Introduction

Williams–Beuren syndrome (WBS, OMIM-194050) is a rare genetic disorder characterized by multisystemic manifestations resulting from a heterozygous loss of contiguous 26–28 genes on chromosome 7, typically occurring de novo1. The prevalence of WBS is approximately 1:7500 live births2. Individuals with WBS exhibit distinctive facial dysmorphisms, cardiovascular anomalies, growth retardation, mild-to-moderate intellectual disability, and a heightened sociability. Patients with WBS also frequently experience medical problems in the gastrointestinal tract and urinary system, such as feeding difficulties, inguinal hernia, diarrhea, and hypercalciuria. Cardiovascular risk factors, including hypertension, impaired glucose tolerance, hyperlipidemia, and high intima-media thickness, are commonly observed in children with Williams syndrome, contributing to an increased likelihood of developing cardiovascular diseases in adulthood3. These manifestations necessitate ongoing management and are currently a primary focus of clinical care.

The pathophysiology of WBS beyond developmental issues in the cardiovascular and neurological systems remains poorly understood, yet it plays a critical role in the manifestation of clinical phenotypes and treatment of WBS. Individuals with WBS frequently experience endocrine and metabolic complications such as disrupted glucose metabolism and diabetes, elevated cholesterol levels and obesity, thyroid dysfunction, hypercalcemia, and premature onset of puberty. Metabolic disturbances are implicated in the manifestation of functional declines, including cognitive impairment and increased risk of cardiovascular disease, which is a leading cause of mortality in individuals with WBS4,5. The prevalence of metabolic disturbances in WBS is notable, with reports of mild thyroid hypoplasia in around 70% of children6 and impaired glucose tolerance in up to 75% of adults7. Additionally, hyperbilirubinemia is observed in 18.3% of WBS patients and is linked to subclinical hypotriglyceridemia and hypothyroidism4. Metabolism and endocrine characteristics play a significant role in the pathophysiology of developmental issues associated with WBS. Nevertheless, there is a scarcity of information regarding the metabolomic profiles of children with WBS. Examination of metabolomic disruptions in WBS may offer insights into the etiology of the disease and the evolution of clinical phenotypes, as well as aid in disease monitoring and treatment.

The objective of this study was to analyze the global metabolomic changes and associated metabolic pathways in individuals with WBS using untargeted serum metabolomics profiling via Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS/MS) within a case–control framework.

Methods

Study population

We recruited 25 children diagnosed with WBS who visited the Department of Child Health Care in Children’s Hospital, Zhejiang University School of Medicine between January 2020 to May 2023. Eligibility criteria were as follows: age under 18 years; and with the presence of microdeletion at 7q11.23 of the genome or the presence of clinical symptoms according to the Lowery score8. Patients with insufficient medical information regarding pregnancy and birth, or clinical records were excluded from the study, as were patients with elastin (ELN) deletion only, and patients whose legal representatives did not give informed consent. The diagnosis of WBS was confirmed by genetic testing, either fluorescence in situ hybridization (FISH) or chromosomal microarray, in 21/25 subjects; the diagnosis was clinically established by experienced senior clinicians in the remaining subjects9,10. One-to-one age and sex matched healthy controls (N = 25) were matched from the Health Management Center in the same hospital.

The study was approved by the Ethics Committee of the Children’s Hospital of Zhejiang University School of Medicine (No. 2019-IRB-122). All legal representatives of the participants in the present analysis provided written informed consent for participation. The study was conducted in accordance with the Helsinki Declaration.

Assessment of WBS-related phenotypes

Anthropometric measurements were measured by trained staff. Detailed WBS-related phenotypes, medical history questionnaire, maternal prenatal history, and genetic test results were retrieved from medical records10. The guardians of WBS children were interviewed for information on demographics and family history.

Metabolomics profiling

Sample preparation

Participants arrived in the morning after an 8-h fast and underwent blood sampling. Serum samples were stored at − 80 °C. Before seqnencing, samples were thawed at 4 °C and vortex-mixed. 100 μL aliquot of each serum sample was mixed with 400 μL methanol. The mixture was vortex-mixed and centrifuged at 12,000 rpm for 10 min at 4 °C. The supernatant was transferred and evaporated to dryness using a centrifugal vacuum evaporator. The dried sample was dissolved in 150 μL methanol–water (80: 20, v/v) containing internal standard (2-chlorol-phenylalanine, 4 ppm) and then was filtered by 0.22 µm membrane and transferred to a vial for UPLC-MS analysis. The quality control (QC) samples were prepared by mixing 20 μL of each sample supernatant and following the same steps.

