Cerebrospinal fluid complement system biomarkers in demyelinating disease.

BACKGROUND: Multiple sclerosis (MS) can be difficult to differentiate from other demyelinating diseases, notably neuromyelitis optica spectrum disorder (NMOSD). We previously showed that NMOSD is distinguished from MS by plasma complement biomarkers. OBJECTIVE: Here, we measure cerebrospinal fluid (CSF) complement proteins in MS, NMOSD and clinically isolated syndrome (CIS), a neurological episode that may presage MS, to test whether these distinguish NMOSD from MS and CIS. MATERIALS AND METHODS: CSF (53 MS, 17 CIS, 11 NMOSD, 35 controls) was obtained; complement proteins (C4, C3, C5, C9, C1, C1q, Factor B (FB)), regulators (Factor I (FI), Factor H (FH), FH-Related Proteins 1, 2 and 5 (FHR125), C1 Inhibitor (C1INH), Properdin) and activation products (terminal complement complex (TCC), iC3b) were quantified by ELISA and results expressed relative to CSF total protein (µg/mg). RESULTS: Compared to control CSF, (1) levels of C4, C1INH and Properdin were elevated in MS; (2) TCC, iC3b, FI and FHR125 were increased in CIS; and (3) all complement biomarkers except TCC, FHR125, Properdin and C5 were higher in NMOSD CSF. A statistical model comprising six analytes (C3, C9, FB, C1q, FI, Properdin) plus age/gender optimally differentiated MS from NMOSD.

Inflammatory biomarkers in Alzheimer's disease plasma.

INTRODUCTION: Plasma biomarkers for Alzheimer's disease (AD) diagnosis/stratification are a "Holy Grail" of AD research and intensively sought; however, there are no well-established plasma markers. METHODS: A hypothesis-led plasma biomarker search was conducted in the context of international multicenter studies. The discovery phase measured 53 inflammatory proteins in elderly control (CTL; 259), mild cognitive impairment (MCI; 199), and AD (262) subjects from AddNeuroMed. RESULTS: Ten analytes showed significant intergroup differences. Logistic regression identified five (FB, FH, sCR1, MCP-1, eotaxin-1) that, age/APOε4 adjusted, optimally differentiated AD and CTL (AUC: 0.79), and three (sCR1, MCP-1, eotaxin-1) that optimally differentiated AD and MCI (AUC: 0.74). These models replicated in an independent cohort (EMIF; AUC 0.81 and 0.67). Two analytes (FB, FH) plus age predicted MCI progression to AD (AUC: 0.71). DISCUSSION: Plasma markers of inflammation and complement dysregulation support diagnosis and outcome prediction in AD and MCI. Further replication is needed before clinical translation.

Complement system biomarkers in first episode psychosis.

Several lines of evidence implicate immunological/inflammatory factors in development of schizophrenia. Complement is a key driver of inflammation, and complement dysregulation causes pathology in many diseases. Here we explored whether complement dysregulation occurred in first episode psychosis (FEP) and whether this provides a source of biomarkers. Eleven complement analytes (C1q, C3, C4, C5, factor B [FB], terminal complement complex [TCC], factor H [FH], FH-related proteins [FHR125], Properdin, C1 inhibitor [C1inh], soluble complement receptor 1 [CR1]) plus C-reactive protein (CRP) were measured in serum from 136 first episode psychosis (FEP) cases and 42 mentally healthy controls using established in-house or commercial ELISA. The relationship between caseness and variables (analytes measured, sex, age, ethnicity, tobacco/cannabis smoking) was tested by multivariate logistic regression. When measured individually, only TCC was significantly different between FEP and controls (p=0.01). Stepwise selection demonstrated interdependence between some variables and revealed other variables that significantly and independently contributed to distinguishing cases and controls. The final model included demographics (sex, ethnicity, age, tobacco smoking) and a subset of analytes (C3, C4, C5, TCC, C1inh, FHR125, CR1). A receiver operating curve analysis combining these variables yielded an area under the curve of 0.79 for differentiating FEP from controls. This model was confirmed by multiple replications using randomly selected sample subsets. The data suggest that complement dysregulation occurs in FEP, supporting an underlying immune/inflammatory component to the disorder. Classification of FEP cases according to biological variables rather than symptoms would help stratify cases to identify those that might most benefit from therapeutic modification of the inflammatory response.

