Parkinson’s disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction

doi:10.21203/rs.3.rs-4980210/v1

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Parkinson’s disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction

https://doi.org/10.21203/rs.3.rs-4980210/v1

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Parkinson’s disease (PD) is a complex multisystem disorder clinically characterized by motor, non-motor, and premotor manifestations. Pathologically, PD involves neuronal loss in the substantia nigra, striatal dopamine deficiency, and accumulation of intracellular inclusions containing aggregates of α-synuclein. Recent studies demonstrate that PD is associated with dysregulated metabolic flux through the kynurenine pathway (KP), in which tryptophan is converted to kynurenine (KYN), and KYN is subsequently metabolized to neuroactive compounds quinolinic acid (QA) and kynurenic acid (KA). This multicenter study used highly sensitive liquid chromatography-tandem mass-spectrometry to compare blood and cerebral spinal fluid (CSF) KP metabolites between 158 unimpaired older adults and 177 participants with PD. Results indicate that increased neuroexcitatory QA/KA ratio in both plasma and CSF of PD participants associated with peripheral and cerebral inflammation and vitamin B6 deficiency. Furthermore, increased QA tracked with CSF tau and severity of both motor and non-motor PD clinical dysfunction. Importantly, plasma and CSF kynurenine metabolites classified PD participants with a high degree of accuracy (AUC = 0.897). Finally, analysis of metabolite data revealed subgroups with distinct KP profiles, and these were subsequently found to display distinct PD clinical features. Together, these data further support the hypothesis that the KP serves as a site of brain and periphery crosstalk, integrating B-vitamin status, inflammation and metabolism to ultimately influence PD clinical manifestation.

Health sciences/Biomarkers/Diagnostic markers

Health sciences/Diseases/Neurological disorders/Parkinson's disease

Parkinson’s disease

plasma

cerebrospinal fluid

Kynurenine pathway

vitamin B

inflammation

metabolism

Parkinson’s disease (PD) is a multisystem neurodegenerative condition characterized by a loss of dopaminergic neurons in the substantia nigra pars compacta and aggregation of misfolded α-synuclein protein in Lewy bodies across the brainstem and other structures¹. Striatal dopamine depletion contributes to classical PD motor and non-motor clinical dysfunction, the latter of which may precede appearance of motor symptoms by years or decades. Furthermore, significant heterogeneity exists in the presentation of PD symptoms – which often fluctuate – and disease progression varies greatly between patients². Given the unpredictable nature in the presentation and progression of PD, there is a pressing need to develop novel biomarkers capable of predicting PD pathophysiology and clinical presentation³.

Multiple lines of evidence implicate central nervous system (CNS) and peripheral inflammation in the PD initiation and progression^4–6. Indeed, genetic studies have identified PD risk variants relating to immune function⁷. Postmortem examinations have demonstrated reactive microglia and incursion of immune cells in PD brain⁸. Moreover, imaging markers of neuroinflammation^9,10 and biofluid biomarkers^11–14 reveal chronic immune activation in PD in vivo. Despite these findings, the mechanisms linking inflammation and PD remain unclear.

The kynurenine pathway (KP) is modified by peripheral and CNS inflammation, and improper KP metabolism leads to the accumulation of proinflammatory and neuroactive intermediate metabolites, collectively termed kynurenines. Under physiological conditions, tryptophan (TRP) is converted to kynurenine (KYN), which is subsequently metabolized via B vitamin-dependent reactions to neuroactive intermediates, including neuroprotective kynurenic acid (KA) and neuroexcitatory quinolinic acid (QA). From QA, quinolinate phosphoribosyl transferase (QPRT) serves as the rate limiting enzyme in de novo nicotinamide adenine dinucleotide (NAD+) synthesis¹⁵, an essential energy source for many cellular functions^16,17. Under inflammatory conditions, interferon (IFN)-γ, IFN-α and tumor necrosis factor (TNF)-α direct TRP metabolism toward KYN by inducing the enzyme indoleamine 2,3-dioxygenase 1 (IDO1)^18,19. Vitamin B₆ deficiency leads to increased 3-hydroxykynurenine (3-HK) and QA, with high 3-HK promoting oxidative stress and apoptotic neuronal death²⁰ and high QA/KA ratios promoting glutamatergic excitotoxicity and further neuroinflammation^21,22. In contrast, KA directly blocks QA excitotoxicity²³ and protects against dopaminergic cell death²⁴. KP activation has been demonstrated to occur across various inflammatory conditions, including aging²⁵, multiple sclerosis²⁶, Alzheimer’s disease^27–29, schizophrenia³⁰, peripartum depression³¹, lung cancer³², and in children with inflammatory neurological disorders³³.