UPLC-MS/MS methodology

Untargeted serum metabolomics were profiled with a UPLC-MS/MS system consisting of a Vanquish UPLC System (Thermo Fisher Scientific, USA) coupled with Q Exactive HF-X mass spectrometer (Thermo Fisher Scientific, USA), and operated in positive and negative polarity modes. Samples were injected 2 μL into a UPLC BEH Amide column (2.1 mm × 100 mm, 1.7 μm). In ESI positive ion mode (ESI+), the mobile phase was consisted of eluent A (0.1% formic acid in water) and eluent B (0.1% formic acid in acetonitrile). In ESI negative ion mode (ESI-), the mobile phases were consisted of eluent A (5 mM ammonium formate in water) and eluent B (acetonitrile). Simultaneous MS1 and MS/MS (Full MS-ddMS2 mode, data-dependent MS/MS) acquisition were used. The raw data were firstly converted to mzXML format by MSConvert in ProteoWizard software package (v3.0.8789) and processed using XCMS for feature detection, retention time correction and alignment. The metabolites were identified by accuracy mass (< 30 ppm) and MS/MS data which were matched with HMDB (http://www.hmdb.ca), massbank (http://www.massbank.jp), LipidMaps (http://www.lipidmaps.org), mzcloud (https://www.mzcloud.org) and KEGG (http://www.genome.jp/kegg)11,12,13. The robust LOESS signal correction (QC-RLSC) was applied for data normalization to correct for any systematic bias. After normalization, only ion peaks with relative standard deviations (RSDs) less than 30% in QC were kept to ensure proper metabolite identification.

Quality control

QC samples were plotted together with samples on the PCA plot based on level one metabolome (Supplementary Figure S1). Closely clustered QC samples indicate good repeatability. Concentrations have to be reported for more than 25% of the samples, otherwise, the metabolite is excluded from the statistical evaluation.

Statistical analysis

Data were expressed as mean ± standard deviation (SD) or median and interquartile range (IQE). T-test or Mann–Whitney U test were used to compare the two groups for normally distributed or non-normally distributed variables, respectively. Chi-square test was performed to compare count variables between groups. A two-sided p value < 0.05 was considered statistically significant. All analyses were conducted using R Studio (version 4.1.2). Z-scores of metabolites were calculated and used in analyses.

Multivariable analysis was performed to evaluate the overall difference of metabolome between WBS and controls. Metabolomics data underwent scaling by adaptive conversion before multivariate analysis. Two different multivariate statistical analysis models, unsupervised and supervised, were applied to discriminate the groups (PCA; PLS-DA; OPLS-DA) by R ropls (v1.22.0) package14. Variable Importance in Projection (VIP) derived from the OPLS-DA model was used to describe the contribution of metabolites to the model which evaluates the influence and ability to distinguish the two groups. A VIP > 1 for the first principal component of the OPLS-DA model was considered meaningful for group differentiation.

To identify characteristic metabolome features that differentiate WBS children from health controls, t-test was performed to identify differences in mean levels of ions or metabolites in WBS and controls. P value < 0.05 and VIP value > 1 were used to screen differentially abundant ions or metabolites. For the top differentially abundant metabolites, the ROC curve and AUC were computed using the predicted probabilities from the logistic regression model with group of participants (WBS or controls) as the outcome and the metabolites as the predictor. The pROC R package (version 1.18.5) was employed to generate the ROC curve and calculate the AUC. Model performance was assessed by comparing to a baseline classifier (AUC = 0.5). After scaling of metabolite matrix using pheatmap R package, heatmap was plotted with clustering for samples and differentially abundant metabolites.

Spearman’s correlation coefficients were calculated and tested among metabolites that are differentially abundant in WBS compared to control groups. Correlations patterns were compared between the two groups.

Differential metabolites were subjected to pathway analysis by MetaboAnalyst15, which combines results from powerful pathway enrichment analysis with the pathway topology analysis. Enrichment analysis was based on the hypergeometric distribution test. Topological analysis based on pointwise centrality degree method was performed to assess whether a given gene or metabolite plays an important role in a biological response based on its position in the pathway. The identified metabolites in metabolomics were then mapped to the KEGG pathway for biological interpretation of higher-level systemic functions. The metabolites and corresponding pathways were visualized using the KEGG Mapper tool. The impact value and p values of pathways were visualized. The top influenced pathways and metabolites were visualized in the network plot.

