Novel immune biomarkers in complex regional pain syndrome

We investigated serum levels of 29 cytokines and immune-activated kynurenine and tetrahydrobiopterin pathway metabolites in 15 complex regional pain syndrome (CRPS) subjects and 14 healthy controls. Significant reductions in interleukin-37 and tryptophan were found in CRPS subjects, along with positive correlations between kynurenine/tryptophan ratio and TNF-α levels with kinesiophobia, tetrahydrobiopterin levels with McGill pain score, sRAGE, and xanthurenic acid and neopterin levels with depression, anxiety and stress scores. Using machine learning, we identified a set of binary variables, including IL-37 and GM-CSF, capable of distinguishing controls from established CRPS subjects. These results suggest possible involvement of various inflammatory markers in CRPS pathogenesis.

Ranking the risk factors for Alzheimer's disease; findings from the UK Biobank study

Background

The cause of the most common form of dementia, sporadic Alzheimer's disease (AD), remains unknown. This may reflect insufficiently powered studies to date for this multi-factorial disorder. The UK Biobank dataset presents a unique opportunity to rank known risk factors and determine novel variables.

Methods

A custom machine learning approach for high dimensionality data was applied to explore prospectively associations between AD in a sub-cohort of 156,209 UK Biobank participants aged 60-70 including more than 2,090 who were subsequently diagnosed with AD.

Results

After the possession of the APOE4 allele, the next highest ranked risk factors were other genetic variants within the TOMM40-APOE-APOC1 locus. When stratified by their apolipoprotein epsilon 4 (APOE4) carrier status, the most prominent risk factors in carriers were AST:ALT ratio, the "number of treatments/ medications" taken as well as "time spent in hospital" while protection was conferred by "Sleeplessness/Insomnia". In non-APOE carriers, lower socioeconomic status and fewer years of education were highly ranked but effect sizes were small relative to APOE4 carriers.

Conclusions

Possession of the APOE4 allele was confirmed as the most important risk factor in AD. Other TOMM40-APOE-APOC1 locus variants further moderate the risk of AD in APOE4 carriers. Liver pathology is a novel risk factor in APOE4 carriers while "Sleeplessness/Insomnia" is protective in AD irrespective of APOE4 status. Other factors such as "Number of treatments/ medications" suggest that multimorbidity is an important risk factor for AD. Future treatments aimed at co-morbidities, including liver disease, may concomitantly lower the risk of sporadic AD.

MACHINE LEARNING ANALYSIS OF THE UK BIOBANK REVEALS PROGNOSTIC AND DIAGNOSTIC IMMUNE BIOMARKERS FOR POLYNEUROPATHY AND NEUROPATHIC PAIN IN DIABETES

AIMS

We assessed the health data of 11,047 people with diabetes in the UK Biobank to rank 329 risk factors for diabetic polyneuropathy (DPN) and DPN with chronic neuropathic pain without a priori assumption.

METHODS

The Integrated Disease Explanation and Risk Scoring (IDEARS) platform applies machine learning algorithms to multimodal data to determine individual disease risk, and rank risk factor importance using mean SHapley Additive exPlanations (SHAP) score.

RESULTS

IDEARS models showed discriminative performances with AUC > 0.64. Lower socioeconomic status, being overweight, poor overall health, cystatin C, HbA1C, and immune activation marker, C-reactive protein (CRP), predict DPN risk. Neutrophils and monocytes were higher in males and lymphocytes lower in females with diabetes that develop DPN. Neutrophil-to-Lymphocyte Ratio (NLR) was increased and IGF-1 levels decreased in people with type 2 diabetes that later develop DPN. CRP was significantly elevated in those with DPN and chronic neuropathic pain compared to DPN without pain.

CONCLUSIONS

Lifestyle factors and blood biomarkers predict the later development of DPN and may relate to DPN pathomechanisms. Our results are consistent with DPN as a disease involving systemic inflammation. We advocate for the use of these biomarkers clinically to predict future DPN risk and improve early diagnosis.

