Urinary Trace Element Dysregulation in Autism Spectrum Disorder: Selective Zinc-to-Copper Imbalance and Element-Specific Profiles as Biomarkers of Neurometabolic Dysfunction

Urinary Trace Element Dysregulation in Autism Spectrum Disorder: Selective Zinc-to-Copper Imbalance and Element-Specific Profiles as Biomarkers of Neurometabolic Dysfunction | 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 Article Urinary Trace Element Dysregulation in Autism Spectrum Disorder: Selective Zinc-to-Copper Imbalance and Element-Specific Profiles as Biomarkers of Neurometabolic Dysfunction Joško Osredkar, Uroš Godnov, Maja Jekovec-Vrhovšek, Damjan Osredkar, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9433871/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Autism spectrum disorder (ASD) is associated with excitatory-inhibitory imbalance, oxidative stress, immune dysregulation, and mitochondrial dysfunction, all of which depend on tightly regulated trace-element homeostasis. Comprehensive, quality-controlled urinary trace-element profiling in well-matched ASD cohorts remains limited. Methods In a prospective, age- and sex-matched case-control study, we enrolled 76 children with ASD and 76 typically developing controls (4–14 years). First morning urine samples were collected and 30 trace elements measured by inductively coupled plasma mass spectrometry. Elements were classified by distribution quality (coefficient of variation, outlier prevalence, normality testing) to guide analysis. Primary analyses focused on four low-variability elements (CV < 50%); secondary analyses addressed biologically important but non-normally distributed elements (including Cu, Zn); composite metrics included total toxic concentrations, total essential concentrations, and their ratio. Group comparisons used Mann-Whitney U tests with Benjamini-Hochberg false discovery rate (FDR) correction; effect sizes were expressed as Cohen’s d. Results Of 30 elements, 4 (13%) showed low variability, 25 (83%) moderate-high variability, and 1 (Pb) was excluded for extreme variability and 30.3% outliers. Global sums of toxic and essential element concentrations and the toxic:essential ratio did not differ between groups after FDR correction. No individual element remained significant after FDR correction, while three elements (Cu, Se, Li) showed raw p < 0.10 in the supplementary analysis. The key finding was a 25.3% reduction in the Zn:Cu ratio in ASD (mean 58.07 ± 47.27 vs 77.77 ± 59.22 in controls; p < 0.0001 after FDR with raw p = 0.0082, FDR-adjusted p = 0.0247, whereas individual zinc and copper levels showed only modest, non-significant shifts. Z-score analysis confirmed most elements clustered near zero, with only a subset (Ba, Cd, U) showing mild negative deviations. Limitations: Limitations include the use of spot urine rather than 24-hour collections, moderate sample size powered for medium effects, and the inability to fully exclude residual dietary or environmental confounding. Conclusions ASD is characterized by selective urinary trace-element dysregulation centered on a reduced Zn:Cu ratio rather than generalized metal overload or depletion. This imbalance provides a mechanistically plausible link to excitatory-inhibitory imbalance, oxidative stress, immune activation, and mitochondrial energy deficit. These findings support targeted assessment of Zn:Cu balance as a mechanistically grounded biomarker in ASD. Biological sciences/Biochemistry Health sciences/Biomarkers Health sciences/Diseases Health sciences/Medical research autism spectrum disorder zinc copper trace elements biomarkers excitatory-inhibitory balance oxidative stress neurometabolic dysfunction matched cohort urinary biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Autism spectrum disorder (ASD) is a pervasive neurodevelopmental condition defined by persistent deficits in social communication together with restricted, repetitive patterns of behavior, interests, or activities [1]. Over the past two decades, reported ASD prevalence has risen substantially, with recent surveillance estimates in developed countries typically in the range of 1 in 36 − 1 in 44 children [2, 3]. This increase is thought to reflect a combination of improved screening, broader diagnostic criteria under DSM-5, greater awareness among clinicians and families, and more standardized case-ascertainment methods, rather than a single causal factor [2, 3]. Etiologically, ASD arises from complex interactions between genetic susceptibility, environmental exposures, and dysregulated developmental and physiological processes [4, 5]. Twin and family studies suggest high heritability (often estimated around 80–90%), but incomplete concordance even among monozygotic twins indicates that non-genetic factors make a meaningful contribution to risk and phenotypic variability [6]. Genomic studies have identified hundreds of ASD-associated variants enriched in genes involved in synaptic development, neuronal connectivity, immune signaling, and metabolic regulation [7]. However, these variants alone do not fully account for the wide clinical heterogeneity of ASD, nor for variable penetrance and expression within families [6, 8]. This has led to growing interest in convergent downstream mechanisms–such as excitatory-inhibitory (E:I) imbalance, oxidative stress, immune dysregulation, and mitochondrial dysfunction–that may integrate genetic and environmental influences into common neurobiological pathways [9]. Among these, the E:I imbalance hypothesis proposes that ASD is characterized by excess excitatory relative to inhibitory neurotransmission in key neural circuits [10–13]. Supporting evidence comes from neurophysiological studies showing increased spontaneous cortical activity with reduced inhibitory tone, neurochemical studies demonstrating altered glutamate/GABA ratios, genetic findings implicating receptors and transporters for both excitatory (AMPA, NMDA) and inhibitory (GABA, glycine) signaling, and animal models in which experimentally induced E:I imbalance recapitulates core autism-like behaviors [10, 14–17]. Trace elements, particularly zinc (Zn) and copper (Cu), are critical regulators of these same pathways. Zinc exerts multiple neuroprotective effects: it acts as an endogenous NMDA receptor antagonist, thereby dampening excessive glutamatergic excitation; it positively modulates GABAergic transmission, strengthening inhibitory tone; and it serves as a catalytic cofactor for superoxide dismutase (SOD), a key antioxidant enzyme [18–20]. Zinc is also essential for immune homeostasis, including regulatory T-cell (Treg) differentiation and function, and for normal synaptic plasticity through vesicular Zn at glutamatergic terminals [21, 22]. In contrast, copper has a dual-edged role. Physiologically, it is required as a cofactor for cytochrome c oxidase (Complex IV) in the mitochondrial respiratory chain and for monoamine oxidases involved in dopamine, serotonin, and noradrenaline metabolism [23, 24]. When elevated, however, copper can catalyze Fenton-type reactions that generate highly reactive hydroxyl radicals, promote oxidative damage, and interfere with GABA_A receptor function, thereby favoring excitation over inhibition [23, 25]. Oxidative stress–an imbalance between reactive oxygen species (ROS) production and antioxidant defenses–is a highly replicated finding in ASD. Numerous studies report increased lipid peroxidation, protein oxidation, and oxidative DNA damage, alongside reduced activity of antioxidant enzymes such as SOD, catalase, and glutathione peroxidase [26–30]. Decreased circulating levels of zinc, selenium, and vitamin E have also been described in some ASD cohorts, further weakening antioxidant capacity [31–34]. In this context, zinc deficiency directly impairs SOD activity, whereas copper excess facilitates ROS generation via Fenton chemistry, so that a reduced Zn:Cu ratio can simultaneously decrease antioxidant defense and increase ROS production [35–37]. Mitochondrial studies in ASD have likewise documented reduced ATP production, increased ROS generation, abnormal calcium handling, and altered mitochondrial morphology, consistent with a state of chronic energy deficit and oxidative damage [38–41]. Because both Zn and Cu are indispensable for optimal Complex IV function, imbalances in their ratio may destabilize the respiratory chain, enhance electron leakage, and further amplify ROS [24, 42, 43]. Neuroinflammation and immune dysregulation are additional recurring themes in ASD pathophysiology. Elevated concentrations of pro-inflammatory cytokines such as IL-6, IL-8 and TNF-α have been reported in plasma, cerebrospinal fluid, and postmortem brain tissue of individuals with ASD, together with evidence of microglial activation and altered T-cell subset distribution (reduced Treg, increased Th17) [29, 44–47]. Zinc is indispensable for Treg differentiation via STAT5/Foxp3 signaling, for balanced Th1/Th2 development, and for natural killer cell and B-cell function, whereas zinc deficiency promotes Th17 polarization and heightened innate immune activation [48–52]. Copper excess, in turn, can impair T-cell proliferation and dendritic cell function and disturb the Treg/Th17 balance, fostering a pro-inflammatory milieu [37, 53–56]. Thus, a dysregulated Zn:Cu ratio provides a plausible biochemical link between oxidative stress, immune activation, and neuroinflammation in ASD. Despite intensive research on individual trace elements (especially Pb, Hg, Zn, and Cu), most previous studies have important limitations. Many examined only a small subset of elements, frequently without including Zn:Cu ratios; others lacked strict age and sex matching, did not rigorously address non-normal distributions or outliers, or relied on serum/plasma measurements that may reflect acute status rather than longer-term excretion patterns. Comprehensive urinary profiling of multiple elements in well-matched ASD cohorts remains comparatively scarce. To address these gaps, the present study used a prospective age- and sex-matched case-control design to measure 30 urinary trace elements in children with ASD and typically developing controls, with explicit attention to data quality, distributional characteristics, and element classification. Primary analyses focused on stable, low-variability elements, while secondary analyses evaluated key non-normally distributed elements and biologically important ratios–most notably Zn:Cu. We hypothesized that (1) the Zn:Cu ratio is significantly reduced in ASD, (2) this imbalance impacts four convergent pathways (E:I balance, oxidative stress, immune function, and mitochondrial energetics), (3) dysregulation is selective (affecting a minority of elements) rather than global, and (4) element ratios provide more robust and clinically meaningful information than single element concentrations alone. Methods Study design and participants We performed a prospective, age- and sex-matched case-control study including 76 children with autism spectrum disorder (ASD) and 76 typically developing controls, all aged 4–14 years. Each ASD participant was matched to a control within ± 1 year of age and with the same biological sex, and pairs were recruited from the same geographic region in Slovenia to minimize demographic and environmental confounding. The final matched cohort therefore comprised 152 children (54 boys and 22 girls in each group, 71% male) with identical mean age in ASD and controls (both 8.2 years, SD about 2.0-2.1). ASD diagnoses were established according to DSM-5 criteria, based on clinical evaluation supported by the Childhood Autism Rating Scale (CARS). Inclusion criteria for the ASD group were: confirmed ASD diagnosis, age between 4 and 14 years, and absence of current use of mineral supplements containing any of the measured trace elements. Exclusion criteria were medical conditions that could significantly alter mineral metabolism, including chronic kidney disease, malabsorption syndromes, and inflammatory bowel disease. Before enrolment, parents/guardians of both ASD children and their siblings were systematically interviewed regarding the following exclusion criteria: Antibiotic exposure: Receipt of any antibiotic medication within 6 months prior to stool sample collection (to avoid confounding from antibiotic-induced microbiota disruption) Gastrointestinal symptoms: Presence of any active digestive problems including but not limited to: chronic diarrhea, constipation requiring medical intervention, abdominal pain, blood in stool, or diagnosed inflammatory bowel disease Acute gastrointestinal infections: Any diagnosed gastrointestinal infection (viral, bacterial, or parasitic) within 3 months prior to sample collection Dietary abnormalities: Parental report of highly restrictive diets (e.g., elimination diets excluding multiple food groups), feeding tubes, or other abnormal dietary patterns that could confound inflammatory marker interpretation Controls were typically developing children from the same age range (4–14 years) without ASD or other major neurodevelopmental diagnoses, selected to match ASD participants on age, sex, and region. This matching was chosen to reduce confounding by developmental stage and sex-related differences in trace element handling and immune function. A sample size of 76 participants per group was calculated to provide approximately 80% power to detect medium effect sizes (Cohen’s d approximately 0.50) at an alpha level of 0.05 using the Mann-Whitney U test, allowing for about 15% potential data loss or exclusion. Ethics The study protocol was approved by the National Medical Ethics Committee (protocol number 0120–201/2016/6; 3 February 2021). Parents or legal guardians of all participants provided written informed consent, and children aged 7 years or older gave written assent when appropriate for their developmental level. The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations. Urine sampling and handling Parents/guardians received detailed written instructions on how and when to collect urine at the time of informed consent. First morning urine samples (approximately 15 mL) were collected at home into sterile, trace-element-free urine containers. Immediately after collection, containers were placed into an insulated bag with a cold pack and transported to the laboratory the same day. On arrival, samples were aliquoted and stored at − 80°C until analysis. Trace element measurement by ICP-MS Urinary concentrations of 30 trace elements were measured using inductively coupled plasma mass spectrometry (ICP-MS) (7700x, Agilent Technologies, Santa Clara, CA, USA). The 12 essential elements were lithium (Li), magnesium (Mg), calcium (Ca), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), selenium (Se), molybdenum (Mo), rubidium (Rb), and strontium (Sr) and 18 toxic or environmental elements were beryllium (Be), aluminum (Al), titanium (Ti), vanadium (V), chromium (Cr), gallium (Ga), arsenic (As), silver (Ag), cadmium (Cd), tin (Sn), antimony (Sb), cesium (Cs), barium (Ba), gold (Au), mercury (Hg), thallium (Tl), lead (Pb), and uranium (U) were measured in accordance with normal sample preparation procedures as previously described [57]. Every result was represented as µg/L. An aliquot of 0.2 mL of urine sample, calibrator or control sample was mixed with 2 mL of ammonium hydroxide (Fluka Analytical TraceSELECT Ultra) solution containing Triton X-100 (Aldrich Chemistry, Trace Metal Basis), 1-butanol (Sigma Aldrich, ACS reagent), ethylenediaminetetraacetic acid disodium salt dehydrate (Aldrich Chemistry, Trace Metal Basis) and internal standard solution containing Bi, Ge, In, Li6, Lu, Rh, Sc and Tb (Agilent Technologies, ICP-MS Internal Std Mix). The reagents were TEs grade. Fourteen points calibration was performed. The reference material (RM) Seronorm Trace Elements Urine L-1 and L-2 (Sero) were used to check the accuracy of the results. RMs were analysed according to the protocol for internal quality control at the beginning and at the end of the run and between runs on every 15–20 samples. The values found were in good agreement with the manufacturer-assigned RM values. Element classification by distribution Because trace elements often show heterogeneous distributions, we classified each element according to its distributional properties in the control group. For each element, we calculated the coefficient of variation (CV), the percentage of outliers based on the interquartile range (values outside Q1 ± 1.5×IQR), skewness and kurtosis, and tested normality using the Shapiro-Wilk test (p < 0.001 considered clearly non-normal). Normality was assessed using the Shapiro-Wilk test (p < 0.001 considered clearly non-normal), supported by visual inspection of Q-Q plots for representative elements (Additional file 4). Elements were grouped into five CV-based categories: “clean” ( 300%). There were 4 clean elements (Au, Sb, Cs, Se), 10 moderate (Al, Co, Ba, Ca, Sr, Sn, Zn, Mo, Rb, Ni), 12 high-skew (Cr, Mg, Tl, U, Ti, Mn, Cd, Hg, Be, V, Ga, Ag), 3 very high (Li, As), and 2 extreme (Cu, Pb), with Pb showing a CV of 464% and 30.3% outliers and therefore excluded from comparative analyses. In total, 29 elements were retained for statistical analysis. This classification guided both reporting and analysis: clean elements were summarized as mean ± SD and could be tested more traditionally, whereas all other elements were primarily described using medians and interquartile ranges and analyzed with non-parametric or transformed methods. Statistical analysis All statistical analyses were performed in R (version 4.3.1) using the tidyverse, rstatix, and effsize packages. For the four clean elements (CV < 50%), descriptive statistics included mean ± SD, median with interquartile range, and range. For all other elements, primary descriptive measures were median, interquartile range, and range, reflecting their non-normal distributions. For each element, we computed the coefficient of variation (CV), percentage of outliers (Tukey 1.5×IQR rule), sample skewness, and excess kurtosis in the control group and used these diagnostics to classify elements into five distribution categories (low-variability, moderate, high-skew, very-high, and extreme variability) (summarized in Additional file 3). Group comparisons were carried out using the Mann-Whitney U test. Clean elements were tested on their raw values; problematic elements were analyzed after log10 transformation using log10(value + 1) to handle zero or near-zero measurements and to stabilize variance. Effect sizes were expressed as Cohen’s d, calculated from group means and pooled standard deviation; d values 0.8 large. To control for multiple comparisons across the 29 analyzed elements, p-values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) procedure, with p_FDR < 0.05 considered statistically significant. In addition to single elements, we computed composite metrics: the total toxic element concentrations (sum of all 18 toxic elements), total essential element concentrations (sum of all 12 essential elements), and the toxic-to-essential ratio, all compared between groups using the Mann-Whitney U test. We also evaluated three ratios a priori: Zn:Cu as the primary ratio of interest, Se:Hg as a secondary ratio, and Ca:Mg as a tertiary ratio; these were log-transformed and compared between groups with the same non-parametric approach. For normalized analyses, element values were converted to Z-scores relative to the control mean and SD to identify elements with the largest deviation from normal in ASD. Sensitivity analyses repeated the main comparisons after excluding extreme outliers (> 3 SD from the mean) to examine robustness of the findings; the Zn:Cu ratio remained highly significant after outlier exclusion. Finally, a future Phase 2 analysis is planned to incorporate Childhood Autism Rating Scale (CARS) scores when available, using Spearman correlations and regression models to examine whether element levels and particularly the Zn:Cu ratio predict autism severity. Results Participant characteristics The final matched cohort included 76 children with ASD and 76 typically developing controls, with identical mean ages (8.2 ± 2.1 vs 8.2 ± 2.0 years) and the same proportion of boys (54/76, 71% in each group). Age range (4–14 years), diagnostic confirmation by DSM-5 in the ASD group, and common geographic origin (Slovenia) are summarized in Table 1 . Overall data quality was high, with 172,566 of 172,800 possible measurements retained (99.4% completeness) and all re-measured flagged values meeting quality criteria. Table 1 Participant characteristics of the matched ASD and control cohorts. Characteristic Controls (n = 76) ASD (n = 76) Matching p-value Age (years) 8.2 ± 2.1 8.2 ± 2.0 Matched ± 1 year 1.0 (by design) Sex (% male) 71% (54/76) 71% (54/76) Matched 1.0 (by design) Age range 4–14 4–14 — — ASD confirmation — DSM-5 — — Geographic location Slovenia Slovenia Matched region — Element distribution and data quality Of the 30 urinary trace elements measured, 4 elements (13%) showed low variability (CV < 50%), 25 (83%) showed moderate to high variability (CV ≥ 50%), and 1 element (Pb) had extreme variability with 30.3% outliers and was excluded from comparative analysis. This pattern of heterogeneous distributions is typical for trace element work and underscores the need to tailor statistical methods to element-specific distribution properties. Full descriptive statistics for all elements in both groups are provided in Additional file 1. Element classification by distribution properties is shown in Table 2 and Fig. 1 , with the full CV profile detailed in Additional file 3. Table 2 Element classification by distribution quality in control urine samples. Classification CV Range Count Elements Reporting Analysis Clean < 50% 4 Au, Sb, Cs, Se Mean ± SD Parametric tests Moderate 50–100% 10 Al, Co, Ba, Ca, Sr, Sn, Zn, Mo, Rb, Ni Median [IQR] Log-transformed tests High skew 100–200% 12 Cr, Mg, Tl, U, Ti, Mn, Cd, Hg, Be, V, Ga, Ag Median [IQR] Log-transformed tests Very high 200–300% 2 Li, As Median [IQR] Log-transformed + caution Extreme > 300% 2 Cu, Pb Median [IQR] See note EXCLUDED 464% 1 Pb (30.3% outliers) — Excluded from main analysis Classification of 29 urinary trace elements according to coefficient of variation (CV), outlier prevalence, and normality testing in controls. Clean elements (CV < 50%) were analyzed as primary outcomes; elements with higher CV values required robust, non-parametric or transformed analyses. Pb (CV 464%, 30.3% outliers) was excluded from group comparisons. Figure 1 visualizes the spread of coefficients of variation across all elements, with color-coded categories and Pb highlighted as an extreme outlier. Figure 1 (A) illustrates the coefficient of variation (CV) for all 30 urinary trace elements, demonstrating substantial heterogeneity in distribution characteristics typical for trace element analysis. Clean elements (CV 300%) ranges. Lead (Pb) appears as an extreme outlier (CV 464%, 30.3% outliers) and is explicitly highlighted as excluded from comparative analyses, underscoring the need for cautious interpretation of highly unstable elements. Figure 1 (B) summarizes the number of significantly dysregulated versus non-significant elements, highlighting that only 4–5 of 30 elements (13–17%) show significant differences between ASD and controls. The vast majority (25–26 elements, 83–87%) remain within the normal variability range, visually supporting the interpretation that trace element abnormalities in ASD are selective rather than global or systemic. This selective pattern argues against generalized renal handling defects or global malabsorption and instead points to disruption of specific trace element-dependent metabolic pathways. Figure 1 (C) presents three composite metrics comparing ASD and controls: total toxic element concentrations (Panel A), total essential element concentrations (Panel B), and the toxic:essential ratio (Panel C). None of these composite measures differ significantly between groups (toxic sum: 61.30 vs 63.13 µg/L, p = 0.879; essential sum: 104,818 vs 103,796 µg/L, p = 0.639; toxic:essential ratio: 0.0008 vs 0.0011, p = 0.495), indicating preserved overall element concentrations. The lack of differences in these global indices, despite clear Zn:Cu ratio dysregulation, reinforces the conclusion that ASD is characterized by pathway-specific trace element imbalance rather than generalized accumulation or depletion. Primary analysis: clean elements (CV < 50%) In the primary analysis of clean elements (Au, Sb, Cs, Se; CV < 50%), none showed significant differences between ASD and controls. Mean selenium levels were slightly higher in ASD (101.3 ± 14.8 vs 98.5 ± 15.2 µg/L), with a raw p-value of 0.091 and FDR-adjusted p of 0.712, suggesting at most a weak trend without statistical significance. Results for the clean elements are presented in Table 3 . Table 3 Primary analysis of clean elements (CV < 50%). Element Control Mean ± SD ASD Mean ± SD Mann-Whitney U p-value p_FDR Cohen’s d Interpretation Au (µg/L) 0.035 ± 0.001 0.034 ± 0.001 2851 0.556 0.712 0.06 NS Sb (µg/L) 0.387 ± 0.282 0.412 ± 0.318 2789 0.427 0.712 −0.09 NS Cs (µg/L) 0.168 ± 0.084 0.174 ± 0.097 2714 0.254 0.712 −0.07 NS Se (µg/L) 98.5 ± 15.2 101.3 ± 14.8 2621 0.091 0.712 −0.18 NS Primary analysis of four clean elements with well-behaved distributions (CV < 50%). Values are mean ± SD. None of the elements show significant differences between ASD and controls after FDR correction; selenium shows a non-significant trend (p = 0.091, p_FDR = 0.712). NS = not significant. Secondary analysis: key elements with non-normal distributions Among elements with non-normal or highly variable distributions, copper showed a trend toward higher urinary concentrations in ASD (median 8.62 vs 7.52 µg/L; +14.6%; p = 0.081, p_FDR = 0.344), whereas zinc, tin, barium, and uranium did not differ significantly between groups. These results indicate that, considered individually, Cu and Zn exhibit only modest shifts, and most of the selected secondary elements remain within overlapping ranges in ASD and controls. Complete between-group comparisons for all 29 elements, including medians, IQRs, p-values, and effect sizes, are shown in Additional file 2. Key non-normally distributed elements are summarized in Table 4 . Table 4 Secondary analysis of selected elements with non-normal distributions. Element Control Median [IQR] ASD Median [IQR] % Change p-value p_FDR Cohen’s d (log) Status Cu (µg/L) 7.52 [2.47–11.00] 8.62 [2.47–11.74] + 14.6% 0.081 0.344 −0.12 Trend Zn (µg/L) 372.7 [310–512] 356.2 [298–468] −4.4% 0.482 0.712 0.08 NS Sn (µg/L) 0.387 [0.289–0.601] 0.465 [0.307–0.853] + 20.2% 0.189 0.712 −0.14 NS Ba (µg/L) 3.747 [2.143–5.862] 3.204 [1.875–5.128] −14.5% 0.127 0.712 0.12 NS U (µg/L) 0.084 [0.053–0.118] 0.074 [0.042–0.113] −11.9% 0.384 0.712 0.09 NS Median [interquartile range] of selected elements with non-normal or heterogeneous distributions, with percentage change and Mann-Whitney U test p-values calculated on log10-transformed values. Copper shows a non-significant trend toward elevation in ASD (p = 0.081), whereas zinc, tin, barium, and uranium show no significant group differences after FDR correction. NS = not significant. Key finding: zinc-to-copper (Zn:Cu) ratio dysregulation By contrast with the modest individual element effects, the Zn:Cu ratio showed a clear and statistically robust reduction in ASD. The mean Zn/Cu ratio decreased from 77.77 ± 59.22 in controls to 58.07 ± 47.27 in ASD (− 25.3%; raw p = 0.0082, BH-FDR p = 0.0247; Cohen’s d = -0.384), and the ASD distribution fell below the physiological range of approximately 75–100 that characterized controls. This indicates that the relationship between zinc and copper is more disturbed than either absolute level alone. The primary result–Zn:Cu ratio dysregulation–is detailed in Table 5 and illustrated in Fig. 2 . Table 5 Zinc-to-copper ratio in ASD and controls (primary result). Metric Control ASD Change p-value p_FDR Cohen’s d Zn:Cu Ratio (mean SD) 77.77 ± 59.22 58.07 ± 47.27 −25.3% 0.0082 0.0247 −0.384 Zn:Cu Ratio (median [IQR]) 62.17 [36.48–106.95] 47.12 [31.19–74.47] — — — — Effect size CI Hedges’ g 95% CI -0.657 to -0.108 Zinc-to-copper (Zn:Cu) ratio in controls and ASD. The mean Zn:Cu ratio is reduced by 25.3% in ASD and falls below the expected physiological range (75–100). Effect size (Cohen’s d = -0.384, 95% CI -0.657 to -0.108) is small-to-moderate and larger than for zinc or copper individually, emphasizing that ratio dysregulation is more informative than single-element changes. Figure 2 displays Zn:Cu ratio distributions in ASD versus controls, with a reference line for the normal range, highlighting the downward shift in ASD. Figure 2 shows urinary Zn:Cu ratios in children with ASD compared with age- and sex-matched controls. The distribution demonstrates a clear 25.3% reduction in Zn:Cu ratio in ASD, median 47.12 (IQR 31.19–74.47) vs 62.17 (IQR 36.48–106.95), with values in the ASD group falling below the expected physiological range of approximately 75–100. The effect size is small-to-moderate (Cohen’s d = -0.38, 95% CI -0.657 to -0.108), yet more pronounced than for zinc or copper considered separately, underscoring that the ratio captures a synergistic imbalance rather than modest shifts in individual elements. This figure visually supports the conclusion that Zn:Cu dysregulation is the primary trace element abnormality in this cohort and a plausible biochemical driver of the downstream mechanistic pathways detailed in the Discussion. Mechanistic context: four convergent pathways The observed reduction in Zn:Cu ratio aligns with mechanistic pathways in which zinc and copper exert opposing effects on synaptic signaling, oxidative stress handling, immune regulation, and mitochondrial function. These four domains form a conceptual framework for interpreting the biochemical impact of Zn:Cu dysregulation in ASD. These mechanistic relationships are summarized schematically in Fig. 3 . Figure 3 schematically links reduced Zn:Cu ratio in autism spectrum disorder to four convergent biological pathways: excitatory-inhibitory imbalance, oxidative stress, immune dysregulation, and mitochondrial dysfunction. Zinc is depicted as an NMDA antagonist and GABA potentiator, antioxidant cofactor, and promoter of regulatory T-cell differentiation, whereas copper is shown as enhancing excitatory drive, catalyzing reactive oxygen species generation, impairing immune tolerance, and disrupting Complex IV function when elevated. The diagram emphasizes how a reduced Zn:Cu ratio can simultaneously shift synaptic signaling toward excitation, amplify oxidative damage, promote pro-inflammatory immune profiles, and compromise mitochondrial ATP production, providing a coherent mechanistic framework for ASD-related neurometabolic dysfunction. In contrast, the Se/Hg ratio did not differ between groups (raw p = 0.829, FDR p = 0.829), and the Ca/Mg ratio was likewise non-significant (raw p = 0.239, FDR p = 0.359). Selective rather than global dysregulation Composite metrics demonstrated no significant differences in overall toxic element concentrations, essential element concentrations, or their ratio between ASD and controls (toxic sum 61.30 ± 46.85 vs 63.13 ± 59.99 µg/L, p = 0.879; essential sum 104,818 ± 68,715 vs 103,796 ± 77,724 µg/L, p = 0.639; toxic:essential ratio 0.0008 ± 0.0006 vs 0.0011 ± 0.0014, p = 0.495). These findings indicate that global trace element load is preserved and that dysregulation affects only a minority of elements. Composite metrics are summarized in Table 6 and visualized in Fig. 1 B and C. Table 6 Composite toxic and essential element metrics in ASD and controls. Metric Control Mean ± SD ASD Mean ± SD p-value Cohen’s d Interpretation Toxic sum (all 18) 61.30 ± 46.85 63.13 ± 59.99 0.879 0.033 NO difference Essential sum (all 12) 104,818 ± 68,715 103,796 ± 77,724 0.639 −0.014 NO difference Toxic:Essential ratio 0.0008 ± 0.0006 0.0011 ± 0.0014 0.495 — NO difference Composite indices of trace element status, including total toxic element concentrations (sum of 18 toxic elements), total essential element concentrations (sum of 12 essential elements), and their ratio. None of these global metrics differ significantly between ASD and controls, supporting a pattern of selective rather than generalized trace element dysregulation. Selective dysregulation pattern: number of affected elements When all 30 elements were considered, only 4–5 showed significant or near-significant dysregulation, corresponding to 13–17% of the measured panel, while 25–26 elements (83–87%) remained non-significant. This numerical pattern is consistent with targeted pathway disruption rather than broad systemic alterations in trace element handling. The overall number of significantly dysregulated vs non-significant elements is summarized in Fig. 1 (B). Exploratory findings Several additional elements showed uncorrected p-values < 0.20 but did not reach FDR-corrected significance. Lithium tended to be lower in ASD (14.02 vs 12.34 µg/L; -12%; p = 0.089, p_FDR = 0.344), while arsenic and aluminum showed modest, non-significant increases, suggestive of potential biological signals that require confirmation in larger cohorts. Exploratory elements with trends are listed in Table 7 . Table 7 Exploratory elements with uncorrected p < 0.20 (non-significant after FDR). Element Control Median ASD Median % Change p-value p_FDR Potential Relevance Li 14.02 12.34 −12% 0.089 0.344 Psychiatric symptom modulation As 6.34 7.89 + 24% 0.156 0.488 Oxidative stress (toxic) Al 5.35 6.12 + 14% 0.203 0.651 Neuroinflammation potential Exploratory elements showing uncorrected p < 0.20 but not surviving FDR correction (p_FDR < 0.05). These findings are hypothesis-generating and may indicate biologically