Demonstrating the reliability of in vivo metabolomics based chemical grouping: towards best practice

While grouping/read-across is widely used to fill data gaps, chemical registration dossiers are often rejected due to weak category justifications based on structural similarity only. Metabolomics provides a route to robust chemical categories via evidence of shared molecular effects across source and target substances. To gain international acceptance, this approach must demonstrate high reliability, and best-practice guidance is required. The MetAbolomics ring Trial for CHemical groupING (MATCHING), comprising six industrial, government and academic ring-trial partners, evaluated inter-laboratory reproducibility and worked towards best-practice. An independent team selected eight substances (WY-14643, 4-chloro-3-nitroaniline, 17α-methyl-testosterone, trenbolone, aniline, dichlorprop-p, 2-chloroaniline, fenofibrate); ring-trial partners were blinded to their identities and modes-of-action. Plasma samples were derived from 28-day rat tests (two doses per substance), aliquoted, and distributed to partners. Each partner applied their preferred liquid chromatography-mass spectrometry (LC-MS) metabolomics workflows to acquire, process, quality assess, statistically analyze and report their grouping results to the European Chemicals Agency, to ensure the blinding conditions of the ring trial. Five of six partners, whose metabolomics datasets passed quality control, correctly identified the grouping of eight test substances into three categories, for both male and female rats. Strikingly, this was achieved even though a range of metabolomics approaches were used. Through assessing intrastudy quality-control samples, the sixth partner observed high technical variation and was unable to group the substances. By comparing workflows, we conclude that some heterogeneity in metabolomics methods is not detrimental to consistent grouping, and that assessing data quality prior to grouping is essential. We recommend development of international guidance for quality-control acceptance criteria. This study demonstrates the reliability of metabolomics for chemical grouping and works towards best-practice.

A pilot clinical study to estimate intracranial pressure utilising cerebral photoplethysmograms in traumatic brain injury patients

PURPOSE

In this research, a non-invasive intracranial pressure (nICP) optical sensor was developed and evaluated in a clinical pilot study. The technology relied on infrared light to probe brain tissue, using photodetectors to capture backscattered light modulated by vascular pulsations within the brain's vascular tissue. The underlying hypothesis was that changes in extramural arterial pressure could affect the morphology of recorded optical signals (photoplethysmograms, or PPGs), and analysing these signals with a custom algorithm could enable the non-invasive calculation of intracranial pressure (nICP).

METHODS

This pilot study was the first to evaluate the nICP probe alongside invasive ICP monitoring as a gold standard. nICP monitoring occurred in 40 patients undergoing invasive ICP monitoring, with data randomly split for machine learning. Quality PPG signals were extracted and analysed for time-based features. The study employed Bland-Altman analysis and ROC curve calculations to assess nICP accuracy compared to invasive ICP data.

RESULTS

Successful acquisition of cerebral PPG signals from traumatic brain injury (TBI) patients allowed for the development of a bagging tree model to estimate nICP non-invasively. The nICP estimation exhibited 95% limits of agreement of 3.8 mmHg with minimal bias and a correlation of 0.8254 with invasive ICP monitoring. ROC curve analysis showed strong diagnostic capability with 80% sensitivity and 89% specificity.

CONCLUSION

The clinical evaluation of this innovative optical nICP sensor revealed its ability to estimate ICP non-invasively with acceptable and clinically useful accuracy. This breakthrough opens the door to further technological refinement and larger-scale clinical studies in the future.

TRIAL REGISTRATION

NCT05632302, 11th November 2022, retrospectively registered.

Neuromuscular disease genetics in under-represented populations: increasing data diversity

Neuromuscular diseases (NMDs) affect ∼15 million people globally. In high income settings DNA-based diagnosis has transformed care pathways and led to gene-specific therapies. However, most affected families are in low-to-middle income countries (LMICs) with limited access to DNA-based diagnosis. Most (86%) published genetic data is derived from European ancestry. This marked genetic data inequality hampers understanding of genetic diversity and hinders accurate genetic diagnosis in all income settings. We developed a cloud-based transcontinental partnership to build diverse, deeply-phenotyped and genetically characterized cohorts to improve genetic architecture knowledge, and potentially advance diagnosis and clinical management. We connected 18 centres in Brazil, India, South Africa, Turkey, Zambia, Netherlands and the UK. We co-developed a cloud-based data solution and trained 17 international neurology fellows in clinical genomic data interpretation. Single gene and whole exome data were analysed via a bespoke bioinformatics pipeline and reviewed alongside clinical and phenotypic data in global webinars to inform genetic outcome decisions. We recruited 6001 participants in the first 43 months. Initial genetic analyses 'solved' or 'possibly solved' ∼56% probands overall. In-depth genetic data review of the four commonest clinical categories (limb girdle muscular dystrophy, inherited peripheral neuropathies, congenital myopathy/muscular dystrophies and Duchenne/Becker muscular dystrophy) delivered a ∼59% 'solved' and ∼13% 'possibly solved' outcome. Almost 29% of disease causing variants were novel, increasing diverse pathogenic variant knowledge. Unsolved participants represent a new discovery cohort. The dataset provides a large resource from under-represented populations for genetic and translational research. In conclusion, we established a remote transcontinental partnership to assess genetic architecture of NMDs across diverse populations. It supported DNA-based diagnosis, potentially enabling genetic counselling, care pathways and eligibility for gene-specific trials. Similar virtual partnerships could be adopted by other areas of global genomic neurological practice to reduce genetic data inequality and benefit patients globally.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

Dementia with Lewy Bodies Drug Therapies in Clinical Trials: Systematic Review up to 2022

INTRODUCTION

Reviews of randomized clinical trials (RCTs) in dementia with Lewy bodies (DLB) are essential for informing ongoing research efforts of symptomatic therapies and potentially disease-modifying therapies (DMTs).

