Metabolomics profiling reveals altered lipid metabolism and identifies a panel of lipid metabolites as biomarkers for Parkinson's disease related anxiety disorder

Neurolipidomic insights into anxiety disorders: Uncovering lipid dynamics for potential therapeutic advances

Anxiety disorders constitute a spectrum of psychological conditions affecting millions of individuals worldwide, imposing a significant health burden. Historically, the development of anxiolytic medications has been largely focused on neurotransmitter function and modulation. However, in recent years, neurolipids emerged as a prime target for understanding psychiatric pathogenesis and developing novel medications. Neurolipids influence various neural activities such as neurotransmission and cellular functioning, as well as maintaining cell membrane integrity. Therefore, this review aims to elucidate the alterations in neurolipids associated with an anxious mental state and explore their potential as targets of novel anxiolytic medications. Existing evidence tentatively associates dysregulated neurolipid levels with the etiopathology of anxiety disorders. Notably, preclinical investigations suggest that several neurolipids, including endocannabinoids and polyunsaturated fatty acids, may hold promise as potential pharmacological targets. Overall, the current literature tentatively suggests the involvement of lipids in the pathogenesis of anxiety disorders, hinting at potential prospects for future pharmacological interventions.

Identification of metabolites reproducibly associated with Parkinson's Disease via meta-analysis and computational modelling

Many studies have reported metabolomic analysis of different bio-specimens from Parkinson's disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson's disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.

Untargeted serum metabolic profiling of diabetes mellitus among Parkinson's disease patients

Type 2 diabetes mellitus (T2DM) is a common comorbidity among Parkinson's disease (PD) patients. Yet, little is known about dysregulated pathways that are unique in PD patients with T2DM. We applied high-resolution metabolomic profiling in serum samples of 636 PD and 253 non-PD participants recruited from Central California. We conducted an initial discovery metabolome-wide association and pathway enrichment analysis. After adjusting for multiple testing, in positive (or negative) ion mode, 30 (25) metabolic features were associated with T2DM in both PD and non-PD participants, 162 (108) only in PD participants, and 32 (7) only in non-PD participants. Pathway enrichment analysis identified 17 enriched pathways associated with T2DM in both the PD and non-PD participants, 26 pathways only in PD participants, and 5 pathways only in non-PD participants. Several amino acid, nucleic acids, and fatty acid metabolisms were associated with T2DM only in the PD patient group suggesting a possible link between PD and T2DM.

How close are we to a breakthrough? The hunt for blood biomarkers in Parkinson's disease diagnosis

Parkinson's disease (PD), being the second largest neurodegenerative disease, poses challenges in early detection, resulting in a lack of timely treatment options to effectively manage the disease. By the time clinical diagnosis becomes possible, more than 60% of dopamine neurons in the substantia nigra (SN) of patients have already degenerated. Therefore, early diagnosis or identification of warning signs is crucial for the prompt and timely beginning of the treatment. However, conducting invasive or complex diagnostic procedures on asymptomatic patients can be challenging, making routine blood tests a more feasible approach in such cases. Numerous studies have been conducted over an extended period to search for effective diagnostic biomarkers in blood samples. However, thus far, no highly effective biomarkers have been confirmed. Besides classical proteins like α-synuclein (α-syn), phosphorylated α-syn and oligomeric α-syn, other molecules involved in disease progression should also be given equal attention. In this review, we will not only discuss proposed biomarkers that are currently under investigation but also delve into the mechanisms underlying the disease, focusing on processes such as α-syn misfolding, intercellular transmission and the crossing of the blood-brain barrier (BBB). Our aim is to provide an updated overview of molecules based on these processes that may potentially serve as blood biomarkers.

A crazy trio in Parkinson's disease: metabolism alteration, α-synuclein aggregation, and oxidative stress

Parkinson's disease (PD) is an aging-associated neurodegenerative disorder, characterized by the progressive loss of dopaminergic neurons in the pars compacta of the substantia nigra and the presence of Lewy bodies containing α-synuclein within these neurons. Oligomeric α-synuclein exerts neurotoxic effects through mitochondrial dysfunction, glial cell inflammatory response, lysosomal dysfunction and so on. α-synuclein aggregation, often accompanied by oxidative stress, is generally considered to be a key factor in PD pathology. At present, emerging evidences suggest that metabolism alteration is closely associated with α-synuclein aggregation and PD progression, and improvement of key molecules in metabolism might be potentially beneficial in PD treatment. In this review, we highlight the tripartite relationship among metabolic changes, α-synuclein aggregation, and oxidative stress in PD, and offer updated insights into the treatments of PD, aiming to deepen our understanding of PD pathogenesis and explore new therapeutic strategies for the disease.

