Untargeted Metabolomics of Body Dysmorphic Disorder

doi:10.21203/rs.3.rs-2310033/v1

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Untargeted Metabolomics of Body Dysmorphic Disorder

https://doi.org/10.21203/rs.3.rs-2310033/v1

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published 28 Feb, 2023

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BDD (Body Dysmorphic Disorder) is a disorder associated with depression and eating disorders, often arising from minor defects in appearance or an individual's imagining that he or she is defective. However, the pathogenesis and mechanism of BDD are not clear, and its pathogenesis and adjuvant treatment methods still need to be explored. We employed an liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach to identify key metabolic differences in BDD. Plasma samples from 2 independent cohorts (8 BDD patients and 8 healthy controls) were collected to characterize metabolic changes in BDD patients. The raw data were subjected to the Compound Discovery program for peak alignment, retention time correction, and extraction of peak areas. Metabolite structure identification was performed using the Compound Discovery program by means of accurate mass matching (<10ppm) and secondary spectral matching to search the database. Multidimensional statistical analysis was performed using the R statistical software ropls tool, including unsupervised PCA (principal component analysis) analysis, supervised PLS-DA (partial least squares discriminant analysis) and OPLS-DA (orthogonal partial least squares discriminant analysis). Identify the most promising metabolic signatures associated with BDD across all metabolomic datasets. Occurrence of BDD may be related to ABC transporters, purine metabolism, Glycine, serine and threonine metabolism, Pyrimidine, Pyrimidine metabolism, Biosynthesis of 12-, 14- and 16-membered macrolides, microbial metabolism in diverse environments, Biosynthesis of secondary metabolites, Caffeine and Insect hormone biosynthesis.

Body dysmorphic disorder

metabolomics

metabolites

Biosynthesis

BDD, also known as BID (Body image disturbance), refers to the fact that an individual's body or appearance is objectively free of defects, or has only slight defects or defects, while the individual imagines that his body is defective, or exaggerates the defects, thus resulting in a preoccupational concept of psychological pain, It is a common mental and psychological problem (Lacroix and Ranson 2021). A large number of studies have confirmed that the development of social culture and economy has made people pay more and more attention to their own body image, so more and more people suffer from body image disorder (Akram et al. 2022). Female body image disorder has long been a common phenomenon, while male body image disorder occurs quietly (Day et al. 2022). However, many patients tend to conceal their symptoms, so we may be underestimating the prevalence of these individuals (Moccia et al. 2021). The literature review found that there is no specific therapy for body image disorders at home and abroad, and most of them use drug therapy and psychological therapy (Graboyes et al. 2019). At present, the pathogenesis of body image disorder is still unclear, and its clinical treatment methods need to be further explored and improved.

With the development of the times, more and more people are troubled by this appearance, resulting in physical disorder, which can lead to depression, serious illness, and even self-mutilation, suicide and suicide (Brooks et al. 2020). If medical staff lacks awareness of BDD, it is easy to cause BDD patients to be dissatisfied with the treatment effect, trigger resistance, develop psychological and physical problems, and be more likely to attack medical staff or cause legal disputes (Demirdel and Ülger 2021). Appearance-induced depression is often accompanied by negative personality traits, such as lower self-esteem, higher self-exclusion and shame (Graboyes et al. 2020). Individuals with high levels of depression often choose escapism in response to social demands, along with conversation anxiety and social avoidance intentions (A Pauzé et al. 2020).

Metabolites are downstream of gene expression and protein translation, and are also the main substances interacting with environmental exposure; therefore, metabolites are considered to be closely related to individual phenotypes (Laíns et al. 2019). Metabolome is a kind of research technology for the quantitative and qualitative research of endogenous metabolome in vivo, with high-throughput and high-sensitivity detection technology (Roque and Romero 2021). Combined with multivariate statistical analysis methods, metabolites with molecular weights less than 1 kDa in biological systems such as body fluids, tissues, and cells can be analyzed. Through joint analysis with bioinformatics, key pathogenic metabolites and pathogenic metabolic pathways can be found (Kumar et al. 2021). By metabolomic analysis of the effect of methamphetamine on human monocyte-derived macrophages, 11 differential metabolomes were identified, and a new metabolite with m/z of 18 secreted by macrophages was found (Pawlak et al. 2019).

