Combination and Interaction of Seven Trace Elements and the Risk of Protein-Energy Malnutrition in School-Aged Children in Shenzhen, China

Combination and Interaction of Seven Trace Elements and the Risk of Protein-Energy Malnutrition in School-Aged Children in Shenzhen, 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 Combination and Interaction of Seven Trace Elements and the Risk of Protein-Energy Malnutrition in School-Aged Children in Shenzhen, China Mingtao Yu, Leyun Tan, Yuhui Chen, Jianhui Shang, Yingbin You, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6029572/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jun, 2025 Read the published version in Biological Trace Element Research → Version 1 posted 9 You are reading this latest preprint version Abstract Background The imbalance of trace elements plays an important role in childhood malnutrition, but previous studies are usually specific to certain elements. We aimed to examine the individual and joint associations between multiple elements and the risk of protein-energy malnutrition (PEM) in young school children. Methods This study measured the serum levels of Zinc (Zn), Copper (Cu), Chromium (Cr), Cobalt (Co), Vanadium (V), Manganese (Mn), and Nickel (Ni) in 1832 out of 5152 children aged 6 to 9 years by using inductively coupled plasma mass spectrometry. The individual and joint association of element and risk of PEM were appraised using logistic regression, restricted cubic splines model (RCS), bayesian kernel machine regression (BKMR), and weighted quantile sum regression (WQS) model, respectively. Results Serum concentrations of Zn, Cu, Co, V, Mn, and Ni were significantly lower in the PEM group than in controls (all P < 0.005). Higher quartile concentrations of Zn (OR = 0.52), Cu (0.59), V (0.52), Mn (0.51), and Ni (0.68) were associated with lower PEM risk (all Ptrend < 0.05). RCS model indicated non-linear relationships between Zn, Cu, Cr, Co, V, Mn, and PEM risk. Interactions were found between Zn, Mn, and Co on the risk of PEM. Both BKMR and WQS models revealed a negative joint association of seven elements with PEM risk (OR = -0.102), Mn (40.4%), and Zn (19.1%) as the strongest contributors. Conclusion Serum concentrations of Zn, Cu, Co, V, and Mn were relatively lower in Children with PEM and exhibited non-linear associations with the risk of PEM. The joint association of seven trace elements was negative with the risk of PEM, in which Mn and Zn contribute the most. Additionally, Mn, Zn, and Co exhibited pairwise interactions. These findings highlight the importance of maintaining balanced trace element levels to mitigate PEM in children. manganese zinc Protein energy malnutrition (PEM) Restricted cubic spline (RCS) bayesian kernel machine regression (BKMR) Weighted quantile sum regression (WQS) Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Protein-Energy Malnutrition (PEM) is a common form of subclinical undernutrition and has significantly jeopardized the health and development of children globally [ 1 , 2 ]. PEM in children has long-term detrimental effects, including impaired physical growth, increased susceptibility to infections, anemia, chronic kidney disease, and the potential development of conditions such as hypertension in adulthood. Additionally, PEM adversely affects psychological health, leading to lower cognitive function, diminished attention span, and reduced intelligence [ 3 – 5 ]. PEM affects one-quarter of children worldwide, with 70% of cases occurring in Asia, China still accounts for 5% of the global stunting burden [ 6 ]. In China, even in developed areas like Beijing, the prevalence of PEM reached 10% in children aged 3–14 years, whereas, in other undeveloped areas, the prevalence of PEM is up to 15.8–19.2% [ 7 – 9 ]. As we all know, macronutrients including protein, fat, and carbohydrates are directly linked to the growth and development of humans and are well-established contributors to PEM in children [ 10 , 11 ]. However, micronutrients, such as vitamins and minerals, are also required by the body in small amounts but also play a crucial role in maintaining health and supporting vital life processes [ 11 ]. Essential trace elements, such as Zinc (Zn), Copper (Cu), Cobalt (Co), and Manganese (Mn) play an important role in the growth and development of children. Thereinto, Zn is the most closely related trace element, due to its involvement in catalyzing more than 100 enzymes, playing an important role in facilitating protein folding, protein and DNA synthesis, cell signaling, and division, etc. [ 12 ]. Additionally, Long-term low levels of Zn can lead to Cu accumulation, as Zn and Cu have antagonistic effects. Excessive Cu can be toxic, which may increase the risk of oxidative damage to cells and impair normal growth and development [ 13 , 14 ]. Mn is involved in the synthesis and activation of many enzymes and the regulation of the metabolism of glucose and lipids in humans [ 15 ]. The associations between serum levels of Mn and nutritional status had been reported in children and adolescents, but the results were inconsistent [ 16 , 17 ]. In addition to the trace elements mentioned above, potentially essential trace elements, such as Chromium (Cr), Nickel (Ni), and Vanadium (V) were acknowledged to have a relationship with malnutrition, however, the relationships have not been thoroughly described [ 18 ]. Except for nutrients, some traditional risk factors such as maternal education, the circumstances of birth, lifestyle, genetic factors, illness, and even environmental pollution have been discussed adequately [ 19 , 20 ]. Regarding the different requirements for micronutrients during the different times of pediatric age can be a useful guide for clinical practice and dietary instruction. In pre-adolescence, children typically do not require supplements, as their micronutrient needs can be adequately met through a diverse diet [ 21 ]. However, school age spans from around six years until the onset of puberty. Young school-aged children need extra attention as they adapt to new environments and learning demands, which can lead to mental stress, sleep disturbances, and digestive issues. Additionally, as children begin managing aspects of their routines, such as eating out or participating in lunchtime care, their diets may become less balanced. As we know, trace elements cannot be synthesized in the human body, but are absorbed from food. Consequently, Children in this age group are particularly vulnerable to micronutrient imbalance. Limited to detection equipment or statistical methods, previous studies have mostly acknowledged the role of individual trace elements in PEM. It's worth noting that trace elements generally do not function independently in the body [ 22 ]. The combined effect or interactions of trace elements on PEM remain unclear. Consequently, the objectives of this study are to compare the concentrations of seven trace elements (essential: Zn, Cu, Mn; potentially essential: Cr, Co, V, Ni) between the PEM and normal groups, and to explore both individual and combined associations of these elements with the risk of PEM in children aged 6 to 9 years in Baoan District, Shenzhen, China. Several statistical methods were applied in this study: logistic regression and restricted cubic spline (RCS) models were used to assess individual associations between trace elements and PEM risk in children, while Bayesian kernel machine regression (BKMR) and weighted quantile sum regression (WQS) explored their combined effects. These findings provide a scientific basis for developing effective interventions to prevent childhood malnutrition, improve nutritional status, and support healthy child development. 2. Materials and Methods 2.1 Study population A total of 5,152 students from 19 primary schools in Bao’an District, Shenzhen, China, were surveyed using cluster sampling, with 4,829 responses (response rate: 93.5%). After excluding 316 students due to severe cardiac, hepatic, or renal diseases, tumors, recent micronutrient or protein supplement use, or unwillingness to participate, 4,513 completed physical exams and questionnaires. Among these, 916 children (20.1%) were diagnosed with PEM and matched 1:1 with controls by age (± 0.5 years) and gender. Serum samples from 1,832 children were analyzed for Zn, Cu, Cr, Co, V, Mn, and Ni using inductively coupled plasma-mass spectrometry (ICP-MS). (Fig. 1 ). 2.2 Demographic information collection Demographic data were collected using structured questionnaires completed by guardians under the guidance of investigators who had undergone standardized training. The questionnaire included general information (gender, age), family background (economic status, parents' height, weight, and educational level, exposure to secondhand smoke), birth conditions (birth weight, delivery method such as cesarean section), and the child's dietary habits and lifestyle (picky eating, nighttime eating habits, frequency of snack, vegetable, and fruit consumption, and duration of physical exercise). 2.3 Blood samples collection and elemental detection Four milliliters of fasting blood were collected into coagulation tubes and transported to the biochemistry laboratory at Baoan Central Hospital within 30 minutes. The samples were then transferred to centrifuge tubes pre-soaked overnight in 0.5% ultrapure-grade nitric acid. After centrifugation at 1,000 r/min for 5–10 minutes, the serum was extracted and stored at -80°C. Serum levels of Zn, Cu, Cr, Co, V, Mn, and Ni were measured using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7900, Agilent Technologies, USA). Multi-element calibration standards (10 µg/mL, Agilent Technologies) in a 5% HNO₃ matrix were prepared by serial dilution with ultrapure water (18.2 MΩ·cm) containing 3% (v/v) nitric acid, resulting in final concentrations of 0, 1, 2.5, 5, 10, 20, 40, 80, 100, and 160 ppb (Table S1 ). An ICP-MS Stock Tuning Solution (10 µg/mL, Agilent Technologies, in a 2% HNO₃ matrix) and an ICP-MS Internal Standard Mix (100 µg/mL, Agilent Technologies, in a 10% HNO₃ matrix) were each diluted 1:10,000 in a 3% nitric acid solution. For sample preparation, 100 µL of serum was transferred into a PFA digestion tube and dried in an oven at 80°C for 6 hours until a crystalline layer formed. After cooling to room temperature, 250 µL of 65% nitric acid was added in a fume hood, the tube was securely capped, and the sample was incubated in an 80°C water bath for 1 hour. Once cooled, 250 µL of 30% hydrogen peroxide was added to a fume hood, followed by another 5-minute incubation at 80°C. After cooling, the sample was stored overnight at 4°C for acidification. Before analysis, 4.5 mL of 3% nitric acid was added to the digestion tube to dilute the sample to a final volume of 5 mL. The solution was thoroughly mixed and transferred to a polypropylene tube for ICP-MS analysis, ensuring consistency with the prepared standards for calibration. This protocol ensured high accuracy and reproducibility in trace element quantification. 2.4 Covariates The covariates included family economic status, assessed using the Engel coefficient (> 49%, 40–49%, 30–39%, < 30%), parental Body Mass Index (BMI) categories (low, normal, overweight, obese), and parental education levels (high school or lower, junior college, bachelor’s degree or higher). Additional covariates comprised exposure to secondhand smoke (yes/no), cesarean delivery (yes/no), birth weight categories (low birth weight, normal weight, macrosomia), daily late-night snack consumption (yes/no), picky eating habits (yes/no), snack consumption (yes/no), frequency of vegetable and fruit intake (≥ 3 times/week, < 3 times/week), and duration of physical activity (≥ 1 hour/day, < 1 hour/day). All covariate information was collected through structured questionnaires completed by the parents of the children. 