GDF15 is a cell activation and stress response cytokine of the glial cell line–derived neurotrophic factor family within the TGF-β superfamily. It acts through a recently identified orphan member of the GFRα family called GFRAL and signals through the Ret coreceptor. Cell stress and disease lead to elevated GDF15 serum levels, causing anorexia, weight loss, and alterations to metabolism, largely by actions on regions of the hindbrain. These changes restore homeostasis and, in the case of obesity, cause a reduction in adiposity. In some diseases, such as advanced cancer, serum GDF15 levels can rise by as much as 10–100-fold, leading to an anorexia-cachexia syndrome, which is often fatal. This review discusses how GDF15 regulates appetite and metabolism, the role it plays in resistance to obesity, and how this impacts diseases such as diabetes, nonalcoholic fatty liver disease, and anorexia-cachexia syndrome. It also discusses potential therapeutic applications of targeting the GDF15-GFRAL pathway and lastly suggests some potential unifying hypotheses for its biological role.

Background: Autoimmune encephalitis (AE) is an important cause of refractory epilepsy, rapidly progressive cognitive decline, and unexplained movement disorders in adults. Whilst there is identification of an increasing number of associated autoantibodies, patients remain with a high clinical probability of autoimmune encephalitis but no associated characterized autoantibody. These patients represent a diagnostic and treatment dilemma. Objective: To evaluate routine and novel diagnostic tests of cerebrospinal fluid (CSF) in patients with a high probability of AE to attempt to identify better biomarkers of neuroinflammation. Methods: Over 18 months (2016–2018), adult patients with a high clinical probability of AE were recruited for a pilot cross-sectional explorative study. We also included viral polymerase-chain-reaction (PCR) positive CSF samples and CSF from neurology patients with “non-inflammatory” (NI) diagnoses for comparison. CSF was examined with standard investigations for encephalitis and novel markers (CSF light chains, and cytokines). Results and Conclusions: Thirty-two AE patients were recruited over 18 months. Twenty-one viral controls, 10 NI controls, and five other autoimmune neurological disease controls (AOND) were also included in the analysis. Our study found that conventional markers: presence of CSF pleocytosis, oligoclonal bands, anti-neuronal immunofluorescence, and magnetic resonance imaging (MRI) changes could be suggestive of AE, but these investigations were neither sensitive nor specific. Promising novel makers of autoimmune encephalitis were the CSF cytokines IL-21 and IP10 which may provide better delineation between viral infections and autoimmune encephalitis than conventional markers, potentially leading to more immediate diagnosis and management of these patients.

Alzheimer’s disease (AD) is a growing global health crisis affecting millions and incurring substantial economic costs. However, clinical diagnosis remains challenging, with misdiagnoses and underdiagnoses being prevalent. There is an increased focus on putative, blood-based biomarkers that may be useful for the diagnosis as well as early detection of AD. In the present study, we used an unbiased combination of machine learning and functional network analyses to identify blood gene biomarker candidates in AD. Using supervised machine learning, we also determined whether these candidates were indeed unique to AD or whether they were indicative of other neurodegenerative diseases, such as Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Our analyses showed that genes involved in spliceosome assembly, RNA binding, transcription, protein synthesis, mitoribosomes, and NADH dehydrogenase were the best-performing genes for identifying AD patients relative to cognitively healthy controls. This transcriptomic signature, however, was not unique to AD, and subsequent machine learning showed that this signature could also predict PD and ALS relative to controls without neurodegenerative disease. Combined, our results suggest that mRNA from whole blood can indeed be used to screen for patients with neurodegeneration but may be less effective in diagnosing the specific neurodegenerative disease.

Spinal cord injury (SCI) has devastating consequences, with limited therapeutic options; therefore, improving its functional outcome is a major goal. The outcome of SCI is contributed to by neuroinflammation, which may be a target for improved recovery and quality of life after injury. Macrophage inhibitory cytokine-1/growth differentiation factor 15 (MIC-1/GDF15) has been identified as a potential novel therapy for central nervous system (CNS) injury because it is an immune regulatory cytokine with neurotrophic properties. Here we used MIC-1/GDF15 knockout (KO) and overexpressing/transgenic (Tg) and wild type (WT) animals to explore its putative therapeutic benefits in a mouse model of contusive SCI. MIC-1/GDF15 Tg mice had superior locomotor recovery and reduced secondary tissue loss at 28 days compared with their KO and WT counterparts. Overexpression of MIC-1/GDF15 coincided with increased expression of monocyte chemoattractant protein-1 (MCP-1)/C-C Motif Chemokine Ligand 2 (CCL2) at the lesion site (28 days post-SCI) and enhanced recruitment of inflammatory cells to the injured spinal cord. This inflammatory cellular infiltrate included an increased frequency of macrophages and dendritic cells (DCs) that mostly preceded recruitment of cluster of differentiation (CD)4⁺ and CD8⁺ T cells. Collectively, our findings suggest hat MIC-1/GDF15 is associated with beneficial changes in the clinical course of SCI that are characterized by altered post-injury inflammation and improved functional outcome. Further investigation of MIC-1/GDF15 as a novel therapeutic target for traumatic SCI appears warranted.