Severe KP disruption has also been reported in PD, involving increases in 3-HK, increases in the immune activation marker neopterin, and decreases in neuroprotective KA^27,34–43. Yet, our understanding of the peripheral and central contributions to KP activation in PD remains incomplete. In addition, it is unclear whether there exists PD clinical subgroups with specific patterns of KP dysmetabolism. This study aimed to identify such endophenotypes to further support the development of individualized therapeutic strategies in PD.

This multicenter study included 335 participants drawn from 8 research centers and consisted of 158 control older adult participants and 177 participants with PD. Demographic information for the study participants is shown in Table 1 and details on the originating research centers for study population participants are presented in Supplemental Fig. 1. Control participants were on average 67.70 years old (SD = 7.58) while the PD participants were 67.41 years old (SD = 8.23). Consistent with disease prevalence⁴⁴, males were overrepresented in the PD participants compared to the controls (69.4% vs. 50.6%; P < 0.001, Chi-Square test). There were no differences between PD and controls with respect to APOE-ε4 carriage (23.8% vs. 24.7%; P > 0.05, Chi-Square test) or years of education (16.22 vs. 16.53; P > 0.05, two-sided Student’s t test). PD participants had an average time since diagnosis of 5.53 years (range = 0 to 26) and Levodopa equivalent daily dose (LEDD) of 566.15 mg (range = 0 to 2150). PD participants had lower Montreal Cognitive Assessment (MoCA) scores (24.30 ± 3.94) compared to controls (27.57 ± 2.01; P = 0.0002, two-sided Student’s t test). No differences were observed in CSF biomarkers p-tau181 or total tau (P > 0.05, two-sided Student’s t test), although CSF Aβ₄₂/Aβ₄₀ was slightly higher in the PD group (P < 0.05, two-sided Student’s t test).

Blood and CSF KP metabolites and B-vitamin cofactors required for their enzymatic synthesis were measured using a liquid chromatography-tandem mass spectrometry (LC-MS/MS) assay with high sensitivity and precision (Fig. 1a-b). Notably, many metabolites are transported from the blood across the blood-brain barrier (BBB) by the large neural amino acid transporter^45,46 and basal BBB permeability is susceptible to change in inflammatory disease states⁴⁷ (Fig. 1c). Accordingly, CSF and plasma pools for many metabolites were often correlated (Supplemental Fig. 2a-c).

KP activation in Parkinson’s disease

LC-MS/MS analysis revealed that plasma and CSF concentrations of the essential amino acid and KP precursor TRP did not differ between control and PD participants (P > 0.05, ANOCVA with age and sex included as covariates), nor did levels of KYN (P > 0.05; Supplemental Fig. 3a-b). However, differences were observed in some KP metabolites between PD participants and controls. In plasma, higher concentrations of the neuroexcitatory 3-HK were found in PD (P < 0.001; Fig. 1d). Similarly, PD participants had higher plasma levels of the neuroexcitatory QA/KA ratio (P < 0.001; Fig. 1e). Conversely, levels of neuroprotective KA were lower in PD plasma (P < 0.001; Supplemental Fig. 3a). In CSF, 3-HK was not different (P > 0.05; Fig. 1f), although the QA/KA ratio was higher in PD compared to controls (P < 0.001; Fig. 1g). CSF QA was also higher in PD compared to control (P < 0.05; Supplemental Fig. 3b). Lower concentrations of KA (P < 0.001) and AA (P < 0.05), NMN (P < 0.001) – an anti-inflammatory metabolite⁴⁸ – were observed in PD. KP metabolic maps for plasma and CSF demonstrate lower plasma KA and higher 3-HK and higher CSF QA in PD (Fig. 1h). While QA is not actively transported across the BBB²⁷, its substrate 3-HK is transported from the blood across the BBB by the large-neutral amino acid transporters⁴⁹. Thus, peripheral-CNS communication through 3-HK BBB transport may contribute to the increase in neuroexcitatory CSF QA in PD. In support of this, plasma 3-HK was found to be positively associated with CSF 3-HK (β = +0.248, P < 0.05; Fig. 1i) and with CSF QA (β = +0.274, P < 0.001; Fig. 1j).