Lipids in Psychiatric Disorders: Functional and Potential Diagnostic Role as Blood Biomarkers

Lipids are a crucial component of the human brain, serving important structural and functional roles. They are involved in cell function, myelination of neuronal projections, neurotransmission, neural plasticity, energy metabolism, and neuroinflammation. Despite their significance, the role of lipids in the development of mental disorders has not been well understood. This review focused on the potential use of lipids as blood biomarkers for common mental illnesses, such as major depressive disorder, anxiety disorders, bipolar disorder, and schizophrenia. This review also discussed the impact of commonly used psychiatric medications, such as neuroleptics and antidepressants, on lipid metabolism. The obtained data suggested that lipid biomarkers could be useful for diagnosing psychiatric diseases, but further research is needed to better understand the associations between blood lipids and mental disorders and to identify specific biomarker combinations for each disease.

Plasma lipidomic profiling reveals six candidate biomarkers for the prediction of incident stroke in patients with hypertension

INTRODUCTION

The burden of stroke in patients with hypertension is very high, and its prediction is critical.

OBJECTIVES

We aimed to use plasma lipidomics profiling to identify lipid biomarkers for predicting incident stroke in patients with hypertension.

METHODS

This was a nested case-control study. Baseline plasma samples were collected from 30 hypertensive patients with newly developed stroke, 30 matched patients with hypertension, 30 matched patients at high risk of stroke, and 30 matched healthy controls. Lipidomics analysis was performed by ultrahigh-performance liquid chromatography-tandem mass spectrometry, and differential lipid metabolites were screened using multivariate and univariate statistical methods. Machine learning methods (least absolute shrinkage and selection operator, random forest) were used to identify candidate biomarkers for predicting stroke in patients with hypertension.

RESULTS

Co-expression network analysis revealed that the key molecular alterations of the lipid network in stroke implicate glycerophospholipid metabolism and choline metabolism. Six lipid metabolites were identified as candidate biomarkers by multivariate statistical and machine learning methods, namely phosphatidyl choline(40:3p)(rep), cholesteryl ester(20:5), monoglyceride(29:5), triglyceride(18:0p/18:1/18:1), triglyceride(18:1/18:2/21:0) and coenzyme(q9). The combination of these six lipid biomarkers exhibited good diagnostic and predictive ability, as it could indicate a risk of stroke at an early stage in patients with hypertension (area under the curve = 0.870; 95% confidence interval: 0.783-0.957).

CONCLUSIONS

We determined lipidomic signatures associated with future stroke development and identified new lipid biomarkers for predicting stroke in patients with hypertension. The biomarkers have translational potential and thus may serve as blood-based biomarkers for predicting hypertensive stroke.

Integrated metabolomics and proteomics analysis of plasma lipid metabolism in Parkinson's disease

INTRODUCTION

Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain.

AREAS COVERED

In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism.

EXPERT OPINION

Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

Exploring the interplay between metabolomics and genetics in Parkinson's disease: Insights from ongoing research and future avenues

Parkinson's disease (PD) is a widespread neurodegenerative disorder, whose complex aetiology remains under construction. While rare variants have been associated with the monogenic PD form, most PD cases are influenced by multiple genetic and environmental aspects. Nonetheless, the pathophysiological pathways and molecular networks involved in monogenic/idiopathic PD overlap, and genetic variants are decisive in elucidating the convergent underlying mechanisms of PD. In this scenario, metabolomics has furnished a dynamic and systematic picture of the synergy between the genetic background and environmental influences that impact PD, making it a valuable tool for investigating PD-related metabolic dysfunctions. In this review, we performed a brief overview of metabolomics current research in PD, focusing on significant metabolic alterations observed in idiopathic PD from different biofluids and strata and exploring how they relate to genetic factors associated with monogenic PD. Dysregulated amino acid metabolism, lipid metabolism, and oxidative stress are the critical metabolic pathways implicated in both genetic and idiopathic PD. By merging metabolomics and genetics data, it is possible to distinguish metabolic signatures of specific genetic backgrounds and to pinpoint subgroups of PD patients who could derive personalized therapeutic benefits. This approach holds great promise for advancing PD research and developing innovative, cost-effective treatments.

