Promising Metabolite Profiles in the Plasma and CSF of Early Clinical Parkinson's Disease.
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ABSTRACT:
SUBMITTER: Stoessel D
PROVIDER: S-EPMC5844983 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Parkinson's disease (PD) starts at the molecular and cellular level long before motor symptoms appear, yet there are no early-stage molecular biomarkers for diagnosis, prognosis prediction, or monitoring therapeutic response. This lack of biomarkers greatly impedes patient care and translational research-L-DOPA remains the standard of care more than 50 years after its introduction. Here, we performed a large-scale, multi-tissue, and multi-platform proteomics study to identify new biomarkers for early diagnosis and disease monitoring in PD. We analyzed 4877 cerebrospinal fluid, blood plasma, and urine samples from participants across seven cohorts using three orthogonal proteomics methods: Olink proximity extension assay, SomaScan aptamer precipitation assay, and liquid chromatography-mass spectrometry proteomics. We discovered that hundreds of proteins were upregulated in the CSF, blood, or urine of PD patients, prodromal PD patients with DAT deficit and REM sleep behavior disorder or anosmia, and non-manifesting genetic carriers of LRRK2 and GBA mutations. We nominate multiple novel hits across our analyses as promising markers of early PD, including DOPA decarboxylase (DDC), also known as L-aromatic acid decarboxylase (AADC), sulfatase-modifying factor 1 (SUMF1), dipeptidyl peptidase 2/7 (DPP7), glutamyl aminopeptidase (ENPEP), WAP four-disulfide core domain 2 (WFDC2), and others. DDC, which catalyzes the final step in dopamine synthesis, particularly stands out as a novel hit with a compelling mechanistic link to PD pathogenesis. DDC is consistently upregulated in the CSF and urine of treatment-naïve PD, prodromal PD, and GBA or LRRK2 carrier participants by all three proteomics methods. We show that CSF DDC levels correlate with clinical symptom severity in treatment-naïve PD patients and can be used to accurately diagnose PD and prodromal PD. This suggests that urine and CSF DDC could be a promising diagnostic and prognostic marker with utility in both clinical care and translational research.
| S-EPMC10927779 | biostudies-literature
Project description: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.
| S-EPMC8706715 | biostudies-literature
Project description:Parkinson's disease (PD) is a progressive neurodegenerative disease, causing loss of motor and nonmotor function. Diagnosis is based on clinical symptoms that do not develop until late in the disease progression, at which point the majority of the patients' dopaminergic neurons are already destroyed. While many PD cases are idiopathic, hereditable genetic risks have been identified, including mutations in the gene for LRRK2, a multidomain kinase with roles in autophagy, mitochondrial function, transcription, molecular structural integrity, the endo-lysosomal system, and the immune response. A definitive PD diagnosis can only be made post-mortem, and no noninvasive or blood-based disease biomarkers are currently available. Alterations in metabolites have been identified in PD patients, suggesting that metabolomics may hold promise for PD diagnostic tools. In this study, we sought to identify metabolic markers of PD in plasma. Using a 1H-13C heteronuclear single quantum coherence spectroscopy (HSQC) NMR spectroscopy metabolomics platform coupled with machine learning (ML), we measured plasma metabolites from approximately age/sex-matched PD patients with G2019S LRRK2 mutations and non-PD controls. Based on the differential level of known and unknown metabolites, we were able to build a ML model and develop a Biomarker of Response (BoR) score, which classified male LRRK2 PD patients with 79.7% accuracy, 81.3% sensitivity, and 78.6% specificity. The high accuracy of the BoR score suggests that the metabolomics/ML workflow described here could be further utilized in the development of a confirmatory diagnostic for PD in larger patient cohorts. A diagnostic assay for PD will aid clinicians and their patients to quickly move toward a definitive diagnosis, and ultimately empower future clinical trials and treatment options.
| S-EPMC8876175 | biostudies-literature
Project description:The development of biomarkers to predict the progression of Parkinson's disease (PD) from its earliest stage through its heterogeneous course is critical for research and therapeutic development. The Parkinson's Progression Markers Initiative (PPMI) study is an ongoing international multicenter, prospective study to validate biomarkers in drug-naïve PD patients and matched healthy controls (HC). We quantified cerebrospinal fluid (CSF) alpha-synuclein (?-syn), amyloid-beta1-42 (A?1-42), total tau (t-tau), and tau phosphorylated at Thr181 (p-tau) in 660 PPMI subjects at baseline, and correlated these data with measures of the clinical features of these subjects. We found that CSF ?-syn, t-tau and p-tau levels, but not A?1-42, were significantly lower in PD compared with HC, while the diagnostic value of the individual CSF biomarkers for PD diagnosis was limited due to large overlap. The level of ?-syn, but not other biomarkers, was significantly lower in PD patients with non-tremor-dominant phenotype compared with tremor-dominant phenotype. In addition, in PD patients the lowest A?1-42, or highest t-tau/A?1-42 and t-tau/?-syn quintile in PD patients were associated with more severe non-motor dysfunction compared with the highest or lowest quintiles, respectively. In a multivariate regression model, lower ?-syn was significantly associated with worse cognitive test performance. APOE ?4 genotype was associated with lower levels of A?1-42, but neither with PD diagnosis nor cognition. Our data suggest that the measurement of CSF biomarkers in early-stage PD patients may relate to disease heterogeneity seen in PD. Longitudinal observations in PPMI subjects are needed to define their prognostic performance.