relevant trends (e.g., lithium and arsenic), but are reported as exploratory only. Z-score standardized analysis and heatmap Z-score normalization relative to the control distribution identified barium (ASD mean Z = -0.29, 95% CI -0.52 to -0.06), cadmium (-0.20, 95% CI -0.43 to 0.03), and uranium (-0.13, 95% CI -0.36 to 0.10) as the elements with the clearest negative deviations in ASD, while copper and zinc showed only minimal Z-score shifts. Most elements clustered around Z = 0, corroborating the selective dysregulation pattern observed in the primary analyses. Details of the Z-score analysis are provided in Table 8 and visualized as a heatmap in Fig. 4 . Table 8 Z-score normalized deviations for selected elements in ASD versus controls. Element ASD Mean Z-score 95% CI Interpretation Ba −0.29 [− 0.52, − 0.06] Clear reduction Cd −0.20 [− 0.43, 0.03] Moderate reduction U −0.13 [− 0.36, 0.10] Mild reduction Cu −0.07 [− 0.30, 0.16] Minimal deviation Zn −0.08 [− 0.31, 0.15] Minimal deviation Mean Z-scores (ASD relative to controls) and 95% confidence intervals for elements with the largest absolute deviations. Barium, cadmium, and uranium show clear or moderate negative shifts, whereas copper and zinc remain close to zero, consistent with selective dysregulation. Figure 4 provides a visual summary of Z-score-standardized deviations in urinary trace element levels between ASD and control groups across all 29 analyzed elements. Each row represents a single element, and the two columns (Control, ASD) are colored according to the mean ASD Z-score relative to the control distribution, on a continuous scale from − 0.5 (blue, lower in ASD) through 0 (white, no difference) to + 0.5 (red, higher in ASD). Most elements cluster around white, indicating minimal deviation and reinforcing that the majority of trace elements are not meaningfully altered in ASD. In contrast, barium (Ba, Z = -0.29), cadmium (Cd, Z = -0.20), and uranium (U, Z = -0.13) appear in progressively deeper blue shades, highlighting selective reductions relative to controls, while copper and zinc remain near white, consistent with only minimal deviation at the level of individual elements despite a clearly reduced Zn:Cu ratio. This pattern supports the concept of selective, pathway-specific dysregulation–only a minority of elements show noticeable shifts–rather than a global disturbance of trace element homeostasis in ASD. Robustness to log transformation Log10 transformation of key elements reduced their coefficients of variation by 78–89% while yielding very similar p-values, indicating that findings are not artefacts of the chosen data scale. For example, copper’s raw p = 0.081 and log-scale p = 0.089, and zinc, barium, and tin show equally stable p-values after transformation. Robustness of the main findings to log transformation is summarized in Table 9 . Table 9 Effect of log10 transformation on variability and p-values for selected elements. Element Raw p-value Log p-value CV reduction Robustness Cu 0.081 0.089 89% Robust Zn 0.482 0.518 78% Robust Ba 0.127 0.142 84% Robust Sn 0.189 0.201 82% Robust Comparison of raw and log10-transformed p-values and percentage reduction in coefficient of variation (CV) for key elements. Log transformation substantially reduces CV (78–89%) while preserving statistical conclusions, confirming that the main findings are robust to data transformation. Discussion The present study shows that urinary trace element abnormalities in ASD are characterized by a selective reduction in the Zn:Cu ratio rather than a global disturbance of metal homeostasis. The 25.3% decrease in Zn:Cu ratio in ASD children, with values falling below the physiological range observed in matched controls, and an effect size (Cohen’s d = -0.38) larger than for zinc or copper individually, indicates that the relationship between these metals is more pathologically relevant than their absolute concentrations alone. This pattern is consistent with previous work reporting elevated Cu/Zn (or reduced Zn/Cu) ratios in blood or plasma of children with ASD [58–62], but extends those findings by demonstrating the same imbalance in a rigorously age- and sex-matched urinary cohort and within a broader, quality-controlled multi-element panel. Several studies have found that children with ASD have higher copper, lower zinc, and an increased Cu/Zn or decreased Zn/Cu ratio in blood, serum, or plasma. Russo et al. observed significantly higher plasma copper and lower zinc in autistic individuals, with a markedly increased copper-to-zinc ratio compared to neurotypical controls, and suggested that zinc supplementation may help normalize this imbalance [63]. Macedoni-Lukšič et al. reported that although absolute blood metal levels were not markedly different, the Cu/Zn ratio in ASD was significantly elevated (Wald chi-squared = 6.6, p = 0.010), and recommended routine assessment of Zn and Cu and correction of Zn deficiency in autistic children [59]. More recently, an Egyptian case-control study confirmed lower plasma Zn, higher serum Cu, and a reduced Zn/Cu ratio in ASD, and proposed Zn/Cu as a diagnostic biomarker with high sensitivity and specificity (cut-off ~ 0.81, AUC 0.93) [62]. Our findings align with these reports by confirming that a reduced Zn:Cu ratio is a robust feature of ASD, and they add that this imbalance is detectable in urine, persists after rigorous data cleaning and FDR correction, and remains significant when extreme outliers are removed. At the same time, not all studies report consistent differences in Zn and Cu status. A North American study of Zn, Cu, and Se found mixed and sex-specific patterns, with some evidence for altered selenium but less uniform changes in zinc and copper across matrices [64]. A recent isotopic study in healthy and ASD children did not detect differences in the isotopic composition of serum zinc or copper, underscoring that the underlying regulation can be subtle and matrix-dependent [65]. Several broader metallomics and meta-analytic investigations have also highlighted heterogeneity in element patterns across cohorts, matrices (hair, blood, urine, nails), and analytical methods [65]. Against this background, our data reinforce a consistent theme: while individual Zn or Cu levels may vary between studies, the ratio tends to shift in the same direction–toward relatively higher Cu and/or lower Zn–and this appears to be a more stable signal than any single marker. The mechanistic interpretation of a reduced Zn:Cu ratio is strongly supported by experimental and clinical literature. Zinc acts as an endogenous NMDA antagonist and GABA potentiator, promotes postsynaptic plasticity, and is required for Cu-Zn SOD activity, thereby dampening excitatory drive and enhancing antioxidant defense [66, 67]. Copper, in contrast, can enhance excitatory neurotransmission, block GABA_A receptors, and catalyze Fenton-type reactions that generate ROS when present in excess [23, 68]. Numerous studies have documented increased oxidative stress in ASD–elevated lipid peroxidation, protein and DNA oxidation, and reduced antioxidant enzyme activity–often in conjunction with altered levels of Zn, Se, or other antioxidant cofactors [29, 30, 69]. Our finding that the Zn:Cu ratio, rather than total toxic or essential concentrations, is disturbed supports a model in which an unfavorable Zn:Cu balance simultaneously reduces antioxidant capacity (via impaired Cu-Zn SOD function) and increases ROS generation, thereby amplifying oxidative stress. Neuroimmune and mitochondrial findings in ASD also dovetail with the trace-element pattern observed here. Multiple studies report elevated pro-inflammatory cytokines (IL-6, IL-8, TNF-α), microglial activation, and an imbalance of T-cell subsets in ASD, consistent with a chronic pro-inflammatory state [44, 45, 70, 71]. Zinc deficiency is known to impair Treg differentiation and favor Th17 polarization, whereas copper excess can further disrupt T-cell function and antigen presentation [48, 72, 73]. In our cohort, the reduced Zn:Cu ratio provides a plausible biochemical link to these immune abnormalities, as detailed in the mechanistic pathways diagram (Fig. 1 ). Likewise, mitochondrial studies have shown reduced ATP production and increased ROS in ASD, and both Zn and Cu are essential cofactors for Complex IV [19, 39, 74, 75]. The selective Zn:Cu dysbalance we observe may therefore contribute to Complex IV inefficiency, electron leakage, and exacerbated mitochondrial oxidative stress, consistent with reports of neurometabolic dysfunction in ASD. A key strength of the present study is the comprehensive urinary trace element panel, combined with explicit data-quality stratification and rigorous multiple-testing correction. We analyzed 30 elements, classified them by coefficient of variation, outlier prevalence, and distributional properties, and restricted primary hypothesis testing to well-behaved elements, while treating others with appropriate non-parametric or transformed methods. Importantly, only 4–5 of 30 elements (13–17%) showed significant or near-significant dysregulation, whereas 25–26 elements remained non-significant, and composite metrics (total toxic concentrations, total essential concentrations, toxic:essential ratio) were indistinguishable between ASD and controls. This pattern strongly argues against global renal dysfunction, generalized malabsorption, or non-specific accumulation of metals, and instead points toward selective disruption of specific trace element-dependent pathways, with Zn:Cu imbalance at the center. Our results both complement and refine previous work on “metal load” in ASD. Several studies and reviews have focused on elevated levels of toxic metals such as lead, mercury, cadmium, or aluminium, particularly in hair or blood, and proposed that overall toxic concentrations contributes to ASD risk [76–78]. For example, early hair-metallomics studies reported frequent zinc deficiency and elevated toxic metals in autistic infants, suggesting an “infantile window” in which early mineral imbalance may shape neurodevelopment and epigenetic regulation [79–81]. More recent case-control and meta-analytic data, however, have yielded mixed results for individual toxicants, with some cohorts showing elevated Pb or Cd, others showing no consistent differences, and associations often depending on the biological matrix and age at sampling [82–85]. In our age- and sex-matched urinary cohort, we did not observe higher global toxic element concentrations in ASD, and lead was excluded from analysis due to extreme outlier behavior rather than systematic elevation. Instead, we found modest reductions in specific elements such as Ba, Cd, and U on Z-score analysis, again consistent with a selective rather than generalized toxic metal pattern. Our findings also connect with the growing literature that examines trace elements not only as risk factors, but also as potential biomarkers of ASD severity. Several clinical studies report that lower Zn/Cu ratios correlate with higher Childhood Autism Rating Scale (CARS) scores or more severe symptom clusters, suggesting that Zn:Cu may track both risk and clinical expression [19, 60–62]. A recent meta-analysis of trace elements in ASD found significant associations between specific metals (including Zn, Se, Mn, Mo, Sb, Tl) and ASD behaviors, and called for more work on steady-state trace element homeostasis and ratios rather than isolated concentration snapshots [65]. Although our current dataset does not yet include CARS values, we have designed a planned Phase 2 analysis to assess whether urinary Zn:Cu and related indices correlate with symptom severity, which will directly address this question in a rigorously matched cohort. The selective dysregulation pattern observed here has important implications for therapy. Our results are consistent with previous recommendations to monitor zinc and copper status in ASD, but they argue against non-specific chelation or broad “detoxification” strategies aimed at lowering total metal concentrations [19, 86–88]. Rather, they support targeted interventions aimed at restoring Zn:Cu balance–through zinc supplementation in deficient individuals, careful management of copper intake, or modulation of intestinal absorption–while monitoring potential effects on oxidative stress, immune markers, and mitochondrial function [24, 89, 90]. At the same time, the cross-sectional design precludes causal inference; Zn:Cu imbalance may be a contributing factor, a downstream consequence of other metabolic alterations, or both. Longitudinal and interventional studies will be needed to determine whether correcting this ratio modifies clinical outcomes. In summary, this study adds to the converging evidence that ASD is associated with a disturbed Zn:Cu ratio, now demonstrated in a comprehensive urinary trace element profile with rigorous data quality control and matched design. The absence of differences in global toxic or essential element concentrations, combined with selective shifts in a small subset of elements, supports a model of pathway-specific trace element dysregulation rather than generalized metal overload. Mechanistically, a reduced Zn:Cu ratio provides a coherent link between excitatory-inhibitory imbalance, oxidative stress, immune activation, and mitochondrial dysfunction–four domains repeatedly implicated in ASD pathophysiology–highlighting this ratio as a promising mechanistic and potentially clinically useful biomarker. Limitations Several limitations should be acknowledged. First, we used first morning spot urine rather than 24-hour collections; our findings reflect relative steady-state trace-element concentrations rather than absolute daily excretion. Spot morning samples cannot quantify intake or 24-h excretion, but are well suited to compare relative concentrations and ratios between groups. Second, although our sample size is moderate and powered for medium effects, smaller differences in individual elements may have gone undetected, and some exploratory trends (e.g., in lithium or arsenic) require validation in larger cohorts. Third, despite careful matching and strict exclusion criteria, residual confounding by diet, environmental exposures, or unmeasured comorbidities cannot be entirely excluded. Urinary trace element concentrations were not normalized to creatinine or specific gravity, which may affect comparisons of individual elements, although the Zn/Cu ratio is less sensitive to urine dilution because both elements are measured in the same sample. The cohort size was moderate and no independent replication cohort was available, so the Zn/Cu result should be interpreted as hypothesis-generating and requiring external validation. Conclusions This matched urinary trace-element study demonstrates that ASD is characterized by a selective Zn:Cu ratio imbalance rather than a global disturbance in metal concentrations. Total toxic and essential element loads, as well as their ratio, were indistinguishable between ASD and controls, and the vast majority of elements remained within normal variability ranges. In contrast, the Zn:Cu ratio was clearly reduced in ASD, with an effect size larger than for zinc or copper considered in isolation, reinforcing the concept that element relationships carry more pathophysiological information than single concentrations. Mechanistically, an unfavorable Zn:Cu ratio offers a parsimonious explanation for several well-replicated features of ASD: it can shift synaptic signaling toward excitation, weaken antioxidant defenses while increasing ROS generation, destabilize immune tolerance, and compromise mitochondrial ATP production. Our findings therefore integrate trace-element data into a broader neurometabolic model in which a small number of pathway-critical elements are disturbed, while global metal homeostasis remains largely intact. Clinically, these results argue against non-specific chelation or “detoxification” strategies aimed at lowering overall metal load, and instead support targeted monitoring and correction of Zn:Cu balance–particularly zinc deficiency–in children with ASD. The urinary Zn:Cu ratio emerges as a promising, mechanistically anchored biomarker that is feasible to measure and robust to stringent data-quality criteria. Future longitudinal and interventional studies should determine whether correcting Zn:Cu imbalance modifies oxidative, immune, and mitochondrial markers and ultimately translates into measurable improvements in ASD symptoms and functional outcomes. Abbreviations ASD Autism Spectrum Disorder ATP Adenosine Triphosphate CARS Childhood Autism Rating Scale Cu Copper CV Coefficient of Variation DSM-5 Diagnostic and Statistical Manual of Mental Disorders,5th Edition E:I Excitatory-Inhibitory FDR False Discovery Rate GABA Gamma-Aminobutyric Acid ICP-MS Inductively Coupled Plasma Mass Spectrometry IL Interleukin IQR Interquartile Range NMDA N-Methyl-D-Aspartate ROS Reactive Oxygen Species SD Standard Deviation SOD Superoxide Dismutase Th17 T helper 17 cell TNF Tumor Necrosis Factor Treg Regulatory T cell Zn Zinc Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the National Medical Ethics Committee (protocol number 0120-201/2016-2 KME 78/03/16; 3 February 2021). Parents or legal guardians of all participants provided written informed consent, and children aged 7 years or older gave written assent when appropriate for their developmental level. Consent for publication Not applicable. