. The R language \u0026ldquo;pROC\u0026rdquo; package was used to create ROC curve to assess the cut-off values of U-Cd for inducing abnormalities in the URBP, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and UNAG. All statistical descriptions, analyses, and plots were conducted using SPSS 26.0, R 4.3.2 softwares.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDemographic characteristics\u003c/h2\u003e \u003cp\u003eIn this study, 231 participants (55%) were boys and 189 (45%) were girls. The \u003cem\u003eGM\u003c/em\u003e of U-Cd for all participants was 7.58 \u0026micro;g/g cr. Notably, U-Cd was higher in girls (\u003cem\u003eGM\u003c/em\u003e: 8.45 \u0026micro;g/g cr) compared to boys (\u003cem\u003eGM\u003c/em\u003e: 6.70 \u0026micro;g/g cr), with a statistically significant difference observed between the genders (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Furthermore, spatial distribution maps of U-Cd among preschool children in community and kindergarten revealed similar trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, U-Cd was higher in the eastern region compared to the western region, with the lowest level observed in the southwest and the highest in the northeast, indicating a decreasing trend from northeast to southwest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eU-Cd (\u0026micro;g/g cr) in preschool children with different characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eU-Cd (\u0026micro;g/g cr)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eZ/H\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e25\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e50\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e75\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eGM\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e420 (100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231 (55.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGirl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e189 (45.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65 (15.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e170 (40.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98 (23.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6~\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87 (20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnderweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6 (1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e360 (85.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverweight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31 (7.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e17.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObesity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23 (5.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinalysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.799\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e166 (39.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e254 (60.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe concentrations of UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP were 3.76 (1.18, 9.95) U/g cr, 322.40 (164.76, 613.99) \u0026micro;g/g cr, and 343.87 (184.86, 657.40) \u0026micro;g/g cr, respectively. No significant differences were observed between boys and girls concerning the three renal injury biomarkers (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation analysis between U-Cd and renal injury biomarkers\u003c/h3\u003e\n\u003cp\u003ePearson correlation analysis was conducted to examine the relationship between U-Cd and biomarkers of renal injury in preschool children. The \u003cem\u003elog\u003c/em\u003e-transformed U-Cd exhibited positive correlations with UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The correlation coefficients for UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP in relation to U-Cd were 0.41, 0.80, and 0.72, respectively. Additionally, significant correlation was observed among UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP, with URBP showing the strong correlation with Uβ\u003csub\u003e2\u003c/sub\u003e-MG, evidenced by correlation coefficient of 0.79 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). The findings indicated that U-Cd was positively correlated with UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP, with \u003cem\u003eR\u0026sup2;\u003c/em\u003e values of 0.17, 0.64, and 0.52, respectively. Notably, Uβ\u003csub\u003e2\u003c/sub\u003e-MG and URBP demonstrated stronger correlations with U-Cd compared to UNAG (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eQuantile regression was used to analyze the regression of three renal injury biomarkers across ten quantile points: \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e5\u003c/em\u003e\u003c/sub\u003e、\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e15\u003c/em\u003e\u003c/sub\u003e、\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e25\u003c/em\u003e\u003c/sub\u003e、\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e35\u003c/em\u003e\u003c/sub\u003e、\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e45\u003c/em\u003e\u003c/sub\u003e、P\u003csub\u003e55\u003c/sub\u003e、\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e65\u003c/em\u003e\u003c/sub\u003e、\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e75\u003c/em\u003e\u003c/sub\u003e、\u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e85\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e95\u003c/em\u003e\u003c/sub\u003e (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). Generally, the \u003cem\u003eβs\u003c/em\u003e for UNAG and U-Cd were positive, with statistically significant differences observed among the \u003cem\u003eβs\u003c/em\u003e at each quantile. Notably, the differences between boys and girls after \u003cem\u003eP\u003c/em\u003e\u003csub\u003e\u003cem\u003e25\u003c/em\u003e\u003c/sub\u003e were also statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Overall, the \u003cem\u003eβs\u003c/em\u003e exhibited a pronounced increasing trend, which was particularly evident in boys, suggesting that the impact of UNAG intensifies with increasing quantiles. Furthermore, the \u003cem\u003eβs\u003c/em\u003e for Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and U-Cd were positive, with statistically significant differences noted across the entire participants, as well as between boys and girls at each quantile (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The trend in the \u003cem\u003eβs\u003c/em\u003e for these biomarkers displayed slight fluctuations alongside gradual growth pattern, indicating that U-Cd positively influences both Uβ\u003csub\u003e2\u003c/sub\u003e-MG and URBP (Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and S1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAssociation analysis between U-Cd and abnormal renal injury biomarkers\u003c/h3\u003e\n\u003cp\u003eThe tertiles of U-Cd among preschool children were classified into \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e (\u0026le;\u0026thinsp;4.12), \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (4.13\u0026thinsp;~\u0026thinsp;11.24), and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e (\u0026ge;\u0026thinsp;11.25) groups, allowing for the comparative analysis of UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP across participants in different U-Cd groups. Trend analysis results indicated that the abnormal rates of UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and the combined rates across the three groups significantly increased with rising U-Cd concentrations in both boys and girls. Notably, the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e group exhibited the highest abnormal rate, with statistically significant differences observed in abnormal rates among the different U-Cd groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Subgroup analysis of urinalysis and BMI revealed that the highest rates were observed in the SG and normal weight groups. However, the difference was statistically significant only in the BLD group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe multivariate logistic regression model analysis demonstrated that UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and combined biomarker were all positively correlated with U-Cd. However, different levels of U-Cd were not associated with UNAG in girls. In comparison to the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e group, the risk of UNAG abnormalities in preschool children from the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e group increased by 2% and 3% in the overall participants and in boys, respectively, with \u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e) values of 1.02 (1.01, 1.04) and 1.03 (1.01, 1.05) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When compared to the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e group, the risk of abnormalities in Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and combined biomarker in preschool children from the \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003e3\u003c/em\u003e\u003c/sub\u003e group rose by 5%, 6%, and 5% in the overall participants, respectively, with \u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e) values of 1.05 (1.02, 1.07), 1.06 (1.02, 1.09), and 1.05 (1.02, 1.08) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In boys, the risk increased by 4%, 4%, and 3%, respectively, with \u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e) values of 1.04 (1.01, 1.06), 1.04 (1.01, 1.07), and 1.03 (1.01, 1.06) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In girls, the risk increased by 8%, 12%, and 10%, respectively, with \u003cem\u003eOR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e) values of 1.08 (1.03, 1.13), 1.12 (1.05, 1.19), and 1.10 (1.03, 1.17) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Due to the low abnormal rates of BIL