| S-EPMC5031365 | biostudies-literature
Project description:BackgroundFew efficient and simple models for the early prediction of Parkinson's disease (PD) exists.ObjectiveTo develop and validate a novel nomogram for early identification of PD by incorporating microRNA (miRNA) expression profiles and clinical indicators.MethodsExpression levels of blood-based miRNAs and clinical variables from 1,284 individuals were downloaded from the Parkinson's Progression Marker Initiative database on June 1, 2022. Initially, the generalized estimating equation was used to screen candidate biomarkers of PD progression in the discovery phase. Then, the elastic net model was utilized for variable selection and a logistics regression model was constructed to establish a nomogram. Additionally, the receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves were utilized to evaluate the performance of the nomogram.ResultsAn accurate and externally validated nomogram was constructed for predicting prodromal and early PD. The nomogram is easy to utilize in a clinical setting since it consists of age, gender, education level, and transcriptional score (calculated by 10 miRNA profiles). Compared with the independent clinical model or 10 miRNA panel separately, the nomogram was reliable and satisfactory because the area under the ROC curve achieved 0.72 (95% confidence interval, 0.68-0.77) and obtained a superior clinical net benefit in DCA based on external datasets. Moreover, calibration curves also revealed its excellent prediction power.ConclusionThe constructed nomogram has potential for large-scale early screening of PD based upon its utility and precision.
| S-EPMC10357140 | biostudies-literature
Project description:To date, the development of disease-modifying therapies for Alzheimer's disease (AD) has largely focused on the removal of amyloid beta Aβ fragments from the CNS. Proteomic profiling of patient fluids may help identify novel therapeutic targets and biomarkers associated with AD pathology. Here, we applied the Olink™ ProSeek immunoassay to measure 270 CSF and plasma proteins across 415 Aβ- negative cognitively normal individuals (Aβ- CN), 142 Aβ-positive CN (Aβ+ CN), 50 Aβ- mild cognitive impairment (MCI) patients, 75 Aβ+ MCI patients, and 161 Aβ+ AD patients from the Swedish BioFINDER study. A validation cohort included 59 Aβ- CN, 23 Aβ- + CN, 44 Aβ- MCI and 53 Aβ+ MCI. To compare protein concentrations in patients versus controls, we applied multiple linear regressions adjusting for age, gender, medications, smoking and mean subject-level protein concentration, and corrected findings for false discovery rate (FDR, q < 0.05). We identified, and replicated, altered levels of ten CSF proteins in Aβ+ individuals, including CHIT1, SMOC2, MMP-10, LDLR, CD200, EIF4EBP1, ALCAM, RGMB, tPA and STAMBP (- 0.14 < d < 1.16; q < 0.05). We also identified and replicated alterations of six plasma proteins in Aβ+ individuals OSM, MMP-9, HAGH, CD200, AXIN1, and uPA (- 0.77 < d < 1.28; q < 0.05). Multiple analytes associated with cognitive performance and cortical thickness (q < 0.05). Plasma biomarkers could distinguish AD dementia (AUC = 0.94, 95% CI = 0.87-0.98) and prodromal AD (AUC = 0.78, 95% CI = 0.68-0.87) from CN. These findings reemphasize the contributions of immune markers, phospholipids, angiogenic proteins and other biomarkers downstream of, and potentially orthogonal to, Aβ- and tau in AD, and identify candidate biomarkers for earlier detection of neurodegeneration.