To identify potential metabolites relating to WBS phenotypes, the associations between differentially abundant metabolites and phenotypes were assessed in linear or logistic regression models adjusting for age and sex. Differentially abundant metabolites were compared to those reported for other diseases in MetaboAnalyst 5.0 as an exploration of shared molecular links and pathophysiology16.

Results

Descriptives of patients and controls

A total of 25 children diagnosed with WBS were included in the current study. The mean age was 5.0 years (SD: 2.6 years), ranging from 1.1 to 10.4 years old. 40% were female. Table 1 presents the general and clinical characteristics of the WBS children. Of them, 22 (88%) children experienced developmental delay, 23 (92%) had congenital heart disease, and 18 (72%) had indirect inguinal hernia. Supravalvular aortic stenosis and peripheral pulmonic stenosis were also prevalent in 20 (80%) and 18 (72%) children, respectively.

Table 1 General and clinical characteristics of children with Williams–Beuren syndrome (WBS).
Full size table

UPLC-MS detected 20,600 signals in positive ion mode and 12,775 in negative ion mode. A total of 465 metabolites were assigned putatively by matching retention time and available tandem MS information with reference databases. These metabolites belong mainly to carboxylic acids and derivatives, fatty acyls, benzene, and substituted derivatives, steroids and steroid derivatives, and organooxygen compounds.

Metabolite difference between WBS patients and healthy controls

Multivariate analysis of serum metabolomics

Hierarchical clustering in the heatmap of ion signals reveals a clear separation between WBS cohort and controls. Group-specific patterns of signals are also present among participants of the two groups (Supplementary Figure S2). Children with WBS and controls clustered distinctly in the OPLS-DA plot, with individuals of each group clustering closely (Fig. 1) along PC1. Volcano plot showed that the levels of a large panel of metabolic signals in WBS differed significantly from controls (p < 0.05, Fig. 2 C).

Fig. 1
figure 1

OPLS-DA plot of ions detected by LC–MS between WBS children and the controls. (A) OPLS-DA plot in positive ion mode; (B) OPLS-DA plot in negative ion mode. Abbreviation OPLS-DA, Orthogonal Projections to Latent Structures Discriminant Analysis, OPLS-DA. Notes The PC1 and PC2 ordinates represent the scores of first and second orthogonal component score. The dot represents the each study participants with colors representing their groups. This plot shows the difference between and within groups. The more concentrated the samples within the group and the more dispersed between the groups indicate the separation is more reliable.

Full size image
Fig. 2
figure 2

Differentially abundant metabolites in children with WBS in comparison to the controls. (A) Heatmap of differentially abundant metabolites in children with WBS and the controls. The columns represent samples and rows represent metabolites. The gradient of colors indicates the magnitude of the metabolite levels, with red representing a high expression and blue representing a lower expression. (B) OPLS-DA plot of putative metabolites between WBS children and the controls; (C) Volcano plot of fold changes and p-values of differentially abundant metabolites between groups.

Full size image

Differentially abundant analysis

Among the 20,600 signals detected by LC–MS in positive ion mode, 7111 were differentially abundant (5229 elevated and 1882 decreased) in WBS compared to controls (p < 0.05, VIP > 1.0). In negative ion mode, 4093 out of 12,775 signals were different in WBS compared to controls (2177 elevated and 1916 decreased). A total of 169 putative metabolites were differentially abundant in WBS patients compared to the controls (P < 0.05 and VIP > 1), with 105 increased and 64 decreased (Supplementary Table S1). Table 2 list the top differentially abundant metabolites with a FC > 2 or < 0.5 and a VIP > 1. Differentially abundant metabolites are visualized in Fig. 2A Heatmap of differentially abundant metabolites in children with WBS and controls; (B) OPLS-DA plot of differentially abundant metabolites in the two groups; (C) Volcano plot shows fold changes and p-values of significantly different metabolites between groups).