2.5 Outcome assessment PEM in children was diagnosed only if both stunting and wasting criteria were met, as defined by the WS/T 456–2014 standard [ 1 ]. Stunting was determined for children whose height (cm) fell at or below the stunting threshold for their respective gender and age group. Wasting was classified based on BMI: children with a BMI at or below the moderate to severe wasting threshold for their gender and age group were categorized as moderately to severely wasted, while those with a BMI at or below the mild wasting threshold were classified as mildly wasted. BMI was calculated by dividing weight (kg) by height squared (m²). 2.6 Statistical analysis The data were presented as median (M) and interquartile range (IQR) due to the non-normal distribution of the seven trace element levels, as indicated by normality tests. Group comparisons were performed using the nonparametric Mann-Whitney U test. Qualitative data were expressed as rates or composition ratios, and group differences were assessed using the chi-square test. To examine potential dose-response relationships, conditional logistic regression and RCS models were performed. For the logistic regression model, trace element concentrations were divided into four quartiles, and two logistic regression models were constructed: Model 1 (unadjusted) and Model 2 (adjusted for covariates such as parental weight, education level, cesarean delivery, birth weight, and picky eating habits). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, with the lowest quartile serving as the reference. Median values within each quartile were used as continuous variables for trend tests. For the RCS model, five knots were positioned at the 5th, 35th, 50th, 65th, and 95th percentiles, with the median (50th percentile) as the reference. Relevant covariates were included in the RCS models to ensure robust adjustment. Further exploring the relationships between levels of serum trace elements and the risk of PEM in children, we adopted the BKMR and WQS models, which are good at handling high-dimensional data and accommodating nonlinear and nonadditive relationships [ 23 ] [ 24 ]. Using a Markov Chain Monte Carlo (MCMC) sampler with 10,000 iterations, we mainly conducted the following analyses in the BKMR model: (1) Calculated the relative importance of seven trace elements in PEM risk using the posterior inclusion probability (PIP). (2) Examined interactive effects between pairs of trace elements on PEM risk. (3) Analyzed the overall effect of the combined trace element mixture, fixing all elements at the 50th percentile and assessing outcome changes at 5-percentile increments (from the 5th to 95th percentile). In addition, a negative WQS model was constructed with the index generated through 10,000 bootstrap iterations, quantifying the combined effect of exposures on the outcome. The model also assigns weights to each exposure, highlighting its contribution to the overall mixture effect. R version 4.4.0 performed all statistical analyses, and P < 0.05 was considered statistically significant. 3. Results 3.1 Baseline characteristics of the study population As shown in Table 1 , the study population had a mean age of 7.1 ± 0.3 years, with 56.9% being boys. Children with lower parental BMI, lower parental education levels, non-cesarean births, and low birth weight were significantly more likely to have PEM (all P < 0.001). Additionally, picky eating was strongly associated with a higher prevalence of PEM (P 49% 345 (18.8) 171 (49.6) 174 (50.4) 40 ~ 49% 384 (21.0) 185 (48.2) 199 (51.8) 30 ~ 39% 648 (35.4) 322 (49.7) 326 (50.3) < 30% 455 (24.8) 238 (52.3) 217 (47.7) BMI of father < 0.001 Low 44 (2.4) 11 (25.0) 33 (75.0) Normal 1065 (58.1) 493 (46.3) 572 (53.7) Overweight 618 (33.7) 351 (56.8) 267 (43.2) Fat 105 (5.7) 61 (58.1) 44 (41.9) BMI of mother < 0.001 Low 277 (15.1) 104 (37.5) 173 (62.5) Normal 1326 (72.4) 665 (50.2) 661 (49.8) Overweight 204 (11.1) 134 (65.7) 70 (34.3) Fat 25 (1.4) 13 (52.0) 12 (48.0) Education level of parents < 0.001 High school or lower 788 (43.0) 349 (44.3) 439 (55.7) Junior college 483 (26.4) 256 (53.0) 227 (47.0) Bachelor’s degree or higher 561 (30.6) 311 (55.4) 250 (44.6) Secondhand smoke exposure (Yes) 895 (48.9) 454 (50.7) 441 (49.3) 0.575 Cesarean section (Yes) 732 (40.0) 401 (54.8) 331 (45.2) 0.001 Birth weight < 0.001 Low birth weight 93 (5.1) 35 (37.6) 58 (62.4) Normal birth weight 1648 (90.0) 824 (50.0) 824 (50.0) Macrosomia 91 (5.0) 57 (62.6) 34 (37.4) Eat snacks every night (Yes) 605 (33.0) 306 (50.6) 299 (49.4) 0.766 Picky eaters (Yes) 1693 (92.4) 818 (48.3) 875 (51.7) < 0.001 Snacks (Yes) 1483 (85.5) 757 (51.0) 726 (49.0) 0.518 Vegetable consumption frequency (≥ 3 times/week) 1332 (72.7) 659 (49.5) 673 (50.5) 0.495 Fruit consumption frequency (≥ 3times/week) 960 (68.5) 504 (52.5) 456 (47.5) 0.825 Physical exercise (≥ 1hours/day) 1358 (74.1) 697 (51.3) 661 (48.7) 0.062 Family economic proportion assessed via Engel's coefficient; the BMI is calculated based on the ratio of weight (in kilograms, kg) to the square of height (in meters, m). According to the Health Industry Standard of the People's Republic of China, WS/T 428–2013 "Criteria of weight for adults", parent’s BMI is categorized into underweight (BMI < 18.5 kg/m²), normal weight (18.5 kg/m² ≤ BMI < 24.0 kg/m²), overweight (24.0 kg/m² ≤ BMI < 28.0 kg/m²), and obesity (BMI ≥ 28.0 kg/m²) [1] ; According to the standards established by the World Health Organization (references [72, 73]), birth weight is classified into low birth weight (birth weight < 2500 g), normal weight (2500 g ≤ birth weight < 4000 g), and macrosomia (birth weight ≥ 4000 g) [2] ; differences in distribution between the two groups were analyzed using Chi-square test or Mann-Whitney U test; P-values in bold indicate statistically significant differences. 3.2 Levels of serum trace element and its comparison between two groups Table 2 presents the median levels of seven trace elements and their comparison between the PEM and control groups. The median concentrations of Zn (106.00 vs. 117.67 µg/dL), Cu (104.93 vs. 113.35 µg/dL), Co (0.06 vs. 0.07 µg/dL), V (0.09 vs. 0.10 µg/dL), Mn (0.77 vs. 0.91 µg/dL), and Ni (1.64 vs. 1.82 µg/dL) were significantly lower in the PEM group compared to the control group (all P < 0.01). However, while the median Cr level was also lower in the PEM group than in the control group (2.52 vs. 2.66 µg/dL), this difference was not statistically significant (P = 0.145). Table 2 Concentration of trace elements in case-control groups (µg/dl) [M (P 25 , P 75 )] Element Total (n = 1832) Non-PEM(n = 916) PEM (n = 916) P Zn 111.50 (89.18,141.75) 117.67 (94.73, 147.71) 106.00 (83.80, 133.82) < 0.001 Cu 108.41(85.57,137.45) 113.35 (88.58, 143.25) 104.93 (83.70, 129.36) < 0.001 Cr 2.58 (1.66, 3.93) 2.66 (1.77, 3.94) 2.52 (1.55, 3.92) 0.145 Co 0.06 (0.04, 0.11) 0.07 (0.04, 0.13) 0.06 (0.04, 0.10) 0.005 V 0.10 (0.07, 0.13) 0.10 (0.08, 0.14) 0.09 (0.07, 0.12) < 0.001 Mn 0.83 (0.61, 1.28) 0.91 (0.65, 1.42) 0.77 (0.58, 1.15) < 0.001 Ni 1.72 (1.15, 3.53) 1.82 (1.20, 4.07) 1.64 (1.11, 3.21) 0.002 The differences in concentrations between the two groups were analyzed using the Mann-Whitney U test. P-values in bold indicate statistically significant differences. 3.3 The association between levels of serum trace elements and the risk of PEM The conditional logistic regression model demonstrated an association between trace element levels and PEM in children (Table 3 ). In Model 2, compared to the lowest quartile, the highest quartile concentrations of Zn (OR: 0.52; 95% CI: 0.39–0.69), Mn (OR: 0.51; 95% CI: 0.38–0.67), V (OR: 0.52; 95% CI: 0.40–0.69), Cu (OR: 0.59; 95% CI: 0.44–0.79), and Ni (OR: 0.68; 95% CI: 0.52–0.89) were significantly negatively associated with PEM in children. The trends for these five trace elements remained statistically significant. However, the ORs for Co and Cr did not exhibit significant changes at their highest quartile levels. Table 3 Association between trace element exposure and the risk of PEM (OR, 95%CI) Element Model PEM P trend Q1 (≤ P 25 ) Q2 (P 25 ~ P 50 ) Q3 (P 50 ~ P 75 ) Q4 (> P 75 ) Zn 1 1.00 0.83 (0.64, 1.08) 0.54 (0.41, 0.70) 0.50 (0.39, 0.66) < 0.001 2 1.00 0.81 (0.61, 1.07) 0.56 (0.42, 0.74) 0.52 (0.39, 0.69) < 0.001 Cu 1 1.00 1.00 (0.76, 1.32) 0.89 (0.68, 1.16) 0.58 (0.44, 0.75) < 0.001 2 1.00 0.96 (0.72, 1.29) 0.87 (0.65, 1.15) 0.59 (0.44, 0.79) 0.001 Cr 1 1.00 0.65 (0.50, 0.84) 0.68 (0.53, 0.89) 0.75 (0.58, 0.97) 0.005 2 1.00 0.71 (0.54, 0.93) 0.68 (0.52, 0.90) 0.78 (0.59, 1.02) 0.028 Co 1 1.00 0.92 (0.71, 1.20) 0.78 (0.60, 1.01) 0.70 (0.54, 0.91) 0.032 2 1.00 0.92 (0.70, 1.22) 0.78 (0.59, 1.03) 0.78 (0.59, 1.03) 0.203 V 1 1.00 0.77 (0.59, 0.99) 0.56 (0.43, 0.74) 0.52 (0.40, 0.67) < 0.001 2 1.00 0.77 (0.58, 1.01) 0.53 (0.40, 0.70) 0.52 (0.40, 0.69) < 0.001 Mn 1 1.00 0.73 (0.56, 0.96) 0.65 (0.50, 0.85) 0.48 (0.36, 0.63) < 0.001 2 1.00 0.75 (0.56, 0.99) 0.64 (0.48, 0.85) 0.51 (0.38, 0.67) < 0.001 Ni 1 1.00 0.80 (0.62, 1.04) 0.72 (0.55, 0.93) 0.68 (0.53, 0.88) 0.016 2 1.00 0.77 (0.58, 1.01) 0.67 (0.51, 0.89) 0.68 (0.52, 0.89) 0.016 Concentrations of trace elements were log-transformed prior to analysis. Model 1 was unadjusted, while Model 2 was adjusted for covariates, including parental BMI and education level, child's birth weight, picky eating behaviors, and whether the child was delivered by cesarean section. OR values in bold indicate a statistically significant difference from the reference group; P values in bold indicate a significant trend. 3.4 The non-linear relationships between levels of serum trace elements and PEM risk The RCS model revealed significant non-linear associations between serum trace element concentrations and PEM risk in children (Fig. 2 ). For Zn, a U-shaped relationship was observed, with the lowest risk of PEM at LN Zn = 5.26, followed by a slight increase at higher levels, and the highest risk of PEM at LN Zn = 4.31, followed by a decreased trend until LN Zn = 5.26 (Fig. 2 b). Mn showed an initial increase in PEM risk, peaking at LN Mn = -0.62 which is lower than its median (-0.186), and a subsequent decline (Fig. 2 a). Co exhibited a similar U-shaped trend, with PEM risk peaking at LN Co = -3.21 and reaching its lowest at LN Co = -1.55 (Fig. 2 c). Cu was negatively associated with PEM risk, with the lowest OR at LN Cu = 5.15 (Fig. 2 f). Cr and V exhibited non-linear associations (Fig. 2 d, Fig. 2 g), with V showing a peak risk at LN V = -3.03 (OR = 1.80) before declining. However, Ni did not show a significantly non-linear trend (P = 0.055) (Fig. 2 e). The BKMR model, presented in the supplementary figure (FigS1), also showed the same nonlinear relationships as the RCS model. 3.5 The interactive and combined relationships among trace elements on the risk of PEM Table S1 . shows that only Mn (PIP = 1.000), Zn (PIP = 0.827), and Co (PIP = 0.707) significantly contributed to PEM risk (PIP > 0.50) [ 25 ]. The bivariate exposure-response analysis revealed that Zn exhibited antagonistic interactions with Mn and Co, while Mn showed a significant synergistic interaction with Co in relation to PEM risk in children, with the remaining five trace elements fixed at their 50th percentile (Fig. 3 a). For the overall effect of combined levels of seven trace elements on PEM risk in children, when all trace elements were set at specific percentiles (ranging from the 5th to the 95th) compared to the 50th percentile, both higher and lower concentrations significantly influenced PEM risk (Table S2). Notably, a decreasing trend in PEM risk was observed as the overall concentration percentile of trace elements increased from the 25th to the 75th percentile (Fig. 3 b). The results from the WQS regression model demonstrated a significant negative association between the combined levels of trace elements and PEM risk (OR: -0.102; 95% CI: -0.162, -0.042) (Table S3). Among the seven trace elements, Mn and Zn contributed the most to the association (Mn: 40.4%, Zn: 19.1%), followed by V, which accounted for 13.9% of the total weight (Fig. 4 ). 4. Discussion In this study, we evaluated PEM in 4513 children aged 6 to 9 years in Shenzhen, China. 