| S-EPMC6836495 | biostudies-literature
Project description:BackgroundParkinson's disease is a heterogeneous neurodegenerative disorder with distinctive gut microbiome patterns suggesting that interventions targeting the gut microbiota may prevent, slow, or reverse disease progression and severity.ObjectiveBecause secretory IgA (SIgA) plays a key role in shaping the gut microbiota, characterization of the IgA-Biome of individuals classified into either the akinetic rigid (AR) or tremor dominant (TD) Parkinson's disease clinical subtypes was used to further define taxa unique to these distinct clinical phenotypes.MethodsFlow cytometry was used to separate IgA-coated and -uncoated bacteria from stool samples obtained from AR and TD patients followed by amplification and sequencing of the V4 region of the 16 S rDNA gene on the MiSeq platform (Illumina).ResultsIgA-Biome analyses identified significant alpha and beta diversity differences between the Parkinson's disease phenotypes and the Firmicutes/Bacteroides ratio was significantly higher in those with TD compared to those with AR. In addition, discriminant taxa analyses identified a more pro-inflammatory bacterial profile in the IgA+ fraction of those with the AR clinical subclass compared to IgA-Biome analyses of those with the TD subclass and with the taxa identified in the unsorted control samples.ConclusionIgA-Biome analyses underscores the importance of the host immune response in shaping the gut microbiome potentially affecting disease progression and presentation. In the present study, IgA-Biome analyses identified a unique proinflammatory microbial signature in the IgA+ fraction of those with AR that would have otherwise been undetected using conventional microbiome analysis approaches.
| S-EPMC10357173 | biostudies-literature
Project description:BackgroundInflammation contributes substantially to the pathogenesis of Parkinson's disease (PD). Plasma extracellular vesicle (EV)-derived cytokines are emerging biomarkers of inflammation. We conducted a longitudinal study of the plasma EV-derived cytokine profiles of people with PD (PwP).MethodsA total of 101 people with mild to moderate PD and 45 healthy controls (HCs) were recruited, and they completed motor assessments (Unified Parkinson Disease Rating Scale [UPDRS]) and cognitive tests at baseline and 1-year follow-up. We isolated the participants' plasma EVs and analyzed their levels of cytokines, including interleukin (IL)-1β, IL-6, IL-10, tumor necrosis factor (TNF)-α, and transforming growth factor (TGF)-β.ResultsWe noted no significant changes in the plasma EV-derived cytokine profiles of the PwPs and HCs between baseline and the 1-year follow-up. Among the PwP, changes in plasma EV-derived IL-1β, TNF-α and IL-6 levels were significantly associated with changes in the severity of postural instability and gait disturbance (PIGD) and cognition. Baseline plasma EV-derived IL-1β, TNF-α, IL-6, and IL-10 levels were significantly associated with the severity of PIGD and cognitive symptoms at follow-up, and PwP with elevated IL-1β and IL-6 levels exhibited significant progression of PIGD over the study period.ConclusionThese results suggested the role of inflammation in PD progression. In addition, baseline levels of plasma EV-derived proinflammatory cytokines can be used to predict the progression of PIGD, the most severe motor symptom of PD. Additional studies with longer follow-up periods are necessary, and plasma EV-derived cytokines may serve as effective biomarkers of PD progression.
| S-EPMC10042681 | biostudies-literature
Project description:ObjectivesIsradipine is a dihydropyridine calcium channel inhibitor that has demonstrated concentration-dependent neuroprotective effects in animal models of Parkinson's disease (PD) but failed to show efficacy in a phase 3 clinical trial. The objectives of this study were to model the plasma pharmacokinetics of isradipine in study participants from the phase 3 trial; and, to investigate associations between drug exposure and longitudinal clinical outcome measures of PD progression.MethodsPlasma samples from nearly all study participants randomized to immediate-release isradipine 5-mg twice daily (166 of 170) were collected for population pharmacokinetic modeling. Estimates of isradipine exposure included apparent oral clearance and area under the concentration-time curve. Isradipine exposure parameters were tested for correlations with 36-month changes in disease severity clinical assessment scores, and time-to-event analyses for initiation of antiparkinson therapy.ResultsIsradipine exposures did not correlate with the primary clinical outcome, changes in the antiparkinson therapy-adjusted Unified Parkinson's Disease Rating Scale parts I-III score over 36 months (Spearman rank correlation coefficient, rs : 0.09, P = 0.23). Cumulative levodopa equivalent dose at month 36 was weakly correlated with isradipine plasma clearance (rs : 0.18, P = 0.035). This correlation was sex dependent and significant in males, but not females. Those with higher isradipine exposure had decreased risk of needing antiparkinson treatment over 36 months compared with placebo (hazard ratio: 0.87, 95% CI: 0.78-0.98, P = 0.02).InterpretationIn this clinical trial, higher isradipine plasma exposure did not affect clinical assessment measures of PD severity but modestly decreased cumulative levodopa equivalent dose and the time needed for antiparkinson treatment initiation.Trial registrationClinicalTrials.gov NCT02168842.
| S-EPMC7951102 | biostudies-literature
Project description:BackgroundHuman plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized.Methodology/principal findingsWe analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p?=?0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r?=?0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices.Conclusions/significanceOur study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.
| S-EPMC3132215 | biostudies-literature