Table 2 Top 10 elevated and top 10 decreased metabolites in WBS children in comparison to the controls.
Full size table

The groups of metabolites altered in WBS included organic acids, glucogenic amino acids, bile acids and among others. As depicted in the heatmap, the top 10 metabolites with concentrations in WBS less than 50% of controls (FC ≤ 0.5) were 5-hydroxyindoleacetic acid, 9,10-dihydroxystearate, deoxyribose 5-phosphate, trimethoprim, indole glycerol phosphate, quadrone, 17a-estradiol, arachidic acid, lithocholic acid, and docosahexaenoic acid. The top 10 metabolites with concentrations more than 2 folds in WBS (FC ≥ 2) were palmitoleic acid, taurocholic acid, l-kynurenine, adenosine 5′-phosphate disodium, mannitol, homogentisate, creatinine, thymidine, (R)-4-hydroxymandelate, gentisic acid. Among these metabolites, palmitoleic acid, 17a-estradiol, deoxyribose 5-phosphate, l-kynurenine, indole glycerol phosphate, adenosine 5′-phosphate disodium, taurocholic acid, 9,10-dihydroxystearate, and gentisic acid had a VIP > 2 from OPLS-DA in distinguishing WBS children and the controls. We also provided the receiver operating characteristic (ROC) curves and area under the curve (AUC) for these metabolites in Supplementary Figures S3 and S4. The major classes of differentially abundant metabolites include carboxylic acids and derivatives, benzene and substituted derivatives, fatty acyls, organooxygen compounds, and steroids and steroid derivatives. Figure 3 depicts the classes of differentially abundant metabolites and Fig. 4 shows distributions of top differentially abundant metabolites in WBS children.

Fig. 3
figure 3

Classes of differentially abundant metabolites.

Full size image
Fig. 4
figure 4

Distributions of top 5 elevated and top 5 decreased metabolites in WBS children and healthy controls. The red dots represent levels of metabolites for controls and the blue dots represent levels of metabolites for WBS children. The horizontal line in the box represents the median value of log2 (intensity) of each metabolites.

Full size image

KEGG pathway enrichment analysis

The differentially abundant metabolites were mapped to KEGG pathways. Significantly (p < 0.05) enriched pathways include (ranging from large to small p-value) nicotine addiction, cholesterol metabolism, arginine biosynthesis, retrograde endocannabinoid signaling, ovarian steroidogenesis, cocaine addiction, phenylalanine metabolism, histidine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, central carbon metabolism in cancer, protein digestion and absorption, aminoacyl-tRNA biosynthesis, neuroactive ligand–receptor interaction, ABC transporters, glycine, serine and threonine metabolism (Table 3). Some of the suggestive (p < 0.1 but p > 0.05) enriched pathways include glutamatergic synapse, GABAergic synapse, linoleic acid metabolism, cerebellar long-term depression, taurine and hypotaurine metabolism, primary bile acid biosynthesis, mineral absorption, pentose phosphate pathway. Supplementary Table S2 shows the full list of pathway enrichment analysis results. Enriched KEGG pathways and their respective disturbed metabolites in WBS were visualized together in the network visualization plot (Fig. 5), revealing a centered place of altered levels of amino acids among enriched metabolic pathways, especially a decreased l-isoleucine and l-histidine, an elevated l-glutamic acid, l-glutamine, l-aspartic acid, l-phenylalanine, and l-threonine, connecting multiple enriched pathways. The disturbance of selected KEGG pathways is visualized in Supplementary Figure S5.

Table 3 Enriched KEGG pathways in children with WBS.
Full size table
Fig. 5
figure 5

KEGG pathway enrichment plots. (A) Bar plot of enriched pathways. The horizontal axis represents impact values enriched into different metabolic pathways and the vertical axis shows metabolic pathways. The numbers indicate the corresponding number of metabolites enriched in the pathway. The color correlates with the P-value, with red indicating small p values and blue indicating large p values. (B) Network plot of enriched pathways and metabolites. Blue dots indicate pathways and other dots indicate metabolites. The size of the pathway point indicates the number of metabolites connected to it, the more connected the number, the larger the point, the color of the metabolite point indicates the magnitude of the log2(FC) value, red indicates up-regulation of the difference, blue indicates down-regulation of the difference, the darker the color, the greater the degree of difference, and there is no information on the metabolite log2(FC) in the multi-group comparison.

Full size image

Correlation between differentially abundant metabolites

Many of the differentially abundant metabolites were intercorrelated and the correlation pattern in WBS was disrupted as compared to controls (Supplementary Figure S6, correlation plots for differentially abundant metabolites in controls (A) and WBS children (B)). In WBS children, for instance, 17-hydroxyprogesterone, 13E-11a-hydroxy-9,15-dioxoprost-13-enoic acid, pergolide, and sphingosine-1-phosphate, became positively correlated with multiple metabolites. N-acetylserotonin was inversely correlated with several metabolites in WBS instead of being positively correlated in controls.

