Marker Of Progression Research Articles

Airway bacterial microbiome signatures correlate with occupational pneumoconiosis progression

Recent evidence has pinpointed a key role of the microbiome in human respiratory health and disease. However, significant knowledge gaps still exist regarding the connection between bacterial communities and adverse effects caused by particulate matters (PMs). Here, we characterized the bacterial microbiome along different airway sites in occupational pneumoconiosis (OP) patients. The sequencing data revealed that OP patients exhibited distinct dysbiosis in the composition and function of the respiratory microbiota. To different extents, there was an overall increase in the colonization of microbiota, such as Streptococcus, implying a possible intrusion pathway provided by exogenous PMs. Compared to those of healthy subjects, unhealthy living habits (i.e., smoking) had a greater impact on microbiome changes in OP patients. Importantly, the associations between the bacterial community and disease indicators indicated that specific bacterial species, including Prevotella, Actinobacillus, and Leptotrichia, might be surrogate markers of OP disease progression. Collectively, our results highlighted the potential participation of the bacterial microbiota in the pathogenesis of respiratory diseases and helped in the discovery of microbiome-based diagnostics for PM-induced disorders.

Prediction of Future Parkinson Disease Using Plasma Proteins Combined With Clinical-Demographic Measures.

Identification of individuals at high risk of developing Parkinson disease (PD) several years before diagnosis is crucial for developing treatments to prevent or delay neurodegeneration. This study aimed to develop predictive models for PD risk that combine plasma proteins and easily accessible clinical-demographic variables. Using data from the UK Biobank (UKB), which recruited participants across the United Kingdom, we conducted a longitudinal study to identify predictors for incident PD. Participants with baseline plasma proteins and no PD were included. Through machine learning, we narrowed down predictors from a pool of 1,463 plasma proteins and 93 clinical-demographic. These predictors were then externally validated using the Parkinson's Progression Marker Initiative (PPMI) cohort. To further investigate the temporal trends of predictors, a nested case-control study was conducted within the UKB. A total of 52,503 participants without PD (median age 58, 54% female) were included. Over a median follow-up duration of 14.0 years, 751 individuals were diagnosed with PD (median age 65, 37% female). Using a forward selection approach, we selected a panel of 22 plasma proteins for optimal prediction. Using an ensemble tree-based Light Gradient Boosting Machine (LightGBM) algorithm, the model achieved an area under the receiver operating characteristic curve (AUC) of 0.800 (95% CI 0.785-0.815). The LightGBM prediction model integrating both plasma proteins and clinical-demographic variables demonstrated enhanced predictive accuracy, with an AUC of 0.832 (95% CI 0.815-0.849). Key predictors identified included age, years of education, history of traumatic brain injury, and serum creatinine. The incorporation of 11 plasma proteins (neurofilament light, integrin subunit alpha V, hematopoietic PGD synthase, histamine N-methyltransferase, tubulin polymerization promoting protein family member 3, ectodysplasin A2 receptor, Latexin, interleukin-13 receptor subunit alpha-1, BAG family molecular chaperone regulator 3, tryptophanyl-TRNA synthetase, and secretogranin-2) augmented the model's predictive accuracy. External validation in the PPMI cohort confirmed the model's reliability, producing an AUC of 0.810 (95% CI 0.740-0.873). Notably, alterations in these predictors were detectable several years before the diagnosis of PD. Our findings support the potential utility of a machine learning-based model integrating clinical-demographic variables with plasma proteins to identify individuals at high risk for PD within the general population. Although these predictors have been validated by PPMI, additional validation in a more diverse population reflective of the general community is essential.

Exposure to household pesticides and Parkinson's disease in the Parkinson's Progression Markers Initiative cohort.