and NIT in urinalysis, as well as the underweight group in the BMI of preschool children, these variables were excluded from the logistic regression model analysis. Overweight and obesity were combined into a single variable for this analysis. Subgroup analysis revealed that varying levels of U-Cd were not associated with the risk of abnormal urinalysis or BMI (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig. S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of U-Cd cut-off value for abnormal biomarkers of induced renal injury\u003c/h2\u003e \u003cp\u003eThe results of the ROC curve (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) showed that the optimal cut-off values of overall U-Cd for inducing abnormalities in UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and combined biomarker were 7.78, 14.74, 12.75, and 10.42 \u0026micro;g/g cr, respectively, with corresponding sensitivities of 81%, 89%, 94%, and 81%. The specificities for these values were 61%, 85%, 83%, and 84%, respectively. For boys, the optimal U-Cd cut-off values were 4.66, 15.66, 10.42, and 7.90 \u0026micro;g/g cr, with sensitivities of 97%, 81%, 97%, and 81%, and specificities of 49%, 88%, 80%, and 75%, respectively. In contrast, the optimal U-Cd cut-off values for girls were 11.07, 14.59, 12.75, and 11.07 \u0026micro;g/g cr, with corresponding sensitivities of 84%, 97%, 94%, and 93%, and specificities of 67%, 84%, 83%, and 79%, respectively.\u003c/p\u003e \u003cp\u003eWhen the sensitivity was set at 95%, the cut-off values of U-Cd for inducing abnormalities in UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and combined biomarker were 4.70, 10.42, 11.07, and 5.18 \u0026micro;g/g cr, respectively, with corresponding specificities of 42%, 76%, 78%, and 52%. For boys, the cut-off values of U-Cd were 4.69, 6.81, 10.48, and 4.69 \u0026micro;g/g cr, yielding specificities of 49%, 63%, 80%, and 55%, respectively. In contrast, the cut-off values of U-Cd for girls were 5.70, 11.19, 13.03, and 7.77 \u0026micro;g/g cr, with corresponding specificities of 41%, 74%, 84%, and 65%, respectively (Table S3).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that \u003cem\u003eGM\u003c/em\u003e of U-Cd in preschool children was 7.58 \u0026micro;g/g cr. the \u003cem\u003eGM\u003c/em\u003e for boys and girls were 6.94 \u0026micro;g/g cr and 8.45 \u0026micro;g/g cr, respectively. Although these values were lower than the threshold of 15 \u0026micro;g/g cr set by the Determination Standard for Environmental Cadmium Pollution Health Hazard Area (GB/T17221-1998) [20], they exceeded the threshold of 5 \u0026micro;g/g cr established for the \u0026lsquo;potential high-risk population\u0026rsquo; in the Technical Guidelines for Diagnosis and Treatment of Heavy Metal Pollution (trial) [21, 22]. Notably, the \u003cem\u003eGM\u003c/em\u003e in girls was higher than that in boys. Previous studies have indicated gender differences in U-Cd in China, with women typically exhibiting higher level than men, which aligns with the findings of this study [23]. Furthermore, research conducted in the United States and Canada has similarly demonstrated that U-Cd concentrations are higher in the normal adult female population compared to their male counterparts [24]. Suggesting that Cd might accumulated more readily in women than in men, particularly in polluted areas. This phenomenon may be related to decreased iron stores in women due to physiological factors, which could enhance Cd absorption [25, 26]. The \u003cem\u003eGM\u003c/em\u003e of U-Cd in preschool children within the study area was approximately 5.3 times greater than the upper limit of average U-Cd levels reported in foreign literature for children aged 3 to 14 years (0.07\u0026ndash;1.43 \u0026micro;g/g cr) [27]. Furthermore, the highest U-Cd level recorded among preschool children in Wuxi and Shanghai (4.86 \u0026micro;g/g cr), along with the median level in preschool children from the Dachang mining area of Nandan County in Guangxi (1.19 \u0026micro;g/g cr), were both lower than the findings of this study [28, 29]. Also, the spatial interpolation analysis indicated that Cd in the northeastern part of the study area was higher than in other regions. Furthermore, the data revealed that the total number of mines in the study area reached 51 by the end of 2021, with the majority of these mines located in the northeastern part of the area [30]. This correlation suggests that U-Cd levels are associated with the distribution of mines. These results indicate that preschool children in the study area are exposed to high level of environmental Cd exposure.