Low B-vitamin status associates with inflammation and KP activation in Parkinson’s disease

Enzymatic conversion of some KP metabolites to their subsequent products requires the active forms of vitamins B₂ (flavine adenine dinucleotide, FAD) and B₆ (pyridoxal 5’-phosphate; PLP) to serve as enzyme cofactors (Fig. 2a). B-vitamin levels are determined by the balance of intestinal absorption and liver catabolism to impact overall availability. The HK ratio (HKr) is a functional measure of vitamin B₆ status, calculated as the ratio of 3-HK to the four kynurenine products of PLP-dependent enzymes KAT and KYNU (i.e., 3-HK/(KA+AA+XA+HAA))⁵⁰. Similarly, the PAr Index is a blood-based measure of vitamin B₆ catabolism, calculated as the ratio of plasma B₆ catabolite 4-pyridoxic acid (4-PA) to the sum of its active form PLP and its transport form pyridoxal (PL) (4-PA/(PLP + PL)⁵¹ to assess vitamin B₆ status. The PAr Index is elevated in inflammatory conditions^51,52, cancer^53,54, coronary artery disease⁵⁵, stroke⁵⁶ and depression⁵⁷.

In examining whether B-vitamin deficiency links proinflammatory responses and KP activation in PD, it was found that plasma flavin mononucleotide (FMN, P < 0.001), PLP (P < 0.001), pyridoxal (P < 0.001) and 4-pyridoxic acid (4-PA, P < 0.001) were all lower in PD participants when compared to controls (Fig. 2b and Supplemental Fig. 4a). Similarly, plasma HKr (P < 0.001) and the PAr Index (P < 0.001) were higher in PD compared to control, confirming lower vitamin B₆ status in the PD group. In CSF, PLP (P < 0.05) and pyridoxal (P < 0.001) were both lower in PD compared to control (Fig. 2c; Supplemental Fig. 4b) and the PD group had higher CSF HKr compared to the control group (P < 0.001).

Next, levels of the peripheral inflammatory marker neopterin – an indicator of monocyte/macrophage activation⁵⁸— were assessed to further investigate the link between inflammatory responses and vitamin B₆ catabolism. Indeed, plasma neopterin was higher in PD compared to controls (P < 0.01; Fig. 2d). Moreover, a positive association was demonstrated between plasma neopterin and the PAr Index (β = +0.234, P < 0.001; Fig. 2e). Further correlation analyses examined associations between the kynurenines and B-vitamin derivatives within plasma (Fig. 2f) and CSF (Fig. 2g) pools in the PD group. Notably, 3-HK and QA levels were higher at lower vitamin B₆ (PLP) levels, further highlighting the possibility that vitamin B₆ deficiency serves as a link between peripheral inflammation and the altered KP profile observed in PD.

CSF QA associates with motor symptom severity and neurodegeneration in Parkinson’s disease

Associations between CSF QA and PD symptoms were next investigated using participant data from the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS⁵⁹; Fig. 3a). CSF QA was positively associated with MDS-UPDRS Part II total score, which is sensitive to motor aspects of experiences of daily living (β = +0.175, P < 0.044), with Part III OFF, which is a measure of motor severity (β = +0.277, P < 0.042), and Part IV, which is related to motor complications of Levodopa replacement therapy (β = +0.198, P < 0.028). Additionally, associations between CSF QA with Alzheimer’s disease biomarkers CSF amyloid and tau were examined given the high comorbidity of Alzheimer’s disease neuropathology in PD^60-64. This analysis revealed that CSF QA was associated with CSF total tau (β = +0.210, P < 0.016), but not CSF p-tau181 (P > 0.05) or CSF Aβ₄₂/Aβ₄₀ (P > 0.05; Fig. 3b), suggesting a specific link between QA-induced neurotoxicity and neurodegeneration.