METHODS

We performed a systematic review of all clinical trials conducted until September 27, 2022, by examining 3 international registries: ClinicalTrials.gov, the European Union Drug Regulating Authorities Clinical Trials Database, and the International Clinical Trials Registry Platform, to identify drugs in trials in DLB.

RESULTS

We found 25 agents in 40 trials assessing symptomatic treatments and DMTs for DLB: 7 phase 3, 31 phase 2, and 2 phase 1 trials. We found an active pipeline for drug development in DLB, with most ongoing clinical trials in phase 2. We identified a recent trend towards including participants at the prodromal stages, although more than half of active clinical trials will enroll mild to moderate dementia patients. Additionally, repurposed agents are frequently tested, representing 65% of clinical trials.

CONCLUSION

Current challenges in DLB clinical trials include the need for disease-specific outcome measures and biomarkers, and improving representation of global and diverse populations.

Neurocognitive trajectory and proteomic signature of inherited risk for Alzheimer's disease

For Alzheimer's disease-a leading cause of dementia and global morbidity-improved identification of presymptomatic high-risk individuals and identification of new circulating biomarkers are key public health needs. Here, we tested the hypothesis that a polygenic predictor of risk for Alzheimer's disease would identify a subset of the population with increased risk of clinically diagnosed dementia, subclinical neurocognitive dysfunction, and a differing circulating proteomic profile. Using summary association statistics from a recent genome-wide association study, we first developed a polygenic predictor of Alzheimer's disease comprised of 7.1 million common DNA variants. We noted a 7.3-fold (95% CI 4.8 to 11.0; p < 0.001) gradient in risk across deciles of the score among 288,289 middle-aged participants of the UK Biobank study. In cross-sectional analyses stratified by age, minimal differences in risk of Alzheimer's disease and performance on a digit recall test were present according to polygenic score decile at age 50 years, but significant gradients emerged by age 65. Similarly, among 30,541 participants of the Mass General Brigham Biobank, we again noted no significant differences in Alzheimer's disease diagnosis at younger ages across deciles of the score, but for those over 65 years we noted an odds ratio of 2.0 (95% CI 1.3 to 3.2; p = 0.002) in the top versus bottom decile of the polygenic score. To understand the proteomic signature of inherited risk, we performed aptamer-based profiling in 636 blood donors (mean age 43 years) with very high or low polygenic scores. In addition to the well-known apolipoprotein E biomarker, this analysis identified 27 additional proteins, several of which have known roles related to disease pathogenesis. Differences in protein concentrations were consistent even among the youngest subset of blood donors (mean age 33 years). Of these 28 proteins, 7 of the 8 proteins with concentrations available were similarly associated with the polygenic score in participants of the Multi-Ethnic Study of Atherosclerosis. These data highlight the potential for a DNA-based score to identify high-risk individuals during the prolonged presymptomatic phase of Alzheimer's disease and to enable biomarker discovery based on profiling of young individuals in the extremes of the score distribution.

Associations between maternal iron supplementation in pregnancy and offspring growth and cardiometabolic risk outcomes in infancy and childhood

It was previously observed that maternal iron supplementation in pregnancy was associated with increased offspring size and adiposity at birth, possibly mediated through increased risk of gestational diabetes. In this study we investigated potential long-term associations of maternal iron supplementation in pregnancy with offspring growth in infancy, and growth and cardiometabolic risk factors in mid-childhood to seek evidence of nutritional programming. Using a nested case-control format, markers of growth and adiposity were measured at 3, 12 and 24 months of age in 341 infants from the Cambridge Baby Growth Study whose mothers supplemented with iron in pregnancy and 222 infants whose mothers did not. Measures of growth, glucose tolerance (using a 30 minute 1.75 g glucose/kg body weight oral glucose tolerance test), insulin sensitivity (HOMA IR) and blood pressure were collected in 122 and 79 of these children, respectively, at around 9.5 years of age. In infancy adiposity-promoting associations with maternal iron supplementation in pregnancy were evident at 3 months of age (e.g. mean difference in skinfold thickness: β = +0.15 mm, p = 0.02, in n = 341 whose mothers supplemented versus 222 that did not; waist circumference: β = +0.7 cm, p = 0.04, in n = 159 and 78, respectively) but differences lessened after this time (e.g. 3–12 month change in mean difference in skinfold thickness: β = -0.2 mm, p = 0.03, in n = 272 and 178, respectively). At ~9.5 years of age children whose mothers supplemented with iron in pregnancy had lower mean arterial blood pressures (β = -1.0 mmHg, p = 0.03, in n = 119 and 78, respectively). There were no apparent differences in markers of growth or other cardiometabolic factors. These results suggest that most of the associations of maternal iron supplementation in pregnancy on growth and adiposity evident at birth disappear during infancy, but there may be some evidence of long-term nutritional programming of blood pressure in mid-childhood.