In the last decades, the association of household pesticide usage with Parkinson's disease (PD) has been poorly explored, with discordant results. Based on the Parkinson's Progression Markers Initiative (PPMI) cohort study, we analyzed (1) the association of household pesticide exposure with the development of PD and (2) the effect of household pesticides on progression of PD. Data from participants of the "FOllow Up persons with Neurologic Disease" (FOUND study) included in the PPMI cohort database were analyzed. The PPMI FOUND study applied the Parkinson's Disease Risk Factor Questionnaire to collect information regarding the use of pesticides in non-work settings during periods of life, and the lifetime pesticide exposure for each participant was estimated. We defined a high use of pesticides if the exposure estimate had a z-score higher than one standard deviation from the mean. Also, we evaluated longitudinal data of people with PD to analyze the effect of high use of household pesticides on disease progression according to motor impairment, cognitive dysfunction, depressive symptoms, and modification of motor clinical phenotype. We analyzed data from 206 people with PD and 64 healthy controls, almost all from the USA. High use of household pesticides was not associated with the odds of developing PD. Regarding PD progression, only cognitive dysfunction was associated with the high use of household fungicides (HR 5.64 per standard deviation increase in exposure estimate, 95% CI 1.41-22.6). Chronic exposure to household pesticides may impact the clinical progression of PD, especially cognitive symptoms.

Protocol and rationale for a randomized controlled SGLT2 inhibitor trial in pediatric and young adult populations with chronic kidney disease: DOUBLE PRO-TECT Alport.

Clinical trials have demonstrated positive cardiovascular and kidney outcomes of sodium-glucose-co-transporter-2 (SGLT2) inhibitors in adult patients with diabetic and other chronic kidney diseases (CKD). Whether benefits extend to children, teenagers, and young adults with early-stage CKD is unknown. For this reason, the DOUBLE PRO-TECT Alport trial (NCT05944016) will study the progression of albuminuria in young patients with Alport syndrome (AS), the most common hereditary CKD, to assess the safety and efficacy of the SGLT2-inhibitor dapagliflozin. Patients living with AS and chronically elevated albuminuria have a high risk of kidney failure before the age of 50 years. DOUBLE PRO-TECT Alport is a multicenter, randomized, double-blind, placebo-controlled trial (RCT). Participants (aged 10 to 39 years) must have a diagnosis of AS by genetic testing or kidney biopsy, be on a stable (> 3 months) maximum tolerated dose of a renin-angiotensin-system-inhibitor (RASi) and must have a Urinary Albumin to Creatinine Ratio (UACR) of >300 mg/g (pediatric) or >500 mg/g (adult).Eligible participants will be randomly assigned at a 2:1 ratio to 48 weeks of treatment with dapaglifozin 10 mg/day -to- matched placebo. Most participants are expected to be children with a normal glomerular filtration rate (eGFR). In addition to safety, the primary (change in UACR from baseline to Week 48) and key secondary (eGFR change from baseline to Week 52) efficacy outcomes will be analyzed with a mixed model repeated measures approach. Efficacy analyses will be performed primarily in the full analysis set according to the intention-to-treat principle. A sensitivity analysis will be performed using reference-based multiple imputation. DOUBLE PRO-TECT Alport will assess whether SGLT2-inhibitors can safely reduce change from baseline in UACR as a marker for progression of CKD in young patients living with AS.

Cognitive Performance Between Latino and White Non-Latino Individuals With Parkinson's Disease.

Cognitive impairment is a common nonmotor symptom in Parkinson's disease (PD). Individuals of Latino background are traditionally underrepresented in research on PD. Despite the fact that Latinos comprise 18% of the U.S. population, they commonly make up less than 5% of samples in studies of PD. Emerging evidence suggests that Latino individuals with PD may experience disparities relative to White non-Latinos in terms of having more severe motor symptoms, more severe depressive symptoms, and worse health-related quality of life. The purpose of the present study was to investigate differences in cognitive performance between Latino and White non-Latino individuals with PD and examine correlates of cognitive performance. Data were obtained from the Parkinson's Progression Markers Initiative. Participants included 60 Latino individuals with PD and 1,009 White non-Latino individuals with PD, all of whom were followed annually for up to 5 years. Participants completed neuropsychological tests of attention and working memory, processing speed, visuospatial functioning, verbal fluency, and immediate and delayed memory and recall. Relative to White non-Latino individuals with PD, Latino individuals with PD had significantly lower scores on the global measure of cognitive functioning, a test of processing speed, and tests of working memory and attention. Years of education was the strongest correlate of performance in these three cognitive domains among individuals in the Latino group. These findings provide initial evidence of disparities in cognitive functioning among Latino individuals with PD. Educational disadvantages may be one potential driver of these disparities.