CSF and plasma KP metabolites accurately predict Parkinson’s disease

We next evaluated whether CSF and plasma kynurenines and related B-vitamin levels might be useful in distinguishing PD from controls. For this analysis all kynurenines, their ratios and related B vitamins were included in the models and a cross validation strategy was used to simultaneously optimize the integrative model on a subset of metabolite data and then test its performance on previously unseen participants. We found that both CSF (AUC: 0.740) and plasma (AUC: 0.893) metabolites correlated with PD diagnosis (AUC combined model: 0.897; Fig. 4a). Indeed, KP metabolite levels were robust predictors of PD diagnosis, sex, and years since diagnosis, but not age or years of education (Fig. 4b). Correlation-based network analysis was used to evaluate the relationships between plasma and CSF metabolite features in a bottom-up approach. In this scenario, the metabolite features are presented as nodes in the network map and links are drawn between them when a relationship is present. KP metabolite features that effectively predicted PD diagnosis included the KMO-related product/substrate ratio 3-HK/Kyn, 3-HK/KA, 3-HKr, 3-HK, PLP, QA/KA, KA, and TRP in plasma and QA/KA, 3-HK/KA – an excitotoxic ratio³⁶ – along with HKr, KA, PL, PLP, 3-HK and 3-HAA in CSF (Fig. 4c). Together, these results demonstrate the utility of the KP, particularly in plasma, to serve as a reliable biofluid biomarker for distinguishing PD from controls.

Parkinson’s disease clinical subgroups display distinct patterns of KP alterations and distinct clinical features

Characterizing the clinical and biochemical heterogeneity of Parkinson’s disease is critical to understanding its origins and devising targeted management strategies³. Therefore, given the size of our multi-center patient cohort, we applied unbiased t-Sne clustering to better define the heterogeneity of our study population using metabolite data (Fig. 5a, Supplemental Fig. 5a). Note that clinical features were not included in the clustering at this stage. Thus, by using metabolite data alone, we preliminarily identified three small yet distinct PD clinical subgroups with specific KP metabolic profiles which clustered separately from the majority of other PD patients and all controls. Further exploratory analyses were undertaken to better define their distinguishing features, and we were able to define these subgroups as: 1) the Dystonia Subgroup (n=25); 2) the Rigid Subgroup (n=20); and 3) the Vitamin B1 Subgroup (n=10; Fig. 5b). These PD clinical subgroups did not show differences in years since diagnosis or age (Supplementary Fig. 5b,c), suggesting they represent separate pathophysiologic trajectories as opposed to distinct stages along the same trajectory. PD participants in the Dystonia Subgroup had high plasma 3-HK, CSF HKr, CSF 3-HK/KA and CSF QA/KA ratio in comparison to PD participants in the other subgroups. In contrast, the Rigid Subgroup had high plasma 3-HK/Kyn ratio without correspondingly high QA. The Vitamin B1 Subgroup had higher levels of plasma thiamine and thiamine monophosphate (TMP; Supplemental Fig. 5d).

MDS-UPDRS clinical scores across subgroups were assessed to determine whether the identified PD clinical subgroups were related to specific clinical dysfunction (Fig. 5c). Indeed, the Dystonia Subgroup had more severe dystonia and were more likely to report a functional impact of motor fluctuations on their daily activities and social interactions compared to the other subgroups. In terms of non-motor symptoms, they also reported greater anxiety, depression, insomnia, and more painful OFF-state dystonia. In contrast, the Rigid Subgroup reported higher rigidity in the left upper extremities and higher body bradykinesia. The Rigid Subgroup also had a lower mean LEDD compared to the other PD subgroups (Supplemental Fig. 5e). In terms of symptoms, the Vitamin B1 Subgroup reported greater right-sided postural tremor in comparison to the other PD clinical subgroups.