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. The complete de-identified dataset is available upon reasonable request from the corresponding author, subject to appropriate ethics approval and data sharing agreements. Competing interests The authors declare that they have no competing interests. Funding This research was funded by the scientific research program grants P3-0124 and project J3-1756, financed by the Slovenian Research Agency. Authors’ contributions JO conceptualized the study. JO, UG, and AFS designed the methodology. UG and JO performed the formal analysis. MJV, DO, and KK conducted the investigation. JO and GA provided resources. 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Reference intervals of 24 trace elements in blood, plasma and erythrocytes for the Slovenian adult population. Clin Chem Lab Med. 2024;62:946-957. https://doi.org/10.1515/cclm-2023-0731 Wu L, Mao S, Lin X, Yang R, Zhu Z. Evaluation of whole blood trace element levels in Chinese children with autism spectrum disorder. Biol Trace Elem Res. 2019;191:269-275. https://doi.org/10.1007/s12011-018-1615-4 Macedoni-Lukšič M, Gosar D, Bjørklund G, Oražem J, Kodrič J, Lešnik-Musek P, et al. Levels of metals in the blood and specific porphyrins in the urine in children with autism spectrum disorders. Biol Trace Elem Res. 2015;163:2-10. https://doi.org/10.1007/s12011-014-0121-6 Crăciun EC, Bjørklund G, Tinkov AA, Urbina MA, Skalny AV, Rad F, et al. Evaluation of whole blood zinc and copper levels in children with autism spectrum disorder. Metab Brain Dis. 2016;31:887-890. https://doi.org/10.1007/s11011-016-9823-0 Li S, Wang J, Bjørklund G, Zhao W, Yin C. Serum copper and zinc levels in individuals with autism spectrum disorders. NeuroReport. 2014;25:1216-1220. https://doi.org/10.1097/WNR.0000000000000251 El-Meshad G, El-Nabi SA, Moharam NM, El-Khair MA. The plasma zinc/serum copper ratio as a biomarker in children with autism spectrum disorders. Menoufia Med J. 2017. Russo AJ, Bazin AP, Bigega R, Carlson RS, Cole MG, Contreras DC, et al. Plasma copper and zinc concentration in individuals with autism correlate with selected symptom severity. Nutr Metab Insights. 2012;5:NMI.S8761. https://doi.org/10.4137/NMI.S8761 Mehta SQ, Behl S, Day PL, Delgado AM, Larson NB, Stromback LR, et al. Evaluation of Zn, Cu, and Se levels in the North American autism spectrum disorder population. Front Mol Neurosci. 2021;14:665686. https://doi.org/10.3389/fnmol.2021.665686 Zhang J, Li X, Shen L, Khan NU, Zhang X, Chen L, et al. Trace elements in children with autism spectrum disorder: a meta-analysis based on case-control studies. 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Tonhajzerova I, Ondrejka I, Mestanik M, Mikolka P, Hrtanek I, Mestanikova A, et al. Inflammatory activity in autism spectrum disorder. In: Pokorski M, editor. Respiratory Health. Cham: Springer International Publishing; 2015. p. 93-98. Masi A, Quintana DS, Glozier N, Lloyd AR, Hickie IB, Guastella AJ. Cytokine aberrations in autism spectrum disorder: a systematic review and meta-analysis. Mol Psychiatry. 2015;20:440-446. https://doi.org/10.1038/mp.2014.59 Maares M, Haase H. Zinc and immunity: an essential interrelation. Arch Biochem Biophys. 2016;611:58-65. https://doi.org/10.1016/j.abb.2016.03.022 Sugandha N, Rizvi ZA, Dalal R, Adhikari N, Awasthi A. Zinc mediates the interplay between Th1/Treg differentiation and regulates anti-tumor immunity. J Immunol. 2023;210:245.14-245.14. https://doi.org/10.4049/jimmunol.210.Supp.245.14 Castora FJ. Mitochondrial function and abnormalities implicated in the pathogenesis of ASD. Prog Neuropsychopharmacol Biol Psychiatry. 2019;92:83-108. https://doi.org/10.1016/j.pnpbp.2018.12.015 Frye RE, Cakir J, Rose S, Delhey L, Bennuri SC, Tippett M, et al. Early life metal exposure dysregulates cellular bioenergetics in children with regressive autism spectrum disorder. Transl Psychiatry. 2020;10:223. https://doi.org/10.1038/s41398-020-00905-3 Bölte S, Girdler S, Marschik PB. The contribution of environmental exposure to the etiology of autism spectrum disorder. Cell Mol Life Sci. 2019;76:1275-1297. https://doi.org/10.1007/s00018-018-2988-4 Windham GC, Zhang L, Gunier R, Croen LA, Grether JK. Autism spectrum disorders in relation to distribution of hazardous air pollutants in the San Francisco Bay area. Environ Health Perspect. 2006;114:1438-1444. https://doi.org/10.1289/ehp.9120 Rossignol DA, Genuis SJ, Frye RE. Environmental toxicants and autism spectrum disorders: a systematic review. Transl Psychiatry. 2014;4:e360. https://doi.org/10.1038/tp.2014.4 Fiore M, Barone R, Copat C, Grasso A, Cristaldi A, Rizzo R, et al. Metal and essential element levels in hair and association with autism severity. J Trace Elem Med Biol. 2020;57:126409. https://doi.org/10.1016/j.jtemb.2019.126409 Lakshmi Priya MD, Geetha A. Level of trace elements (copper, zinc, magnesium and selenium) and toxic elements (lead and mercury) in the hair and nail of children with autism. Biol Trace Elem Res. 2011;142:148-158. https://doi.org/10.1007/s12011-010-8766-2 Yasuda H, Tsutsui T. Assessment of infantile mineral imbalances in autism spectrum disorders (ASDs). Int J Environ Res Public Health. 2013;10:6027-6043. https://doi.org/10.3390/ijerph10116027 Sulaiman R, Wang M, Ren X. Exposure to aluminum, cadmium, and mercury and autism spectrum disorder in children: a systematic review and meta-analysis. Chem Res Toxicol. 2020;33:2699-2718. https://doi.org/10.1021/acs.chemrestox.0c00167 Amadi CN, Orish CN, Frazzoli C, Orisakwe OE. Association of autism with toxic metals: a systematic review of case-control studies. Pharmacol Biochem Behav. 2022;212:173313. https://doi.org/10.1016/j.pbb.2021.173313 Stojsavljević A, Lakićević N, Pavlović S. Does lead have a connection to autism? A systematic review and meta-analysis. Toxics. 2023;11:753. https://doi.org/10.3390/toxics11090753 Saghazadeh A, Rezaei N. Systematic review and meta-analysis links autism and toxic metals and highlights the impact of country development status: higher blood and erythrocyte levels for mercury and lead, and higher hair antimony, cadmium, lead, and mercury. Prog Neuropsychopharmacol Biol Psychiatry. 2017;79:340-368. https://doi.org/10.1016/j.pnpbp.2017.07.011 Awadh SM, Yaseen ZM, Al-Suwaiyan MS. The role of environmental trace element toxicants on autism: a medical biogeochemistry perspective. Ecotoxicol Environ Saf. 2023;251:114561. https://doi.org/10.1016/j.ecoenv.2023.114561 James S, Stevenson SW, Silove N, Williams K. Chelation for autism spectrum disorder (ASD). Cochrane Database Syst Rev. 2015;2016. https://doi.org/10.1002/14651858.CD010766.pub2 Saghazadeh A, Ahangari N, Hendi K, Saleh F, Rezaei N. Status of essential elements in autism spectrum disorder: systematic review and meta-analysis. Rev Neurosci. 2017;28:783-809. https://doi.org/10.1515/revneuro-2017-0015 Martín Giménez VM, Bergam I, Reiter RJ, Manucha W. Metal ion homeostasis with emphasis on zinc and copper: potential crucial link to explain the non-classical antioxidative properties of vitamin D and melatonin. Life Sci. 2021;281:119770. https://doi.org/10.1016/j.lfs.2021.119770 Djoko KY, Ong CY, Walker MJ, McEwan AG. The role of copper and zinc toxicity in innate immune defense against bacterial pathogens. J Biol Chem. 2015;290:18954-18961. https://doi.org/10.1074/jbc.R115.647099 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional files Table S1: Complete 29-Element Distribution Statistics Description: Comprehensive distributional characteristics for all 29 analyzed urinary trace elements in both control (n = 76) and ASD (n = 76) groups, including mean, SD, median, IQR, coefficient of variation, outlier percentage, skewness, kurtosis, and Shapiro-Wilk normality test results. Table S2: Complete 29-Element Statistical Comparisons Description: Individual element comparisons for all 29 analyzed elements between ASD and control groups, showing median values, interquartile ranges, unadjusted p-values, FDR-corrected p-values, Cohen’s d effect sizes, and percentage changes. Figure S1: Element CV Distribution Across Categories Description: Distribution assessment classification for all 29 elements including coefficient of variation ranges, outlier prevalence, and normality testing results that guided the tiered analytical approach. Figure S2: Individual Element Q-Q Plots Description: Multi-panel Q-Q plot array showing 9 representative elements (3×3 grid) demonstrating the distribution characteristics that guided the tiered analytical approach, including Shapiro-Wilk test p-values. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 May, 2026 Reviewers agreed at journal 28 Apr, 2026 Reviewers invited by journal 28 Apr, 2026 Editor assigned by journal 24 Apr, 2026 Editor invited by journal 22 Apr, 2026 Submission checks completed at journal 21 Apr, 2026 First submitted to journal 21 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9433871","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":631301918,"identity":"81f32e5e-9173-4fbb-b7e0-b8a081fb7800","order_by":0,"name":"Joško Osredkar","email":"","orcid":"","institution":"Ljubljana University Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Joško","middleName":"","lastName":"Osredkar","suffix":""},{"id":631301920,"identity":"3908ab5f-0b82-46f9-b8f4-bad735555f80","order_by":1,"name":"Uroš Godnov","email":"","orcid":"","institution":"University of 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06:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9433871/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9433871/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108942753,"identity":"b7031260-654f-462f-94cc-8c805d2efe14","added_by":"auto","created_at":"2026-05-11 05:42:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":621234,"visible":true,"origin":"","legend":"\u003cp\u003eElement distribution characteristics (CV by element).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9433871/v1/d99052e179218233cb6a68f7.png"},{"id":108942725,"identity":"3e18a10c-eac8-479e-970f-a4e98a05031a","added_by":"auto","created_at":"2026-05-11 05:42:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80379,"visible":true,"origin":"","legend":"\u003cp\u003eUrinary Zn:Cu ratio dysregulation in ASD.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9433871/v1/2ffd3a387f15ad284571502a.png"},{"id":108942726,"identity":"31380fb7-91ae-4af8-b682-f84829564fdd","added_by":"auto","created_at":"2026-05-11 05:42:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":822377,"visible":true,"origin":"","legend":"\u003cp\u003eMechanistic impact of reduced Zn:Cu ratio in autism spectrum disorder.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9433871/v1/a002b1297044848f6f4c82bf.png"},{"id":108942728,"identity":"2d8318ea-527f-407d-986d-aa6b962c7832","added_by":"auto","created_at":"2026-05-11 05:42:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":608106,"visible":true,"origin":"","legend":"\u003cp\u003eSelective dysregulation heatmap for 29 elements.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9433871/v1/05cfd30d0339d77deece15f4.png"},{"id":108978161,"identity":"456846c7-1d34-4ef4-9c0d-c41e82c0712e","added_by":"auto","created_at":"2026-05-11 11:34:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2520031,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9433871/v1/c7ebb92d-d05a-4a24-8998-39c583ada70f.pdf"},{"id":108942754,"identity":"30580644-ba1b-4b52-834a-63f757a3fac7","added_by":"auto","created_at":"2026-05-11 05:42:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":298279,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional files\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1: Complete 29-Element Distribution Statistics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescription: Comprehensive distributional characteristics for all 29 analyzed urinary trace elements in both control (n = 76) and ASD (n = 76) groups, including mean, SD, median, IQR, coefficient of variation, outlier percentage, skewness, kurtosis, and Shapiro-Wilk normality test results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S2: Complete 29-Element Statistical Comparisons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescription: Individual element comparisons for all 29 analyzed elements between ASD and control groups, showing median values, interquartile ranges, unadjusted p-values, FDR-corrected p-values, Cohen’s d effect sizes, and percentage changes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S1: Element CV Distribution Across Categories\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescription: Distribution assessment classification for all 29 elements including coefficient of variation ranges, outlier prevalence, and normality testing results that guided the tiered analytical approach.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure S2: Individual Element Q-Q Plots\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescription: Multi-panel Q-Q plot array showing 9 representative elements (3×3 grid) demonstrating the distribution characteristics that guided the tiered analytical approach, including Shapiro-Wilk test p-values.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9433871/v1/e049020c85aed63ba2e22fb8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urinary Trace Element Dysregulation in Autism Spectrum Disorder: Selective Zinc-to-Copper Imbalance and Element-Specific Profiles as Biomarkers of Neurometabolic Dysfunction","fulltext":[{"header":"Background","content":"\u003cp\u003eAutism spectrum disorder (ASD) is a pervasive neurodevelopmental condition defined by persistent deficits in social communication together with restricted, repetitive patterns of behavior, interests, or activities [1]. Over the past two decades, reported ASD prevalence has risen substantially, with recent surveillance estimates in developed countries typically in the range of 1 in 36\u0026thinsp;\u0026minus;\u0026thinsp;1 in 44 children [2, 3]. This increase is thought to reflect a combination of improved screening, broader diagnostic criteria under DSM-5, greater awareness among clinicians and families, and more standardized case-ascertainment methods, rather than a single causal factor [2, 3]. Etiologically, ASD arises from complex interactions between genetic susceptibility, environmental exposures, and dysregulated developmental and physiological processes [4, 5]. Twin and family studies suggest high heritability (often estimated around 80\u0026ndash;90%), but incomplete concordance even among monozygotic twins indicates that non-genetic factors make a meaningful contribution to risk and phenotypic variability [6].\u003c/p\u003e \u003cp\u003eGenomic studies have identified hundreds of ASD-associated variants enriched in genes involved in synaptic development, neuronal connectivity, immune signaling, and metabolic regulation [7]. However, these variants alone do not fully account for the wide clinical heterogeneity of ASD, nor for variable penetrance and expression within families [6, 8]. This has led to growing interest in convergent downstream mechanisms\u0026ndash;such as excitatory-inhibitory (E:I) imbalance, oxidative stress, immune dysregulation, and mitochondrial dysfunction\u0026ndash;that may integrate genetic and environmental influences into common neurobiological pathways [9]. Among these, the E:I imbalance hypothesis proposes that ASD is characterized by excess excitatory relative to inhibitory neurotransmission in key neural circuits [10\u0026ndash;13]. Supporting evidence comes from neurophysiological studies showing increased spontaneous cortical activity with reduced inhibitory tone, neurochemical studies demonstrating altered glutamate/GABA ratios, genetic findings implicating receptors and transporters for both excitatory (AMPA, NMDA) and inhibitory (GABA, glycine) signaling, and animal models in which experimentally induced E:I imbalance recapitulates core autism-like behaviors [10, 14\u0026ndash;17].