Predicting Cerebrospinal Fluid Alpha-Synuclein Seed Amplification Assay Status from Demographics and Clinical Data.

To develop and externally validate models to predict probabilities of alpha-synuclein (a-syn) positive or negative status in vivo in a mixture of people with and without Parkinson's disease (PD) using easily accessible clinical predictors. Uni- and multi-variable logistic regression models were developed in a cohort of participants from the Parkinson Progression Marker Initiative (PPMI) study to predict cerebrospinal fluid (CSF) a-syn status as measured by seeding amplification assay (SAA). Models were externally validated in a cohort of participants from the Systemic Synuclein Sampling Study (S4) that had also measured CSF a-syn status using SAA. The PPMI model training/testing cohort consisted of 1260 participants, of which 76% had manifest PD with a mean (± standard deviation) disease duration of 1.2 (±1.6) years. Overall, 68.7% of the overall PPMI cohort (and 88.0% with PD of those with manifest PD) had positive CSF a-syn SAA status results. Variables from the full multivariable model to predict CSF a-syn SAA status included age- and sex-specific University of Pennsylvania Smell Identification Test (UPSIT) percentile values, sex, self-reported presence of constipation problems, leucine-rich repeat kinase 2 (LRRK2) genetic status and pathogenic variant, and GBA status. Internal performance of the model on PPMI data to predict CSF a-syn SAA status had an area under the receiver operating characteristic curve (AUROC) of 0.920, and sensitivity/specificity of 0.881/0.845. When this model was applied to the external S4 cohort, which included 71 participants (70.4% with manifest PD for a mean 5.1 (±4.8) years), it performed well, achieving an AUROC of 0.976, and sensitivity/specificity of 0.958/0.870. Models using only UPSIT percentile performed similarly well upon internal and external testing. Data-driven models using non-invasive clinical features can accurately predict CSF a-syn SAA positive and negative status in cohorts enriched for people living with PD. Scores from the UPSIT were highly significant in predicting a-syn SAA status.

A standardized clinical database for research in Chagas disease: The NHEPACHA network.

The NHEPACHA Iberoamerican Network, founded on the initiative of a group of researchers from Latin American countries and Spain, aims to establish a research framework for Chagas disease that encompasses diagnosis and treatment. For this purpose, the network has created a questionnaire to gather relevant data on epidemiological, clinical, diagnostic, and therapeutic aspects of the disease. This questionnaire was developed based on a consensus of expert members of the network, with the intention of collecting high-quality standardized data, which can be used interchangeably by the different research centers that make up the NHEPACHA network. Furthermore, the network intends to offer a clinical protocol that can be embraced by other researchers, facilitating comparability among published studies, as well as the development of therapeutic response and progression markers.

Motor progression trajectories and risk of mild cognitive impairment in Parkinson's disease: A latent class trajectory model from PPMI cohort.

Rare studies have investigated the association between heterogeneity of motor progression and risk of early cognitive impairment in Parkinson's disease (PD). In this study, we aim to identify distinct trajectories of motor progression longitudinally and investigate their impact on predicting mild cognitive impairment (MCI). A 5-year cohort including 415 PD patients at baseline was collected from the Parkinson's Progression Markers Initiative. The severity of motor symptoms was evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale part III. The latent class trajectory model and nonlinear mixed-effects model were used to analyze and delineate the longitudinal changes in motor symptoms. Propensity score matching (PSM) was used to minimize the impact of potential confounders. Cox proportional hazard models were applied to calculate hazard ratios for MCI, and a Kaplan-Meier curve was generated using the occurrence of MCI during the follow-up as the time-to-event. Two latent trajectories were identified: a mild and remitting motor symptoms class (Class 1, 33.01%) and a severe and progressive motor symptom class (Class 2, 66.99%). Patients in Class 2 initially exhibited severe motor symptoms that worsened progressively despite receiving anti-PD medications. In comparison, patients in Class 1 exhibited milder symptoms that improved following drug therapy and a slower progression. During a 5-year follow-up, patients in Class 2 showed a higher risk of developing MCI compared to those in Class 1 before PSM (Log-Rank 28.58, p < 0.001) and after PSM (Log-Rank 8.20, p = 0.004). PD patients with severe and progressive motor symptoms are more likely to develop MCI than those with mild and stable motor symptoms.