The present study revealed KP dysregulation in PD is linked to peripheral and CNS inflammation and low vitamin B₆ status. The shift in KYN metabolism was characterized by lower neuroprotective KA and higher 3-HK and QA, which are both neuroexcitatory compounds. KP metabolic maps for plasma and CSF showed that lower KA and higher 3-HK were observed in both plasma and CSF pools in PD, however, higher QA was only observed in CSF. This observation suggests BBB permeable 3-HK may serve as a conduit for CNS-periphery crosstalk, whereby peripheral 3-HK traffics to increase CNS 3-HK pools and ultimately increase neuroexcitatory QA. While future studies should aim to provide experimental evidence of the BBB transfer of 3-HK directly, we found that 3-HK concentrations in CSF and plasma were significantly correlated. Amid high CSF QA we observed more severe PD motor symptoms and higher CSF tau. An exploratory analysis of PD patient subgroups suggests the possibility that specific alterations in KP metabolism may be linked to distinct clinical dysfunction.

Results of the current study suggest KP dysfunction might result from vitamin B₆ deficiency shifting production away from the neuroprotective KA and AA and towards overproduction of neuroexcitatory 3-HK and QA. As a key cofactor, vitamin B₆ is required for the enzymatic activities of KYNU and KAT⁶⁵, which produce AA and KA, respectively. Consistent with previous work⁵², the current study demonstrated that the plasma marker of inflammation neopterin was linked to B₆ deficiency, directly implicating peripheral inflammation in low vitamin B₆ status. The proposed connection between B₆ deficiency, KP dysmetabolism, and consequent neuroinflammation may further explain epidemiological studies showing reduced dietary B₆ intake is tied to increased PD risk⁶⁶. Vitamin B₆ catabolism (and consequently PAr) rises in inflammatory conditions⁵¹ and peripheral inflammation may both drive or potentiate PD-associated B₆ deficiency. B₆ deficiency might also be related to levodopa administration, as previous work has shown that individuals taking more than 2,000 mg LEDD were nearly always found to have B₆ deficiency⁶⁷. In this case, B₆ deficiency may be explained by consumption of PLP during levodopa metabolism and deactivation of PLP by carbidopa^68–71. Thus, additional work is required to determine the source of B₆ deficiency in PD and data on intake of B₆ and the other vitamins would be useful.

The current study also supports the KP may be capable of distinguishing PD clinical subgroups with distinct pathophysiology and clinical profiles. While these findings should be validated in larger studies, interesting preliminary associations were observed. For example, the Dystonia Subgroup was biologically defined by high levels of neuroexcitatory and inflammatory CSF QA/KA. This group also showed greater involuntary muscle contractions (dystonia) and reported greater impact of their fluctuations. The Dystonia Subgroup also showed greater anxiety, depression and pain compared to the other subgroups. Interestingly, these symptoms may be consistent with the “Anxious fluctuators” clinical endophenotype previously identified using only clinical variables⁷². This clinical subgroup was uncovered in the current study using only metabolite data, potentially providing a biological substrate for this previously identified clinical endophenotype. The KP alterations specific to the Dystonia Subgroup were increased neuroexcitatory and neuroinflammatory CSF QA/KA. In contrast, the Rigid Subgroup showed higher rigidity and body bradykinesia. Together with akinesia, these are symptoms commonly defined as motor deficits relating to the akinetic/rigid (AR) type of PD. AR symptoms have been linked to gray matter decline and altered functional connectivity within frontal-parietal networks critical for motor planning and execution⁷³, and as such, the AR motor subtype is associated with poorer prognosis and increased risk of dementia compared to the tremor subtype^74,75. The Rigid Subgroup did not show increased CSF QA/KA, but rather increased 3-HK/Kyn, which may indicate elevated KMO activity. That differences in clinical symptoms correspond to differences in KP profiles, with increased CSF QA/KA and normal 3-HK/Kyn in the Dystonia Subgroup, and normal CSF QA/KA but abnormal 3-HK/Kyn in the Rigid Subgroup, suggests the KP may be sensitive to specific PD endophenotypes.

