Plasma neurofilament light levels show elevation two years prior to diagnosis of amyotrophic lateral sclerosis in the UK Biobank.

OBJECTIVE: Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disease with profound unmet need. In patients carrying genetic mutations, elevations in neurofilament light (NfL) have been shown to precede symptom onset, however, the natural history of NfL in general ALS patients is less characterized. METHODS: We performed a secondary analysis of the UK Biobank Pharma Proteomics Project (UKB-PPP), a subset of the UK Biobank, a population-based cohort study in the United Kingdom, to examine plasma NfL levels in 237 participants subsequently diagnosed with ALS. We applied logistic and Cox proportional hazards regression to compare cases to 42,752 population-based and 948 age and sex-matched controls. Genetic information was obtained from exome and genotype array data.Results and Conclusions: We observed that NfL was 1.42-fold higher in cases vs population-based controls. At two to three years pre-diagnosis, NfL levels in patients exceeded the 95th percentile of age and sex-matched controls. A time-to-diagnosis analysis showed that a 2-fold increase in NfL levels was associated with a 3.4-fold risk of diagnosis per year, with NfL being most predictive of case status at two years (AUC = 0.96). Participants with genetic variation that might put them at risk for familial disease (N = 46) did not show a different pattern of association than those without (N = 191). DISCUSSION: Our findings show that NfL is elevated and discriminative of future ALS diagnosis up to two years prior to diagnosis in patients with and without genetic risk variants.

Zoish: A Novel Feature Selection Approach Leveraging Shapley Additive Values for Machine Learning Applications in Healthcare.

In the intricate landscape of healthcare analytics, effective feature selection is a prerequisite for generating robust predictive models, especially given the common challenges of sample sizes and potential biases. Zoish uniquely addresses these issues by employing Shapley additive values-an idea rooted in cooperative game theory-to enable both transparent and automated feature selection. Unlike existing tools, Zoish is versatile, designed to seamlessly integrate with an array of machine learning libraries including scikit-learn, XGBoost, CatBoost, and imbalanced-learn.The distinct advantage of Zoish lies in its dual algorithmic approach for calculating Shapley values, allowing it to efficiently manage both large and small datasets. This adaptability renders it exceptionally suitable for a wide spectrum of healthcare-related tasks. The tool also places a strong emphasis on interpretability, providing comprehensive visualizations for analyzed features. Its customizable settings offer users fine-grained control over feature selection, thus optimizing for specific predictive objectives.This manuscript elucidates the mathematical framework underpinning Zoish and how it uniquely combines local and global feature selection into a single, streamlined process. To validate Zoish's efficiency and adaptability, we present case studies in breast cancer prediction and Montreal Cognitive Assessment (MoCA) prediction in Parkinson's disease, along with evaluations on 300 synthetic datasets. These applications underscore Zoish's unparalleled performance in diverse healthcare contexts and against its counterparts.

Genetically-informed prediction of short-term Parkinson's disease progression.

Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.

Gene expression analysis in Fmr1KO mice identifies an immunological signature in brain tissue and mGluR5-related signaling in primary neuronal cultures.

BACKGROUND: Fragile X syndrome (FXS) is a neurodevelopmental disorder whose biochemical manifestations involve dysregulation of mGluR5-dependent pathways, which are widely modeled using cultured neurons. In vitro phenotypes in cultured neurons using standard morphological, functional, and chemical approaches have demonstrated considerable variability. Here, we study transcriptomes obtained in situ in the intact brain tissues of a murine model of FXS to see how they reflect the in vitro state. METHODS: We used genome-wide mRNA expression profiling as a robust characterization tool for studying differentially expressed pathways in fragile X mental retardation 1 (Fmr1) knockout (KO) and wild-type (WT) murine primary neuronal cultures and in embryonic hippocampal and cortical murine tissue. To study the developmental trajectory and to relate mouse model data to human data, we used an expression map of human development to plot murine differentially expressed genes in KO/WT cultures and brain. RESULTS: We found that transcriptomes from cell cultures showed a stronger signature of Fmr1KO than whole tissue transcriptomes. We observed an over-representation of immunological signaling pathways in embryonic Fmr1KO cortical and hippocampal tissues and over-represented mGluR5-downstream signaling pathways in Fmr1KO cortical and hippocampal primary cultures. Genes whose expression was up-regulated in Fmr1KO murine cultures tended to peak early in human development, whereas differentially expressed genes in embryonic cortical and hippocampal tissues clustered with genes expressed later in human development. CONCLUSIONS: The transcriptional profile in brain tissues primarily centered on immunological mechanisms, whereas the profiles from cell cultures showed defects in neuronal activity. We speculate that the isolation and culturing of neurons caused a shift in neurological transcriptome towards a "juvenile" or "de-differentiated" state. Moreover, cultured neurons lack the close coupling with glia that might be responsible for the immunological phenotype in the intact brain. Our results suggest that cultured cells may recapitulate an early phase of the disease, which is also less obscured with a consequent "immunological" phenotype and in vivo compensatory mechanisms observed in the embryonic brain. Together, these results suggest that the transcriptome of cultured primary neuronal cells, in comparison to whole brain tissue, more robustly demonstrated the difference between Fmr1KO and WT mice and might reveal a molecular phenotype, which is typically hidden by compensatory mechanisms present in vivo. Moreover, cultures might be useful for investigating the perturbed pathways in early human brain development and genes previously implicated in autism.