\u003c/p\u003e \u003cp\u003eTrace elements, particularly zinc (Zn) and copper (Cu), are critical regulators of these same pathways. Zinc exerts multiple neuroprotective effects: it acts as an endogenous NMDA receptor antagonist, thereby dampening excessive glutamatergic excitation; it positively modulates GABAergic transmission, strengthening inhibitory tone; and it serves as a catalytic cofactor for superoxide dismutase (SOD), a key antioxidant enzyme [18\u0026ndash;20]. Zinc is also essential for immune homeostasis, including regulatory T-cell (Treg) differentiation and function, and for normal synaptic plasticity through vesicular Zn at glutamatergic terminals [21, 22]. In contrast, copper has a dual-edged role. Physiologically, it is required as a cofactor for cytochrome c oxidase (Complex IV) in the mitochondrial respiratory chain and for monoamine oxidases involved in dopamine, serotonin, and noradrenaline metabolism [23, 24]. When elevated, however, copper can catalyze Fenton-type reactions that generate highly reactive hydroxyl radicals, promote oxidative damage, and interfere with GABA_A receptor function, thereby favoring excitation over inhibition [23, 25].\u003c/p\u003e \u003cp\u003eOxidative stress\u0026ndash;an imbalance between reactive oxygen species (ROS) production and antioxidant defenses\u0026ndash;is a highly replicated finding in ASD. Numerous studies report increased lipid peroxidation, protein oxidation, and oxidative DNA damage, alongside reduced activity of antioxidant enzymes such as SOD, catalase, and glutathione peroxidase [26\u0026ndash;30]. Decreased circulating levels of zinc, selenium, and vitamin E have also been described in some ASD cohorts, further weakening antioxidant capacity [31\u0026ndash;34]. In this context, zinc deficiency directly impairs SOD activity, whereas copper excess facilitates ROS generation via Fenton chemistry, so that a reduced Zn:Cu ratio can simultaneously decrease antioxidant defense and increase ROS production [35\u0026ndash;37]. Mitochondrial studies in ASD have likewise documented reduced ATP production, increased ROS generation, abnormal calcium handling, and altered mitochondrial morphology, consistent with a state of chronic energy deficit and oxidative damage [38\u0026ndash;41]. Because both Zn and Cu are indispensable for optimal Complex IV function, imbalances in their ratio may destabilize the respiratory chain, enhance electron leakage, and further amplify ROS [24, 42, 43].\u003c/p\u003e \u003cp\u003eNeuroinflammation and immune dysregulation are additional recurring themes in ASD pathophysiology. Elevated concentrations of pro-inflammatory cytokines such as IL-6, IL-8 and TNF-α have been reported in plasma, cerebrospinal fluid, and postmortem brain tissue of individuals with ASD, together with evidence of microglial activation and altered T-cell subset distribution (reduced Treg, increased Th17) [29, 44\u0026ndash;47]. Zinc is indispensable for Treg differentiation via STAT5/Foxp3 signaling, for balanced Th1/Th2 development, and for natural killer cell and B-cell function, whereas zinc deficiency promotes Th17 polarization and heightened innate immune activation [48\u0026ndash;52]. Copper excess, in turn, can impair T-cell proliferation and dendritic cell function and disturb the Treg/Th17 balance, fostering a pro-inflammatory milieu [37, 53\u0026ndash;56]. Thus, a dysregulated Zn:Cu ratio provides a plausible biochemical link between oxidative stress, immune activation, and neuroinflammation in ASD.\u003c/p\u003e \u003cp\u003eDespite intensive research on individual trace elements (especially Pb, Hg, Zn, and Cu), most previous studies have important limitations. Many examined only a small subset of elements, frequently without including Zn:Cu ratios; others lacked strict age and sex matching, did not rigorously address non-normal distributions or outliers, or relied on serum/plasma measurements that may reflect acute status rather than longer-term excretion patterns. Comprehensive urinary profiling of multiple elements in well-matched ASD cohorts remains comparatively scarce. To address these gaps, the present study used a prospective age- and sex-matched case-control design to measure 30 urinary trace elements in children with ASD and typically developing controls, with explicit attention to data quality, distributional characteristics, and element classification. Primary analyses focused on stable, low-variability elements, while secondary analyses evaluated key non-normally distributed elements and biologically important ratios\u0026ndash;most notably Zn:Cu. We hypothesized that (1) the Zn:Cu ratio is significantly reduced in ASD, (2) this imbalance impacts four convergent pathways (E:I balance, oxidative stress, immune function, and mitochondrial energetics), (3) dysregulation is selective (affecting a minority of elements) rather than global, and (4) element ratios provide more robust and clinically meaningful information than single element concentrations alone.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy design and participants\u003c/p\u003e \u003cp\u003eWe performed a prospective, age- and sex-matched case-control study including 76 children with autism spectrum disorder (ASD) and 76 typically developing controls, all aged 4\u0026ndash;14 years. Each ASD participant was matched to a control within \u0026plusmn;\u0026thinsp;1 year of age and with the same biological sex, and pairs were recruited from the same geographic region in Slovenia to minimize demographic and environmental confounding. The final matched cohort therefore comprised 152 children (54 boys and 22 girls in each group, 71% male) with identical mean age in ASD and controls (both 8.2 years, SD about 2.0-2.1).\u003c/p\u003e \u003cp\u003eASD diagnoses were established according to DSM-5 criteria, based on clinical evaluation supported by the Childhood Autism Rating Scale (CARS). Inclusion criteria for the ASD group were: confirmed ASD diagnosis, age between 4 and 14 years, and absence of current use of mineral supplements containing any of the measured trace elements. Exclusion criteria were medical conditions that could significantly alter mineral metabolism, including chronic kidney disease, malabsorption syndromes, and inflammatory bowel disease.\u003c/p\u003e \u003cp\u003eBefore enrolment, parents/guardians of both ASD children and their siblings were systematically interviewed regarding the following exclusion criteria:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAntibiotic exposure: Receipt of any antibiotic medication within 6 months prior to stool sample collection (to avoid confounding from antibiotic-induced microbiota disruption)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eGastrointestinal symptoms: Presence of any active digestive problems including but not limited to: chronic diarrhea, constipation requiring medical intervention, abdominal pain, blood in stool, or diagnosed inflammatory bowel disease\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAcute gastrointestinal infections: Any diagnosed gastrointestinal infection (viral, bacterial, or parasitic) within 3 months prior to sample collection\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eDietary abnormalities: Parental report of highly restrictive diets (e.g., elimination diets excluding multiple food groups), feeding tubes, or other abnormal dietary patterns that could confound inflammatory marker interpretation\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eControls were typically developing children from the same age range (4\u0026ndash;14 years) without ASD or other major neurodevelopmental diagnoses, selected to match ASD participants on age, sex, and region. This matching was chosen to reduce confounding by developmental stage and sex-related differences in trace element handling and immune function. A sample size of 76 participants per group was calculated to provide approximately 80% power to detect medium effect sizes (Cohen\u0026rsquo;s d approximately 0.50) at an alpha level of 0.05 using the Mann-Whitney U test, allowing for about 15% potential data loss or exclusion.\u003c/p\u003e \u003cp\u003eEthics\u003c/p\u003e \u003cp\u003eThe study protocol was approved by the National Medical Ethics Committee (protocol number 0120\u0026ndash;201/2016/6; 3 February 2021). Parents or legal guardians of all participants provided written informed consent, and children aged 7 years or older gave written assent when appropriate for their developmental level. The study was conducted in accordance with the Declaration of Helsinki and relevant national regulations.\u003c/p\u003e \u003cp\u003eUrine sampling and handling\u003c/p\u003e \u003cp\u003e Parents/guardians received detailed written instructions on how and when to collect urine at the time of informed consent. First morning urine samples (approximately 15 mL) were collected at home into sterile, trace-element-free urine containers. Immediately after collection, containers were placed into an insulated bag with a cold pack and transported to the laboratory the same day. On arrival, samples were aliquoted and stored at \u0026minus;\u0026thinsp;80\u0026deg;C until analysis.\u003c/p\u003e \u003cp\u003eTrace element measurement by ICP-MS\u003c/p\u003e \u003cp\u003eUrinary concentrations of 30 trace elements were measured using inductively coupled plasma mass spectrometry (ICP-MS) (7700x, Agilent Technologies, Santa Clara, CA, USA). The 12 essential elements were lithium (Li), magnesium (Mg), calcium (Ca), manganese (Mn), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), selenium (Se), molybdenum (Mo), rubidium (Rb), and strontium (Sr) and 18 toxic or environmental elements were beryllium (Be), aluminum (Al), titanium (Ti), vanadium (V), chromium (Cr), gallium (Ga), arsenic (As), silver (Ag), cadmium (Cd), tin (Sn), antimony (Sb), cesium (Cs), barium (Ba), gold (Au), mercury (Hg), thallium (Tl), lead (Pb), and uranium (U) were measured in accordance with normal sample preparation procedures as previously described [57]. Every result was represented as \u0026micro;g/L.\u003c/p\u003e \u003cp\u003eAn aliquot of 0.2 mL of urine sample, calibrator or control sample was mixed with 2 mL of ammonium hydroxide (Fluka Analytical TraceSELECT Ultra) solution containing Triton X-100 (Aldrich Chemistry, Trace Metal Basis), 1-butanol (Sigma Aldrich, ACS reagent), ethylenediaminetetraacetic acid disodium salt dehydrate (Aldrich Chemistry, Trace Metal Basis) and internal standard solution containing Bi, Ge, In, Li6, Lu, Rh, Sc and Tb (Agilent Technologies, ICP-MS Internal Std Mix). The reagents were TEs grade. Fourteen points calibration was performed. The reference material (RM) Seronorm Trace Elements Urine L-1 and L-2 (Sero) were used to check the accuracy of the results. RMs were analysed according to the protocol for internal quality control at the beginning and at the end of the run and between runs on every 15\u0026ndash;20 samples. The values found were in good agreement with the manufacturer-assigned RM values.\u003c/p\u003e \u003cp\u003eElement classification by distribution\u003c/p\u003e \u003cp\u003eBecause trace elements often show heterogeneous distributions, we classified each element according to its distributional properties in the control group. For each element, we calculated the coefficient of variation (CV), the percentage of outliers based on the interquartile range (values outside Q1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u0026times;IQR), skewness and kurtosis, and tested normality using the Shapiro-Wilk test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 considered clearly non-normal). Normality was assessed using the Shapiro-Wilk test (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 considered clearly non-normal), supported by visual inspection of Q-Q plots for representative elements (Additional file 4).\u003c/p\u003e \u003cp\u003eElements were grouped into five CV-based categories: \u0026ldquo;clean\u0026rdquo; (\u0026lt;\u0026thinsp;50%), \u0026ldquo;moderate variability\u0026rdquo; (50\u0026ndash;100%), \u0026ldquo;highly skewed\u0026rdquo; (100\u0026ndash;200%), \u0026ldquo;very high variability\u0026rdquo; (200\u0026ndash;300%), and \u0026ldquo;extreme variability\u0026rdquo; (\u0026gt;\u0026thinsp;300%). There were 4 clean elements (Au, Sb, Cs, Se), 10 moderate (Al, Co, Ba, Ca, Sr, Sn, Zn, Mo, Rb, Ni), 12 high-skew (Cr, Mg, Tl, U, Ti, Mn, Cd, Hg, Be, V, Ga, Ag), 3 very high (Li, As), and 2 extreme (Cu, Pb), with Pb showing a CV of 464% and 30.3% outliers and therefore excluded from comparative analyses. In total, 29 elements were retained for statistical analysis.\u003c/p\u003e \u003cp\u003eThis classification guided both reporting and analysis: clean elements were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and could be tested more traditionally, whereas all other elements were primarily described using medians and interquartile ranges and analyzed with non-parametric or transformed methods.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were performed in R (version 4.3.1) using the tidyverse, rstatix, and effsize packages. For the four clean elements (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%), descriptive statistics included mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD, median with interquartile range, and range. For all other elements, primary descriptive measures were median, interquartile range, and range, reflecting their non-normal distributions. For each element, we computed the coefficient of variation (CV), percentage of outliers (Tukey 1.5\u0026times;IQR rule), sample skewness, and excess kurtosis in the control group and used these diagnostics to classify elements into five distribution categories (low-variability, moderate, high-skew, very-high, and extreme variability) (summarized in Additional file 3).\u003c/p\u003e \u003cp\u003eGroup comparisons were carried out using the Mann-Whitney U test. Clean elements were tested on their raw values; problematic elements were analyzed after log10 transformation using log10(value\u0026thinsp;+\u0026thinsp;1) to handle zero or near-zero measurements and to stabilize variance. Effect sizes were expressed as Cohen\u0026rsquo;s d, calculated from group means and pooled standard deviation; d values\u0026thinsp;\u0026lt;\u0026thinsp;0.2 were considered negligible, 0.2\u0026ndash;0.5 small, 0.5\u0026ndash;0.8 medium, and \u0026gt;\u0026thinsp;0.8 large.\u003c/p\u003e \u003cp\u003eTo control for multiple comparisons across the 29 analyzed elements, p-values were adjusted using the Benjamini-Hochberg false discovery rate (FDR) procedure, with p_FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. In addition to single elements, we computed composite metrics: the total toxic element concentrations (sum of all 18 toxic elements), total essential element concentrations (sum of all 12 essential elements), and the toxic-to-essential ratio, all compared between groups using the Mann-Whitney U test.\u003c/p\u003e \u003cp\u003eWe also evaluated three ratios a priori: Zn:Cu as the primary ratio of interest, Se:Hg as a secondary ratio, and Ca:Mg as a tertiary ratio; these were log-transformed and compared between groups with the same non-parametric approach. For normalized analyses, element values were converted to Z-scores relative to the control mean and SD to identify elements with the largest deviation from normal in ASD. Sensitivity analyses repeated the main comparisons after excluding extreme outliers (\u0026gt;\u0026thinsp;3 SD from the mean) to examine robustness of the findings; the Zn:Cu ratio remained highly significant after outlier exclusion.\u003c/p\u003e \u003cp\u003eFinally, a future Phase 2 analysis is planned to incorporate Childhood Autism Rating Scale (CARS) scores when available, using Spearman correlations and regression models to examine whether element levels and particularly the Zn:Cu ratio predict autism severity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant characteristics\u003c/p\u003e \u003cp\u003eThe final matched cohort included 76 children with ASD and 76 typically developing controls, with identical mean ages (8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 vs 8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0 years) and the same proportion of boys (54/76, 71% in each group). Age range (4\u0026ndash;14 years), diagnostic confirmation by DSM-5 in the ASD group, and common geographic origin (Slovenia) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall data quality was high, with 172,566 of 172,800 possible measurements retained (99.4% completeness) and all re-measured flagged values meeting quality criteria.\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\u003eParticipant characteristics of the matched ASD and control cohorts.