Distinct KP alterations may provide insight into the clinical trajectory associated with each symptom dimension/profile and inform person-tailored therapeutic strategies. For example, PD patients with altered KP metabolism may benefit from strategies aiming to restore vitamin B₆ levels and reducing peripheral inflammation. Indeed, KMO inhibition has been tested as therapeutic strategy for Alzheimer’s and PD, although these drugs are complicated by low BBB permeability. However, recent second generation KMO inhibitors with increased BBB permeability have shown promise in reducing the production of neuroexcitatory KP metabolites and in shifting the pathway towards neuroprotective KA⁷⁶. An alternative strategy to address KP dysfunction might involve exercise, which can increase KAT expression by directing peripheral KYN metabolism toward KA production⁷⁷. Combinational therapies incorporating anti-inflammatory agents, KMO inhibitors or KAT enhancers, in addition to lifestyle factors such as exercise, may have synergistic effects.

This study has several strengths, including its multicenter design, analysis of kynurenines and B-vitamins in matched CSF and plasma pools in the same sample, and application of unbiased machine learning approaches. Several opportunities to extend on our findings remain open. First, because of the cross-sectional design, longitudinal studies should be undertaken to examine the changes in KYN metabolism over the course of disease development. For example, it is possible that metabolites fluctuate within different clinical populations or across an individual person’s disease trajectory, and longitudinal study may reveal coupling between metabolites and clinical dysfunction. Second, in keeping with PD prevalence, our study had an overrepresentation of males in the PD cohort, which makes it difficult to assess potential sex differences. Importantly, previous studies have shown sex-specific differences in the KP^78,79. Third, given that this is an observational study, it is difficult to determine whether the observed KP disruptions are a cause or consequence of PD pathophysiology. Interventional studies and preclinical experiments will be essential in determining causality. Fourth, PD diagnosis was made based on patient-reported history and neurological exam using the United Kingdom Parkinson’s Disease Society Brain Bank diagnostic criteria, but still may not be fully accurate⁸⁰. Synuclein seeding assays would be useful to confirm synucleinopathy across the cohort⁸¹. Fifth, the PD clinical subgroup populations were of modest sample size and these findings should be expanded, ideally in a large cohort of de novo PD participants. Nevertheless, that these subgroups emerged in the current multicenter design lends support to the external validity of the subgroup findings. Finally, information on dietary intake of B-vitamins or use of vitamin supplements was not available for all participants. This information would be useful in future studies aimed at directly assessing relationships between vitamin status and KP activation in PD participants.

In conclusion, the current study identifies the KP as a site of peripheral-cerebral crosstalk, integrating inflammation and metabolic dysfunction. Furthermore, this study provides new evidence to suggest that the KP can reflect specific PD endophenotypes. Identification of PD clinical subgroups widens opportunities for developing biomarker candidates, novel avenues for investigating disease pathogenesis and new strategies for person-tailored therapeutic targeting.

Study participants and study design

Three-hundred and twenty-five participants were included in this multicenter study and were drawn from 8 research centers (Supplementary Fig. 1). This included participants from 1) the Stanford Movement Disorders Clinic (Stanford MDC); 2) the Stanford Alzheimer’s Disease Research Center (Stanford ADRC); 3) the Stanford Aging and Memory Study (SAMS); 4) the VA Puget Sound Health Care System/University of Washington (Seattle VA); 5) the VA Portland Medical Center (Portland VA); 6) the Oregon Health & Science University Layton Aging and Alzheimer’s Disease Research Center (OHSU ADRC); 7) the University of Pennsylvania Alzheimer’s Disease Center (Penn ADC); and 8) the University of Pennsylvania Frontal Temporal Dementia Center (Penn FTDC). Participants from the Stanford MDC and Stanford ADRC were included if they were cognitively normal healthy adults or if they met United Kingdom Parkinson’s Disease Society Brain Bank clinical diagnostic criteria for PD. Healthy control participants were recruited from the family of PD participants and from the surrounding community. They had no history of PD, other neurodegenerative diseases, or chronic neuropsychiatric disorders. SAMS participants were clinically unimpaired older adults with Clinical Dementia Rating global score of zero⁸². A full neuropsychological battery assessing multiple domains was given to assign a cognitive diagnosis. Participants from Stanford were defined as cognitively impaired if scores were ≥ 1.5 standard deviations below age- and education-matched normative values on at least two separate neuropsychological measures, regardless of domain⁸³. Seattle VA and Portland VA and OHSU ADRC assigned diagnoses at a clinical consensus conference. Control participants from Penn ADC and Penn FTDC underwent cognitive testing and determination of cognitive diagnosis by clinical consensus after the time of sampling. Levodopa equivalent daily dose (LEDD) was determined as previously described⁸⁴. Study protocols were approved by Institutional Review Boards at each center. In accordance with the Declaration of Helsinki, written informed consent was obtained from each study participant or their legally authorized representative.