I148M variant in PNPLA3 reduces central adiposity and metabolic disease risks while increasing nonalcoholic fatty liver disease.

BACKGROUND & AIMS: The I148M variant because of the substitution of C to G in PNPLA3 (rs738409) is associated with the increased risk of nonalcoholic fatty liver disease (NAFLD). In liver, I148M variant reduces hydrolytic function of PNPLA3, which results in hepatic steatosis; however, its association with the other clinical phenotype such as adiposity and metabolic diseases is not well established. METHODS: To identify the impact of I148M variant on clinical risk factors of NAFLD, we recruited 1363 generally healthy Korean males after excluding alcoholic and secondary causes of hepatic steatosis. Central adiposity was assessed by computed tomography, and hepatic steatosis was evaluated by abdominal ultrasonography. RESULTS: The participants were predominantly middle-aged (49.0 ± 7.1 years; range 30-60 years), and the frequency of NAFLD was 44.2%. The rs738409-G allele carriers had a 1.19-fold increased risk for NAFLD (minor allele frequency 0.43; allelic odds ratio 1.38; P = 4.3 × 10(-5) ). Interestingly, the rs738409 GG carriers showed significantly lower levels of visceral and subcutaneous adiposity (P < 0.001 and = 0.015, respectively), BMI (P < 0.001), triglycerides (P < 0.001) and insulin resistance (P = 0.002) compared to CC carriers. These negative associations between clinical risk factors and rs738409-G dosage were more prominent in non-NAFLD group compared to those in NAFLD group. CONCLUSIONS: The I148M variant, although increasing the risk of NAFLD, was associated with reduced levels of central adiposity, BMI, serum triglycerides and insulin resistance, suggesting differential roles in fat storage and distribution according to cell types and metabolic status.

iPSC-derived neurons as a higher-throughput readout for autism: promises and pitfalls.

The elucidation of disease etiologies and establishment of robust, scalable, high-throughput screening assays for autism spectrum disorders (ASDs) have been impeded by both inaccessibility of disease-relevant neuronal tissue and the genetic heterogeneity of the disorder. Neuronal cells derived from induced pluripotent stem cells (iPSCs) from autism patients may circumvent these obstacles and serve as relevant cell models. To date, derived cells are characterized and screened by assessing their neuronal phenotypes. These characterizations are often etiology-specific or lack reproducibility and stability. In this review, we present an overview of efforts to study iPSC-derived neurons as a model for autism, and we explore the plausibility of gene expression profiling as a reproducible and stable disease marker.

Classification of infectious diseases based on chemiluminescent signatures of phagocytes in whole blood.