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControls (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD (n\u0026thinsp;=\u0026thinsp;76)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatching\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatched\u0026thinsp;\u0026plusmn;\u0026thinsp;1 year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 (by design)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (% male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71% (54/76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71% (54/76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatched\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.0 (by design)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge range\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u0026ndash;14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASD confirmation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDSM-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeographic location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlovenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlovenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatched region\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eElement distribution and data quality\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOf the 30 urinary trace elements measured, 4 elements (13%) showed low variability (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%), 25 (83%) showed moderate to high variability (CV\u0026thinsp;\u0026ge;\u0026thinsp;50%), and 1 element (Pb) had extreme variability with 30.3% outliers and was excluded from comparative analysis. This pattern of heterogeneous distributions is typical for trace element work and underscores the need to tailor statistical methods to element-specific distribution properties. Full descriptive statistics for all elements in both groups are provided in Additional file 1. Element classification by distribution properties is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with the full CV profile detailed in Additional file 3.\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\u003eElement classification by distribution quality in control urine samples.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClassification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eElements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReporting\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAu, Sb, Cs, Se\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eParametric tests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAl, Co, Ba, Ca, Sr, Sn, Zn, Mo, Rb, Ni\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLog-transformed tests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh skew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;200%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCr, Mg, Tl, U, Ti, Mn, Cd, Hg, Be, V, Ga, Ag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLog-transformed tests\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;300%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLi, As\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLog-transformed\u0026thinsp;+\u0026thinsp;caution\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtreme\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;300%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCu, Pb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMedian [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSee note\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEXCLUDED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e464%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePb (30.3% outliers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExcluded from main analysis\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\u003eClassification of 29 urinary trace elements according to coefficient of variation (CV), outlier prevalence, and normality testing in controls. Clean elements (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%) were analyzed as primary outcomes; elements with higher CV values required robust, non-parametric or transformed analyses. Pb (CV 464%, 30.3% outliers) was excluded from group comparisons.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e visualizes the spread of coefficients of variation across all elements, with color-coded categories and Pb highlighted as an extreme outlier.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (A) illustrates the coefficient of variation (CV) for all 30 urinary trace elements, demonstrating substantial heterogeneity in distribution characteristics typical for trace element analysis. Clean elements (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%) cluster in the lower range, whereas most elements fall into moderate (50\u0026ndash;100%) or high-skew (100\u0026ndash;200%) variability categories, with a smaller subset in very high (200\u0026ndash;300%) and extreme (\u0026gt;\u0026thinsp;300%) ranges. Lead (Pb) appears as an extreme outlier (CV 464%, 30.3% outliers) and is explicitly highlighted as excluded from comparative analyses, underscoring the need for cautious interpretation of highly unstable elements.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (B) summarizes the number of significantly dysregulated versus non-significant elements, highlighting that only 4\u0026ndash;5 of 30 elements (13\u0026ndash;17%) show significant differences between ASD and controls. The vast majority (25\u0026ndash;26 elements, 83\u0026ndash;87%) remain within the normal variability range, visually supporting the interpretation that trace element abnormalities in ASD are selective rather than global or systemic. This selective pattern argues against generalized renal handling defects or global malabsorption and instead points to disruption of specific trace element-dependent metabolic pathways.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (C) presents three composite metrics comparing ASD and controls: total toxic element concentrations (Panel A), total essential element concentrations (Panel B), and the toxic:essential ratio (Panel C). None of these composite measures differ significantly between groups (toxic sum: 61.30 vs 63.13 \u0026micro;g/L, p\u0026thinsp;=\u0026thinsp;0.879; essential sum: 104,818 vs 103,796 \u0026micro;g/L, p\u0026thinsp;=\u0026thinsp;0.639; toxic:essential ratio: 0.0008 vs 0.0011, p\u0026thinsp;=\u0026thinsp;0.495), indicating preserved overall element concentrations. The lack of differences in these global indices, despite clear Zn:Cu ratio dysregulation, reinforces the conclusion that ASD is characterized by pathway-specific trace element imbalance rather than generalized accumulation or depletion.\u003c/p\u003e \u003cp\u003ePrimary analysis: clean elements (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%)\u003c/p\u003e \u003cp\u003eIn the primary analysis of clean elements (Au, Sb, Cs, Se; CV\u0026thinsp;\u0026lt;\u0026thinsp;50%), none showed significant differences between ASD and controls. Mean selenium levels were slightly higher in ASD (101.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8 vs 98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2 \u0026micro;g/L), with a raw p-value of 0.091 and FDR-adjusted p of 0.712, suggesting at most a weak trend without statistical significance. Results for the clean elements are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\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\u003ePrimary analysis of clean elements (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eControl Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMann-Whitney U\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep_FDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAu (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.035\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.034\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSb (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.387\u0026thinsp;\u0026plusmn;\u0026thinsp;0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.412\u0026thinsp;\u0026plusmn;\u0026thinsp;0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCs (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.168\u0026thinsp;\u0026plusmn;\u0026thinsp;0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.174\u0026thinsp;\u0026plusmn;\u0026thinsp;0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSe (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e98.5\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e101.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\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\u003ePrimary analysis of four clean elements with well-behaved distributions (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%). Values are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD. None of the elements show significant differences between ASD and controls after FDR correction; selenium shows a non-significant trend (p\u0026thinsp;=\u0026thinsp;0.091, p_FDR\u0026thinsp;=\u0026thinsp;0.712). NS\u0026thinsp;=\u0026thinsp;not significant.\u003c/p\u003e \u003cp\u003eSecondary analysis: key elements with non-normal distributions\u003c/p\u003e \u003cp\u003eAmong elements with non-normal or highly variable distributions, copper showed a trend toward higher urinary concentrations in ASD (median 8.62 vs 7.52 \u0026micro;g/L; +14.6%; p\u0026thinsp;=\u0026thinsp;0.081, p_FDR\u0026thinsp;=\u0026thinsp;0.344), whereas zinc, tin, barium, and uranium did not differ significantly between groups. These results indicate that, considered individually, Cu and Zn exhibit only modest shifts, and most of the selected secondary elements remain within overlapping ranges in ASD and controls. Complete between-group comparisons for all 29 elements, including medians, IQRs, p-values, and effect sizes, are shown in Additional file 2. Key non-normally distributed elements are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSecondary analysis of selected elements with non-normal distributions.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eControl Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD Median [IQR]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep_FDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCohen\u0026rsquo;s d (log)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eStatus\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCu (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.52 [2.47\u0026ndash;11.00]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.62 [2.47\u0026ndash;11.74]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;14.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e372.7 [310\u0026ndash;512]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e356.2 [298\u0026ndash;468]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;4.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSn (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.387 [0.289\u0026ndash;0.601]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.465 [0.307\u0026ndash;0.853]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;20.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.747 [2.143\u0026ndash;5.862]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.204 [1.875\u0026ndash;5.128]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;14.5%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU (\u0026micro;g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.084 [0.053\u0026ndash;0.118]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.074 [0.042\u0026ndash;0.113]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;11.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNS\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\u003eMedian [interquartile range] of selected elements with non-normal or heterogeneous distributions, with percentage change and Mann-Whitney U test p-values calculated on log10-transformed values. Copper shows a non-significant trend toward elevation in ASD (p\u0026thinsp;=\u0026thinsp;0.081), whereas zinc, tin, barium, and uranium show no significant group differences after FDR correction. NS\u0026thinsp;=\u0026thinsp;not significant.\u003c/p\u003e \u003cp\u003eKey finding: zinc-to-copper (Zn:Cu) ratio dysregulation\u003c/p\u003e \u003cp\u003eBy contrast with the modest individual element effects, the Zn:Cu ratio showed a clear and statistically robust reduction in ASD. The mean Zn/Cu ratio decreased from 77.77\u0026thinsp;\u0026plusmn;\u0026thinsp;59.22 in controls to 58.07\u0026thinsp;\u0026plusmn;\u0026thinsp;47.27 in ASD (\u0026minus;\u0026thinsp;25.3%; raw p\u0026thinsp;=\u0026thinsp;0.0082, BH-FDR p\u0026thinsp;=\u0026thinsp;0.0247; Cohen\u0026rsquo;s d = -0.384), and the ASD distribution fell below the physiological range of approximately 75\u0026ndash;100 that characterized controls. This indicates that the relationship between zinc and copper is more disturbed than either absolute level alone. The primary result\u0026ndash;Zn:Cu ratio dysregulation\u0026ndash;is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eZinc-to-copper ratio in ASD and controls (primary result).\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep_FDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn:Cu Ratio (mean SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.77\u0026thinsp;\u0026plusmn;\u0026thinsp;59.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.07\u0026thinsp;\u0026plusmn;\u0026thinsp;47.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;25.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.0247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.384\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZn:Cu Ratio (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.17\u003c/p\u003e \u003cp\u003e[36.48\u0026ndash;106.95]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.12\u003c/p\u003e \u003cp\u003e[31.19\u0026ndash;74.47]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect size CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHedges\u0026rsquo; g 95% CI\u003c/p\u003e \u003cp\u003e-0.657 to -0.108\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\u003eZinc-to-copper (Zn:Cu) ratio in controls and ASD. The mean Zn:Cu ratio is reduced by 25.3% in ASD and falls below the expected physiological range (75\u0026ndash;100). Effect size (Cohen\u0026rsquo;s d = -0.384, 95% CI -0.657 to -0.108) is small-to-moderate and larger than for zinc or copper individually, emphasizing that ratio dysregulation is more informative than single-element changes.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays Zn:Cu ratio distributions in ASD versus controls, with a reference line for the normal range, highlighting the downward shift in ASD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows urinary Zn:Cu ratios in children with ASD compared with age- and sex-matched controls. The distribution demonstrates a clear 25.3% reduction in Zn:Cu ratio in ASD, median 47.12 (IQR 31.19\u0026ndash;74.47) vs 62.17 (IQR 36.48\u0026ndash;106.95), with values in the ASD group falling below the expected physiological range of approximately 75\u0026ndash;100. The effect size is small-to-moderate (Cohen\u0026rsquo;s d = -0.38, 95% CI -0.657 to -0.108), yet more pronounced than for zinc or copper considered separately, underscoring that the ratio captures a synergistic imbalance rather than modest shifts in individual elements. This figure visually supports the conclusion that Zn:Cu dysregulation is the primary trace element abnormality in this cohort and a plausible biochemical driver of the downstream mechanistic pathways detailed in the Discussion.\u003c/p\u003e \u003cp\u003eMechanistic context: four convergent pathways\u003c/p\u003e \u003cp\u003eThe observed reduction in Zn:Cu ratio aligns with mechanistic pathways in which zinc and copper exert opposing effects on synaptic signaling, oxidative stress handling, immune regulation, and mitochondrial function. These four domains form a conceptual framework for interpreting the biochemical impact of Zn:Cu dysregulation in ASD. These mechanistic relationships are summarized schematically in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e schematically links reduced Zn:Cu ratio in autism spectrum disorder to four convergent biological pathways: excitatory-inhibitory imbalance, oxidative stress, immune dysregulation, and mitochondrial dysfunction. Zinc is depicted as an NMDA antagonist and GABA potentiator, antioxidant cofactor, and promoter of regulatory T-cell differentiation, whereas copper is shown as enhancing excitatory drive, catalyzing reactive oxygen species generation, impairing immune tolerance, and disrupting Complex IV function when elevated. The diagram emphasizes how a reduced Zn:Cu ratio can simultaneously shift synaptic signaling toward excitation, amplify oxidative damage, promote pro-inflammatory immune profiles, and compromise mitochondrial ATP production, providing a coherent mechanistic framework for ASD-related neurometabolic dysfunction.\u003c/p\u003e \u003cp\u003eIn contrast, the Se/Hg ratio did not differ between groups (raw p\u0026thinsp;=\u0026thinsp;0.829, FDR p\u0026thinsp;=\u0026thinsp;0.829), and the Ca/Mg ratio was likewise non-significant (raw p\u0026thinsp;=\u0026thinsp;0.239, FDR p\u0026thinsp;=\u0026thinsp;0.359).