MDS-UPDRS

The Movement Disorder’s Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) was used to evaluate various aspects of Parkinson’s disease including non-motor and motor experiences of daily living and motor complications⁵⁹.

Plasma and CSF collection

For the Stanford MDC, Stanford ADRC and SAMS, fasting plasma was collected within 2 weeks of lumbar puncture. For the remaining cohorts, CSF and plasma blood draws occurred on the same morning. No hemolysis or discoloration was apparent in any of the included CSF or plasma samples (visual inspection). CSF samples were restricted to two freeze-thaw cycles, as recommended by Consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry⁸⁵.

Liquid chromatography-tandem mass spectrometry

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed on CSF and plasma samples by Bevital AS (Bergen, Norway, www.bevital.no)) and measured kynurenine pathway metabolites and related vitamins including tryptophan, kynurenine, kynurenic acid, quinolinic acid, picolinic acid, 3-hydroxykynurenine, anthranilic acid, 3-hydroxanthranillic acid, nicotinamide, N¹-methylnicotinamide, pyridoxal, pyridoxal 5’-phosphate, thiamine, riboflavin, flavin mononucleotide, thiamine monophosphate, and neopterin, as previously described ⁸⁶.

Measurement of AD CSF core biomarkers

CSF amyloid and tau were measured by the Stanford ADRC Biomarker Core using the fully automated Lumipulse G assays (Fujirebio Diagnostics, US, Malvern, PA) on the Lumipulse G1200 instrument as previously described^12,87. Investigators were blind to clinical and demographic information of the sample.

Predictive modeling and subgroup analysis

A flexible machine learning method was applied to predict whether CSF and plasma levels of kynurenine pathway metabolites and related B-vitamins might be used to distinguish PD from control individuals. All participants having both plasma and CSF data were included in machine learning analysis, resulting in cohort of PD participants (n = 149) from control participants (n = 158). Metabolites and ratios included were thiamine, thiamine monophosphate, flavin mononucleotide, riboflavin, nicotinamide, N1 methylnicotinamide, pyridoxal 5’-phosphate, pyridoxal, tryptophan, kynurenine, kynurenic acid, anthranilic acid, 3-hydroxykynurenine, 3-hydroxyanthranilic acid, quinolinic acid, picolinic acid, 3-HK/KA, HKr, QA/KA in CSF and thiamine, thiamine monophosphate, flavin mononucleotide, riboflavin, nicotinamide, N1-methylnicotinamide, pyridoxal 5’-phosphate, pyridoxal, 4-pyridoxic acid, tryptophan, kynurenine, kynurenic acid, anthranilic acid, 3-hydroxykynurenine, xanthurenic acid, 3-hydroxyanthranilic acid, quinolinic acid, picolinic acid, 3-HK/KA, HKr, QA/KA, Par, and 3-HK/Kyn in plasma. The outcome of interest was PD vs control. Specifically, a two-step procedure was used to train the prediction model. The first step involved building two prediction models using random forest with metabolic features from CSF and plasma as input and the second step combined the predictions from two models built in the first step to generate a final prediction. Prediction models from the first step were also kept for comparison purpose. A cross validation strategy was used to simultaneously build the model on a subset of metabolite data and then to test its performance on previously unseen participants as previously described⁸⁸. The model performance in cross-validation was measured via area under the ROC curve (AUC). Also tested was whether kynurenine pathway metabolite levels were robust predictors of sex, years since diagnosis, age or years of education. Correlation network analysis was used to evaluate the relationships between plasma and CSF metabolite features in a bottom-up approach. Separately, unsupervised dimension-reduction using t-distributed stochastic neighbor embedding (tSNE) algorithm⁸⁹ was performed on all metabolites to project the individual participants into a two-dimensional plane for potential pattern detection. Based on tSNE projection, multiple PD participant clusters were identified.