Metabolites phenotype associations

To investigate if circulating metabolites were related to phenotypes of WBS, we regressed the 465 metabolites over 25 phenotypes after adjusting for age and sex. A total of 222 associations from 125 metabolites were identified (p < 0.05), but no association survived FDR multiple testing correction. An overview of the presence of WBS-related phenotypes in WBS children is visualized in the heatmap (Supplementary Figure S7). Metabolites associated with the phenotypes are listed in Supplementary Table S3. Short stature, gene deletion size, indirect inguinal hernia, a history of neonatal jaundice, and PPS were associated with most metabolite alterations. Larger gene deletion sizes were in general associated with lower levels of several metabolites. Higher levels of trimethoprim, ergothioneine, salicyluric acid, and lower taurine were associated with hypercalcemia. Lower anhalamine, mannitol, l-glutamine, tryptophanol, creatine, propionylcarnitine, isopyridoxal, cortisone, carbosulfan, aspartame, indole-3-acetate, 4-quinolinecarboxylic acid, and higher indoleglycerol phosphate, kyotorphin, pelargonic acid, acetylcholine, beta-leucine were associated with larger head circumference. However, birth weight, birth height, pregnancy week, SGA, SVAS, and SVPS did not show much correlation.

For exploring purpose, we checked in the MetaboAnalyst Online platform the similarity of differentially abundant metabolites with those reported for other diseases. The enriched metabolite sets share a higher similarity with those reported for dicarboxylic aminoaciduria, nicotinamide adenine dinucleotide deficiency, celiac disease, galactosemia type 1, schizophrenia, Alzheimer’s disease, autism, etc. Further, we explored the enriched KEGG pathways of metabolites associated with gene deletion sizes and short stature (Supplementary Figure S8).

Discussion

Through the utilization of a comprehensive, untargeted metabolomics methodology, the present investigation demonstrated a distinct differentiation in the circulating metabolome between individuals with WBS and healthy individuals. A total of 465 compounds were characterized based on their retention time and tandem mass spectrometry data, with 169 exhibiting differential abundance in individuals with WBS. The perturbed metabolites were found to be enriched in various pathways associated with crucial neurological functions, including retrograde endocannabinoid signaling, phenylalanine metabolism, nicotine and cocaine addiction, and bile acids metabolism. These findings suggest potential implications for the pathophysiological alterations observed in individuals with WBS.

In our analysis, a significant number of metabolites and their correlations were found to be disrupted in children with WBS compared to healthy controls. Despite being measured in blood, many of these disturbed metabolites were associated with the metabolism of neurotransmitters. Specifically, shared metabolites for these pathways included L-glutamic acid, l-glutamine, l-phenylalanine, l-aspartic acid, l-threonine, l-histidine, and l-isoleucine. These findings not only suggest potential underlying pathophysiological mechanisms but also present new opportunities for disease management. For instance, we detected disruptions in N, N-dimethylglycine and d-glycerate levels, which were linked to decreased phosphoserine phosphatase activity in a patient with Williams–Beuren syndrome. Supplementation with serine and glycine was found to be advantageous in this case17.

5-Hydroxyindoleacetic acid (5-HIAA), the main metabolite of serotonin, was among the top decreased metabolites among WBS children. Lower urinary or circulating 5-HIAA was found in depression, migraines, and autism spectrum disorders previously and low cerebral spinal fluid (CSF) levels were associated with anxiety and aggression and were found in WBS18.

The primary source of circulating serotonin is the intestine, with additional synthesis occurring in the brain from L-tryptophan. This neurotransmitter plays a crucial role in modulating various physiological processes including learning, memory, sleep, mood, attention, fear and appetite regulation,19,20 as well as gastrointestinal motility and vasoconstriction. Decreased levels of serotonin have been associated with conditions such as depression, anxiety, obsessive–compulsive disorder, sleep disturbances, and gastrointestinal issues.21,22. The dysregulation of serotonin synaptic activities in the brain has been implicated in both WBS and autism, as evidenced by mouse models23. Consistent with our findings, a decrease in serotonergic axon density has been observed in postmortem brains of individuals with WBS compared to neurotypical brains24. Serotonin metabolism has been suggested to play a role in the social phenotypes of WBS25 and peripheral arterial stenosis26. Additionally, a decreased density of serotonin transporter, responsible for removing serotonin from the synaptic cleft to the presynaptic terminal, has been observed in the amygdala of infants with WBS24. Consistent with this finding, adjusting serotonin levels may be advantageous in addressing neurological symptoms, as indicated by the effectiveness of selective serotonin reuptake inhibitors in individuals with WBS27. Our study further supports the potential utility of medications that target the serotonin system, such as antidepressants, anxiolytics, and antiemetics, in managing psychiatric symptoms in individuals with WBS28.