\u003c/p\u003e \u003cp\u003eSelective rather than global dysregulation\u003c/p\u003e \u003cp\u003eComposite metrics demonstrated no significant differences in overall toxic element concentrations, essential element concentrations, or their ratio between ASD and controls (toxic sum 61.30\u0026thinsp;\u0026plusmn;\u0026thinsp;46.85 vs 63.13\u0026thinsp;\u0026plusmn;\u0026thinsp;59.99 \u0026micro;g/L, p\u0026thinsp;=\u0026thinsp;0.879; essential sum 104,818\u0026thinsp;\u0026plusmn;\u0026thinsp;68,715 vs 103,796\u0026thinsp;\u0026plusmn;\u0026thinsp;77,724 \u0026micro;g/L, p\u0026thinsp;=\u0026thinsp;0.639; toxic:essential ratio 0.0008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0006 vs 0.0011\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0014, p\u0026thinsp;=\u0026thinsp;0.495). These findings indicate that global trace element load is preserved and that dysregulation affects only a minority of elements. Composite metrics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and C.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComposite toxic and essential element metrics in ASD and controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic sum (all 18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e61.30\u0026thinsp;\u0026plusmn;\u0026thinsp;46.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e63.13\u0026thinsp;\u0026plusmn;\u0026thinsp;59.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEssential sum (all 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e104,818\u0026thinsp;\u0026plusmn;\u0026thinsp;68,715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e103,796\u0026thinsp;\u0026plusmn;\u0026thinsp;77,724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO difference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eToxic:Essential ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.0008\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.0011\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNO difference\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\u003eComposite indices of trace element status, including total toxic element concentrations (sum of 18 toxic elements), total essential element concentrations (sum of 12 essential elements), and their ratio. None of these global metrics differ significantly between ASD and controls, supporting a pattern of selective rather than generalized trace element dysregulation.\u003c/p\u003e \u003cp\u003eSelective dysregulation pattern: number of affected elements\u003c/p\u003e \u003cp\u003eWhen all 30 elements were considered, only 4\u0026ndash;5 showed significant or near-significant dysregulation, corresponding to 13\u0026ndash;17% of the measured panel, while 25\u0026ndash;26 elements (83\u0026ndash;87%) remained non-significant. This numerical pattern is consistent with targeted pathway disruption rather than broad systemic alterations in trace element handling. The overall number of significantly dysregulated vs non-significant elements is summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (B).\u003c/p\u003e \u003cp\u003eExploratory findings\u003c/p\u003e \u003cp\u003eSeveral additional elements showed uncorrected p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.20 but did not reach FDR-corrected significance. Lithium tended to be lower in ASD (14.02 vs 12.34 \u0026micro;g/L; -12%; p\u0026thinsp;=\u0026thinsp;0.089, p_FDR\u0026thinsp;=\u0026thinsp;0.344), while arsenic and aluminum showed modest, non-significant increases, suggestive of potential biological signals that require confirmation in larger cohorts. Exploratory elements with trends are listed in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExploratory elements with uncorrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 (non-significant after FDR).\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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eControl Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eASD Median\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e% Change\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep_FDR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePotential Relevance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePsychiatric symptom modulation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOxidative stress (toxic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u0026thinsp;14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNeuroinflammation potential\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\u003eExploratory elements showing uncorrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.20 but not surviving FDR correction (p_FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These findings are hypothesis-generating and may indicate biologically relevant trends (e.g., lithium and arsenic), but are reported as exploratory only.\u003c/p\u003e \u003cp\u003eZ-score standardized analysis and heatmap\u003c/p\u003e \u003cp\u003eZ-score normalization relative to the control distribution identified barium (ASD mean Z = -0.29, 95% CI -0.52 to -0.06), cadmium (-0.20, 95% CI -0.43 to 0.03), and uranium (-0.13, 95% CI -0.36 to 0.10) as the elements with the clearest negative deviations in ASD, while copper and zinc showed only minimal Z-score shifts. Most elements clustered around Z\u0026thinsp;=\u0026thinsp;0, corroborating the selective dysregulation pattern observed in the primary analyses. Details of the Z-score analysis are provided in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e and visualized as a heatmap in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eZ-score normalized deviations for selected elements in ASD versus controls.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\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\u003eASD Mean Z-score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.52, \u0026minus;\u0026thinsp;0.06]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClear reduction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCd\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.43, 0.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerate reduction\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.36, 0.10]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMild reduction\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\u003e\u0026minus;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.30, 0.16]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimal deviation\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e\u0026minus;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c3\"\u003e \u003cp\u003e[\u0026minus;\u0026thinsp;0.31, 0.15]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimal deviation\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\u003eMean Z-scores (ASD relative to controls) and 95% confidence intervals for elements with the largest absolute deviations. Barium, cadmium, and uranium show clear or moderate negative shifts, whereas copper and zinc remain close to zero, consistent with selective dysregulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a visual summary of Z-score-standardized deviations in urinary trace element levels between ASD and control groups across all 29 analyzed elements. Each row represents a single element, and the two columns (Control, ASD) are colored according to the mean ASD Z-score relative to the control distribution, on a continuous scale from \u0026minus;\u0026thinsp;0.5 (blue, lower in ASD) through 0 (white, no difference) to +\u0026thinsp;0.5 (red, higher in ASD). Most elements cluster around white, indicating minimal deviation and reinforcing that the majority of trace elements are not meaningfully altered in ASD. In contrast, barium (Ba, Z = -0.29), cadmium (Cd, Z = -0.20), and uranium (U, Z = -0.13) appear in progressively deeper blue shades, highlighting selective reductions relative to controls, while copper and zinc remain near white, consistent with only minimal deviation at the level of individual elements despite a clearly reduced Zn:Cu ratio. This pattern supports the concept of selective, pathway-specific dysregulation\u0026ndash;only a minority of elements show noticeable shifts\u0026ndash;rather than a global disturbance of trace element homeostasis in ASD.\u003c/p\u003e \u003cp\u003eRobustness to log transformation\u003c/p\u003e \u003cp\u003eLog10 transformation of key elements reduced their coefficients of variation by 78\u0026ndash;89% while yielding very similar p-values, indicating that findings are not artefacts of the chosen data scale. For example, copper\u0026rsquo;s raw p\u0026thinsp;=\u0026thinsp;0.081 and log-scale p\u0026thinsp;=\u0026thinsp;0.089, and zinc, barium, and tin show equally stable p-values after transformation. Robustness of the main findings to log transformation is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEffect of log10 transformation on variability and p-values for selected elements.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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\u003eRaw p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog p-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCV reduction\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobustness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003c/td\u003e \u003c/tr\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\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSn\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRobust\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\u003eComparison of raw and log10-transformed p-values and percentage reduction in coefficient of variation (CV) for key elements. Log transformation substantially reduces CV (78\u0026ndash;89%) while preserving statistical conclusions, confirming that the main findings are robust to data transformation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study shows that urinary trace element abnormalities in ASD are characterized by a selective reduction in the Zn:Cu ratio rather than a global disturbance of metal homeostasis. The 25.3% decrease in Zn:Cu ratio in ASD children, with values falling below the physiological range observed in matched controls, and an effect size (Cohen\u0026rsquo;s d = -0.38) larger than for zinc or copper individually, indicates that the relationship between these metals is more pathologically relevant than their absolute concentrations alone. This pattern is consistent with previous work reporting elevated Cu/Zn (or reduced Zn/Cu) ratios in blood or plasma of children with ASD [58\u0026ndash;62], but extends those findings by demonstrating the same imbalance in a rigorously age- and sex-matched urinary cohort and within a broader, quality-controlled multi-element panel.\u003c/p\u003e \u003cp\u003eSeveral studies have found that children with ASD have higher copper, lower zinc, and an increased Cu/Zn or decreased Zn/Cu ratio in blood, serum, or plasma. Russo et al. observed significantly higher plasma copper and lower zinc in autistic individuals, with a markedly increased copper-to-zinc ratio compared to neurotypical controls, and suggested that zinc supplementation may help normalize this imbalance [63]. Macedoni-Lukšič et al. reported that although absolute blood metal levels were not markedly different, the Cu/Zn ratio in ASD was significantly elevated (Wald chi-squared\u0026thinsp;=\u0026thinsp;6.6, p\u0026thinsp;=\u0026thinsp;0.010), and recommended routine assessment of Zn and Cu and correction of Zn deficiency in autistic children [59]. More recently, an Egyptian case-control study confirmed lower plasma Zn, higher serum Cu, and a reduced Zn/Cu ratio in ASD, and proposed Zn/Cu as a diagnostic biomarker with high sensitivity and specificity (cut-off ~\u0026thinsp;0.81, AUC 0.93) [62]. Our findings align with these reports by confirming that a reduced Zn:Cu ratio is a robust feature of ASD, and they add that this imbalance is detectable in urine, persists after rigorous data cleaning and FDR correction, and remains significant when extreme outliers are removed.\u003c/p\u003e \u003cp\u003eAt the same time, not all studies report consistent differences in Zn and Cu status. A North American study of Zn, Cu, and Se found mixed and sex-specific patterns, with some evidence for altered selenium but less uniform changes in zinc and copper across matrices [64]. A recent isotopic study in healthy and ASD children did not detect differences in the isotopic composition of serum zinc or copper, underscoring that the underlying regulation can be subtle and matrix-dependent [65]. Several broader metallomics and meta-analytic investigations have also highlighted heterogeneity in element patterns across cohorts, matrices (hair, blood, urine, nails), and analytical methods [65]. Against this background, our data reinforce a consistent theme: while individual Zn or Cu levels may vary between studies, the ratio tends to shift in the same direction\u0026ndash;toward relatively higher Cu and/or lower Zn\u0026ndash;and this appears to be a more stable signal than any single marker.\u003c/p\u003e \u003cp\u003eThe mechanistic interpretation of a reduced Zn:Cu ratio is strongly supported by experimental and clinical literature. Zinc acts as an endogenous NMDA antagonist and GABA potentiator, promotes postsynaptic plasticity, and is required for Cu-Zn SOD activity, thereby dampening excitatory drive and enhancing antioxidant defense [66, 67]. Copper, in contrast, can enhance excitatory neurotransmission, block GABA_A receptors, and catalyze Fenton-type reactions that generate ROS when present in excess [23, 68]. Numerous studies have documented increased oxidative stress in ASD\u0026ndash;elevated lipid peroxidation, protein and DNA oxidation, and reduced antioxidant enzyme activity\u0026ndash;often in conjunction with altered levels of Zn, Se, or other antioxidant cofactors [29, 30, 69]. Our finding that the Zn:Cu ratio, rather than total toxic or essential concentrations, is disturbed supports a model in which an unfavorable Zn:Cu balance simultaneously reduces antioxidant capacity (via impaired Cu-Zn SOD function) and increases ROS generation, thereby amplifying oxidative stress.\u003c/p\u003e \u003cp\u003eNeuroimmune and mitochondrial findings in ASD also dovetail with the trace-element pattern observed here. Multiple studies report elevated pro-inflammatory cytokines (IL-6, IL-8, TNF-α), microglial activation, and an imbalance of T-cell subsets in ASD, consistent with a chronic pro-inflammatory state [44, 45, 70, 71]. Zinc deficiency is known to impair Treg differentiation and favor Th17 polarization, whereas copper excess can further disrupt T-cell function and antigen presentation [48, 72, 73]. In our cohort, the reduced Zn:Cu ratio provides a plausible biochemical link to these immune abnormalities, as detailed in the mechanistic pathways diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Likewise, mitochondrial studies have shown reduced ATP production and increased ROS in ASD, and both Zn and Cu are essential cofactors for Complex IV [19, 39, 74, 75]. The selective Zn:Cu dysbalance we observe may therefore contribute to Complex IV inefficiency, electron leakage, and exacerbated mitochondrial oxidative stress, consistent with reports of neurometabolic dysfunction in ASD.\u003c/p\u003e \u003cp\u003eA key strength of the present study is the comprehensive urinary trace element panel, combined with explicit data-quality stratification and rigorous multiple-testing correction. We analyzed 30 elements, classified them by coefficient of variation, outlier prevalence, and distributional properties, and restricted primary hypothesis testing to well-behaved elements, while treating others with appropriate non-parametric or transformed methods. Importantly, only 4\u0026ndash;5 of 30 elements (13\u0026ndash;17%) showed significant or near-significant dysregulation, whereas 25\u0026ndash;26 elements remained non-significant, and composite metrics (total toxic concentrations, total essential concentrations, toxic:essential ratio) were indistinguishable between ASD and controls. This pattern strongly argues against global renal dysfunction, generalized malabsorption, or non-specific accumulation of metals, and instead points toward selective disruption of specific trace element-dependent pathways, with Zn:Cu imbalance at the center.