Exploratory Metabolomic and Lipidomic Profiling in a Manganese-Exposed Parkinsonism-Affected Population in Northern Italy

Abstract

Background/objectives: Chronic manganese (Mn) exposure is a recognized environmental contributor to Parkinsonian syndromes, including Mn-induced Parkinsonism (MnIP). This study aimed to evaluate whole-blood Mn levels and investigate disease/exposure-status-related alterations in metabolomic and lipidomic profiles.

Methods: A case-control study (N = 97) was conducted in Brescia, Italy, stratifying participants by Parkinsonism diagnosis and residential Mn exposure. Whole-blood Mn was quantified using ICP-MS. Untargeted metabolomic and lipidomic profiling was conducted using LC-MS. Statistical analyses included Mann-Whitney U tests, conditional logistic regression, ANCOVA, and pathway analysis.

Results: Whole-blood Mn levels were significantly elevated in Parkinsonism cases vs. controls (median: 1.55 µg/dL [IQR: 0.75] vs. 1.02 µg/dL [IQR: 0.37]; p = 0.001), with Mn associated with increased odds of Parkinsonism (OR = 2.42, 95% CI: 1.13-5.17; p = 0.022). The disease effect metabolites included 3-sulfoxy-L-tyrosine (β = 1.12), formiminoglutamic acid (β = 0.99), and glyoxylic acid (β = 0.83); all FDR p < 0.001. The exposure effect was associated with elevated glycocholic acid (β = 0.51; FDR p = 0.006) and disrupted butanoate (Impact = 0.03; p = 0.004) and glutamate metabolism (p = 0.03). Additionally, SLC-mediated transmembrane transport was enriched (p = 0.003). The interaction effect identified palmitelaidic acid (β = 0.30; FDR p < 0.001), vitamin B6 metabolism (Impact = 0.08; p = 0.03), and glucose homeostasis pathways. In lipidomics, triacylglycerols and phosphatidylethanolamines were associated with the disease effect (e.g., TG(16:0_10:0_18:1), β = 0.79; FDR p < 0.01). Ferroptosis and endocannabinoid signaling were enriched in both disease and interaction effects, while sphingolipid metabolism was specific to the interaction effect.

Conclusions: Mn exposure and Parkinsonism are associated with distinct metabolic and lipidomic perturbations. These findings support the utility of omics in identifying environmentally linked Parkinsonism biomarkers and mechanisms.

Keywords: Parkinsonism; biomonitoring; environmental neurotoxicology; lipidomics; manganese exposure; metabolomics.

Figure 1

Study design and geographic overview of the Parkinsonism case–control population in the Province of Brescia, Italy.

Figure 2

Distribution of whole-blood manganese concentrations by disease status. Violin plots show the distribution of whole-blood manganese levels (µg/dL) among individuals with Parkinsonism (n = 32) and controls (n = 46). The median manganese concentration was higher in the Parkinsonism group (1.55 µg/dL; IQR = 0.75) compared to the control group (1.02 µg/dL; IQR = 0.37). A Mann–Whitney U test indicated a statistically significant difference between groups (p = 0.0010). *** p-Value ≤ 0.001.

Figure 3

Metabolites associated with disease effect identified by ANCOVA. (A) Volcano plot displaying metabolite associations with disease status. The x-axis shows beta coefficients (effect size and direction), while the y-axis shows the (−log10(p-value)). Red points represent significantly upregulated metabolites (positive beta coefficients), and blue points represent significantly downregulated metabolites (negative beta coefficients). Light-pink and light-blue points indicate metabolites with significant p-values but smaller effect sizes (<|0.3|). Gray points are not statistically significant. The vertical green dashed lines mark beta coefficient thresholds of ±0.301, and the horizontal blue dashed line represents the significance threshold (p = 0.05). (B) Bar chart showing the top 25 metabolites ranked by partial eta squared (η²_p), representing the proportion of variance in metabolite levels explained by disease status. Gray bars reflect this explained variance. Overlaid red and blue circles represent beta coefficients (positive and negative, respectively), with error bars indicating 95% confidence intervals (CIs). All metabolites shown passed statistical thresholds for inclusion.

Figure 4

Metabolites associated with exposure effect identified by ANCOVA. (A) Volcano plot displaying the relationship between effect size (β coefficients) and statistical significance (−log10(p-value)) for individual metabolites associated with exposure effect. Each point represents a single metabolite. Red dots indicate metabolites significantly upregulated with exposure (β > 0.301, p < 0.05), while blue dots represent significantly downregulated metabolites (β < −0.301, p < 0.05). Light-pink and light-blue dots represent metabolites significant by p-value but with lower effect sizes. The vertical green dashed lines mark the beta coefficient thresholds of ±0.301, and the horizontal blue dashed line indicates the significance threshold of p = 0.05. (B) Pathway enrichment and topology analysis for exposure-effect-associated metabolites. Each bubble represents a metabolic pathway, where the x-axis indicates pathway impact based on relative-betweenness centrality and the y-axis shows statistical significance (−log10(p-value)) from a hypergeometric overrepresentation test. Bubble size reflects pathway impact, and color intensity corresponds to the FDR-corrected p-value. Pathways above the red dashed line (−log10(p) > 1.3, corresponding to p < 0.05) are considered significantly enriched. Analysis was performed using MetaboAnalyst 6.0 with Homo sapiens-specific KEGG pathway libraries.

Figure 5

Metabolites associated with interaction effect identified by ANCOVA. (A) Volcano plot illustrating the interaction effect on metabolites, displaying beta coefficients (x-axis) versus (−log10(p-values)) (y-axis). Red dots represent metabolites significantly upregulated in the interaction group (β > 0.301, p < 0.05), and blue dots indicate significant downregulation (β < −0.301, p < 0.05). Horizontal and vertical dashed lines represent p-value and beta coefficient thresholds, respectively. Annotated metabolites reflect those passing both thresholds and among the most significant based on p-value. (B) Bubble plot representing the top 25 enriched metabolite sets associated with the interaction effect. Bubble size corresponds to the enrichment ratio, and color represents statistical significance (p-value), with darker colors indicating stronger enrichment. Pathways are ranked by (−log10(p-value)), with the most statistically enriched pathways appearing at the top.

Figure 6

Lipids associated with disease effect and interaction effect identified by ANCOVA. (A) Volcano plot illustrating the disease effect on lipid species, where each point represents its associated beta coefficient (x-axis) and its (−log10(p-value)) (y-axis). Significant positive associations (β > 0.301, p < 0.05) are shown in red and significant negative associations in blue (β < −0.301, p < 0.05). Dashed vertical green lines indicate β = ± 0.301, and the horizontal dashed blue line indicates the significance threshold of p = 0.05. (B) Bar chart of the top 25 lipid species associated with the disease effect, ranked by partial eta squared (η²_p), indicating effect size. Overlaid points reflect the direction and magnitude of beta coefficients with their 95% confidence intervals (CIs)colored by sign of association — red for positive associations, blue for negative. (C) Volcano plot showing lipid species associated with the interaction effect, following the same plotting conventions as Panel A. (D) Bar chart of the top 25 lipid species associated with the interaction effect, following the same plotting conventions as Panel B.