In contrast, mouse models of WBS exhibited elevated levels of serotonin metabolite 5-HIAA and increased 5-HT1A currents in various brain regions, which are known to influence anxiety-like behaviors25,29. The potential disparity between peripheral and cerebral serotonin metabolite levels requires further investigation, but these findings underscore the dysregulation of serotonin metabolism in the neurological aspects of WBS. In addition to its known functions, serotonin may also impact glucose metabolism30, and contribute to vascular abnormalities in WBS26.

In individuals with WBS, levels of the neurotoxic metabolite l-kynurenine, derived from l-tryptophan, were found to be significantly elevated compared to serotonin levels31. This suggests a deviation in tryptophan metabolism from serotonin towards the kynurenine pathway, which has been associated with neurodegenerative disorders32. This metabolic shift may be influenced by oxidative stress and inflammation, and has been linked to the development of type 2 diabetes33, cardiovascular diseases, cognitive deficits, and depression34,35. Psychological stressors, as well as kynurenine itself, have the potential to activate the aryl hydrocarbon receptor and stimulate the production of kynurenine36. These findings suggest that restoring equilibrium in tryptophan metabolism, particularly in the serotonin and l-kynurenine pathways, could be advantageous in addressing mood disorders associated with WBS.

Increased levels of l-glutamic acid and arachidic acid, as well as decreased levels of trehalose 6-phosphate and 2-arachidonoylglycerol, were enriched in the retrograde endocannabinoid signaling pathway, which has been implicated in Williams syndrome5. The endocannabinoid system is integral to various physiological processes, including brain development, anxiety regulation, stress response, pain perception, memory formation, appetite control, digestion, sleep regulation, inflammation modulation, and cardiovascular health37,38. Elevated levels of endogenous cannabinoids have been associated with anxiety disorders, which may contribute to the heightened prevalence of anxiety in individuals with WBS. Modulation of the cannabinoid pathway in WBS mouse models has shown promising results in enhancing social and cognitive behaviors, as well as improving cardiovascular function5. A higher level of arachidic acid, a polyunsaturated fatty acid, was linked to inflammation39, but it also supports the performance of brain function40. The alteration of metabolites we observed in WBS may contribute to anxiety and other cognitive and cardiovascular issues in WBS, suggesting potential treatment avenues.

The taurocholic acid level of WBS children was eight times higher than that of controls. Other derivatives of primary bile acid metabolism including taurine, and glycocholic acid were also elevated, indicating active bile acid biosynthesis in WBS. Circulating bile acids are considered mediators linking the gut, liver, and brain, and have been linked to Pakinson’s disease related depression41, Alzheimer’s disease41,42, diabetes, obesity43, and liver injury44,45. Taurine plays an important role in modulating calcium homeostasis and vitamin D absorption46, and is relevant for cardiac disease47,48 and bone health46. In line with this, a lower taurine level was associated with hypercalcemia in our WBS cohort.

Additionally, the gut microbiota plays a role in the synthesis of secondary bile acids, and the composition of the microbiota can be influenced by the host’s bile acid metabolism49. For example, individuals with irritable bowel disease have been found to exhibit elevated levels of fecal primary bile acids, which are positively associated with symptoms, while levels of secondary bile acids are reduced50. In our investigation, we observed a decrease in the secondary bile acid lithocholic acid in WBS. This dysregulation in bile acid metabolism may contribute to the common gastrointestinal symptoms experienced by WBS patients51. This is further supported by a recent study which documented reduced taurine and hypotaurine metabolism through analysis of gut microbiota proteome52. Additionally, while biliary hypoplasia, a factor in bile acid transfer, was not previously recognized as a characteristic of WBS, it has been documented in a patient with neonatal cholestatic jaundice53. Given the high incidence of cholestatic jaundice in our WBS cohort, vigilant monitoring of biliary development is warranted.

