Peripheral inflammatory immune response differs among sporadic and familial Parkinson’s disease

Muñoz‐Delgado, Laura; Macías‐García, Daniel; Periñán, María Teresa; Jesús, Silvia; Gómez, A.; Toribio, Marta Bonilla; Rueda, Dolores Buiza; Jiménez-Jaraba, María del Valle; Zamora, Belén Benítez; Belloso, Rafael Díaz; García-Díaz, Sergio; Martín-Bórnez, Miguel; Sánchez, Rocío Pineda; Carrillo, Fátima; Gómez-Garre, Pilar; Mir, Pablo

doi:10.1038/s41531-023-00457-5

Cited by 22 publications

(19 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Inflammatory markers like cytokines and chemokines in the peripheral system that play a key role in immune regulation, cross the BBB and impact the central nervous system, Therefore, peripheral inflammation could be a significant contributor to the etiology of PD as well as to disease progression [52], [55].…”

Section: Discussionmentioning

confidence: 99%

“…Quantitative and qualitative changes in leukocytes (which are the immune cells in peripheral blood) and their subpopulations have also been observed in PD patients. [51], [52]. Besides, blood cells also get affected by PD in different ways.…”

Section: Discussionmentioning

confidence: 99%

“…The BBB, along with other protective barriers, shields brain neurons from external threats, and changes in the BBB associated with aging are a significant risk factor for PD [55]. Inflammatory markers like cytokines and chemokines in the peripheral system that play a key role in immune regulation, cross the BBB and impact the central nervous system, Therefore, peripheral inflammation could be a significant contributor to the etiology of PD as well as to disease progression [52], [55].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Bhattacharjee,

Jamal,

Ahammad

et al. 2023

Preprint

View full text Add to dashboard Cite

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the central nervous system, often diagnosed in its advanced stages due to the absence of sensitive biomarkers. With this objective in mind, our study conducted a comprehensive analysis of differentially expressed genes (DEGs) sourced from blood-based microarray datasets to uncover potential biomarkers and developed a machine learning based classifier to conduct two step validations. By analyzing gene expression of three projects, we identified 678 DEGs, consisting of 337 genes showing upregulation and 341 genes presenting downregulation. Additionally, insights from functional enrichment and the protein-protein network analysis indicate that HLA-F, IRF-1, and RPS28 have the potential to serve as biomarkers for diagnosing PD. Simultaneously, we employed feature selection techniques such as Least Absolute Shrinkage and Selection Operator with Cross Validation (LassoCV) followed by Recursive Feature Elimination with Cross Validation (REFCV) to filter our initial dataset of 13,249 genes down to 43 genes, which were subsequently used to train the machine learning-based classifier models. These 43 genes formed the basis for training and testing various machine learning models, including logistic regression, random forest, naive Bayes, k-nearest neighbors, support vector machine, and deep learning based artificial neural networks. Our models demonstrated robust performance, with Support Vector Machine outperforming others by 0.65 accuracy (95%CI: 0.58-0.66), 0.70 AUC-ROC (95%CI: 0.70-0.71) and 0.35 MCC (95%CI: 0.34-0.39). The model was implemented to develop the PitArray tool for non-invasive detection of PD from blood. PitArray is available at: https://github.com/Arittra95/PitArray.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

Bhattacharjee,

Jamal,

Ahammad

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The association between LRRK2 variants and inflammation in PD has been challenged by a recent study, 74 showing how LRRK2 ‐PD patients did not differ from healthy controls in the count of any leukocyte subpopulation or neutrophil‐to‐lymphocyte ratio, a biomarker of systemic inflammation. These results suggest that LRRK2 might mediate immune processes through different pathways, with a more prominent role of inflammatory mediators and a dysregulation of specific immune cell subpopulations like microglia, while having a marginal effect on the number of peripheral leukocytes 74 …”