916 children were identified with PEM contributing a rate of 20.3%. The levels of most trace elements (Zn, Mn, V, Cu, Co, Ni) in the serum of children were relatively lower in the PEM group and presented non-linear dose-response relationships with the risk of PEM. Moreover, the contribution of Zn and Mn was the largest regarding the negative joint association of seven trace elements with the risk of PEM. Among seven trace elements, Mn, Zn, and Co exhibited pairwise interactions. First, we quantified the levels of seven serum trace elements in children and compared the total levels of trace elements with a study on establishing reference intervals of 11 minerals in children of Liaoning province of China using ICP-MS [ 26 ], we found that Zn, Cu, and Co concentrations overlapping with those of Liaoning, whereas, Mn, Ni, and Cr had significant deviation. Nevertheless, the normal reference value in children is limited, we further compared our results with the normal reference value in adults provided by WHO [ 27 ]. We found that Zn, Cu, and Co were similar; however, Mn and Ni were about 8 times, and Cr was 129 times the WHO reference value. Throughout the testing process, we implemented strict quality control measures, and the normal levels of Zn, Cu, and Co further validated the reliability of our results. Therefore, the abnormal elevations of Mn, Ni, and Cr will need to be clarified in future studies. Next, we found that the levels of Zn, Cu, Co, V, Mn, and Ni in the PEM group were significantly lower than control group, and the outcome of logistic regression also presented that the odds of PEM were much lower at their highest percentiles compared to their lowest percentiles. These findings were aligned with the results of RCS model. Furthermore, significantly nonlinear relationships were presented in the RCS model. For Zn, Co, and Cu, relatively high or low levels had a positive relationship with the risk of PEM; and in middle level, there were negative associations with the risk of PEM, respectively. In addition, an inverted U-shaped relationship existed between Mn, V, and the risk of PEM, while a U-shaped relationship was observed between Cr and the risk of PEM. Interactions were also found between Mn and Zn, Co on the PEM risk. High Co or Zn levels reduced the influence of Mn on PEM risk. And the interaction between Zn and Co appeared to show a buffering effect, where higher Co levels may attenuate the impact of Zn on PEM risk. Finally, for evaluating combined association, both the BKMR and WQS models were adopted and demonstrated that as the combined concentrations of the seven trace elements increased, the risk of PEM in children decreased, showing a significant negative association, with Zn and Mn contributing predominantly. Up to now, due to limitations in detection techniques or statistical methods, the evidence regarding the relationship between trace elements and PEM is absent, especially for the interactions and combined associations. For the relationships between individual trace elements and PEM, our results are consistent with most previous studies. Lack or too high level of essential trace elements like Zn, Mn, Cu, and Co in our study can affect human health status, which may increase the risk of PEM. However, at the median level, there are negative associations between the concentration of these essential trace elements and the risk of PEM. A study involving 425 children aged between 5 and 7 years also concluded that serum levels of Zn were inversely associated with PEM [ 28 ]. Regarding Mn, several studies have identified that Mn was at very low levels in malnourished children, but the evidence suggesting that Mn deficiency cause poor growth in children is still limited [ 29 ]. In our study, the concentration of Cu was significantly lower in the PEM group and negatively associated with the risk of PEM. Savitri Thakur also identified that the decrease in Cu level was associated with the increase in the risk of PEM [ 30 ]. Several previous studies showed that children with malnutrition have lower levels of Cu in serum [ 29 , 31 ]. For Co, V, and Ni, research on its relationship with the risk of PEM is not enough, but as essential or potentially essential trace elements, they play important roles in human health status, more research is needed on the relationship between these trace elements and PEM. A negative association was found between the combined level of seven trace elements and the risk of PEM in our study, which Mn and Zn contributed the most. Our previous research also showed that combined levels of Zn, Mn, and Co were significantly associated with the risk of insufficient muscle development level [ 32 ]. Zn, or zinc ions, act as cofactors for enzymes that regulate carbohydrate, fat, and protein metabolism can control protein function [ 33 ]. Therefore, Zn is crucial for normal growth and development. Studies have shown that Zn deficiency or excessive Zn level can disrupt metabolic processes, and lead to a loss of appetite, which further can delay the growth of children [ 34 ]. However, at appropriate levels, relatively high levels of Zn can promote healthy growth and development [ 35 , 36 ]. Multiple population studies have consistently shown that zinc deficiency is a significant factor contributing to malnutrition in children, while an increase in serum zinc levels can significantly improve growth indicators [ 36 , 37 ]. All those indicate a U-shaped association between Zn level and the risk of PEM in children, which is consistent with our study. Mn, as a cofactor for various metalloproteins such as superoxide dismutase, plays a crucial role in oxidative stress [ 38 ]. Both Mn deficiency and excess can lead to an increase in the production of superoxide dismutase, which in turn causes oxidative damage associated with neuropathological conditions linked to enhanced glucocorticoid expression. Glucocorticoids are key regulators of the biosynthesis and metabolism of carbohydrates, lipids, and proteins [ 15 , 39 ]. Therefore, Mn plays an important role in the metabolism of these processes. Previous studies have also reported that Mn deficiency was associated with malnutrition in children [ 40 ]. Our results underscored the importance of maintaining Mn levels within an optimal range to reduce the risk of PEM in children. We found antagonistic and synergistic interactions among Zn, Mn, and Co. Both Zn and Mn play essential roles in various metabolic pathways. Increasing evidence suggests that Zn or zinc ions, particularly ZNT10, ZIP8, and ZIP14, play key roles in Mn metabolism [ 41 – 43 ]. Michaelis et al. [ 44 ] also found that Zn could decrease the bioavailability of Mn and alter the expression of transport-related genes, thereby reducing the cytotoxic damage induced by Mn. That may explain why there is an antagonistic relationship between Zn and Mn in the risk of PEM in our study. Co, as part of the cobalamin structure, may support the role of Zn in cellular growth processes, thereby reducing the impact of Zn on PEM risk when Co levels are adequate [ 45 ]. Additionally, Zn and Co may share common transport mechanisms or binding sites, which could lead to a balancing effect when both elements are present at optimal levels [ 46 ]. The antagonistic interaction between Mn and Co may stem from competition in biochemical pathways or transport systems. Mn and Co are both involved in oxidative stress management and cellular energy production, yet high levels of one may inhibit the function or absorption of the other due to competitive inhibition at transport sites [ 47 ]. High Co levels could reduce the absorption or efficacy of Mn, leading to diminished Mn impact on PEM risk. Research suggests that Mn and Co may compete for mitochondrial pathways and antioxidant functions, where excess Co can disrupt Mn-dependent enzymatic activities [ 48 ]. This study possesses several noteworthy strengths. First, serum trace element concentrations were measured using ICP-MS, which provides a wide linear dynamic range (10^8), low detection limits, and high precision and accuracy. Second, the study population was relatively large, including 5,152 elementary school students, with 1,832 qualified participants selected, ensuring the reliability and robustness of the findings. Third, multiple statistical methods were employed to enhance result reliability, while also exploring interactions among trace elements and their combined effects. The use of different statistical methods for cross-validation further minimized potential errors. Finally, the RCS model offered reference ranges for both the positive and negative impacts of trace elements on PEM status in children, providing valuable guidance for managing trace element levels in pediatric populations. However, this study has some limitations. First, its cross-sectional design may introduce recall bias in questionnaire responses and limit the ability to establish causal relationships between trace elements and PEM. Second, residual confounding from unmeasured factors, such as genetic predisposition or environmental exposures, cannot be entirely ruled out in this observational study. 5. Conclusions In conclusion, this study suggests that the distributions of Zn, Cu, Co, V, Mn, and Ni are different between the two groups. And Mn, Zn are the most influential trace elements on the risk of PEM in children. This study also reveals the antagonistic interactions between Zn and Mn as well as Co, and the synergistic effect of Co with Mn. Additionally, seven other trace elements were negatively associated with the risk of PEM. These findings highlight the complex interactions among trace elements and their critical role in nutritional health, suggesting the need for further investigation into their mechanisms to inform effective nutritional interventions for reducing PEM risk in children. Abbreviations PEM Protein-energy malnutrition RCS restricted cubic spline BKMR bayesian kernel machine regression WQS weighted quantile sum ICP-MS inductively coupled plasma mass spectrometry WHO world health organization Declarations Competing of interests The authors have no relevant financial or non-financial interests to disclose. Ethical approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Baoan Central Hospital of Shenzhen (protocol code IRB-PJ-2018-002). Consent to participate: Written informed consent was obtained from the parents. Funding: This word was supported by The Natural Science Foundation of Guangdong Province (grant no.: 2022A1515012218). Author Contribution M.T. Yu: Writing – original draft, Writing – review & editing, Methodology, Formal analysis, Software, Visualization. L.Y. Tan: Methodology, Software, Visualization, Writing – original draft. Y.H. Chen., J.F. Shang., Y.B. You., H.M. Xie., N. Pang, R.M. Liang: Investigation. Y.B. You: Data curation. Q.-Y. Zhang: Writing – review & editing, Supervision, Funding acquisition, Project administration, Conceptualization. Acknowledgement We express our sincere gratitude to the participating children and their families for their invaluable cooperation and understanding throughout this study. The study received support from all the physicians and nurses in the Department of Laboratory Medicine, Baoan Central Hospital, Shenzhen. 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Li, Z.; Lai Z Fau - Ya, K.; Ya K Fau - Fang, D.; Fang D Fau - Ho, Y.W.; Ho Yw Fau - Lei, Y.; Lei Y Fau - Ming, Q.Z.; Ming, Q.Z. Correlation between the expression of divalent metal transporter 1 and the content of hypoxia-inducible factor-1 in hypoxic HepG2 cells, doi: 10.1111/j.1582-4934.2007.00145.x Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 24 Jun, 2025 Read the published version in Biological Trace Element Research → Version 1 posted Editorial decision: Revision requested 25 Mar, 2025 Reviews received at journal 25 Mar, 2025 Reviewers agreed at journal 17 Feb, 2025 Reviews received at journal 16 Feb, 2025 Reviewers agreed at journal 16 Feb, 2025 Reviewers invited by journal 15 Feb, 2025 Editor assigned by journal 14 Feb, 2025 Submission checks completed at journal 14 Feb, 2025 First submitted to journal 14 Feb, 2025 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. 