Genetic influence on microstructure integrity and motor progression in Parkinson's disease

BackgroundUp to 10 % of Parkinson's disease (PD) populations carry a genetic risk variant, which may not only increase one's chance of developing PD but also affect disease presentation and progression. We hypothesize motor impairment in genetic carriers of PD correlate to different patterns of microstructural changes over time. Design/methodsData were accessed from the Parkinson's Progression Markers Initiative (PPMI) project. Connectometry analyses were performed for GBA1+ PD, LRRK2+ PD, and sporadic PD correlating white matter structural changes, as measured by quantitative anisotropy (QA), with motor impairment, as measured by MDS-UPDRS III. ResultsThere was a negative correlation between QA and MDS-UPDRS III in all 3 cohorts at 48 months. In GBA1+ PD (n = 12), the white matter tracts identified were cortical and subcortical, while in the LRRK2+ PD (n = 18) and sporadic PD (n = 45) cohorts white tracts identified were primarily subcortical and within the brainstem. ConclusionsOur findings highlight the association between motor symptom progrerssion and structural connectivity in individuals with GBA1+ PD, LRRK2+ PD, and sporadic PD. Due to the small sample size, larger studies are needed in the future to confirm the findings.

Protection from oxygen–glucose deprivation by neurosteroid treatment in primary neurons and oligodendrocytes

Preterm birth results in an increased risk of neonatal brain injury and neurobehavioural disorders. Despite the seriousness of these adverse outcomes, there are currently no effective therapies to protect the vulnerable developing brain. We propose that neurosteroid replacement therapy may be a novel approach in reducing detrimental neurological outcomes following preterm birth. The use of guinea pig primary neuronal and oligodendrocyte cultures with relevance to late gestation allows insight into the mechanisms behind the effectiveness of these treatments. Primary neuronal and oligodendrocyte cultures were derived from fetal guinea pig frontal cortex brain tissue at gestational age 62 (GA62). Cell cultures were pre-treated with either etifoxine (5 µM) or zuranolone (1 µm) for 24 h prior to insult. Cells were then exposed to either oxygen–glucose deprivation (OGD; 0% O2 and no glucose DMEM; preterm birth insult) or sham (standard cell culture conditions; 25 mM DMEM) for 2 h. Lactate dehydrogenase assay (LDH) was performed following OGD as a measure of cytotoxicity. Relative mRNA expression of key neuronal and oligodendrocyte markers, as well as neuronal receptors and transporters, were quantified using high throughput (Fluidigm) RT-PCR. OGD significantly increased cellular cytotoxicity in both neurons and oligodendrocytes. Additionally, key neuronal marker mRNA expression was reduced following OGD, and oligodendrocytes displayed arrested mRNA expression of key markers of lineage progression. Treatment with etifoxine restored a number of parameters back to control levels, whereas treatment with zuranolone provided a robust improvement in all parameters examined. This study has demonstrated the neuroprotective potential of neurosteroid replacement therapy in a model of hypoxia related to preterm birth. Neuroprotection appears to be mediated through glutamate reduction and increased brain derived neurotrophic factor (BDNF). Future work is warranted in examining these treatments in vivo, with the overall aim to suppress preterm associated brain damage and reduce long term outcomes for affected offspring.

Pentraxin 3 as a marker of development and severity of stable coronary artery disease