Omics data integration suggests a potential idiopathic Parkinson's disease signature

The vast majority of Parkinson's disease cases are idiopathic. Unclear etiology and multifactorial nature complicate the comprehension of disease pathogenesis. Identification of early transcriptomic and metabolic alterations consistent across different idiopathic Parkinson's disease (IPD) patients might reveal the potential basis of increased dopaminergic neuron vulnerability and primary disease mechanisms. In this study, we combine systems biology and data integration approaches to identify differences in transcriptomic and metabolic signatures between IPD patient and healthy individual-derived midbrain neural precursor cells. Characterization of gene expression and metabolic modeling reveal pyruvate, several amino acid and lipid metabolism as the most dysregulated metabolic pathways in IPD neural precursors. Furthermore, we show that IPD neural precursors endure mitochondrial metabolism impairment and a reduced total NAD pool. Accordingly, we show that treatment with NAD precursors increases ATP yield hence demonstrating a potential to rescue early IPD-associated metabolic changes.

Consumption of cashew nut induced anxiolytic-like behavior in dyslipidemic rats consuming a high fat diet

This study aimed to evaluate the effect of cashew nut consumption on anxiety-like behavior in dyslipidemic rats. The groups formed were: Control (CONT), Dyslipidemic (DL) and Dyslipidemic cashew nuts (DLCN). Tests to assess anxiety parameters were performed after the treatment period. Brain fatty acid profiles were analyzed. The animals in the DLCN group showed more rearing than DL, without differing from the CONT and less grooming than either the DL and CONT in the Open Field. In the Elevated Plus Maze, DLCN spent more time on the open arms and in the central area compared to the other groups. As for brain fatty acids, there was a reduction in polyunsaturated fatty acids for the DLCN compared to the other groups. The cashew nut, rich in fatty acids, phenolic and flavonoid compounds, reduced the anxiogenic-like behavior caused by dyslipidemia in rats without altering brain fatty acids.

Serum metabolomics analysis revealed metabolic disorders in Parkinson's disease

BACKGROUND

Parkinson's disease (PD) is by now the second of the most prevalent neurodegenerative diseases in the world, and its incidence is increasing rapidly as the global population ages, with 14.2 million PD patients expected worldwide by 2040.

METHODS

We gathered a completion of 45 serum samples, including 15 of healthy controls and 30 from the PD group. We used non-targeted metabolomics analysis based on liquid chromatography-mass spectrometry to identify the molecular changes in PD patients, and conducted bioinformatics analysis on this basis to explore the possible pathogenesis of PD.

RESULTS

We found significant metabolomics changes in the levels of 30 metabolites in PD patients compared with healthy controls.

CONCLUSION

Lipids and lipid-like molecules accounted for the majority of the 30 differentially expressed metabolites. Also, pathway enrichment analysis showed significant enrichment in sphingolipid metabolic pathway. These assessments can improve our perception on the underlying mechanism of PD as well as facilitate a better targeting on therapeutic interventions.

Effects of Lead and Cadmium Combined Heavy Metals on Liver Function and Lipid Metabolism in Mice

Although a large number of studies have been conducted on lead (Pb) and cadmium (Cd) exposure individually, information regarding the toxicity of combined Pb and Cd exposure is relatively limited. The present study aims to investigate the toxicity of Pb-Cd combination exposure and the corresponding mechanism. A heavy metal exposure model was established in mice by subcutaneous intragastric administration of Pb-Cd (50:1) for 35 days. Body weight, diet, hair state, mental state, liver index, haematological index, biochemical indicators and pathological section analysis were used to comprehensively evaluate toxicity. Then, classical oxidative stress indexes and lipidomics techniques were used to explore the potential mechanism. The results showed that Pb-Cd caused the mice to have low appetite, poor spirit, significantly reduced activity, slow weight gain and irritated or drying hair. Pb-Cd also caused liver enlargement, significantly increased aspartate aminotransferase (AST) and alanine aminotransferase (ALT) enzyme activities, and resulted in pathological changes to the liver. Prolonged Pb-Cd exposure led to significantly increased thrombocyte haematocrit (PCT), white blood cell (WBC), platelet (PLT) and monocyte (MON) counts and decreased red blood cell (RBC), haemoglobin (HGB), haematocrit (HCT) and lymphocyte (LYM) counts. Pb-Cd increased oxidative stress by increasing the activity of superoxide dismutase (SOD) and lactate dehydrogenase (LDH) and the content of malondialdehyde (MDA). Finally, Pb-Cd triggered lipid metabolism disorders by regulating linoleic acid, sphingolipid and glycerolipid metabolism.