BACKGROUND: Checkpoint inhibitor–associated autoimmune diabetes mellitus (CIADM) is a rare but life-altering complication of immune checkpoint inhibitor (ICI) therapy. Biomarkers that predict type 1 diabetes (T1D) are unreliable for CIADM. AIM: In the present study, we sought to identify biomarkers for the prediction of CIADM. METHODS: From our prospective biobank, we identified 14 patients with CIADM who had metastatic melanoma treated with anti–programed antibody death 1 (anti–PD-1) with or without anti–cytotoxic T lymphocyte–associated antibody protein 4 (anti-CTLA4). Controls were selected from the same biobank, matched 2:1. Pretreatment, on-ICI, and post-CIADM serum and PBMCs were analyzed. Serum was analyzed for T1D autoantibodies, C-peptide, glucose, and cytokines. PBMCs were profiled using flow cytometry. Pancreatic volume was measured using CT volumetry. RESULTS: Before treatment, patients with CIADM had smaller pancreatic volume (27% reduction, P = 0.044) and higher anti–glutamic acid decarboxylase autoantibody (anti-GAD) titers (median 2.9 vs. 0, P = 0.01). They had significantly higher baseline proportions of Th17 cells ( P = 0.03), higher CD4 ⁺ central memory cells ( P = 0.04), and lower naive CD4 ⁺ T cells ( P = 0.01). With ICI treatment, greater declines in pancreatic volume were seen in patients with CIADM ( P < 0.0001). Activated CD4 ⁺ T cell subsets increased significantly in CIADM and controls with immune-related adverse effects (IRAEs) but not in controls without IRAEs. Using only pretreatment results, we found that pancreatic volume, anti-GAD antibody titers, and the baseline immune flow profile were highly predictive of CIADM development, with an AUC of greater than 0.96. CONCLUSIONS: People who develop CIADM are immunologically predisposed and have antecedent pancreatic and immunological changes that accurately predict disease with excellent sensitivity. These biomarkers could be used to guide ICI use, particularly when planning treatment for low-risk tumors. FUNDING: National Health and Medical Research Council (NHMRC) Investigator grants 2033228, 2009476, and 2007839.

Introduction The autoimmune encephalitides (AE) are a heterogeneous group of neurological disorders with significant morbidity and healthcare costs. Despite advancements in understanding their pathophysiology, uncertainties persist regarding long-term prognosis and optimal management. This study aims to address these gaps, focusing on immunotherapeutic strategies, neoplastic associations and functional outcomes. Methods and analysis The Retrospective Linkage Study of Autoimmune Encephalitis project will use data linkage techniques to establish a retrospective 10-year population cohort of Australian patients with AE. Two cohorts will be analysed, the Reference Cohort (clinically confirmed AE cases obtained from hospital medical records, n=145) and the Operationally Defined Cohort (AE cases identified through administrative coding data, n≈5000). Univariate statistical methods will identify candidate coding elements for use in the operational case definition and multivariate models and evaluation methods used to identify and internally validate the optimal coding algorithms. The two study cohorts will be analysed separately due to the high likelihood of overlap. Primary outcomes include relapse rate, prevalence and control of epilepsy, cognitive disability, poor educational attainment, delayed tumour diagnosis and mortality. Statistical analyses, including random mixed-effects regression models, will assess treatment effects, covariates and outcomes. Ethics and dissemination This project has been approved by the leading investigators’ institutional Human Research Ethics Committee (HREC), the St Vincent’s Hospital Melbourne HREC, as well as the Australian Institute of Health and Welfare HREC and relevant jurisdictional HRECs where required. The dissemination of findings through peer-reviewed publications and patient advocacy channels will maximise the impact of this research.