Modelling idiopathic Parkinson disease as a complex illness can inform incidence rate in healthy adults: the P<sub>R</sub>EDIGT score

Schlossmacher, Michael G.; Tomlinson, Julianna J.; Santos, Goncalo; Shutinoski, Bojan; Brown, Earl G.; Dujovny, Manuel; Mestre, Tiago

doi:10.1111/ejn.13476

Cited by 19 publications

(45 citation statements)

References 160 publications

(272 reference statements)

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“…Inhibition of the lysosomal acid ceramidase activity, in the presence of GCase impairment, rescues ceramide levels and prevents α-syn accumulation in vitro (Kim et al, 2018). These data help to reconcile genetic evidence that not all carriers of risk-associated GBA or SMPD1 mutations develop PD, PDD, or DLB (Schlossmacher et al, 2017). Building on this hypothesis, we suggest that an individual’s sphingolipid metabolome confers susceptibility (or resistance) to PDD and DLB phenoconversion via molecular modulation of ceramide homeostasis.…”

Section: Mutations In Gba Promote Toxic Conversion Of α-Synucleinmentioning

confidence: 67%

Dysregulated Lipid Metabolism and Its Role in α-Synucleinopathy in Parkinson’s Disease

2019

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Parkinson’s disease (PD) is the second most common neurodegenerative disease, the main pathological hallmark of which is the accumulation of α-synuclein (α-syn) and the formation of filamentous aggregates called Lewy bodies in the brainstem, limbic system, and cortical areas. Lipidomics is a newly emerging field which can provide fresh insights and new answers that will enhance our capacity for early diagnosis, tracking disease progression, predicting critical endpoints, and identifying risk in pre-symptomatic persons. In recent years, lipids have been implicated in many aspects of PD pathology. Biophysical and lipidomic studies have demonstrated that α-syn binds preferentially not only to specific lipid families but also to specific molecular species and that these lipid-protein complexes enhance its interaction with synaptic membranes, influence its oligomerization and aggregation, and interfere with the catalytic activity of cytoplasmic lipid enzymes and lysosomal lipases, thereby affecting lipid metabolism. The genetic link between aberrant lipid metabolism and PD is even more direct, with mutations in GBA and SMPD1 enhancing PD risk in humans and loss of GALC function increasing α-syn aggregation and accumulation in experimental murine models. Moreover, a number of lipidomic studies have reported PD-specific lipid alterations in both patient brains and plasma, including alterations in the lipid composition of lipid rafts in the frontal cortex. A further aspect of lipid dysregulation promoting PD pathogenesis is oxidative stress and inflammation, with proinflammatory lipid mediators such as platelet activating factors (PAFs) playing key roles in arbitrating the progressive neurodegeneration seen in PD linked to α-syn intracellular trafficking. Lastly, there are a number of genetic risk factors of PD which are involved in normal lipid metabolism and function. Genes such as PLA2G6 and SCARB2 , which are involved in glycerophospholipid and sphingolipid metabolism either directly or indirectly are associated with risk of PD. This review seeks to describe these facets of metabolic lipid dysregulation as they relate to PD pathology and potential pathomechanisms involved in disease progression, while highlighting incongruous findings and gaps in knowledge that necessitate further research.

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Section: Mutations In Gba Promote Toxic Conversion Of α-Synucleinmentioning

confidence: 67%

Dysregulated Lipid Metabolism and Its Role in α-Synucleinopathy in Parkinson’s Disease

2019

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“…Those identified as high risk had a subtle motor slowing on keyboard testing, olfactory loss, symptoms of RBD, and loss of striatal innervation on DAT scan . Similarly, the PREIDGT algorithm combined genetic with risk and prodromal markers to estimate prodromal disease; it has not yet been formally tested for predictive value …”

Section: Key Advances In the Last Decadementioning

confidence: 99%

“…115,116 Similarly, the PREIDGT algorithm combined genetic with risk and prodromal markers to estimate prodromal disease; it has not yet been formally tested for predictive value. 117 In 2015, the MDS published the MDS prodromal PD criteria. This used a unique Bayesian statistical approach, which estimates, on a case-by-case basis, the probability that an individual has prodromal PD.…”