\u003c/p\u003e \u003cp\u003eThe results of the correlation analysis in this study showed that U-Cd was positively correlated with UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and URBP. Sun Hong et al. reported a strong linear correlation between UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, and U-Cd, which align with the findings of the present study [21, 31]. A 35-year cohort study involving 2,213 adults in Japan demonstrated a positive and significant dose-response relationship between U-Cd and both Uβ\u003csub\u003e2\u003c/sub\u003e-MG and URBP [32]. Quantile regression analysis revealed that U-Cd positively influenced the three renal injury biomarkers; specifically, as U-Cd concentration increased, each quantile of renal injury biomarkers exhibited an upward trend. This indicated that U-Cd has a significant positive effect on early renal injury within the study participants. Furthermore, the relationship between U-Cd and renal injury biomarkers were assessed using tertiles. It was observed that high U-Cd significantly increased the abnormal rates of UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and the combined biomarker, with the increasing trend being statistically significant. These findings suggested that UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and the combined biomarker serve as indicators of early renal injury. Du Yu et al. conducted a long-term dynamic observation of the population in Cd-contaminated area in Jiangxi, concluding that urinary protein did not significantly increase in the early stages when glomerular and renal function remained intact. During this initial period, the primary indicators were elevated levels of U-Cd and urinary enzymes. As Cd exposure increased, both U-Cd and urinary enzymes rose concurrently, followed by an increase in urinary protein, indicating that organic lesions in the renal system had already developed. In the later stages of Cd poisoning, the rise in U-Cd and urinary protein became predominant concerns [33]. However, variability in pollution levels, population characteristics, methodologies, and inconsistent exposure assessment indicators and endpoints across population-based studies limits the comparability of the results. The relationship between U-Cd and biomarkers in the progression of early renal injury among preschool children remains unclear, necessitating a substantial number of population-based epidemiological studies to clarify this relationship. Furthermore, it was observed that U-Cd was significantly higher in individuals with lower BMI [14], while this study found no correlation between U-Cd and BMI, this discrepancy may be attributed to the age and exposure of the participants selected for the study.\u003c/p\u003e \u003cp\u003eIn ROC curve analysis, selecting the optimal cut-off value is crucial for maximizing both sensitivity and specificity [34]. We utilized the coordinates of the ROC curves to identify the optimal cut-off values for U-Cd that yielded the highest sensitivity and specificity. In this study, the optimal U-Cd cut-off values for inducing abnormal UNAG, Uβ\u003csub\u003e2\u003c/sub\u003e-MG, URBP, and combined biomarker in preschool children, both overall and for boys and girls, were found to be lower than the established standard of 15 \u0026micro;g/g cr for populations in Cd-contaminated areas in China [20]. With the exception of the optimal U-Cd cut-off values for inducing abnormal UNAG in boys, the other cut-off values exceeded the Cd poisoning standard for occupational populations in China, as well as the 'potential high-risk population' cut-off value of 5 \u0026micro;g/g cr outlined in the Technical Guidelines for the Diagnosis and Treatment of Heavy Metal Pollution (trial) [17, 22]. Notably, research has shown that even U-Cd below 0.5 \u0026micro;g/g cr can lead to adverse renal effects [35]. Therefore, to better safeguard the health of preschool children, it is recommended that lower U-Cd thresholds be considered, as UNAG demonstrated greater sensitivity for the early identification of renal injury, followed by the combined biomarker.\u003c/p\u003e \u003cp\u003eWe also set a fixed sensitivity (95%, which meant 5% false negative rate), and identified the U-Cd values that achieved this sensitivity on the ROC curves. In comparison to the U-Cd thresholds previously estimated using the benchmark dose (BMD) method in the study area, when UNAG as the outcome biomarker, the optimal cut-off value of U-Cd and the cut-off value obtained under 95% sensitivity were similar to those of BMD/BMDL\u003csub\u003e10\u003c/sub\u003e (8.87/6.14 \u0026micro;g/g cr) and BMD/BMDL\u003csub\u003e05\u003c/sub\u003e (4.76/2.76 \u0026micro;g/g cr), respectively. When Uβ\u003csub\u003e2\u003c/sub\u003e-MG or URBP as the outcome biomarker, the cut-off value of U-Cd obtained under 95% sensitivity was also similar to that of BMD/BMDL\u003csub\u003e05\u003c/sub\u003e (Uβ\u003csub\u003e2\u003c/sub\u003e-MG: 9.08/7.59 \u0026micro;g/g cr, URBP: 8.31/6.82 \u0026micro;g/g cr) [36]. However, the choice of higher sensitivity comes at the expense of lower specificity. In the current study, we observed that as sensitivity increased, the concentration of U-Cd required to induce abnormal biomarkers of renal injury decreased. While utilizing high-sensitivity determination thresholds may have identified more individuals with abnormalities in renal injury biomarkers, it would also have resulted in a higher rate of false positives. This situation suggests that children who are unlikely to develop renal injury may require more frequent examinations and visits, thereby inflating the perceived prevalence of Cd-induced renal injury among preschool children. Consequently, in ROC curve analysis, selecting cut-off value should take into account the clinical and practical objectives of screening or diagnosis [34, 37, 38].