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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-6029572","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":416466541,"identity":"b49617e9-971a-4086-8f98-7b950e969271","order_by":0,"name":"Mingtao Yu","email":"","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mingtao","middleName":"","lastName":"Yu","suffix":""},{"id":416466542,"identity":"5f0c8472-e302-4abf-b093-5948a79cbec0","order_by":1,"name":"Leyun Tan","email":"","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Leyun","middleName":"","lastName":"Tan","suffix":""},{"id":416466543,"identity":"930b0cb8-db5a-4c7f-ad48-c9ef7d814cea","order_by":2,"name":"Yuhui Chen","email":"","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yuhui","middleName":"","lastName":"Chen","suffix":""},{"id":416466544,"identity":"c7dcfd45-cd12-4a55-bba4-cac95529929f","order_by":3,"name":"Jianhui Shang","email":"","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Jianhui","middleName":"","lastName":"Shang","suffix":""},{"id":416466545,"identity":"ac8ae373-e7e9-4a0e-b549-7ae877f358d2","order_by":4,"name":"Yingbin You","email":"","orcid":"","institution":"Baoan central hospital of shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Yingbin","middleName":"","lastName":"You","suffix":""},{"id":416466546,"identity":"a194af19-aeac-4794-a5ce-37cf4cc9bc56","order_by":5,"name":"Haimin Xie","email":"","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Haimin","middleName":"","lastName":"Xie","suffix":""},{"id":416466547,"identity":"15314dba-97b6-46cf-bef4-8f23cfc8e942","order_by":6,"name":"Nan Pang","email":"","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Pang","suffix":""},{"id":416466548,"identity":"4fb2a650-bf84-4ce3-a871-0bf4c25bc485","order_by":7,"name":"Rimei Liang","email":"","orcid":"","institution":"Baoan central hospital of shenzhen","correspondingAuthor":false,"prefix":"","firstName":"Rimei","middleName":"","lastName":"Liang","suffix":""},{"id":416466549,"identity":"7b4a3828-5f66-4449-b67b-308be1047cc0","order_by":8,"name":"Qingying Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHACNhAhh8QmUosxhCJFS2ID0Vr4bqQ/e/BzR236/Pk9Bgwfyg4z8M9uwK9F8kZCumHvmeO5G47xGDDOOHeYQeLOAfxaDG4kHJPgbTuWu4GNx4CZt+0wg4FEAiEtiW2Sf9uOpcu3AbX8JU5LMps0b1tNAgPQYcyMxGiRPPOMTVq27YDhhmNpBQd7zqXzSNwgoIXvePozybdtdfLyzYc3PvhRZi3HP4OAFoYLYAWHwewDQMxDQD0QnAepY6gjrHAUjIJRMApGLgAAPpBEXieHuigAAAAASUVORK5CYII=","orcid":"","institution":"Shantou University Medical College","correspondingAuthor":true,"prefix":"","firstName":"Qingying","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-02-14 10:09:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6029572/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6029572/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12011-025-04721-y","type":"published","date":"2025-06-24T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":76687083,"identity":"0470f2bb-4899-431b-ae05-8b7a5bd0c877","added_by":"auto","created_at":"2025-02-19 16:22:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":21881,"visible":true,"origin":"","legend":"\u003cp\u003eScreening Flow Chart of the Study Population\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6029572/v1/cd5704d36ff82d2bdf8529e7.png"},{"id":76687085,"identity":"f5457be7-2973-4da6-bd32-ba74cff4d7e0","added_by":"auto","created_at":"2025-02-19 16:22:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208444,"visible":true,"origin":"","legend":"\u003cp\u003eNon-linear dose-response relationships between certain element and the risk of PEM were assessed by RCS model controlling covariates, including parental BMI and education level, child's birth weight, picky eating behaviors, and whether the child was delivered by cesarean section. The knots of RCS model were located at the 25th, 50th, and 75th percentiles.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6029572/v1/2949448fa23950a37ddcea95.png"},{"id":76687985,"identity":"af8e108a-b4cc-47e3-8d15-98a80a1b96cd","added_by":"auto","created_at":"2025-02-19 16:30:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":136517,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Interactions between paired trace elements and PEM risk were examined by fixing the second trace element at various quantiles and the remaining five at the 50th percentile, to evaluate the dose-response relationship of the first trace element with PEM risk. \u003cstrong\u003e(b) \u003c/strong\u003eUsing the concentration at the 50th percentile of all elements as the reference, the significance of differences in risk at other percentiles (minimum:25\u003csup\u003eth\u003c/sup\u003e, maximum:75\u003csup\u003eth\u003c/sup\u003e, step = 5\u003csup\u003eth\u003c/sup\u003e.) compared to the reference was assessed. The results were adjusted by covariates, including parental BMI and education level, child's birth weight, picky eating behaviors, and whether the child was delivered by cesarean section.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6029572/v1/575d8a7f76d28d8b346b1ef5.png"},{"id":76687086,"identity":"76e4efaf-6d25-4af6-9af7-ff7612369a4d","added_by":"auto","created_at":"2025-02-19 16:22:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25042,"visible":true,"origin":"","legend":"\u003cp\u003eEstimated weights assigned to each exposure based on WQS regression modeled in the negative direction with respect to the outcome. Models were adjusted for covariates.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6029572/v1/b0c496f45f95ea004aecd3c5.png"},{"id":85686143,"identity":"0131faaa-4c2a-4706-a424-c089fa6c6594","added_by":"auto","created_at":"2025-06-30 16:03:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1727469,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6029572/v1/2c931409-2823-44b0-a523-08f4fbc76054.pdf"},{"id":76687094,"identity":"bbea899e-477b-4456-be28-39e64f8dcd46","added_by":"auto","created_at":"2025-02-19 16:22:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8580008,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6029572/v1/df6d2e4c4fe0b316c15d7ca9.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Combination and Interaction of Seven Trace Elements and the Risk of Protein-Energy Malnutrition in School-Aged Children in Shenzhen, China","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eProtein-Energy Malnutrition (PEM) is a common form of subclinical undernutrition and has significantly jeopardized the health and development of children globally [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. PEM in children has long-term detrimental effects, including impaired physical growth, increased susceptibility to infections, anemia, chronic kidney disease, and the potential development of conditions such as hypertension in adulthood. Additionally, PEM adversely affects psychological health, leading to lower cognitive function, diminished attention span, and reduced intelligence [\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. PEM affects one-quarter of children worldwide, with 70% of cases occurring in Asia, China still accounts for 5% of the global stunting burden [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In China, even in developed areas like Beijing, the prevalence of PEM reached 10% in children aged 3\u0026ndash;14 years, whereas, in other undeveloped areas, the prevalence of PEM is up to 15.8\u0026ndash;19.2% [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAs we all know, macronutrients including protein, fat, and carbohydrates are directly linked to the growth and development of humans and are well-established contributors to PEM in children [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, micronutrients, such as vitamins and minerals, are also required by the body in small amounts but also play a crucial role in maintaining health and supporting vital life processes [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Essential trace elements, such as Zinc (Zn), Copper (Cu), Cobalt (Co), and Manganese (Mn) play an important role in the growth and development of children. Thereinto, Zn is the most closely related trace element, due to its involvement in catalyzing more than 100 enzymes, playing an important role in facilitating protein folding, protein and DNA synthesis, cell signaling, and division, etc. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, Long-term low levels of Zn can lead to Cu accumulation, as Zn and Cu have antagonistic effects. Excessive Cu can be toxic, which may increase the risk of oxidative damage to cells and impair normal growth and development [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Mn is involved in the synthesis and activation of many enzymes and the regulation of the metabolism of glucose and lipids in humans [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The associations between serum levels of Mn and nutritional status had been reported in children and adolescents, but the results were inconsistent [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition to the trace elements mentioned above, potentially essential trace elements, such as Chromium (Cr), Nickel (Ni), and Vanadium (V) were acknowledged to have a relationship with malnutrition, however, the relationships have not been thoroughly described [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Except for nutrients, some traditional risk factors such as maternal education, the circumstances of birth, lifestyle, genetic factors, illness, and even environmental pollution have been discussed adequately [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e Regarding the different requirements for micronutrients during the different times of pediatric age can be a useful guide for clinical practice and dietary instruction. In pre-adolescence, children typically do not require supplements, as their micronutrient needs can be adequately met through a diverse diet [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, school age spans from around six years until the onset of puberty. Young school-aged children need extra attention as they adapt to new environments and learning demands, which can lead to mental stress, sleep disturbances, and digestive issues. Additionally, as children begin managing aspects of their routines, such as eating out or participating in lunchtime care, their diets may become less balanced. As we know, trace elements cannot be synthesized in the human body, but are absorbed from food. Consequently, Children in this age group are particularly vulnerable to micronutrient imbalance. Limited to detection equipment or statistical methods, previous studies have mostly acknowledged the role of individual trace elements in PEM. It's worth noting that trace elements generally do not function independently in the body [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The combined effect or interactions of trace elements on PEM remain unclear.\u003c/p\u003e\u003cp\u003eConsequently, the objectives of this study are to compare the concentrations of seven trace elements (essential: Zn, Cu, Mn; potentially essential: Cr, Co, V, Ni) between the PEM and normal groups, and to explore both individual and combined associations of these elements with the risk of PEM in children aged 6 to 9 years in Baoan District, Shenzhen, China. Several statistical methods were applied in this study: logistic regression and restricted cubic spline (RCS) models were used to assess individual associations between trace elements and PEM risk in children, while Bayesian kernel machine regression (BKMR) and weighted quantile sum regression (WQS) explored their combined effects. These findings provide a scientific basis for developing effective interventions to prevent childhood malnutrition, improve nutritional status, and support healthy child development.