Statistical analysis

Differences in demographic variables between PD and control participants were assessed using Student’s t test or Chi-square test. CSF and plasma biomarkers were log-transformed to approximate Gaussian distribution. To evaluate differences between PD and control diagnostic groups, data were analyzed using analysis of covariance (ANCOVA) adjusting for age and sex. Similarly, multiple linear regression analyses were used to assess associations between continuous biomarker variables also after adjusting for age and sex. ANOVA was used to assess differences in selected variables between identified PD participant clusters. For metabolite data, data points below first quartile minus 1.5*IQR, or above third quartile plus 1.5*IQR were identified as outliers and removed from analysis. Outliers were not excluded from the subgroup analysis. Statistical analyses were performed using R, version 3.6.1 (R Foundation for Statistical Computing) and GraphPad Prism, version 10 (GraphPad Software, Boston, MA) and figures were prepared using BioRender (Toronto, Ontario, Canada). All statistical tests were two-sided and significance was defined as P < 0.05.

Acknowledgements: We thank contributors who collected samples used in this study, as well as research participants and their families, whose help and participation made this work possible. Funding: This work was supported by NIH/NIA grants RF1AG058047, R01AG048232 to KIA, NS115114, K23 NS075097 and Michael J. Fox Foundation grant 6440.0 to KLP, and NIH grant R35GM138353 and the American Heart Association grant (19PABHI34580007) to NA. NIH R01AG048076 (ADW) and R01AG074339 (ECM). NIH/NIA P30 AG072979 (ACP and DAW), NIH/NINDS RO1NS115139 (ACP). ACP is also supported by the Parker Family Chair. NIH grant P30 AG066518 to JFQ and NEG. CSF and plasma samples were obtained from the Stanford ADRC (P30 AG066515), the University of Pennsylvania ADRC (P30 AG072979), the Pacific Udall Center (NINDS) P50 NS062684, and the National Centralized Repository for Alzheimer Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA). This material is the result of work supported with resources and the use of facilities at the VA Puget Sound and VA Portland Health Care Systems. The authors would also like to acknowledge the generous support of the Jean Perkins Foundation and the Scully Research Initiative. KIA is a Chan Zuckerberg Biohub-San Francisco Investigator. ENW, TWC, KLP, and KIA are supported by the Phil & Penny Knight Initiative for Brain Resilience.

Data availability

Anonymized data are available to qualified researchers upon request to the corresponding author or by contacting the individual cohort studies.

Competing interests: The authors declare that they have no competing interests.

Author contributions: E.N.W. and J.U. performed experimental design, data collection, statistical analysis, and manuscript writing. M.S.S. performed data collection and statistical analysis. P.S.M. performed experimental design and data collection. Ø.M., A.U. and P.M.U. led mass spectrometry experiments. M.S.-B., P.L., S.D.M., Q.W., D.C., N.K.C. performed data collection. L.T. contributed to statistical analysis. C.A.F., G.A.K., E.D.P., B.C., T.J.M., C.P.Z., N.E.G., J.F.Q., S.J.S., V.W.H., T.W-C., F.M.L., D.A.W., A.C-P., A.D.W., E.C.M., K.L.P., and K.I.A. provided samples and clinical data. N.A. led the machine learning analysis. K.L.P. and K.I.A. contributed to experimental design and manuscript writing. All authors revised and approved the manuscript.

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Parkinson’s disease is characterized by vitamin B6-dependent inflammatory kynurenine pathway dysfunction

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INTRODUCTION

RESULTS

KP activation in Parkinson’s disease

Low B-vitamin status associates with inflammation and KP activation in Parkinson’s disease

CSF QA associates with motor symptom severity and neurodegeneration in Parkinson’s disease

CSF and plasma KP metabolites accurately predict Parkinson’s disease

Parkinson’s disease clinical subgroups display distinct patterns of KP alterations and distinct clinical features

DISCUSSION

MATERIALS AND METHODS

MDS-UPDRS

Statistical analysis

Declarations

References

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Supplementary Files

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