PurposeInflammation plays a crucial role in the development of atherosclerotic plaques. Pentraxin 3 (PTX3) is produced at the site of inflammation and has been identified as a specific marker of atherosclerosis, vascular inflammation, and progression of the coronary artery disease (CAD).The aim of the study was to establish if PTX3 has potential relations with classical markers of cardiovascular risk, and if PTX3 may act as an independent risk factor of CAD occurrence and advancement. Materials and methodsThe study included 98 patients with stable CAD confirmed in coronary angiography (CAD group) (median age 65 interquartile range [IQR] 61–72 years; 72 ​% men). The control group consisted of 40 patients without CAD. ResultsThe CAD group had significantly higher PTX3 concentration compared to the control group. There was a correlation with age, male gender, lipid profile and intima-media thickness. There was no correlation between PTX3 concentration and the number of coronary vessels with significant atherosclerotic lesions and the advancement of atherosclerotic lesions on the Gensini scoring scale. The cut-off point was determined for 0.89 ​ng/ml for the exclusion of angiographically significant atherosclerotic lesions. ConclusionsPatients with CAD have significantly higher concentration of PTX3. There was no correlation between PTX3 and the advancement of angiographically significant atherosclerotic lesions in coronary arteries. Low PTX3 concentration may serve as an indicator for the absence of atherosclerosis.

Leveraging Hypotension Prediction Index to Forecast LPS-Induced Acute Lung Injury and Inflammation in a Porcine Model: Exploring the Role of Hypoxia-Inducible Factor in Circulatory Shock.

Acute respiratory distress syndrome (ARDS) is a critical illness in critically unwell patients, characterized by refractory hypoxemia and shock. This study evaluates an early detection tool and investigates the relationship between hypoxia and circulatory shock in ARDS, to improve diagnostic precision and therapy customization. We used a porcine model, inducing ARDS with mechanical ventilation and intratracheal plus intravenous lipopolysaccharide (LPS) injection. Hemodynamic changes were monitored using an Acumen IQ sensor and a ForeSight Elite sensor connected to the HemoSphere platform. We evaluated tissue damage, inflammatory response, and hypoxia-inducible factor (HIF) alterations using enzyme-linked immunosorbent assay and immunohistochemistry. The results showed severe hypotension and increased heart rates post-LPS exposure, with a notable rise in the hypotension prediction index (HPI) during acute lung injury (p = 0.024). Tissue oxygen saturation dropped considerably in the right brain region. Interestingly, post-injury HIF-2α levels were lower at the end of the experiment. Our findings imply that the HPI can effectively predict ARDS-related hypotension. HIF expression levels may serve as possible markers of rapid ARDS progression. Further research should be conducted on the clinical value of this novel approach in critical care, as well as the relationship between the HIF pathway and ARDS-associated hypotension.

Gender variability in machine learning based subcortical neuroimaging for Parkinson’s disease diagnosis

PurposeThis study evaluates machine learning (ML) classifiers for diagnosing Parkinson’s disease (PD) using subcortical brain region data from 3D T1 magnetic resonance imaging (MRI) Parkinson’s Progression Markers Initiative (PPMI database). We aim to identify top-performing algorithms and assess gender-related differences in accuracy.Design/methodology/approachMultiple ML algorithms will be compared for their ability to classify PD vs healthy controls using MRI scans of the brain structures like the putamen, thalamus, brainstem, accumbens, amygdala, caudate, hippocampus and pallidum. Analysis will include gender-specific performance comparisons.FindingsThe study reveals that ML classifier performance in diagnosing PD varies across subcortical brain regions and shows gender differences. The Extra Trees classifier performed best in men (86.36% accuracy in the putamen), while Naive Bayes performed best in women (69.23%, amygdala). Regions like the accumbens, hippocampus and caudate showed moderate accuracy (65–70%) in men and poor performance in women. The results point out a significant gender-based performance gap, highlighting the need for gender-specific models to improve diagnostic precision across complex brain structures.Originality/valueThis study highlights the significant impact of gender on machine learning diagnosis of PD using data from subcortical brain regions. Our novel focus on these regions uncovers their diagnostic potential, improves model accuracy and emphasizes the need for gender-specific approaches in medical AI. This work could ultimately lead to earlier PD detection and more personalized treatment.

Decoding the muscle transcriptome of patients with late onset Pompe disease reveals markers of disease progression.