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eU-Cd levels among preschool children in this study were generally high, revealing the correlation between U-Cd and biomarkers of renal injury. Our findings suggest that appropriate cut-off value for U-Cd should be established based on the sensitivity and specificity of various renal injury biomarkers. This approach will enhance diagnostic accuracy and prevent misdiagnosis, thereby alleviating unnecessary distress for families of preschool children and society at large.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was supported by the Natural Science Foundation of Gansu (23JRRA1073) and the Science and Technology Plan of Baiyin (2023-1-53Y).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGulipiyan Balajiang:\u003c/strong\u003e Research design, Sample collection and testing, Data processing, Writing-Original draft preparation, Formal analysis, Methodology. \u003cstrong\u003eYue Du:\u003c/strong\u003e Sample collection, Formal analysis.\u003cstrong\u003e\u0026nbsp;Wenzheng Yuan:\u003c/strong\u003e Sample collection, Formal analysis. \u003cstrong\u003eJingru Xie:\u003c/strong\u003e Sample collection, Formal analysis. \u003cstrong\u003eWenting Zhao:\u003c/strong\u003e Sample collection, Formal analysis. \u003cstrong\u003eShiwei Ai:\u003c/strong\u003e Research design, Conceptualization, Writing\u0026mdash;review \u0026amp; editing, Funding acquisition. \u003cstrong\u003eYuhui Dang:\u003c/strong\u003e Research design, Methodology, Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYan Z, Lei Z, Hongguang C, Haixu S, Xiangfen Cui. 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The choice of methods in determining the optimal cut-off value for quantitative diagnostic test evaluation. \u003cem\u003eStatistical Methods in Medical Research.\u003c/em\u003e 2018;27(8):2374\u0026ndash;2383.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Preschool children, Urinary cadmium, Renal injury, Cut-off value","lastPublishedDoi":"10.21203/rs.3.rs-5770154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5770154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChildren demonstrate increased sensitivity and vulnerability to cadmium exposure compared to adults. Current research predominantly focuses on adults residing in cadmium-contaminated areas, while studies involving children remain relatively scarce. This study aimed to explore the relationship between urinary cadmium (U-Cd) and biomarkers of renal injury, identify sensitive biomarkers associated with cadmium-related renal injury, and evaluate the optimal cut-off value for U-Cd in preschool children. Morning urine samples were collected to detect urinalysis, U-Cd, and renal injury biomarkers, including urinary N-acetyl-β-D-glucosidase (UNAG), urinary β2-microglobulin (Uβ2-MG), and urinary retinol-binding protein (URBP). Pearson correlation, quantile regression, and logistic regression models were utilized to explore the relationships between U-Cd and the renal injury biomarkers. Receiver operating characteristic (ROC) curves were employed to determine the optimal cut-off value of U-Cd for inducing abnormalities in renal injury biomarkers. U-Cd demonstrated positive associations with UNAG, Uβ2-MG, and URBP. The optimal cut-off values of U-Cd for inducing abnormalities in UNAG, Uβ2-MG, URBP, and combined biomarker were 7.78, 14.74, 12.75, and 10.42 \u0026micro;g/g cr, respectively. When the sensitivity was set at 95%, the cut-off values were adjusted to 4.70, 10.42, 11.07, and 5.18 \u0026micro;g/g cr, respectively. U-Cd was significantly associated with renal injury biomarkers. Our findings suggest that the appropriate cut-off value for U-Cd should be established based on the sensitivity and specificity of various renal injury biomarkers.\u003c/p\u003e","manuscriptTitle":"Association of urinary cadmium with renal injury biomarkers and optimal cut-off value of urinary cadmium in preschool children from mining area of northwestern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 19:56:20","doi":"10.21203/rs.3.rs-5770154/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8fd28499-944d-4f8c-9b0e-b16073c487ea","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-21T23:21:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 19:56:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5770154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5770154","identity":"rs-5770154","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}