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 5,152 students from 19 primary schools in Bao\u0026rsquo;an District, Shenzhen, China, were surveyed using cluster sampling, with 4,829 responses (response rate: 93.5%). After excluding 316 students due to severe cardiac, hepatic, or renal diseases, tumors, recent micronutrient or protein supplement use, or unwillingness to participate, 4,513 completed physical exams and questionnaires. Among these, 916 children (20.1%) were diagnosed with PEM and matched 1:1 with controls by age (\u0026plusmn;\u0026thinsp;0.5 years) and gender. Serum samples from 1,832 children were analyzed for Zn, Cu, Cr, Co, V, Mn, and Ni using inductively coupled plasma-mass spectrometry (ICP-MS). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Demographic information collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDemographic data were collected using structured questionnaires completed by guardians under the guidance of investigators who had undergone standardized training. The questionnaire included general information (gender, age), family background (economic status, parents' height, weight, and educational level, exposure to secondhand smoke), birth conditions (birth weight, delivery method such as cesarean section), and the child's dietary habits and lifestyle (picky eating, nighttime eating habits, frequency of snack, vegetable, and fruit consumption, and duration of physical exercise).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Blood samples collection and elemental detection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFour milliliters of fasting blood were collected into coagulation tubes and transported to the biochemistry laboratory at Baoan Central Hospital within 30 minutes. The samples were then transferred to centrifuge tubes pre-soaked overnight in 0.5% ultrapure-grade nitric acid. After centrifugation at 1,000 r/min for 5\u0026ndash;10 minutes, the serum was extracted and stored at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003eSerum levels of Zn, Cu, Cr, Co, V, Mn, and Ni were measured using inductively coupled plasma mass spectrometry (ICP-MS, Agilent 7900, Agilent Technologies, USA). Multi-element calibration standards (10 \u0026micro;g/mL, Agilent Technologies) in a 5% HNO₃ matrix were prepared by serial dilution with ultrapure water (18.2 MΩ\u0026middot;cm) containing 3% (v/v) nitric acid, resulting in final concentrations of 0, 1, 2.5, 5, 10, 20, 40, 80, 100, and 160 ppb (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). An ICP-MS Stock Tuning Solution (10 \u0026micro;g/mL, Agilent Technologies, in a 2% HNO₃ matrix) and an ICP-MS Internal Standard Mix (100 \u0026micro;g/mL, Agilent Technologies, in a 10% HNO₃ matrix) were each diluted 1:10,000 in a 3% nitric acid solution.\u003c/p\u003e \u003cp\u003eFor sample preparation, 100 \u0026micro;L of serum was transferred into a PFA digestion tube and dried in an oven at 80\u0026deg;C for 6 hours until a crystalline layer formed. After cooling to room temperature, 250 \u0026micro;L of 65% nitric acid was added in a fume hood, the tube was securely capped, and the sample was incubated in an 80\u0026deg;C water bath for 1 hour. Once cooled, 250 \u0026micro;L of 30% hydrogen peroxide was added to a fume hood, followed by another 5-minute incubation at 80\u0026deg;C. After cooling, the sample was stored overnight at 4\u0026deg;C for acidification.\u003c/p\u003e \u003cp\u003eBefore analysis, 4.5 mL of 3% nitric acid was added to the digestion tube to dilute the sample to a final volume of 5 mL. The solution was thoroughly mixed and transferred to a polypropylene tube for ICP-MS analysis, ensuring consistency with the prepared standards for calibration. This protocol ensured high accuracy and reproducibility in trace element quantification.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe covariates included family economic status, assessed using the Engel coefficient (\u0026gt;\u0026thinsp;49%, 40\u0026ndash;49%, 30\u0026ndash;39%, \u0026lt;\u0026thinsp;30%), parental Body Mass Index (BMI) categories (low, normal, overweight, obese), and parental education levels (high school or lower, junior college, bachelor\u0026rsquo;s degree or higher). Additional covariates comprised exposure to secondhand smoke (yes/no), cesarean delivery (yes/no), birth weight categories (low birth weight, normal weight, macrosomia), daily late-night snack consumption (yes/no), picky eating habits (yes/no), snack consumption (yes/no), frequency of vegetable and fruit intake (\u0026ge;\u0026thinsp;3 times/week, \u0026lt;\u0026thinsp;3 times/week), and duration of physical activity (\u0026ge;\u0026thinsp;1 hour/day, \u0026lt;\u0026thinsp;1 hour/day). All covariate information was collected through structured questionnaires completed by the parents of the children.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Outcome assessment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePEM in children was diagnosed only if both stunting and wasting criteria were met, as defined by the WS/T 456\u0026ndash;2014 standard [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Stunting was determined for children whose height (cm) fell at or below the stunting threshold for their respective gender and age group. Wasting was classified based on BMI: children with a BMI at or below the moderate to severe wasting threshold for their gender and age group were categorized as moderately to severely wasted, while those with a BMI at or below the mild wasting threshold were classified as mildly wasted. BMI was calculated by dividing weight (kg) by height squared (m\u0026sup2;).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data were presented as median (M) and interquartile range (IQR) due to the non-normal distribution of the seven trace element levels, as indicated by normality tests. Group comparisons were performed using the nonparametric Mann-Whitney U test. Qualitative data were expressed as rates or composition ratios, and group differences were assessed using the chi-square test.\u003c/p\u003e \u003cp\u003eTo examine potential dose-response relationships, conditional logistic regression and RCS models were performed. For the logistic regression model, trace element concentrations were divided into four quartiles, and two logistic regression models were constructed: Model 1 (unadjusted) and Model 2 (adjusted for covariates such as parental weight, education level, cesarean delivery, birth weight, and picky eating habits). Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated, with the lowest quartile serving as the reference. Median values within each quartile were used as continuous variables for trend tests. For the RCS model, five knots were positioned at the 5th, 35th, 50th, 65th, and 95th percentiles, with the median (50th percentile) as the reference. Relevant covariates were included in the RCS models to ensure robust adjustment.\u003c/p\u003e \u003cp\u003eFurther exploring the relationships between levels of serum trace elements and the risk of PEM in children, we adopted the BKMR and WQS models, which are good at handling high-dimensional data and accommodating nonlinear and nonadditive relationships [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Using a Markov Chain Monte Carlo (MCMC) sampler with 10,000 iterations, we mainly conducted the following analyses in the BKMR model: (1) Calculated the relative importance of seven trace elements in PEM risk using the posterior inclusion probability (PIP). (2) Examined interactive effects between pairs of trace elements on PEM risk. (3) Analyzed the overall effect of the combined trace element mixture, fixing all elements at the 50th percentile and assessing outcome changes at 5-percentile increments (from the 5th to 95th percentile). In addition, a negative WQS model was constructed with the index generated through 10,000 bootstrap iterations, quantifying the combined effect of exposures on the outcome. The model also assigns weights to each exposure, highlighting its contribution to the overall mixture effect.\u003c/p\u003e \u003cp\u003eR version 4.4.0 performed all statistical analyses, and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics of the study population\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the study population had a mean age of 7.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3 years, with 56.9% being boys. Children with lower parental BMI, lower parental education levels, non-cesarean births, and low birth weight were significantly more likely to have PEM (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Additionally, picky eating was strongly associated with a higher prevalence of PEM (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003c/div\u003e \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\u003eComparison of baseline characteristics between case and control groups [n (%)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \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\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1832)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;916)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;916)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily economic proportion\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e345 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e171 (49.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e174 (50.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u0026thinsp;~\u0026thinsp;49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e384 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e185 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e199 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026thinsp;~\u0026thinsp;39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e648 (35.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e322 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e326 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e455 (24.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e238 (52.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217 (47.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI of father\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44 (2.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33 (75.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e1065 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e493 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e572 (53.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e618 (33.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e351 (56.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e267 (43.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e105 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61 (58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44 (41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI of mother\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e277 (15.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104 (37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173 (62.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e1326 (72.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e665 (50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e661 (49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\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\u003e204 (11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e134 (65.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12 (48.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level of parents\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e788 (43.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e349 (44.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e439 (55.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJunior college\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e483 (26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e256 (53.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e227 (47.