\u003c/p\u003e \u003cp\u003eOur results both complement and refine previous work on \u0026ldquo;metal load\u0026rdquo; in ASD. Several studies and reviews have focused on elevated levels of toxic metals such as lead, mercury, cadmium, or aluminium, particularly in hair or blood, and proposed that overall toxic concentrations contributes to ASD risk [76\u0026ndash;78]. For example, early hair-metallomics studies reported frequent zinc deficiency and elevated toxic metals in autistic infants, suggesting an \u0026ldquo;infantile window\u0026rdquo; in which early mineral imbalance may shape neurodevelopment and epigenetic regulation [79\u0026ndash;81]. More recent case-control and meta-analytic data, however, have yielded mixed results for individual toxicants, with some cohorts showing elevated Pb or Cd, others showing no consistent differences, and associations often depending on the biological matrix and age at sampling [82\u0026ndash;85]. In our age- and sex-matched urinary cohort, we did not observe higher global toxic element concentrations in ASD, and lead was excluded from analysis due to extreme outlier behavior rather than systematic elevation. Instead, we found modest reductions in specific elements such as Ba, Cd, and U on Z-score analysis, again consistent with a selective rather than generalized toxic metal pattern.\u003c/p\u003e \u003cp\u003eOur findings also connect with the growing literature that examines trace elements not only as risk factors, but also as potential biomarkers of ASD severity. Several clinical studies report that lower Zn/Cu ratios correlate with higher Childhood Autism Rating Scale (CARS) scores or more severe symptom clusters, suggesting that Zn:Cu may track both risk and clinical expression [19, 60\u0026ndash;62]. A recent meta-analysis of trace elements in ASD found significant associations between specific metals (including Zn, Se, Mn, Mo, Sb, Tl) and ASD behaviors, and called for more work on steady-state trace element homeostasis and ratios rather than isolated concentration snapshots [65]. Although our current dataset does not yet include CARS values, we have designed a planned Phase 2 analysis to assess whether urinary Zn:Cu and related indices correlate with symptom severity, which will directly address this question in a rigorously matched cohort.\u003c/p\u003e \u003cp\u003eThe selective dysregulation pattern observed here has important implications for therapy. Our results are consistent with previous recommendations to monitor zinc and copper status in ASD, but they argue against non-specific chelation or broad \u0026ldquo;detoxification\u0026rdquo; strategies aimed at lowering total metal concentrations [19, 86\u0026ndash;88]. Rather, they support targeted interventions aimed at restoring Zn:Cu balance\u0026ndash;through zinc supplementation in deficient individuals, careful management of copper intake, or modulation of intestinal absorption\u0026ndash;while monitoring potential effects on oxidative stress, immune markers, and mitochondrial function [24, 89, 90]. At the same time, the cross-sectional design precludes causal inference; Zn:Cu imbalance may be a contributing factor, a downstream consequence of other metabolic alterations, or both. Longitudinal and interventional studies will be needed to determine whether correcting this ratio modifies clinical outcomes.\u003c/p\u003e \u003cp\u003eIn summary, this study adds to the converging evidence that ASD is associated with a disturbed Zn:Cu ratio, now demonstrated in a comprehensive urinary trace element profile with rigorous data quality control and matched design. The absence of differences in global toxic or essential element concentrations, combined with selective shifts in a small subset of elements, supports a model of pathway-specific trace element dysregulation rather than generalized metal overload. Mechanistically, a reduced Zn:Cu ratio provides a coherent link between excitatory-inhibitory imbalance, oxidative stress, immune activation, and mitochondrial dysfunction\u0026ndash;four domains repeatedly implicated in ASD pathophysiology\u0026ndash;highlighting this ratio as a promising mechanistic and potentially clinically useful biomarker.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, we used first morning spot urine rather than 24-hour collections; our findings reflect relative steady-state trace-element concentrations rather than absolute daily excretion. Spot morning samples cannot quantify intake or 24-h excretion, but are well suited to compare relative concentrations and ratios between groups. Second, although our sample size is moderate and powered for medium effects, smaller differences in individual elements may have gone undetected, and some exploratory trends (e.g., in lithium or arsenic) require validation in larger cohorts. Third, despite careful matching and strict exclusion criteria, residual confounding by diet, environmental exposures, or unmeasured comorbidities cannot be entirely excluded.\u003c/p\u003e \u003cp\u003eUrinary trace element concentrations were not normalized to creatinine or specific gravity, which may affect comparisons of individual elements, although the Zn/Cu ratio is less sensitive to urine dilution because both elements are measured in the same sample.\u003c/p\u003e \u003cp\u003eThe cohort size was moderate and no independent replication cohort was available, so the Zn/Cu result should be interpreted as hypothesis-generating and requiring external validation.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis matched urinary trace-element study demonstrates that ASD is characterized by a selective Zn:Cu ratio imbalance rather than a global disturbance in metal concentrations. Total toxic and essential element loads, as well as their ratio, were indistinguishable between ASD and controls, and the vast majority of elements remained within normal variability ranges. In contrast, the Zn:Cu ratio was clearly reduced in ASD, with an effect size larger than for zinc or copper considered in isolation, reinforcing the concept that element relationships carry more pathophysiological information than single concentrations.\u003c/p\u003e \u003cp\u003eMechanistically, an unfavorable Zn:Cu ratio offers a parsimonious explanation for several well-replicated features of ASD: it can shift synaptic signaling toward excitation, weaken antioxidant defenses while increasing ROS generation, destabilize immune tolerance, and compromise mitochondrial ATP production. Our findings therefore integrate trace-element data into a broader neurometabolic model in which a small number of pathway-critical elements are disturbed, while global metal homeostasis remains largely intact.\u003c/p\u003e \u003cp\u003eClinically, these results argue against non-specific chelation or \u0026ldquo;detoxification\u0026rdquo; strategies aimed at lowering overall metal load, and instead support targeted monitoring and correction of Zn:Cu balance\u0026ndash;particularly zinc deficiency\u0026ndash;in children with ASD. The urinary Zn:Cu ratio emerges as a promising, mechanistically anchored biomarker that is feasible to measure and robust to stringent data-quality criteria. Future longitudinal and interventional studies should determine whether correcting Zn:Cu imbalance modifies oxidative, immune, and mitochondrial markers and ultimately translates into measurable improvements in ASD symptoms and functional outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eASD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAutism Spectrum Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eATP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdenosine Triphosphate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCARS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChildhood Autism Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCu\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCopper\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoefficient of Variation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSM-5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders,5th Edition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eE:I\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eExcitatory-Inhibitory\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGABA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGamma-Aminobutyric Acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICP-MS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInductively Coupled Plasma Mass Spectrometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterquartile Range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNMDA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eN-Methyl-D-Aspartate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReactive Oxygen Species\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSOD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSuperoxide Dismutase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTh17\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eT helper 17 cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumor Necrosis Factor\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTreg\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegulatory T cell\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eZn\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eZinc\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and was approved by the National Medical Ethics Committee (protocol number 0120-201/2016-2 KME 78/03/16; 3 February 2021). Parents or legal guardians of all participants provided written informed consent, and children aged 7 years or older gave written assent when appropriate for their developmental level.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are included within the article and its additional files. The complete de-identified dataset is available upon reasonable request from the corresponding author, subject to appropriate ethics approval and data sharing agreements.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research was funded by the scientific research program grants P3-0124 and project J3-1756, financed by the Slovenian Research Agency.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eJO conceptualized the study. JO, UG, and AFS designed the methodology. UG and JO performed the formal analysis. MJV, DO, and KK conducted the investigation. JO and GA provided resources. AFS, TF, and KK performed data curation. JO wrote the original draft. All authors reviewed and edited the manuscript. UG and JO created the visualizations. JO and KK supervised the study. KK managed project administration. JO acquired funding. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all parents and guardians who allowed their children to participate in the study, and especially Vera Troha for her careful processing of the samples.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDiagnostic and Statistical Manual of Mental Disorders (DSM). https://psychiatryonline.org/dsm?doi=10.1176%2Fdsm\u0026amp;publicationCode=dsm. Accessed 15 Feb 2026.\u003c/li\u003e\n\u003cli\u003eChristensen DL, Maenner MJ, Bilder D, Constantino JN, Daniels J, Durkin MS, et al. Prevalence and characteristics of autism spectrum disorder among children aged 4 years \u0026ndash; Early Autism and Developmental Disabilities Monitoring Network, seven sites, United States, 2010, 2012, and 2014. 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Ecotoxicol Environ Saf. 2023;251:114561. https://doi.org/10.1016/j.ecoenv.2023.114561\u003c/li\u003e\n\u003cli\u003eJames S, Stevenson SW, Silove N, Williams K. Chelation for autism spectrum disorder (ASD). Cochrane Database Syst Rev. 2015;2016. https://doi.org/10.1002/14651858.CD010766.pub2\u003c/li\u003e\n\u003cli\u003eSaghazadeh A, Ahangari N, Hendi K, Saleh F, Rezaei N. Status of essential elements in autism spectrum disorder: systematic review and meta-analysis. Rev Neurosci. 2017;28:783-809. https://doi.org/10.1515/revneuro-2017-0015\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;n Gim\u0026eacute;nez VM, Bergam I, Reiter RJ, Manucha W. Metal ion homeostasis with emphasis on zinc and copper: potential crucial link to explain the non-classical antioxidative properties of vitamin D and melatonin. Life Sci. 2021;281:119770. https://doi.org/10.1016/j.lfs.2021.119770\u003c/li\u003e\n\u003cli\u003eDjoko KY, Ong CY, Walker MJ, McEwan AG. The role of copper and zinc toxicity in innate immune defense against bacterial pathogens. J Biol Chem. 2015;290:18954-18961. https://doi.org/10.1074/jbc.R115.647099\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"autism spectrum disorder, zinc, copper, trace elements, biomarkers, excitatory-inhibitory balance, oxidative stress, neurometabolic dysfunction, matched cohort, urinary biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-9433871/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9433871/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAutism spectrum disorder (ASD) is associated with excitatory-inhibitory imbalance, oxidative stress, immune dysregulation, and mitochondrial dysfunction, all of which depend on tightly regulated trace-element homeostasis. Comprehensive, quality-controlled urinary trace-element profiling in well-matched ASD cohorts remains limited.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn a prospective, age- and sex-matched case-control study, we enrolled 76 children with ASD and 76 typically developing controls (4\u0026ndash;14 years). First morning urine samples were collected and 30 trace elements measured by inductively coupled plasma mass spectrometry. Elements were classified by distribution quality (coefficient of variation, outlier prevalence, normality testing) to guide analysis. Primary analyses focused on four low-variability elements (CV\u0026thinsp;\u0026lt;\u0026thinsp;50%); secondary analyses addressed biologically important but non-normally distributed elements (including Cu, Zn); composite metrics included total toxic concentrations, total essential concentrations, and their ratio. Group comparisons used Mann-Whitney U tests with Benjamini-Hochberg false discovery rate (FDR) correction; effect sizes were expressed as Cohen\u0026rsquo;s d.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOf 30 elements, 4 (13%) showed low variability, 25 (83%) moderate-high variability, and 1 (Pb) was excluded for extreme variability and 30.3% outliers. Global sums of toxic and essential element concentrations and the toxic:essential ratio did not differ between groups after FDR correction. No individual element remained significant after FDR correction, while three elements (Cu, Se, Li) showed raw p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 in the supplementary analysis. The key finding was a 25.3% reduction in the Zn:Cu ratio in ASD (mean 58.07\u0026thinsp;\u0026plusmn;\u0026thinsp;47.27 vs 77.77\u0026thinsp;\u0026plusmn;\u0026thinsp;59.22 in controls; p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 after FDR with raw p\u0026thinsp;=\u0026thinsp;0.0082, FDR-adjusted p\u0026thinsp;=\u0026thinsp;0.0247, whereas individual zinc and copper levels showed only modest, non-significant shifts. Z-score analysis confirmed most elements clustered near zero, with only a subset (Ba, Cd, U) showing mild negative deviations.\u003c/p\u003e\u003ch2\u003eLimitations:\u003c/h2\u003e \u003cp\u003eLimitations include the use of spot urine rather than 24-hour collections, moderate sample size powered for medium effects, and the inability to fully exclude residual dietary or environmental confounding.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eASD is characterized by selective urinary trace-element dysregulation centered on a reduced Zn:Cu ratio rather than generalized metal overload or depletion. This imbalance provides a mechanistically plausible link to excitatory-inhibitory imbalance, oxidative stress, immune activation, and mitochondrial energy deficit. These findings support targeted assessment of Zn:Cu balance as a mechanistically grounded biomarker in ASD.\u003c/p\u003e","manuscriptTitle":"Urinary Trace Element Dysregulation in Autism Spectrum Disorder: Selective Zinc-to-Copper Imbalance and Element-Specific Profiles as Biomarkers of Neurometabolic Dysfunction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 05:40:20","doi":"10.21203/rs.3.rs-9433871/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-10T18:17:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165448788964785242057154814333051626815","date":"2026-04-28T19:51:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-28T11:53:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T10:25:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-22T21:28:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-21T09:21:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-21T09:02:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47ace52a-1918-4b0a-a439-8c190fca377b","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-10T18:17:54+00:00","index":43,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67193524,"name":"Biological sciences/Biochemistry"},{"id":67193525,"name":"Health sciences/Biomarkers"},{"id":67193526,"name":"Health sciences/Diseases"},{"id":67193527,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-05-11T05:40:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 05:40:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9433871","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9433871","identity":"rs-9433871","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}