The untargeted stable isotope-resolved metabolomics obtained by further development of metabolomics technology. Live microbial cells were extracted from fresh mouse feces and cultured anaerobically with 13C-labeled dietary fiber. Utilize the untargeted stable isotope-resolved metabolomics technology obtained by further development of metabolomics technology. The use of inulin was significantly more efficient than the use of cellulose in the biosynthetic pathway, reflecting the different metabolic pathways of dietary fiber in the gut microbiome. This technique contributes to a deeper and more comprehensive understanding of the metabolic function of the gut microbiome (Deng et al. 2021). Using metabolomics to analyze the plasma of 15 healthy adolescents and 15 adolescents with PCOS (Polycystic ovary syndrome), it was concluded that PCOS may be related to abnormal lipid metabolism in the body. Supplementation with these two substances was consistent with improving PCOS symptoms (Çelebier et al. 2020). Metabolome analysis of the urine of BALB/c mice infected with Toxoplasma gondii showed that Toxoplasma gondii infection changed the amino acid metabolism, fatty acid metabolism, and nicotinate and nicotinamide metabolic pathways in mice, which is helpful for understanding the pathogenesis of toxoplasmosis. and improved treatment methods have certain value (Zhou et al. 2022).

Here, we utilized a non-targeted LC-MS-based metabolomic approach to study metabolite changes in BDD serum. Our aim was to identify the most likely relevant metabolic biomarkers and metabolic pathways that may contribute to a better understanding and improvement of the biochemical impairment associated with this disease.

Participants

Disease group: Subjects were assessed by BDD-YBOCS (Yale - Brown Obsessive - Compulsive Scale Modified for Body Dysmorphic Disorder), and patients with a score of 20 and above were included in this group. Finally, 8 patients with depression caused by appearance were selected as the disease group.

Normal group: Subjects with a score of 20 and above diagnosed by BDD-YBOCS. No history of other psychiatric diseases, no clinical physical diseases, no history of drug abuse or allergy, no history of alcoholism, no special eating habits, and no abnormality in routine physical examination. Age 20–75 years old, no gender restriction. Eight healthy people were selected as the normal group.

The study was approved by the Institutional Research Ethics Committee of the Affiliated Hospital of Nanjing University of Chinese Medicine. Each volunteer was informed about the basic information of the study and obtained consent, and signed the informed consent form.

Collect Serum Samples

All volunteers took blood on an empty stomach in the morning (8:00–10:00). 3mL of fresh venous blood was drawn from each patient through the middle cubital vein and placed in a BD blood collection tube coated with a coagulant. The serum was separated by centrifugation at 3000rpm/min for 15min, and 65µL of the supernatant was dispensed into 200µL centrifuge tubes. Make a record and store in a -80℃ refrigerator for later use.

Lc-ms Analysis

Take out the sample from − 80℃ refrigerator, dissolve on ice, take 50µL sample, add 1mL extract solution (methanol:acetonitrile = 3:1, -4℃ pre-cooling), vortex for 5min, sonicate for 15min, stand at 4℃ for 2h. Centrifuge at 12000rpm, 4℃ for 15 min, take the supernatant, and concentrate the dry powder in vacuo. Add 100 µL of 50% methanol in water to reconstitute. 2000rpm, 4℃, vortex for 3min, 12000rpm, 4℃, centrifuge for 15min, take the supernatant for injection analysis.

The samples were placed in an 8℃ autosampler and separated using a HSS T3 column. The injection volume was 2µL, the column temperature was 40℃, and the flow rate was 0.3mL/min. Chromatographic mobile phase(A: 0.1% formic acid in water, B: 0.1% formic acid in methanol, C: 0.05% acetic acid in water, D: 0.05% acetic acid in methanol). The chromatographic gradient elution program is as follows: Positive ion: 0-0.5min, B: 2%; 0.5-6min, B: 2–50%; 6-10min, B: 50–98%; 10-14min, B: 98%; 14-16min, B: 98 − 2%; 16-21min, B: 2%. Negative ion: 0-0.5min, D: 2%; 0.5-6min, D: 2–50%; 6-10min, D: 50–98%; 10-14min, D: 98%; 14-16min, D: 98 − 2%; 16-21min, D: 2%. QC standards are inserted into the samples to monitor and evaluate the stability of the system and the reliability of the experimental data.

The samples were detected by ESI (electrospray ionization) in positive and negative ion modes, respectively. The samples were separated by UHPLC and analyzed by mass spectrometry using a Thermo QE HF-X mass spectrometer. Mass spectrometry conditions: ionization source: ESI ion source, sheath gas flow rate: 30; auxiliary gas: 10; spray voltage: 2.5KV(+)/2.5KV(-); S-Lens RF: 50; capillary temperature: 325℃; Gas temperature: 300°C; secondary collision energy (NCE): 30; isolation window 1.5m/z, Top N = 8. Scanning range: 70-1050m/z, scanning mode: positive and negative ion scanning respectively. Analysis was performed using Xcalibur 4.1.

Data Preprocessing

Raw data were subjected to Compound Discovery for peak alignment, retention time correction, and extraction of peak areas. Metabolite structure identification was performed using Compound Discovery by accurate mass matching (< 10ppm) and secondary spectral matching to search databases for comparison. Multidimensional statistical analysis, including PCA analysis, PLS-DA and OPLS-DA, was performed using the R statistical software ropls tool.