Late-onset Pompe Disease (LOPD) is a rare genetic disorder caused by the deficiency of acid alpha-glucosidase leading to progressive cellular dysfunction due to the accumulation of glycogen in the lysosome. The mechanism of relentless muscle damage - a classic manifestation of the disease - has been extensively studied by analysing the whole muscle tissue; however, little, if any, is known about transcriptional heterogeneity among nuclei within the multinucleated skeletal muscle cells. This is the first report of application of single nuclei RNA sequencing to uncover changes in the gene expression profile in muscle biopsies from eight patients with LOPD and four muscle samples from age and gender matched healthy controls. We matched these changes with histology findings using GeoMx Spatial Transcriptomics to compare the transcriptome of control myofibers from healthy individuals with non-vacuolated (histologically unaffected) and vacuolated (histologically affected) myofibers of LODP patients. We observed an increase in the proportion of slow and regenerative muscle fibers and macrophages in LOPD muscles. The expression of the genes involved in glycolysis was reduced, whereas the expression of the genes involved in the metabolism of lipids and amino acids was increased in non-vacuolated fibers, indicating early metabolic abnormalities. Additionally, we detected upregulation of autophagy genes, and downregulation of the genes involved in ribosomal and mitochondrial function leading to defective oxidative phosphorylation. The upregulation of the genes associated with inflammation, apoptosis and muscle regeneration was observed only in vacuolated fibers. Notably, enzyme replacement therapy - the only available therapy for the disease - showed a tendency to restore metabolism dysregulation, particularly within slow fibers. A combination of single nuclei RNA sequencing and spatial transcriptomics revealed the landscape of normal and the diseased muscle, and highlighted the early abnormalities associated with the disease progression. Thus, the application of these two new cutting-edge technologies provided insight into the molecular pathophysiology of muscle damage in LOPD and identified potential avenues for therapeutic intervention.

Utilising deep learning networks to classify ZEB2 expression images in cervical cancer.

Aims/Background Cervical cancer continues to be a significant cause of cancer-related deaths among women, especially in low-resource settings where screening and follow-up care are lacking. The transcription factor zinc finger E-box-binding homeobox 2 (ZEB2) has been identified as a potential marker for tumour aggressiveness and cancer progression in cervical cancer tissues. Methods This study presents a hybrid deep learning system developed to classify cervical cancer images based on ZEB2 expression. The system integrates multiple convolutional neural network models-EfficientNet, DenseNet, and InceptionNet-using ensemble voting. We utilised the gradient-weighted class activation mapping (Grad-CAM) visualisation technique to improve the interpretability of the decisions made by the convolutional neural networks. The dataset consisted of 649 annotated images, which were divided into training, validation, and testing sets. Results The hybrid model exhibited a high classification accuracy of 94.4% on the test set. The Grad-CAM visualisations offered insights into the model's decision-making process, emphasising the image regions crucial for classifying ZEB2 expression levels. Conclusion The proposed hybrid deep learning model presents an effective and interpretable method for the classification of cervical cancer based on ZEB2 expression. This approach holds the potential to substantially aid in early diagnosis, thereby potentially enhancing patient outcomes and mitigating healthcare costs. Future endeavours will concentrate on enhancing the model's accuracy and investigating its applicability to other cancer types.

The activities of daily living partially mediate the relationship between rapid eye movement sleep behavior disorder and depressive symptoms in Parkinson's disease.

A longitudinal study was conducted to investigate whether rapid eye movement sleep behavior disorder affect depression in patients with Parkinson's disease through activities of daily living. A total of 387 Parkinson's disease patients' six-year follow-up data (one follow-up per year) were obtained from the Parkinson's Progression Markers Initiative. To allow causal effects to manifest, this study increased the lag period and divided the data from the six follow-ups into two groups: wave 1 (wave refers to time points), wave 3, and wave 5 as one group, and wave 2, wave 4, and wave6 as the other group. The time interval between two time points in each group was two years. To comprehensively and deeply analyze the dynamic relationships between variables, accurately infer causal relationships, control for individual differences, and detect the stability of these relationships, this study constructed the fixed effects cross-lagged panel model (CLPM), the random effects CLPM (RE-CLPM) model, and the Equating CLPM and Equating RE-CLPM models with applied restriction conditions. Additionally, a reverse path was added to verify the reverse prediction effect. The most suitable data analysis model was selected to explore the relationships between the study variables. Furthermore, the longitudinal mediating effect of daily living activities between rapid eye movement sleep behavior disorder and depression was investigated. In the models, Equating cross-lagged panel model was the best. The lag effect was positive and significant. In wave 1, 3, 5, activities of daily living mediated 11.82% on the path from rapid eye movement sleep behavior disorder to depression; in wave 2, 4, 6, it mediated 13.13%. Therefore, attention should be paid to the treatment of activities of daily living. Longitudinal changes in activities of daily living have indirect effects on the relationship between rapid eye movement sleep behavior disorder and depression, which highlights the importance of changes in activities of daily living ability in Parkinson's disease patients with rapid eye movement sleep behavior disorder.