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBachelor\u0026rsquo;s degree or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e561 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311 (55.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250 (44.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondhand smoke exposure (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e895 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e454 (50.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e441 (49.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCesarean section (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e732 (40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e401 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e331 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBirth weight\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35 (37.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58 (62.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal birth weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1648 (90.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e824 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e824 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMacrosomia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e91 (5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEat snacks every night (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e605 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e306 (50.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e299 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePicky eaters (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1693 (92.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e818 (48.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e875 (51.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSnacks (Yes)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1483 (85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e757 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e726 (49.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVegetable consumption frequency (\u0026ge;\u0026thinsp;3 times/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1332 (72.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e659 (49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e673 (50.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFruit consumption frequency\u003c/p\u003e \u003cp\u003e(\u0026ge;\u0026thinsp;3times/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e960 (68.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e504 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical exercise (\u0026ge;\u0026thinsp;1hours/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1358 (74.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e697 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e661 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFamily economic proportion assessed via Engel's coefficient; the BMI is calculated based on the ratio of weight (in kilograms, kg) to the square of height (in meters, m). According to the Health Industry Standard of the People's Republic of China, WS/T 428\u0026ndash;2013 \"Criteria of weight for adults\", parent\u0026rsquo;s BMI is categorized into underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5 kg/m\u0026sup2;), normal weight (18.5 kg/m\u0026sup2; \u0026le; BMI\u0026thinsp;\u0026lt;\u0026thinsp;24.0 kg/m\u0026sup2;), overweight (24.0 kg/m\u0026sup2; \u0026le; BMI\u0026thinsp;\u0026lt;\u0026thinsp;28.0 kg/m\u0026sup2;), and obesity (BMI\u0026thinsp;\u0026ge;\u0026thinsp;28.0 kg/m\u0026sup2;)\u003csup\u003e[1]\u003c/sup\u003e; According to the standards established by the World Health Organization (references [72, 73]), birth weight is classified into low birth weight (birth weight\u0026thinsp;\u0026lt;\u0026thinsp;2500 g), normal weight (2500 g\u0026thinsp;\u0026le;\u0026thinsp;birth weight\u0026thinsp;\u0026lt;\u0026thinsp;4000 g), and macrosomia (birth weight\u0026thinsp;\u0026ge;\u0026thinsp;4000 g)\u003csup\u003e[2]\u003c/sup\u003e; differences in distribution between the two groups were analyzed using Chi-square test or Mann-Whitney U test; \u003cb\u003eP-values in bold indicate statistically significant differences.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Levels of serum trace element and its comparison between two groups\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the median levels of seven trace elements and their comparison between the PEM and control groups. The median concentrations of Zn (106.00 vs. 117.67 \u0026micro;g/dL), Cu (104.93 vs. 113.35 \u0026micro;g/dL), Co (0.06 vs. 0.07 \u0026micro;g/dL), V (0.09 vs. 0.10 \u0026micro;g/dL), Mn (0.77 vs. 0.91 \u0026micro;g/dL), and Ni (1.64 vs. 1.82 \u0026micro;g/dL) were significantly lower in the PEM group compared to the control group (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.01). However, while the median Cr level was also lower in the PEM group than in the control group (2.52 vs. 2.66 \u0026micro;g/dL), this difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.145).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConcentration of trace elements in case-control groups (\u0026micro;g/dl) [M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;1832)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-PEM(n\u0026thinsp;=\u0026thinsp;916)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePEM (n\u0026thinsp;=\u0026thinsp;916)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\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\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e111.50 (89.18,141.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117.67 (94.73, 147.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106.00 (83.80, 133.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.41(85.57,137.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113.35 (88.58, 143.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e104.93 (83.70, 129.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.58 (1.66, 3.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.66 (1.77, 3.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.52 (1.55, 3.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.06 (0.04, 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07 (0.04, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06 (0.04, 0.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10 (0.07, 0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.10 (0.08, 0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09 (0.07, 0.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.83 (0.61, 1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.65, 1.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.58, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.72 (1.15, 3.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.82 (1.20, 4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64 (1.11, 3.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe differences in concentrations between the two groups were analyzed using the Mann-Whitney U test. \u003cb\u003eP-values in bold indicate statistically significant differences.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The association between levels of serum trace elements and the risk of PEM\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe conditional logistic regression model demonstrated an association between trace element levels and PEM in children (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Model 2, compared to the lowest quartile, the highest quartile concentrations of Zn (OR: 0.52; 95% CI: 0.39\u0026ndash;0.69), Mn (OR: 0.51; 95% CI: 0.38\u0026ndash;0.67), V (OR: 0.52; 95% CI: 0.40\u0026ndash;0.69), Cu (OR: 0.59; 95% CI: 0.44\u0026ndash;0.79), and Ni (OR: 0.68; 95% CI: 0.52\u0026ndash;0.89) were significantly negatively associated with PEM in children. The trends for these five trace elements remained statistically significant. However, the ORs for Co and Cr did not exhibit significant changes at their highest quartile levels.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between trace element exposure and the risk of PEM (OR, 95%CI)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePEM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003csub\u003etrend\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ1 (\u0026le;\u0026thinsp;P\u003csub\u003e25\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ2 (P\u003csub\u003e25\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;P\u003csub\u003e50\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ3 (P\u003csub\u003e50\u003c/sub\u003e\u0026thinsp;~\u0026thinsp;P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ4 (\u0026gt;\u0026thinsp;P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83 (0.64, 1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.54 (0.41, 0.70)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.50 (0.39, 0.66)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.81 (0.61, 1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.56 (0.42, 0.74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.52 (0.39, 0.69)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (0.76, 1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89 (0.68, 1.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.58 (0.44, 0.75)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96 (0.72, 1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.87 (0.65, 1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.59 (0.44, 0.79)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.65 (0.50, 0.84)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.68 (0.53, 0.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.75 (0.58, 0.97)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.71 (0.54, 0.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.68 (0.52, 0.90)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78 (0.59, 1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.028\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.71, 1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78 (0.60, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.70 (0.54, 0.91)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.032\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.92 (0.70, 1.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.78 (0.59, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78 (0.59, 1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.77 (0.59, 0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.56 (0.43, 0.74)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.52 (0.40, 0.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.58, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.53 (0.40, 0.70)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.52 (0.40, 0.69)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.73 (0.56, 0.96)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.65 (0.50, 0.85)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.48 (0.36, 0.63)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.75 (0.56, 0.99)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.64 (0.48, 0.85)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.51 (0.38, 0.67)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80 (0.62, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.72 (0.55, 0.93)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.68 (0.53, 0.88)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.77 (0.58, 1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.67 (0.51, 0.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.68 (0.52, 0.89)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConcentrations of trace elements were log-transformed prior to analysis. Model 1 was unadjusted, while Model 2 was adjusted for covariates, including parental BMI and education level, child's birth weight, picky eating behaviors, and whether the child was delivered by cesarean section. \u003cb\u003eOR values in bold indicate a statistically significant difference from the reference group; P values in bold indicate a significant trend.