Machine learning analysis of the UK Biobank reveals IGF-1 and inflammatory biomarkers predict Parkinson's disease risk

INTRODUCTION

Parkinson's disease (PD) is the most common movement disorder, and its prevalence is increasing rapidly worldwide with an ageing population. The UK Biobank is the world's largest and most comprehensive longitudinal study of ageing community volunteers. The cause of the common form of PD is multifactorial, but the degree of causal heterogeneity among patients or the relative importance of one risk factor over another is unclear. This is a major impediment to the discovery of disease-modifying therapies.

METHODS

We used an integrated machine learning algorithm (IDEARS) to explore the relative effects of 1,753 measured non-genetic variables in 334,062 eligible UK Biobank participants, including 2,719 who had developed PD since their recruitment into the study.

RESULTS

Male gender was the highest-ranked risk factor, followed by elevated serum insulin-like growth factor 1 (IGF-1), lymphocyte count, and neutrophil/lymphocyte ratio. A group of factors aligned with the symptoms of frailty also ranked highly. IGF-1 and neutrophil/lymphocyte ratio were also elevated in both sexes before PD diagnosis and at the point of diagnosis.

DISCUSSION

The use of machine learning with the UK Biobank provides the best opportunity to explore the multidimensional nature of PD. Our results suggest that novel risk biomarkers, including elevated IGF-1 and NLR, may play a role in, or are indicative of PD pathomechanisms. In particular, our results are consistent with PD being a central manifestation of a systemic inflammatory disease. These biomarkers may be used clinically to predict future PD risk, improve early diagnosis and provide new therapeutic avenues.

Self-organizing nervous systems for robot swarms

We present the self-organizing nervous system (SoNS), a robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multilevel system architectures. For example, a robot swarm consisting of n independent robots could transform into a single n-robot SoNS and then into several independent smaller SoNSs, where each SoNS uses a temporary and dynamic hierarchy. Leveraging the SoNS approach, we showed that sensing, actuation, and decision-making can be coordinated in a locally centralized way without sacrificing the benefits of scalability, flexibility, and fault tolerance, for which swarm robotics is usually studied. In several proof-of-concept robot missions-including binary decision-making and search and rescue-we demonstrated that the capabilities of the SoNS approach greatly advance the state of the art in swarm robotics. The missions were conducted with a real heterogeneous aerial-ground robot swarm, using a custom-developed quadrotor platform. We also demonstrated the scalability of the SoNS approach in swarms of up to 250 robots in a physics-based simulator and demonstrated several types of system fault tolerance in simulation and reality.

ReTimeML: a retention time predictor that supports the LC-MS/MS analysis of sphingolipids

The analysis of ceramide (Cer) and sphingomyelin (SM) lipid species using liquid chromatography-tandem mass spectrometry (LC-MS/MS) continues to present challenges as their precursor mass and fragmentation can correspond to multiple molecular arrangements. To address this constraint, we developed ReTimeML, a freeware that automates the expected retention times (RTs) for Cer and SM lipid profiles from complex chromatograms. ReTimeML works on the principle that LC-MS/MS experiments have pre-determined RTs from internal standards, calibrators or quality controls used throughout the analysis. Employed as reference RTs, ReTimeML subsequently extrapolates the RTs of unknowns using its machine-learned regression library of mass-to-charge (m/z) versus RT profiles, which does not require model retraining for adaptability on different LC-MS/MS pipelines. We validated ReTimeML RT estimations for various Cer and SM structures across different biologicals, tissues and LC-MS/MS setups, exhibiting a mean variance between 0.23 and 2.43% compared to user annotations. ReTimeML also aided the disambiguation of SM identities from isobar distributions in paired serum-cerebrospinal fluid from healthy volunteers, allowing us to identify a series of non-canonical SMs associated between the two biofluids comprised of a polyunsaturated structure that confers increased stability against catabolic clearance.