Section: Inflammationmentioning

confidence: 99%

LRRK2 and Parkinson's disease: from genetics to targeted therapy

Sosero

Gan‐Or

2023

Ann Clin Transl Neurol

View full text Add to dashboard Cite

LRRK2 variants are implicated in both familial and sporadic PD. LRRK2-PD has a generally benign clinical presentation and variable pathology, with inconsistent presence of Lewy bodies and marked Alzheimer's disease pathology. The mechanisms underlying LRRK2-PD are still unclear, but inflammation, vesicle trafficking, lysosomal homeostasis, and ciliogenesis have been suggested, among others. As novel therapies targeting LRRK2 are under development, understanding the role and function of LRRK2 in PD is becoming increasingly important. Here, we outline the epidemiological, pathophysiological, and clinical features of LRRK2-PD, and discuss the arising therapeutic approaches targeting LRRK2 and possible future directions for research.

show abstract

“…Peripheral blood cell count and the neutrophil-to-lymphocyte ratio (NLR) allow tracking the immune activation in the innate or adaptive branches [6]. An increased NLR consistent with a systemic proinflammatory state has been measured in many pathological conditions and neurodegenerative diseases, associated with a worse course, including mortality, and peculiar neuropathological signatures [7][8][9]. Conversely, there are still no data on iNPH.…”

Section: Introductionmentioning

confidence: 99%

Peripheral immunity changes are associated with neurodegeneration and worse clinical outcome in idiopathic normal pressure hydrocephalus

Bissacco,

Simonetta,

Mascioli

et al. 2023

Euro J of Neurology

View full text Add to dashboard Cite

Background and purposeIdiopathic normal pressure hydrocephalus (iNPH) pathogenesis is multifactorial. Systemic inflammation might have a role in gathering clinical–pathological trajectories. We aimed to shape the peripheral immune profile of iNPH and establish correlations with cerebrospinal fluid (CSF) markers, ventricular enlargement, and clinical outcomes.MethodsWe conducted a single‐center retrospective–longitudinal study, including 38 iNPH patients and 38 controls. Baseline iNPH Grading Scale and modified Rankin Scale (mRS) scores were collected with peripheral blood cell count, CSF amyloid‐β42 (Aβ42), total tau (t‐tau), phosphorylated‐181‐tau, and Evans index. Depending on 5‐year outcome, iNPH patients were grouped into “poor outcome” (PO; mRS ≥ 5) and “favorable outcome” (FO; mRS < 5). Biomarkers were compared and correlated with each other. Receiver operating characteristic analysis was performed.ResultsiNPH patients compared to controls had higher neutrophil‐to‐lymphocyte ratio (NLR; 2.43 ± 1.04 vs. 1.61 ± 0.47, p < 0.001), higher neutrophils (4.22 ± 0.86 1000/mL vs. 3.48 ± 1.34, p = 0.033), and lower lymphocytes (1.45 ± 0.55 1000/mL vs. 2.07 ± 0.86, p = 0.038), with the expected CSF biomarkers signature. In the patients' cohort, NLR was associated directly with t‐tau and inversely with Aβ42. NLR directly correlated with Evans index. PO patients compared to those with FO had higher NLR (3.25 ± 1.40 vs. 2.01 ± 0.77, p = 0.035) and higher t‐tau (274.76 ± 114.39 pg/mL vs. 150.28 ± 72.62, p = 0.017), with an area under the curve of 0.786 and 0.793, respectively.ConclusionsiNPH patients present a proinflammatory state associated with neurodegeneration and predicting poor clinical outcome. Systemic inflammation represents a factor in the clinical–pathological progression of iNPH, and the NLR emerges as a potential prognostic index.

show abstract

Peripheral inflammatory immune response differs among sporadic and familial Parkinson’s disease

Cited by 22 publications

References 61 publications

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

A Non-invasive Detection of Parkinson’s Disease using PitArray: An Integrative Meta-Analysis and Machine Learning Approach

LRRK2 and Parkinson's disease: from genetics to targeted therapy

Peripheral immunity changes are associated with neurodegeneration and worse clinical outcome in idiopathic normal pressure hydrocephalus

Contact Info

Product

Resources

About