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 The non-linear relationships between levels of serum trace elements and PEM risk\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe RCS model revealed significant non-linear associations between serum trace element concentrations and PEM risk in children (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For Zn, a U-shaped relationship was observed, with the lowest risk of PEM at LN Zn\u0026thinsp;=\u0026thinsp;5.26, followed by a slight increase at higher levels, and the highest risk of PEM at LN Zn\u0026thinsp;=\u0026thinsp;4.31, followed by a decreased trend until LN Zn\u0026thinsp;=\u0026thinsp;5.26 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Mn showed an initial increase in PEM risk, peaking at LN Mn = -0.62 which is lower than its median (-0.186), and a subsequent decline (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Co exhibited a similar U-shaped trend, with PEM risk peaking at LN Co = -3.21 and reaching its lowest at LN Co = -1.55 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Cu was negatively associated with PEM risk, with the lowest OR at LN Cu\u0026thinsp;=\u0026thinsp;5.15 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef). Cr and V exhibited non-linear associations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eg), with V showing a peak risk at LN V = -3.03 (OR\u0026thinsp;=\u0026thinsp;1.80) before declining. However, Ni did not show a significantly non-linear trend (P\u0026thinsp;=\u0026thinsp;0.055) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). The BKMR model, presented in the supplementary figure (FigS1), also showed the same nonlinear relationships as the RCS model.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The interactive and combined relationships among trace elements on the risk of PEM\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. shows that only Mn (PIP\u0026thinsp;=\u0026thinsp;1.000), Zn (PIP\u0026thinsp;=\u0026thinsp;0.827), and Co (PIP\u0026thinsp;=\u0026thinsp;0.707) significantly contributed to PEM risk (PIP\u0026thinsp;\u0026gt;\u0026thinsp;0.50) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The bivariate exposure-response analysis revealed that Zn exhibited antagonistic interactions with Mn and Co, while Mn showed a significant synergistic interaction with Co in relation to PEM risk in children, with the remaining five trace elements fixed at their 50th percentile (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eFor the overall effect of combined levels of seven trace elements on PEM risk in children, when all trace elements were set at specific percentiles (ranging from the 5th to the 95th) compared to the 50th percentile, both higher and lower concentrations significantly influenced PEM risk (Table S2). Notably, a decreasing trend in PEM risk was observed as the overall concentration percentile of trace elements increased from the 25th to the 75th percentile (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). The results from the WQS regression model demonstrated a significant negative association between the combined levels of trace elements and PEM risk (OR: -0.102; 95% CI: -0.162, -0.042) (Table S3). Among the seven trace elements, Mn and Zn contributed the most to the association (Mn: 40.4%, Zn: 19.1%), followed by V, which accounted for 13.9% of the total weight (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn this study, we evaluated PEM in 4513 children aged 6 to 9 years in Shenzhen, China. 916 children were identified with PEM contributing a rate of 20.3%. The levels of most trace elements (Zn, Mn, V, Cu, Co, Ni) in the serum of children were relatively lower in the PEM group and presented non-linear dose-response relationships with the risk of PEM. Moreover, the contribution of Zn and Mn was the largest regarding the negative joint association of seven trace elements with the risk of PEM. Among seven trace elements, Mn, Zn, and Co exhibited pairwise interactions.\u003c/p\u003e \u003cp\u003eFirst, we quantified the levels of seven serum trace elements in children and compared the total levels of trace elements with a study on establishing reference intervals of 11 minerals in children of Liaoning province of China using ICP-MS [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], we found that Zn, Cu, and Co concentrations overlapping with those of Liaoning, whereas, Mn, Ni, and Cr had significant deviation. Nevertheless, the normal reference value in children is limited, we further compared our results with the normal reference value in adults provided by WHO [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. We found that Zn, Cu, and Co were similar; however, Mn and Ni were about 8 times, and Cr was 129 times the WHO reference value. Throughout the testing process, we implemented strict quality control measures, and the normal levels of Zn, Cu, and Co further validated the reliability of our results. Therefore, the abnormal elevations of Mn, Ni, and Cr will need to be clarified in future studies.\u003c/p\u003e \u003cp\u003eNext, we found that the levels of Zn, Cu, Co, V, Mn, and Ni in the PEM group were significantly lower than control group, and the outcome of logistic regression also presented that the odds of PEM were much lower at their highest percentiles compared to their lowest percentiles. These findings were aligned with the results of RCS model. Furthermore, significantly nonlinear relationships were presented in the RCS model. For Zn, Co, and Cu, relatively high or low levels had a positive relationship with the risk of PEM; and in middle level, there were negative associations with the risk of PEM, respectively. In addition, an inverted U-shaped relationship existed between Mn, V, and the risk of PEM, while a U-shaped relationship was observed between Cr and the risk of PEM. Interactions were also found between Mn and Zn, Co on the PEM risk. High Co or Zn levels reduced the influence of Mn on PEM risk. And the interaction between Zn and Co appeared to show a buffering effect, where higher Co levels may attenuate the impact of Zn on PEM risk. Finally, for evaluating combined association, both the BKMR and WQS models were adopted and demonstrated that as the combined concentrations of the seven trace elements increased, the risk of PEM in children decreased, showing a significant negative association, with Zn and Mn contributing predominantly.\u003c/p\u003e \u003cp\u003eUp to now, due to limitations in detection techniques or statistical methods, the evidence regarding the relationship between trace elements and PEM is absent, especially for the interactions and combined associations. For the relationships between individual trace elements and PEM, our results are consistent with most previous studies. Lack or too high level of essential trace elements like Zn, Mn, Cu, and Co in our study can affect human health status, which may increase the risk of PEM. However, at the median level, there are negative associations between the concentration of these essential trace elements and the risk of PEM. A study involving 425 children aged between 5 and 7 years also concluded that serum levels of Zn were inversely associated with PEM [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Regarding Mn, several studies have identified that Mn was at very low levels in malnourished children, but the evidence suggesting that Mn deficiency cause poor growth in children is still limited [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In our study, the concentration of Cu was significantly lower in the PEM group and negatively associated with the risk of PEM. Savitri Thakur also identified that the decrease in Cu level was associated with the increase in the risk of PEM [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Several previous studies showed that children with malnutrition have lower levels of Cu in serum [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For Co, V, and Ni, research on its relationship with the risk of PEM is not enough, but as essential or potentially essential trace elements, they play important roles in human health status, more research is needed on the relationship between these trace elements and PEM.\u003c/p\u003e \u003cp\u003eA negative association was found between the combined level of seven trace elements and the risk of PEM in our study, which Mn and Zn contributed the most. Our previous research also showed that combined levels of Zn, Mn, and Co were significantly associated with the risk of insufficient muscle development level [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Zn, or zinc ions, act as cofactors for enzymes that regulate carbohydrate, fat, and protein metabolism can control protein function [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Therefore, Zn is crucial for normal growth and development. Studies have shown that Zn deficiency or excessive Zn level can disrupt metabolic processes, and lead to a loss of appetite, which further can delay the growth of children [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, at appropriate levels, relatively high levels of Zn can promote healthy growth and development [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Multiple population studies have consistently shown that zinc deficiency is a significant factor contributing to malnutrition in children, while an increase in serum zinc levels can significantly improve growth indicators [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. All those indicate a U-shaped association between Zn level and the risk of PEM in children, which is consistent with our study. Mn, as a cofactor for various metalloproteins such as superoxide dismutase, plays a crucial role in oxidative stress [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Both Mn deficiency and excess can lead to an increase in the production of superoxide dismutase, which in turn causes oxidative damage associated with neuropathological conditions linked to enhanced glucocorticoid expression. Glucocorticoids are key regulators of the biosynthesis and metabolism of carbohydrates, lipids, and proteins [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, Mn plays an important role in the metabolism of these processes. Previous studies have also reported that Mn deficiency was associated with malnutrition in children [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Our results underscored the importance of maintaining Mn levels within an optimal range to reduce the risk of PEM in children.\u003c/p\u003e \u003cp\u003eWe found antagonistic and synergistic interactions among Zn, Mn, and Co. Both Zn and Mn play essential roles in various metabolic pathways. Increasing evidence suggests that Zn or zinc ions, particularly ZNT10, ZIP8, and ZIP14, play key roles in Mn metabolism [\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Michaelis et al. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] also found that Zn could decrease the bioavailability of Mn and alter the expression of transport-related genes, thereby reducing the cytotoxic damage induced by Mn. That may explain why there is an antagonistic relationship between Zn and Mn in the risk of PEM in our study. Co, as part of the cobalamin structure, may support the role of Zn in cellular growth processes, thereby reducing the impact of Zn on PEM risk when Co levels are adequate [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Additionally, Zn and Co may share common transport mechanisms or binding sites, which could lead to a balancing effect when both elements are present at optimal levels [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The antagonistic interaction between Mn and Co may stem from competition in biochemical pathways or transport systems. Mn and Co are both involved in oxidative stress management and cellular energy production, yet high levels of one may inhibit the function or absorption of the other due to competitive inhibition at transport sites [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. High Co levels could reduce the absorption or efficacy of Mn, leading to diminished Mn impact on PEM risk. Research suggests that Mn and Co may compete for mitochondrial pathways and antioxidant functions, where excess Co can disrupt Mn-dependent enzymatic activities [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study possesses several noteworthy strengths. First, serum trace element concentrations were measured using ICP-MS, which provides a wide linear dynamic range (10^8), low detection limits, and high precision and accuracy. Second, the study population was relatively large, including 5,152 elementary school students, with 1,832 qualified participants selected, ensuring the reliability and robustness of the findings. Third, multiple statistical methods were employed to enhance result reliability, while also exploring interactions among trace elements and their combined effects. The use of different statistical methods for cross-validation further minimized potential errors. Finally, the RCS model offered reference ranges for both the positive and negative impacts of trace elements on PEM status in children, providing valuable guidance for managing trace element levels in pediatric populations.\u003c/p\u003e \u003cp\u003eHowever, this study has some limitations. First, its cross-sectional design may introduce recall bias in questionnaire responses and limit the ability to establish causal relationships between trace elements and PEM. Second, residual confounding from unmeasured factors, such as genetic predisposition or environmental exposures, cannot be entirely ruled out in this observational study.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn conclusion, this study suggests that the distributions of Zn, Cu, Co, V, Mn, and Ni are different between the two groups. And Mn, Zn are the most influential trace elements on the risk of PEM in children. This study also reveals the antagonistic interactions between Zn and Mn as well as Co, and the synergistic effect of Co with Mn. Additionally, seven other trace elements were negatively associated with the risk of PEM. These findings highlight the complex interactions among trace elements and their critical role in nutritional health, suggesting the need for further investigation into their mechanisms to inform effective nutritional interventions for reducing PEM risk in children.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003ePEM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eProtein-energy malnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eRCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003erestricted cubic spline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eBKMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003ebayesian kernel machine regression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eWQS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eweighted quantile sum\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eICP-MS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003einductively coupled plasma mass spectrometry\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 277px;\"\u003e\n \u003cp\u003eworld health organization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting of interests\u003c/h2\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003ch2\u003eEthical approval:\u003c/h2\u003e\n\u003cp\u003e This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Baoan Central Hospital of Shenzhen (protocol code IRB-PJ-2018-002).\u003c/p\u003e\n\u003cp\u003e \u003cstrong\u003eConsent to participate:\u003c/strong\u003e \u003c/p\u003e\n\u003cp\u003e Written informed consent was obtained from the parents.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis word was supported by The Natural Science Foundation of Guangdong Province (grant no.: 2022A1515012218).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eM.T. Yu: Writing – original draft, Writing – review \u0026amp; editing, Methodology, Formal analysis, Software, Visualization. L.Y. Tan: Methodology, Software, Visualization, Writing – original draft. Y.H. Chen., J.F. Shang., Y.B. You., H.M. Xie., N. Pang, R.M. Liang: Investigation. Y.B. You: Data curation. Q.-Y. Zhang: Writing – review \u0026amp; editing, Supervision, Funding acquisition, Project administration, Conceptualization.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe express our sincere gratitude to the participating children and their families for their invaluable cooperation and understanding throughout this study. The study received support from all the physicians and nurses in the Department of Laboratory Medicine, Baoan Central Hospital, Shenzhen. Furthermore, we extend our appreciation to all the school doctors and teachers in Baoan District who actively participated in the survey.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated during the current study are not publicly available due to privacy restrictions but are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLei, Y.T.; Ma J Fau - Hu, P.J.; Hu Pj Fau - Dong, B.; Dong B Fau - Zhang, B.; Zhang B Fau - Song, Y.; Song, Y. [The status of spermarche, menarche and corresponding relationships with nutritional status among students of 13 ethnic minorities in Southwest China in 2014], doi: 10.3760/cma.j.issn.0253-9624.2019.05.011\u003c/li\u003e\n\u003cli\u003eWebb, P.; Stordalen, G.A.; Singh, S.; Wijesinha-Bettoni, R.; Shetty, P.; Lartey, A. Hunger and malnutrition in the 21st century. 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Molecular and Cellular Biochemistry 2023, 478, 2141-2171, doi:10.1007/s11010-022-04638-3.\u003c/li\u003e\n\u003cli\u003eOrtega, R.; Bresson C Fau - Fraysse, A.; Fraysse A Fau - Sandre, C.; Sandre C Fau - Dev\u0026egrave;s, G.; Dev\u0026egrave;s G Fau - Gombert, C.; Gombert C Fau - Tabarant, M.; Tabarant M Fau - Bleuet, P.; Bleuet P Fau - Seznec, H.; Seznec H Fau - Simionovici, A.; Simionovici A Fau - Moretto, P., et al. Cobalt distribution in keratinocyte cells indicates nuclear and perinuclear accumulation and interaction with magnesium and zinc homeostasis, doi: 10.1016/j.toxlet.2009.02.024\u003c/li\u003e\n\u003cli\u003eSule, K.; Umbsaar, J.; Prenner, E.J. Mechanisms of Co, Ni, and Mn toxicity: From exposure and homeostasis to their interactions with and impact on lipids and biomembranes. Biochimica et Biophysica Acta (BBA) - Biomembranes 2020, 1862, 183250, doi:https://doi.org/10.1016/j.bbamem.2020.183250.\u003c/li\u003e\n\u003cli\u003eLi, Z.; Lai Z Fau - Ya, K.; Ya K Fau - Fang, D.; Fang D Fau - Ho, Y.W.; Ho Yw Fau - Lei, Y.; Lei Y Fau - Ming, Q.Z.; Ming, Q.Z. Correlation between the expression of divalent metal transporter 1 and the content of hypoxia-inducible factor-1 in hypoxic HepG2 cells, doi: 10.1111/j.1582-4934.2007.00145.x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biological-trace-element-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bter","sideBox":"Learn more about [Biological Trace Element Research](https://www.springer.com/journal/12011)","snPcode":"12011","submissionUrl":"https://submission.nature.com/new-submission/12011/3","title":"Biological Trace Element Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"manganese, zinc, Protein energy malnutrition (PEM), Restricted cubic spline (RCS), bayesian kernel machine regression (BKMR), Weighted quantile sum regression (WQS)","lastPublishedDoi":"10.21203/rs.3.rs-6029572/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6029572/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe imbalance of trace elements plays an important role in childhood malnutrition, but previous studies are usually specific to certain elements. We aimed to examine the individual and joint associations between multiple elements and the risk of protein-energy malnutrition (PEM) in young school children.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study measured the serum levels of Zinc (Zn), Copper (Cu), Chromium (Cr), Cobalt (Co), Vanadium (V), Manganese (Mn), and Nickel (Ni) in 1832 out of 5152 children aged 6 to 9 years by using inductively coupled plasma mass spectrometry. The individual and joint association of element and risk of PEM were appraised using logistic regression, restricted cubic splines model (RCS), bayesian kernel machine regression (BKMR), and weighted quantile sum regression (WQS) model, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSerum concentrations of Zn, Cu, Co, V, Mn, and Ni were significantly lower in the PEM group than in controls (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.005). Higher quartile concentrations of Zn (OR\u0026thinsp;=\u0026thinsp;0.52), Cu (0.59), V (0.52), Mn (0.51), and Ni (0.68) were associated with lower PEM risk (all Ptrend\u0026thinsp;\u0026lt;\u0026thinsp;0.05). RCS model indicated non-linear relationships between Zn, Cu, Cr, Co, V, Mn, and PEM risk. Interactions were found between Zn, Mn, and Co on the risk of PEM. Both BKMR and WQS models revealed a negative joint association of seven elements with PEM risk (OR = -0.102), Mn (40.4%), and Zn (19.1%) as the strongest contributors.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eSerum concentrations of Zn, Cu, Co, V, and Mn were relatively lower in Children with PEM and exhibited non-linear associations with the risk of PEM. The joint association of seven trace elements was negative with the risk of PEM, in which Mn and Zn contribute the most. Additionally, Mn, Zn, and Co exhibited pairwise interactions. These findings highlight the importance of maintaining balanced trace element levels to mitigate PEM in children.\u003c/p\u003e","manuscriptTitle":"Combination and Interaction of Seven Trace Elements and the Risk of Protein-Energy Malnutrition in School-Aged Children in Shenzhen, China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-19 16:22:52","doi":"10.21203/rs.3.rs-6029572/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-03-25T16:50:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-25T10:08:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"244833052993808572263170276017465590791","date":"2025-02-17T07:19:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-16T15:35:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101664180005444493470598612931281939347","date":"2025-02-16T12:37:05+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-02-16T04:36:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-02-14T13:45:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-02-14T12:19:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Biological Trace Element Research","date":"2025-02-14T10:00:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"biological-trace-element-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bter","sideBox":"Learn more about [Biological Trace Element Research](https://www.springer.com/journal/12011)","snPcode":"12011","submissionUrl":"https://submission.nature.com/new-submission/12011/3","title":"Biological Trace Element Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"66ca7357-558f-4db9-a715-ff306fa10275","owner":[],"postedDate":"February 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-30T15:59:24+00:00","versionOfRecord":{"articleIdentity":"rs-6029572","link":"https://doi.org/10.1007/s12011-025-04721-y","journal":{"identity":"biological-trace-element-research","isVorOnly":false,"title":"Biological Trace Element Research"},"publishedOn":"2025-06-24 15:57:11","publishedOnDateReadable":"June 24th, 2025"},"versionCreatedAt":"2025-02-19 16:22:52","video":"","vorDoi":"10.1007/s12011-025-04721-y","vorDoiUrl":"https://doi.org/10.1007/s12011-025-04721-y","workflowStages":[]},"version":"v1","identity":"rs-6029572","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6029572","identity":"rs-6029572","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}