Section: Data Coalesce Into Criteriamentioning

confidence: 99%

Prodromal Parkinson's Disease: The Decade Past, the Decade to Come

2019

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The past decade has seen a dramatic expansion of the field of prodromal PD. Ten years ago, there were only six known prodromal markers of disease, none of which had more than two studies documenting diagnostic value. We now have at least 16 markers, with as many as 10 prospective studies for a single marker. This review summarizes the major advances over the last decade and speculates about the advances we will see in the decade to come. The most notable advances over the last decade came through the study of high‐risk cohorts (REM sleep behavior disorder and later genetic and autonomic cohorts), the generation of more representative population‐based cohorts for studying prodromal PD, major advances in neuroimaging of early disease stages, the emerging likelihood that tissue biopsy will be able to diagnose prodromal PD, and the coalescence of prodromal markers into discrete criteria. As the next decade dawns, we await increasing precision of sensitivity and specificity estimates of known markers, the discovery of new biomarkers of prodromal disease, improvements in diagnosis using combined methods/criteria (with increasing recognition of prodromal PD as one stage of the full PD spectrum), and ultimately the development of neuroprotective therapy that can be provided at the earliest stages of disease. © 2019 International Parkinson and Movement Disorder Society

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“…We had further hypothesized that from these risk elements a final score can be calculated by assigning values to each category, as computed by the formula: P R =(E+D+I)xGxT. 6 Of note, for some risk categories, where details of variables could be incomplete, such as for an individual's microbial exposure history within his/her nasal cavity (E) and/or regarding genetic polymorphisms (D), we had chosen surrogates, such as hyposmia and family history, respectively. 6 Of importance, motor assessment-based data, as gleaned from routine neurological examinations, were not included in the original PREDIGT model.…”

Section: Et Al Evaluation Of Predigt Score To Identify Pd Patientsmentioning

confidence: 99%

Evaluation of the PREDIGT Score in Discriminating Parkinson Disease from Neurological Health

Mestre

Mollenhauer

et al. 2020

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Background: We previously created the PREDIGT Score as an algorithm to predict the incidence of Parkinson disease. The model rests on a hypothesis-driven formula, [PR=(E+D+I)xGxT], that uses numerical values for five categories known to modulate Parkinson's risk (PR): environmental exposure (E); DNA variants (D); evidence of gene-environment interactions (I); gender (G); and time (T). Notably, the formula does not rely on motor examination results. Methods: To evaluate the PREDIGT Score, we tested it in two established case-control cohorts: 'De Novo Parkinson Study' (DeNoPa) and 'Parkinson's Progression Marker Initiative' (PPMI). Using baseline data from 589 patients and 309 controls enrolled in the DeNoPa and PPMI cohorts, we evaluated the PREDIGT Score's discriminative performance in distinguishing Parkinson's patients from healthy controls by area-under-the-curve (AUC) analyses. Findings: When examining cohorts separately and using all available variables in each cohort to calculate the PREDIGT Score, AUCs were 0.83 (95% CI 0.77-0.89) for DeNoPa and 0.87 (95% CI 0.84-0.9) for PPMI, respectively, in distinguishing Parkinson disease patients from healthy individuals. When combining DeNoPa and PPMI data sets by using eleven variables that had been collected in both cohorts, the PREDIGT Score discriminated patients from controls with an AUC of 0.84 (95% CI 0.81-0.87). The mean score of Parkinson disease patients was significantly higher than that of control individuals at 108.48 (+/-52.08) and 47.33 (+/-34.1), respectively (p < 0.0001). Interpretation: Our results demonstrate a robust performance of the original PREDIGT Score in distinguishing patients diagnosed with Parkinson disease from neurologically healthy subjects without reliance on motor examination data. In future efforts, the predictive performance of the algorithm will be studied in longitudinal cohorts of at-risk persons. Funding: Parkinson Canada, Michael J. Fox Foundation, Department of Medicine (The Ottawa Hospital), Uttra & Subash Bhargava Family, Paracelsus-Elena-Klinik Kassel, Parkinson Fond Deutschland, and Deutsche Parkinson Vereinigung.

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Modelling idiopathic Parkinson disease as a complex illness can inform incidence rate in healthy adults: the P_REDIGT score

Cited by 19 publications

References 160 publications

Dysregulated Lipid Metabolism and Its Role in α-Synucleinopathy in Parkinson’s Disease

Dysregulated Lipid Metabolism and Its Role in α-Synucleinopathy in Parkinson’s Disease

Prodromal Parkinson's Disease: The Decade Past, the Decade to Come

Evaluation of the PREDIGT Score in Discriminating Parkinson Disease from Neurological Health

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Modelling idiopathic Parkinson disease as a complex illness can inform incidence rate in healthy adults: the PREDIGT score

Cited by 19 publications

References 160 publications

Dysregulated Lipid Metabolism and Its Role in α-Synucleinopathy in Parkinson’s Disease

Dysregulated Lipid Metabolism and Its Role in α-Synucleinopathy in Parkinson’s Disease

Prodromal Parkinson's Disease: The Decade Past, the Decade to Come

Evaluation of the PREDIGT Score in Discriminating Parkinson Disease from Neurological Health

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Product

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Modelling idiopathic Parkinson disease as a complex illness can inform incidence rate in healthy adults: the P_REDIGT score