LRRK2-Associated Parkinsonism With and Without In Vivo Evidence of Alpha-Synuclein Aggregates.

Among LRRK2-associated parkinsonism cases with nigral degeneration, over two-thirds demonstrate evidence of pathologic alpha-synuclein, but many do not. Understanding the clinical phenotype and underlying biology in such individuals is critical for therapeutic development. Our objective was to compare clinical and biomarker features, and rate of progression over 4 years follow-up, among LRRK2-associated parkinsonism cases with and without in vivo evidence of alpha-synuclein aggregates. Data were from the Parkinson's Progression Markers Initiative, a multicenter prospective cohort study. The sample included individuals diagnosed with Parkinson disease with pathogenic variants in LRRK2. Presence of CSF alpha-synuclein aggregation was assessed with seed amplification assay. A range of clinician- and patient- reported outcome assessments were administered. Biomarkers included dopamine transporter SPECT scan, CSF amyloid-beta1-42, total tau, phospho-tau181, urine bis(monoacylglycerol)phosphate levels, and serum neurofilament light chain. Linear mixed effects models examined differences in trajectory in CSF negative and positive groups. 148 LRRK2-parkinsonism cases (86% with G2019S variant), 46 negative and 102 positive for CSF alpha-synuclein seed amplification assay were included. At baseline, the negative group were older than the positive group (median [interquartile range] 69.1 [65.2-72.3] vs 61.5 [55.6-66.9] years, p<0.001) and a greater proportion were female (28 (61%) vs 43 (42%), p=0.035). Despite being older, the negative group had similar duration since diagnosis, and similar motor rating scale (16 [11-23] vs 16 [10-22], p=0.480) though lower levodopa equivalents. Only 13 (29%) of the negative group were hyposmic, compared to 75 (77%) of the positive group. Lowest putamen dopamine transporter binding expected for age and sex was greater in the negative vs positive groups (0.36 [0.29-0.45] vs 0.26 [0.22-0.37], p<0.001). Serum neurofilament light chain was higher in the negative group compared to the positive group (17.10 [13.60-22.10] vs 10.50 [8.43-14.70]; age-adjusted p-value=0.013). In terms of longitudinal change, the negative group remained stable in functional rating scale score in contrast to the positive group who had a significant increase (worsening) of 0.729 per year (p=0.037), but no other differences in trajectory were found. Among individuals diagnosed with Parkinson disease with pathogenic variants in the LRRK2 gene, we found clinical and biomarker differences in cases without versus with in vivo evidence of CSF alpha-synuclein aggregates. LRRK2 parkinsonism cases without evidence of alpha-synuclein aggregates as a group exhibit less severe motor manifestations and decline may have more significant cognitive dysfunction. The underlying biology in LRRK2-parkinsonism cases without evidence of alpha-synuclein aggregates requires further investigation.

Plasma sphingolipids, dopaminergic degeneration and clinical progression in idiopathic Parkinson's disease