Variations in additional metabolites may also serve as indicators of the pathophysiological mechanisms underlying WBS. Specifically, children with WBS exhibited notably elevated levels of creatinine, suggesting an increased susceptibility to chronic kidney disease54. Furthermore, heightened levels of sphingosine-1-phosphate, a key modulator of vascular and immune functions, were observed. The increase in palmitoleic acid levels may be indicative of enhanced hepatic lipogenesis. Metabolites associated with steroid hormone biosynthesis exhibit variations between the groups, indicating potential phenotypic implications such as precocious puberty warranting consideration in WBS55. The elevated presence of N-acetylglucosamine may serve as a compensatory mechanism by inhibiting elastase activity, a notable feature in WBS attributed to the deletion of the elastin gene.

The metabolic profile of individuals with WBS was compared to that of individuals with other diseases. Similarities were identified with celiac disease, which has a co-occurrence rate of over 4% with WBS56,57. Additionally, similarities were noted with schizophrenia, Alzheimer’s disease, and autism, as alterations in neurotransmitters were found to be shared among these conditions, supported by gene expression data58. Future research could explore common molecular pathways that contribute to the phenotypes of these conditions. Furthermore, metabolites, which are downstream of pathological changes, may be influenced by genetic variations or alterations in gut microbial metabolic activities. Till now, no genes deleted in WBS were directly linked to metabolic processes, and future studies are warranted to uncover underlying genetic architecture. The WBS gut microbiota was revealed to show altered microbial metabolism implicated in our study, such as alanine, aspartate, and glutamate metabolism52.

The strengths of our study lie in the untargeted metabolome profiling of individuals with WBS and healthy controls, which revealed metabolites from diverse families and pathways not previously examined in the context of WBS. Furthermore, the study predominantly focused on young children with WBS, enabling the identification of metabolic alterations during early developmental stages. The results illuminate metabolomic changes that may be advantageous for the management and early intervention of WBS, as well as enhance comprehension of the molecular underpinnings of neuro-endocrinological symptoms, which may have greater significance for neurological development in comparison to adults.

There are several limitations inherent in the present study. The present study is subject to limitations regarding sample size, with 25 participants per group and a predominantly male composition. While we implemented age and gender matching between the WBS group and healthy controls to mitigate selection bias, the restricted sample size may limit the generalizability of findings, particularly regarding gender-specific effects. This sample size determination aligns with exploratory research paradigms in rare disease studies, yet future investigations would benefit from expanded cohorts incorporating more balanced gender representation and diverse demographic characteristics to enhance statistical power and external validity. Furthermore, in order to explore the relationship and causality among metabolite disturbances, phenotypes, and genotypes, a larger study sample size with a longitudinal design would be necessary. However, increasing the sample size for a rare disease may present challenges. The extent to which serum metabolome measures accurately reflect metabolic status in the central nervous system and other tissues remains inadequately elucidated. Further research is necessary to determine the potential relationship between these biochemical alterations and physiological functions, particularly within the central nervous system. Additionally, the current methodology was unable to ascertain the absolute metabolite concentration, thus hindering the interpretation against normal ranges. Moreover, the influence of dietary factors and gut microbial metabolic activities, which could impact circulating metabolites, were not considered in the present study. Last but not the least, we had to acknowledge that certain metabolites may not be exclusively specific to WBS patients. Urinary organic acid analysis indicates that patients with other inborn metabolic defects (e.g., amino acid metabolism disorders or fatty acid oxidation abnormalities) may excrete similar metabolites. For instance, elevated specific organic acids in urine are also observed in methylmalonic acidemia and propionic acidemia patients, which may overlap with the metabolic profile of WBS. To enhance the identification of disease-specific biomarkers, we recommend genetic testing (e.g., verification of chromosome 7q11.23 deletion) or targeted metabolomic analysis.

Conclusion

In conclusion, our research has identified unique metabolomic profiles in children with Williams–Beuren syndrome compared to healthy controls, even at a young age. These findings suggest significant subclinical metabolic changes that may have implications for neuroendocrinological development. These alterations appear to be associated with imbalances in metabolite production and consumption, potentially stemming from dysregulation of key enzymes. However, further investigation is needed to fully elucidate the underlying regulatory mechanisms. Future studies with larger sample sizes and long-term follow-ups can determine if metabolite differences can predict the onset of related symptoms for early intervention.