OBJECTIVES: Despite medical advances, infectious diseases are still a major cause of mortality and morbidity, disability and socio-economic upheaval worldwide. Early diagnosis, appropriate choice and immediate initiation of antibiotic therapy can greatly affect the outcome of any kind of infection. Phagocytes play a central role in the innate immune response of the organism to infection. They comprise the first-line of defense against infectious intruders in our body, being able to produce large quantities of reactive oxygen species, which can be detected by means of chemiluminescence (CL). The data preparation approach implemented in this work corresponds to a dynamic assessment of phagocytic respiratory burst localization in a luminol-enhanced whole blood CL system. We have previously applied this approach to the problem of identifying various intra-abdominal pathological processes afflicting peritoneal dialysis patients in the Nephrology department and demonstrated 84.6% predictive accuracy with the C4.5 decision-tree algorithm. In this study, we apply the CL-based approach to a larger sample of patients from two departments (Nephrology and Internal Medicine) with the aim of finding the most effective and interpretable feature sets and classification models for a fast and accurate identification of several infectious diseases. MATERIALS AND METHODS: Whole blood samples were collected from 78 patients (comprising 115 instances) with respiratory infections, infections associated with renal replacement therapy and patients without infections. CL kinetic parameters were calculated for each case, which was assigned into a specific clinical group according to the available clinical diagnostics. Feature selection wrapper and filter methods were applied to remove the irrelevant and redundant features and to improve the predictive performance of disease classification algorithms. Three data mining algorithms, C4.5 (J48) decision tree, support vector machines and naive Bayes classifier were applied for inducing disease classification models and their performance in classifying three clinical groups was evaluated by 10 runs of a stratified 10-fold cross-validation. RESULTS AND CONCLUSIONS: The results demonstrate that the predictive power of the best models obtained with the three evaluated algorithms after feature selection was found to be in the range of 63.38 ± 2.18-70.68 ± 1.43%. The highest disease classification accuracy was reached by C4.5, which also provides the most informative model in the form of a decision tree, and the lowest accuracy was obtained with naive Bayes. The feature selection method attaining the best classification performance was the wrapper method in forward direction. Moreover, the classification models exposed biological patterns specific to the clinical states and the predictive features selected were found to be characteristic of a specific disorder. Based on these encouraging results, we believe that the CL-based data pre-processing approach combined with the wrapper forward feature selection procedure and the C4.5 decision-tree algorithm has a clear potential to become a fast, informative, and sensitive tool for predictive diagnostics of infectious diseases in clinics.

Differentiation between viral and bacterial acute infections using chemiluminescent signatures of circulating phagocytes.

Oftentimes the etiological diagnostic differentiation between viral and bacterial infections is problematic, while clinical management decisions need to be made promptly upon admission. Thus, alternative rapid and sensitive diagnostic approaches need to be developed. Polymorphonuclear leukocytes (PMNs) or phagocytes act as major players in the defense response of the host during an episode of infection, and thereby undergo functional changes that differ according to the infections. PMNs functional activity can be characterized by quantification and localization of respiratory burst production and assessed by chemiluminescent (CL) byproduct reaction. We have assessed the functional states of PMNs of patients with acute infections in a luminol-amplified whole blood system using the component CL approach. In this study, blood was drawn from 69 patients with fever (>38 °C), and diagnosed as mainly viral or bacterial infections in origin. Data mining algorithms (C4.5, Support Vector Machines (SVM) and Naïve Bayes) were used to induce classification models to distinguish between clinical groups. The model with the best predictive accuracy was induced using C4.5 algorithm, resulting in 94.7% accuracy on the training set and 88.9% accuracy on the testing set. The method demonstrated a high predictive diagnostic value and may assist the clinician one day in the distinction between viral and bacterial infections and the choice of proper medication.

Dynamic component chemiluminescent sensor for assessing circulating polymorphonuclear leukocyte activity of peritoneal dialysis patients.

Recurrent bacterial peritonitis is a major complication in peritoneal dialysis (PD) patients, which is associated with polymorphonuclear leukocyte (PMN) functional changes and can be assessed by a chemiluminescent (CL) reaction. We applied a new approach of a dynamic component chemiluminescence sensor for the assessment of functional states of PMNs in a luminol-amplified whole-blood system. This method is based on the evaluation of CL kinetic patterns of stimulated PMNs, while the parallel measurements of intracellular and extracellular production of reactive oxygen species (ROS) from the same sample can be conducted. Blood was drawn from diabetic and nondiabetic patients during follow-up, and during peritonitis. Healthy medical personnel served as the control group. Chemiluminescence curves were recorded and presented as a sum of three biological components. CL kinetic parameters were calculated, and functional states of PMNs were assessed. Data mining algorithms were used to build decision tree models that can distinguish between different clinical groups. The induced classification models were used afterward for differentiating and classifying new blind cases and demonstrated good correlation with medical diagnosis (84.6% predictive accuracy). In conclusion, this novel method shows a high predictive diagnostic value and may assist in detection of PD-associated clinical states.