BackgroundSphingolipid dysregulation in Parkinson's disease (PD) may affect the release and uptake of striatal dopamine. However, the longitudinal relationship between sphingolipids, striatal dopaminergic degeneration, and clinical correlates in idiopathic PD (iPD) remain unclear. ObjectiveTo investigate the relationship between plasma sphingolipids, striatal dopamine transporter specific binding ratio (DAT-SBR) and clinical symptoms in iPD. MethodsWe included 283 iPD patients and 121 healthy controls (HC) from the Parkinson's Progression Markers Initiative (PPMI), utilizing available data on plasma sphingolipids (sphingomyelin [SM] and ceramide [CER]), striatal DAT-SBR and clinical assessments. Linear mixed models and mediation analyses were used to examine the relationship between sphingolipids, DAT-SBR, and clinical progression in iPD. ResultsLower baseline SM levels were significantly associated with a faster decline in DAT-SBR in both the caudate (p = 0.015) and putamen (p = 0.002), with the putamen association remaining significant after Bonferroni correction (p = 0.015). No significant association was found for CER. Patients in the lowest quartile of baseline SM showed faster progression in MDS-UPDRS I (p = 0.013) and II (p = 0.011), while those in the lowest quartile of baseline CER showed faster progression in MDS-UPDRS II (p = 0.013) and III (p = 0.033). The progression rate of caudate DAT-SBR partially mediated the relationships between SM and progression in MDS-UPDRS I and II (p < 0.01). ConclusionSphingolipids are associated with worse dopaminergic degeneration and potentially linked to faster progression in iPD, holding the promise for identifying individuals with faster progression in iPD.

Plasma Immune Biomarkers Predictive of Progression to Active Tuberculosis in Household Contacts of TB Patients.

The progression from Mycobacterium tuberculosis infection to active tuberculosis (TB) disease varies among individuals, and identifying biomarkers to predict progression is crucial for guiding interventions. In this study, we aimed to determine plasma immune biomarker profiles in healthy household contacts of index pulmonary TB (PTB) patients who either progressed to TB or remained as non-progressors. A cohort of household contacts of adults with PTB was enrolled, consisting of 15 contacts who progressed to TB disease and 15 non-progressors. Plasma samples were collected at baseline, 4 months, and 12 months to identify predictive TB progression markers. Our findings revealed that individuals in the progressor group exhibited significantly decreased levels of IFNγ, IL-2, TNFα, IL1α, IL1β, IL-17A, and IL-1Ra at baseline, months 4 and 12. In contrast, the progressor group displayed significantly elevated levels of IFNα, IFNβ, IL-6, IL-12, GM-CSF, IL-10, IL-33, CCL2, CCL11, CXCL8, CXCL10, CX3CL1, VEGF, Granzyme-B and PDL-1 compared to the non-progressor group at baseline, months 4 and 12. ROC analysis identified IFNγ, GM-CSF, IL-1Ra, CCL2 and CXCL10 as the most promising predictive markers, with an AUC of ≥90. Furthermore, combinatorial analysis demonstrated that GM-CSF, CXCL10 and IL-1Ra, when used in combination, exhibited high accuracy in predicting progression to active TB disease. Our study suggests that a specific set of plasma biomarkers GM-CSF, CXCL10 and IL-1Ra, can effectively identify household contacts at significant risk of developing TB disease. These findings have important implications for early intervention and preventive strategies in TB-endemic regions.

Neutrophil-to-lymphocyte ratio and longitudinal cognitive performance in Parkinson's disease.

Previous studies have suggested a link between peripheral inflammation and cognitive outcomes in the general population and individuals with Parkinson's disease (PD). We sought to test the association between peripheral inflammation, measured by the neutrophil-to-lymphocyte ratio (NLR), cognitive performance, and mild cognitive impairment (MCI) status in individuals with PD. A retrospective, longitudinal analysis was carried out using data from the Parkinson's Progression Markers Initiative (PPMI), including 422 participants with PD followed over 5 years. Cognitive performance was assessed using a neuropsychological battery including the Montreal Cognitive Assessment (MoCA) and tests of verbal learning, visuospatial function, processing speed, and executive function. Mixed-effect regression models were used to analyze the association between NLR, cognitive performance, and MCI status, controlling for age, sex, education, APOE genotype, and motor severity. There was a negative association between NLR and MoCA, even after adjusting for covariates (b = -0.12, p = 0.033). MoCA scores for individuals in the high NLR category exhibited a more rapid decline over time compared to the low NLR group (b = -0.16, p = 0.012). Increased NLR was associated with decreased performance across all cognitive domains. However, NLR was not associated with MCI status over 5 years of follow-up. This study demonstrates a link between elevated NLR and cognitive performance in PD, but not with MCI status over 5 years. This suggests that NLR is more strongly associated with day-to-day cognitive performance than with incident MCI, but this requires further study in more heterogeneous cohorts.