Experimental quality evaluation

This project evaluates and analyzes the system stability of the project experiment by comparing the TIC (Total Ion Chromatography) map of the QC samples and analyzing the PCA statistics of the overall sample. The total ion chromatograms of the mass spectrometry in the positive and negative ion detection modes of the QC samples were compared respectively. The results showed that the response intensity and retention time of each chromatographic peak basically overlapped, indicating that the instrumental error was small and the data quality was reliable during the whole experiment (Fig. 1A&1B). By comparing the response peak height difference of the QC sample and the detection blank sample, the stability of the detection process and the residual substance can be judged. It can be seen that the retention time and response intensity of the QC samples are relatively stable, and no obvious peaks of the internal standard in the blank samples were detected. It shows that the instrument data collection stability is high, the material residue is very little, and there is no cross-contamination between samples (Fig. 1C&1D). These results show that the LC-MS instrument and detection process are not abnormal, and the data are accurate and reliable.

Metabolome Pca Analysis

PCA is an unsupervised data analysis method. By selecting several comprehensive variables, the information is dimensionally reduced, and PCA analysis is performed on each group of samples. In the PCA one-dimensional distribution diagram (Fig. 2A&2B), the QC and samples are all within the range of ± 2SD, indicating that the data quality of this experiment is good. On the PC1 and PC2 dimensional maps, there was no apparent separation between samples in the POS (positive ion) and NEG (negative ion) mode data (Fig. 2C&2D). After 7-fold cross-validation, the obtained PCA model parameters have a BIDPRE (principal component score) of 5 for POS, 0.523cum for R2X, 5 for NEG, and 0.519cum for R2X. The results showed changes in small-molecule metabolites in patients.

Metabolome Pls-da Analysis And Opls-da Analysis

PLS-DA is a supervised data analysis method. The method uses partial least squares regression to establish the relationship model between the number of metabolites and the category, and predict the category of the sample. Statistically drawn PLS-DA score map (Fig. 3A & 3B) shows that the model has a high degree of interpretability for the Y-axis variable data set. OPLS-DA is also a supervised data analysis method. The method is improved on the basis of PLS-DA, filtering out the noise irrelevant to the classification information, and improving the analytical ability and effectiveness of the model. Statistically drawn OPLS-DA score chart shows that between the disease group and normal group data, the intra-group difference is small, and the inter-group difference is large (Fig. 3C&3D). By analyzing the OPLS-DA model scores of the two groups (Fig. 3E&3F), it can be seen that the OPLS-DA model established by the POS and NEG data in this experiment did not overfit.

Screening For Differential Metabolites

The obtained data were analyzed by univariate statistical analysis, and the linear statistical model and Bayesian algorithm in the R language limma toolkit were applied. The AveExpr(mean normalized quantitative value), FC(Fold change), t value(T test score), P value(significant P.Value) and adj.P.Val of the two groups of differential metabolites were obtained. In this experiment, the difference fold |FC|≥1.3 and P.Value < 0.05 were used as the screening criteria, and 64 metabolites in POS were screened up, 49 metabolites were down-regulated, and 31 metabolites in NEG were up-regulated and 45 metabolites were screened. Metabolites were downregulated (Fig. 4A & 4B). See supplementary table 1 and 2 for details.

Bioinformatics analysis

In order to evaluate the function of differentially expressed metabolites, and at the same time to more comprehensively and intuitively display the relationship between samples and the expression differences of metabolites in BDD, we used the qualitatively significant differential metabolites to perform hierarchical clustering (Hierarchical Clustering), thereby Assist us to accurately screen marker metabolites related to BDD and study the changes of related metabolic processes(Fig. 5A & 5B). The obtained significantly different metabolites were annotated by KEGG ID in Thermo Scientific™ Compound Discoverer™ 3.1 software, and compared with the KEGG metabolic pathway database for enrichment and comparison analysis(Fig. 5C&5D). Among them, POS is mainly related to ABC transporters, purine metabolism, Glycine, serine and threonine metabolism, Pyrimidine metabolism, Biosynthesis of 12-, 14- and 16-membered macrolides, microbial metabolism in diverse environments and Biosynthesis of secondary metabolites. NEG is mainly related to Caffeine metabolism, Insect hormone biosynthesis and Biosynthesis of secondary metabolites. These results provide a reference for our next step to study the mechanism and mechanism of depression in patients with BDD.