Identification of three potential novel biomarkers for early diagnosis of acute ischemic stroke via plasma lipidomics

INTRODUCTION

Acute ischemic stroke (AIS) accounts for the majority of all stroke, globally the second leading cause of death. Due to its rapid development after onset, its early diagnosis is crucial.

OBJECTIVES

We aim to identify potential highly reliable blood-based biomarkers for early diagnosis of AIS using quantitative plasma lipid profiling via a machine learning approach.

METHODS

Lipidomics was used for quantitative plasma lipid profiling, based on ultra-performance liquid chromatography tandem mass spectrometry. Our samples were divided into a discovery and a validation set, each containing 30 AIS patients and 30 health controls (HC). Differentially expressed lipid metabolites were screened based on the criteria VIP > 1, p < 0.05, and fold change > 1.5 or < 0.67. The least absolute shrinkage and selection operator (LASSO) and random forest algorithms in machine learning were used to select differential lipid metabolites as potential biomarkers.

RESULTS

Three key differential lipid metabolites, CarnitineC10:1, CarnitineC10:1-OH and Cer(d18:0/16:0), were identified as potential biomarkers for early diagnosis of AIS. The former two, associated with thermogenesis, were down-regulated, whereas the latter, associated with necroptosis and sphingolipd metabolism, was upregulated. Univariate and multivariate logistic regressions showed that these three lipid metabolites and the resulting diagnostic model exhibited a strong ability in discriminating between AIS patients and HCs in both the discovery and validation sets, with an area under the curve above 0.9.

CONCLUSIONS

Our work provides valuable information on the pathophysiology of AIS and constitutes an important step toward clinical application of blood-based biomarkers for diagnosing AIS.

Dysregulation of AMPK-mTOR signaling leads to comorbid anxiety in Dip2a KO mice

Autism is often comorbid with other psychiatric disorders. We have previously shown that Dip2a knockout (KO) induces autism-like behaviors in mice. However, the role of Dip2a in other psychiatric disorders remains unclear. In this paper, we revealed that Dip2a KO mice had comorbid anxiety. Dip2a KO led to a reduction in the dendritic length of cortical and hippocampal excitatory neurons. Molecular mechanism studies suggested that AMPK was overactivated and suppressed the mTOR cascade, contributing to defects in dendritic morphology. Deletion of Dip2a in adult-born hippocampal neurons (Dip2a conditional knockout (cKO)) increased susceptibility to anxiety upon acute stress exposure. Application of (2R,6R)-hydroxynorketamine (HNK), an inhibitor of mTOR, rescued anxiety-like behaviors in Dip2a KO and Dip2a cKO mice. In addition, 6 weeks of high-fat diet intake alleviated AMPK-mTOR signaling and attenuated the severity of anxiety in both Dip2a KO mice and Dip2a cKO mice. Taken together, these results reveal an unrecognized function of DIP2A in anxiety pathophysiology via regulation of AMPK-mTOR signaling.

Genotypic-Phenotypic Analysis, Metabolic Profiling and Clinical Correlations in Parkinson's Disease Patients from Tamil Nadu Population, India

Parkinson's disease (PD) is an ageing disorder caused by dopaminergic neuron depletion with age. Growing research in the field of metabolomics is expected to play a major role in PD diagnosis, prognosis and therapeutic development. In this study, we looked at how SNCA and GBA1 gene mutations, as well as metabolomic abnormalities of kynurenine and cholesterol metabolites, were linked to alpha-synuclein (α-syn) and clinical characteristics in three different PD age groups. In all three age groups, a metabolomics analysis revealed an increased amount of 27-hydroxycholesterol (27-OHC) and a lower level of kynurenic acid (KYNA). The effect of 27-OHC on SNCA and GBA1 modifications was shown to be significant (P < 0.05) only in the A53T variant of the SNCA gene in late-onset and early-onset PD groups, whereas GBA1 variants were not. Based on the findings, we observed that the increase in 27-OHC would have elevated α-syn expression, which triggered the changes in the SNCA gene but not in the GBA1 gene. Missense variations in the SNCA and GBA1 genes were investigated using the sequencing technique. SNCA mutation A53T has been linked to increased PD symptoms, but there is no phenotypic link between GBA1 and PD. As a result of the data, we hypothesise that cholesterol and kynurenine metabolites play an important role in PD, with the metabolite 27-OHC potentially serving as a PD biomarker. These findings will aid in the investigation of pathogenic causes as well as the development of therapeutic and preventative measures for PD.