Complement system biomarkers in epilepsy.

PURPOSE: To explore whether complement dysregulation occurs in a routinely recruited clinical cohort of epilepsy patients, and whether complement biomarkers have potential to be used as markers of disease severity and seizure control. METHODS: Plasma samples from 157 epilepsy cases (106 with focal seizures, 46 generalised seizures, 5 unclassified) and 54 controls were analysed. Concentrations of 10 complement analytes (C1q, C3, C4, factor B [FB], terminal complement complex [TCC], iC3b, factor H [FH], Clusterin [Clu], Properdin, C1 Inhibitor [C1Inh] plus C-reactive protein [CRP]) were measured using enzyme linked immunosorbent assay (ELISA). Univariate and multivariate statistical analysis were used to test whether combinations of complement analytes were predictive of epilepsy diagnoses and seizure occurrence. Correlation between number and type of anti-epileptic drugs (AED) and complement analytes was also performed. RESULTS: We found: CONCLUSION: This study adds to evidence implicating complement in pathogenesis of epilepsy and may allow the development of better therapeutics and prognostic markers in the future. Replication in a larger sample set is needed to validate the findings of the study.

Effects of freezer storage time on levels of complement biomarkers.

BACKGROUND: There is uncertainty regarding how stable complement analytes are during long-term storage at - 80 °C. As part of our work program we have measured 17 complement biomarkers (C1q, C1 inhibitor, C3, C3a, iC3b, C4, C5, C9, FB, FD, FH, FI, TCC, Bb, sCR1, sCR2, Clusterin) and the benchmark inflammatory marker C-reactive protein (CRP) in a large set of plasma samples (n = 720) that had been collected, processed and subsequently stored at - 80 °C over a period of 6.6-10.6 years, prior to laboratory analysis. The biomarkers were measured using solid-phase enzyme immunoassays with a combination of multiplex assays using the MesoScale Discovery Platform and single-plex enzyme-linked immunosorbent assays (ELISAs). As part of a post hoc analysis of extrinsic factors (co-variables) affecting the analyses we investigated the impact of freezer storage time on the values obtained for each complement analyte. RESULTS: With the exception of five analytes (C4, C9, sCR2, clusterin and CRP), storage time was significantly correlated with measured plasma concentrations. For ten analytes: C3, FI, FB, FD, C5, sCR1, C3a, iC3b, Bb and TCC, storage time was positively correlated with concentration and for three analytes: FH, C1q, and C1 inhibitor, storage time was negatively correlated with concentration. CONCLUSIONS: The results suggest that information on storage time should be regarded as an important co-variable and taken into consideration when analysing data to look for associations of complement biomarker levels and disease or other outcomes.

The Correlation between Inflammatory Biomarkers and Polygenic Risk Score in Alzheimer's Disease.

Plasma biomarkers to aid the early diagnosis of Alzheimer's disease (AD) or to monitor disease progression have long been sought and continue to be widely studied. Biomarkers that correlate with AD polygenic risk score, a measure of the polygenic architecture of the disease and highly predictive of AD status, would be excellent candidates. Therefore, we undertook a preliminary study to assess the association of plasma inflammatory biomarkers with an overall AD polygenic risk score as well as with an inflammation-specific AD polygenic risk score in a sample set of 93 AD cases. We measured five complement biomarkers [complement receptor 1 (CR1), clusterin, complement component 9 (C9), C1 inhibitor (C1inh), terminal complement complex (TCC)] and the benchmark inflammatory marker C-reactive protein (CRP). Plasma clusterin level showed an association with overall AD polygenic risk score, while clusterin, C1inh, and CRP levels each displayed some association with the inflammatory-specific AD polygenic risk score. The results suggest that elevated plasma levels of inflammatory biomarkers, including complement proteins, associate with polygenic risk scores in AD, further strengthening the link between genetic and biomarker disease predictors and indicating a potential role for these markers in disease prediction and patient stratification in AD.