All patients with BDD consider their appearance to be unattractive or ugly, including all parts of the body, and most of them are dissatisfied with the appearance of the face and body (Akram et al. 2020). The diagnosis of BDD mainly relies on clinical diagnosis and scale diagnosis, and clinical diagnosis is mainly based on clinical observation and DSM-IVh or CCMD-3. Due to issues such as patient judgment and private privacy, scale diagnosis is mainly used to assist diagnosis (Häberle et al. 2020). BDD patients are mainly affected by mental disorders. They start with a slight body image distortion and begin to be dissatisfied with their appearance, but they do not affect their lives at this time, and then develop to a moderate degree of body image distortion and begin to produce anxiety and depression (Hamamoto et al. 2019). Finally, due to the aggravation of anxiety and depression, it may start to escape from reality, abuse drugs, drink alcohol, self-abuse, self-inflicted surgery and damage the body, and more serious suicide (Gupta and Kapur 2019). Depression is only one of the comorbidities of BDD, and it is also the most serious mental illness (Sobanski and Schmidt 2000). Due to the lack of qualified psychiatrists in the plastic surgery department, we can only screen patients through scales. BDD-YBOCS was translated and revised by our team with good reliability and validity (Chen et al. 2022).

Depression is a persistent and serious mental illness that affects more than 120 million people worldwide (Peng et al. 2015). In the United States, more than 19 million adults are reported to suffer from depression (Baglioni et al. 2011), More than 26 million people in China currently suffer from depression (He and Zhang 2013). Depression is now the most important risk factor for suicide, with a population-attributable risk ratio of approximately 28% (Miret et al. 2013). By 2030, depression will be the leading cause of death worldwide (Tian et al. 2013). Using a proton NMR-based metabolomics protocol, 23 differentially expressed metabolites were identified between subjects with major depressive disorder and healthy control subjects, of which 5 metabolites were singled out as potential markers of depression thing (Zheng et al. 2013). Metabolomics analysis of plasma of rat CUMS (chronic unpredicted mild stress) depression model, the study found that glucose metabolism is related to depression (Chen et al. 2020). Depression is common, and despite extensive research and a variety of perspectives, its causes and treatments are still not fully understood. In this paper, the metabolomic analysis of the serum of patients with depression caused by BDD was carried out, and potential biomarkers were excavated for the study of the mechanism of depression caused by BDD.

ABC transporters, also known as efflux pumps, are powered by ATP hydrolysis and mediate the transmembrane transport of various endogenous and exogenous molecules (Hartz and Bauer 2011). Has the function of clearing brain toxic peptides (Jha et al. 2019) and compounds to maintain normal brain homeostasis and is associated with MDR (multidrug resistance) in cancer (Wang et al. 2021). Inborn errors of purine metabolism would cause neurological pediatric syndromes (Camici et al. 2010) and Chronic Mountain sickness (Gao et al. 2020). Glycine, serine and threonine metabolism are associated with male infertility (Ma et al. 2019) and Diabetic kidney disease (Hyeon et al. 2020). Pyrimidine metabolism is associated with poor prognosis in liver cancer (Wang et al. 2020) and mitochondrial damage (Micheli and Sestini 2011). There are few reports on Biosynthesis of 12-, 14- and 16-membered macrolides, mainly focusing on the study of antibiotics (Rubinstein 2001). Caffeine metabolism is associated with sleep and anxiety, a site of potential neurodegenerative and psychiatric disorders (Nehlig 2018).

In conclusion, we highlight the metabolic disturbances of Biosynthesis of secondary metabolites, Microbial metabolism in diverse environments and ABC transporters as key metabolic events in BDD Accumulation of purine, Pyrimidine, macrolides and secondary metabolites suggests an important role for substance metabolism in nerves. These predictions could improve our understanding of the pathogenesis of BDD and facilitate disease detection and intervention.

Author Contribution

Jinlong Huang contributed to the conception of the study. Both authors performed the experiment and contributed significantly to analysis and manuscript preparation. Yawen Wang performed the data analysis and wrote the manuscript.

Acknowledgements

We appreciate the technical support of the Meijiayouke Inc. in Wuhan.

Funding

This study received no funding.

Ethics Approval and Consent to Participate

was performed in accordance with the ethical standards as laid down in the Declaration of Helsinki and approved by the ethics committee of Affiliated Hospital of Nanjing University of Chinese Medicine. And verbal consent for publication was obtained from the patients.

Data Availability

All the data during the current study are included in the article.

Conflicts of Interest

The authors declare no competing interests.

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Supplementary Tables S1 and S2 are not available with this version

No competing interests reported.

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You are reading this latest preprint version

Untargeted Metabolomics of Body Dysmorphic Disorder

Status:

Journal Publication

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Abstract

Figures

Introduction

Methods

Participants

Collect Serum Samples

Lc-ms Analysis

Data Preprocessing

Results

Experimental quality evaluation

Metabolome Pca Analysis

Metabolome Pls-da Analysis And Opls-da Analysis

Screening For Differential Metabolites

Discussion

Conclusions

Declarations

References

Supplementary Tables

Additional Declarations

Status:

Journal Publication

Version 1