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.

Lower Blood Lipid Level Is Associated with the Occurrence of Parkinson's Disease: A Meta-Analysis and Systematic Review

BACKGROUND

The changes of blood lipid levels in patients with Parkinson's disease (PD) and its clinical relevance remain unclear. We aimed to evaluate the potential association of blood lipid and the occurrence of PD, to provide evidence to the clinical treatment and nursing care of PD.

METHODS

We searched PubMed, Medline, Web of Science, Cochrane Library, Wanfang Database, Weipu Database, and China National Knowledge Infrastructure for studies related to the blood lipid levels and PD until November 30, 2021. Two researchers independently screened the literature and extricated the data including the levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of included studies. RevMan5.3 and Stata 12.0 software were used for statistical processing and analysis.

RESULTS

A total of 15 cohort studies with 9740 participants involving 2032 PD patients and 7708 controls were included. Meta-analysis indicated that TC (SMD = -0.29, 95% CI -0.55∼-0.03, P=0.04), TG (SMD = -16.83, 95% CI -20.71∼-12.95, P < 0.001), HDL-C (SMD = -0.14, 95% CI -0.26∼-0.02, P < 0.001) and LDL-C (SMD = -0.26, 95% CI -0.50∼-0.01, P=0.04) level in the PD patients was significantly lower than that of health controls. Sensitivity analysis indicated that the results were stable. No significant publication bias was found between the synthesized outcomes.

CONCLUSIONS

Lower blood TC, TG, HDL-C, and LDL-C level are associated with the occurrence of PD. Limited by sample size and study population, further high-quality, large-sample clinical trials in different areas are needed to further determine the relationship between blood lipids and PD in the future.

Plasma Metabolite Markers of Parkinson's Disease and Atypical Parkinsonism

Differentiating between Parkinson’s disease (PD) and the atypical Parkinsonian disorders of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) is difficult clinically due to overlapping symptomatology, especially at early disease stages. Consequently, there is a need to identify metabolic markers for these diseases and to develop them into viable biomarkers. In the present investigation, solution nuclear magnetic resonance and mass spectrometry metabolomics were used to quantitatively characterize the plasma metabolomes (a total of 167 metabolites) of a cohort of 94 individuals comprising 34 PD, 12 MSA, and 17 PSP patients, as well as 31 control subjects. The distinct and statistically significant differences observed in the metabolite concentrations of the different disease and control groups enabled the identification of potential plasma metabolite markers of each disorder and enabled the differentiation between the disorders. These group-specific differences further implicate disturbances in specific metabolic pathways. The two metabolites, formic acid and succinate, were altered similarly in all three disease groups when compared to the control group, where a reduced level of formic acid suggested an effect on pyruvate metabolism, methane metabolism, and/or the kynurenine pathway, and an increased succinate level suggested an effect on the citric acid cycle and mitochondrial dysfunction.

Metabolic Profile in Plasma AND CSF of LEVODOPA-induced Dyskinesia in Parkinson's Disease: Focus on Neuroinflammation

The existence of few biomarkers and the lack of a better understanding of the pathophysiology of levodopa-induced dyskinesia (LID) in Parkinson's disease (PD) require new approaches, as the metabolomic analysis, for discoveries. We aimed to identify a metabolic profile associated with LID in patients with PD in an original cohort and to confirm the results in an external cohort (BioFIND). In the original cohort, plasma and CSF were collected from 20 healthy controls, 23 patients with PD without LID, and 24 patients with PD with LID. LC-MS/MS and metabolomics data analysis were used to perform untargeted metabolomics. Untargeted metabolomics data from the BioFIND cohort were analyzed. We identified a metabolic profile associated with LID in PD, composed of multiple metabolic pathways. In particular, the dysregulation of the glycosphingolipid metabolic pathway was more related to LID and was strongly associated with the severity of dyskinetic movements. Furthermore, bile acid biosynthesis metabolites simultaneously found in plasma and CSF have distinguished patients with LID from other participants. Data from the BioFIND cohort confirmed dysregulation in plasma metabolites from the bile acid biosynthesis pathway. There is a distinct metabolic profile associated with LID in PD, both in plasma and CSF, which may be associated with the dysregulation of lipid metabolism and neuroinflammation.