Evaluating the utility of serum NfL, GFAP, UCHL1 and tTAU as estimates of CSF levels and diagnostic instrument in neuroinflammation and multiple sclerosis.

BACKGROUND: This study aimed to evaluate the utility of neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), ubiquitin C-terminal hydrolase L1 (UCHL1) and total tau (tTAU) serum concentrations as approximation for cerebrospinal fluid (CSF) concentrations of the respective biomarkers in the context of neuroinflammation and multiple sclerosis (MS). METHODS: NfL, GFAP, UCHL1 and tTAU concentrations in serum and CSF were measured in 183 patients (122 with neuroinflammatory disease and 61 neurological or somatoform disease controls) using the single molecule array HD-1 analyzer (Quanterix, Boston, MA). Spearman's rank correlations were computed between serum and CSF concentrations. In a second step, the effects of age, BMI, gadolinium-enhancing lesions in MRI, integrity of the blood-brain barrier (BBB) and presence of acute relapse were accounted for by computing partial correlations. The analyses were repeated for a subsample consisting of MS phenotype patients only (n = 118). EDSS, MS disease activity and acute relapse were considered as additional covariates. Receiver operating characteristic (ROC) analysis was performed for each serum/CSF biomarker concentration to assess how well the particular biomarker concentration differentiates MS patients from somatoform disease controls. Correlations between serum and CSF levels as well as area under the curve (AUC) values were compared for the different biomarkers using z-test statistics. RESULTS: Serum concentrations correlated positively with CSF levels for NfL (r = 0.705, p < 0.01) as well as for GFAP (r = 0.259, p < 0.01). Correlation coefficients were significantly higher for NfL than for GFAP (z = 5.492, p < 0.01). We found no significant serum-CSF correlations for UCHL1 or tTAU. After adjusting for covariates, the results remained unchanged. In the analysis focusing only on MS patients, the results were replicated. ROC analysis demonstrated similarly acceptable performance of serum and CSF NfL values in differentiating MS phenotype patients from somatoform disease controls. AUC values were significantly higher for serum and CSF NfL compared to other biomarkers. CONCLUSION: NfL and GFAP but not UCHL1 or tTAU serum concentrations are associated with CSF levels of the respective biomarker. NfL exhibits more robust correlations between its serum and CSF concentrations as compared to GFAP independently from BBB integrity, clinical and radiological covariates. Both serum and CSF NfL values differentiate between MS and controls.

Matching proposed clinical and MRI criteria of aggressive multiple sclerosis to serum and cerebrospinal fluid markers of neuroaxonal and glial injury.

BACKGROUND: Definitions of aggressive MS employ clinical and MR imaging criteria to identify highly active, rapidly progressing disease courses. However, the degree of overlap between clinical and radiological parameters and biochemical markers of CNS injury is not fully understood. Aim of this cross-sectional study was to match clinical and MR imaging hallmarks of aggressive MS to serum/CSF markers of neuroaxonal and astroglial injury (neurofilament light chain (sNfL, cNfL), and glial fibrillary acidic protein (sGFAP, cGFAP)). METHODS: We recruited 77 patients with relapsing-remitting MS (RRMS) and 22 patients with clinically isolated syndrome. NfL and GFAP levels in serum and CSF were assessed using a single-molecule-array HD-1-analyzer. A general linear model with each biomarker as a dependent variable was computed. Clinical and imaging criteria of aggressive MS, as recently proposed by the ECTRIMS Consensus Group, were modeled as independent variables. Other demographic, clinical or laboratory parameters, were modeled as covariates. Analyses were repeated in a homogenous subgroup, consisting only of newly diagnosed, treatment-naïve RRMS patients presenting with an acute relapse. RESULTS: After adjusting for covariates and multiplicity of testing, sNfL and cNfL concentrations were strongly associated with the presence of ≥2 gadolinium-enhancing lesions (psNfL = 0.00008; pcNfL = 0.004) as well as the presence of infratentorial lesions on MRI (psNfL = 0.0003; pcNfL < 0.004). No other clinical and imaging criteria of aggressive MS correlated significantly with NfL or GFAP in serum and CSF. In the more homogeneous subgroup, sNfL still was associated with the presence of ≥2 gadolinium-enhancing lesions (psNfL = 0.001), presence of more than 20 T2-lesions (psNfL = 0.049) as well as the presence of infratentorial lesions on MRI (psNfL = 0.034), while cNfL was associated with the presence of ≥2 gadolinium-enhancing lesions (psNfL = 0.011) and presence of more than 20 T2-lesions (psNfL = 0.029). CONCLUSIONS: Among proposed risk factors for an aggressive disease course, MRI findings but not clinical characteristics correlated with sNfL and cNfL as a marker of neuroaxonal injury and should be given appropriate weight considering MS prognosis and therapy. No significant correlation was detected for GFAP alone.

Effect of Estimated Blood Volume and Body Mass Index on GFAP and NfL Levels in the Serum and CSF of Patients With Multiple Sclerosis.

BACKGROUND AND OBJECTIVES: To increase the validity of biomarker measures in multiple sclerosis (MS), factors affecting their concentration need to be identified. Here, we test whether the volume of distribution approximated by the patients' estimated blood volume (BV) and body mass index (BMI) affect the serum concentrations of glial fibrillary acidic protein (GFAP). As a control, we also determine the relationship between BV/BMI and GFAP concentrations in CSF. To confirm earlier findings, we test the same hypotheses for neurofilament light chain (NfL). METHODS: NfL and GFAP concentrations were measured in serum and CSF (sNFL/sGFAP and cNFL/cGFAP) in 157 patients (n = 106 with MS phenotype and n = 51 with other neurologic/somatoform diseases). Using multivariate linear regressions, BV was tested in the MS cohort as a predictor for each of the biomarkers while controlling for age, sex, MS phenotype, Expanded Disability Status Scale score, gadolinium-enhancing lesions, and acute relapse. In addition, overweight/obese patients (BMI ≥25 kg/m2) were compared with patients with BMI <25 kg/m2 using the general linear model. The analyses were repeated including the neurologic/somatoform controls. RESULTS: In the MS cohort, BV predicted sGFAP (ß = -0.301, p = 0.014). Overweight/obese patients with MS had lower sGFAP concentrations compared with patients with MS and BMI <25 kg/m2 (F = 4.732, p = 0.032). Repeating the analysis after adding patients with other neurologic/somatoform diseases did not change these findings (ß = -0.276, p = 0.009; F = 7.631, p = 0.006). Although sNfL was inversely correlated with BV (r = -0.275, p = 0.006) and body weight (r = -0.258, p = 0.010), those results did not remain significant after adjusting for covariates. BV and BMI were not associated with cGFAP or cNfL concentrations. DISCUSSION: These findings support the notion that the volume of distribution of sGFAP approximated by BV and BMI is a relevant variable and should therefore be controlled for when measuring sGFAP in MS, while this might not be necessary when measuring cGFAP concentrations.

Serum and cerebrospinal fluid BDNF concentrations are associated with neurological and cognitive improvement in multiple sclerosis: A pilot study.

BACKGROUND: Biomarkers of disease activity have been intensively studied in multiple sclerosis (MS) but knowledge on predictors of disability improvement is limited. The aim of this pilot study was to explore whether increased brain-derived neurotrophic factor concentrations in serum and CSF (sBDNF/cBDNF) precede neurological and cognitive improvement in MS. METHODS: In this pilot, monocentric prospective cohort study we collected serum/CSF samples at baseline together with EDSS (n = 36) and cognitive testing (n = 34) in patients with relapsing-remitting/primary progressive MS or clinically isolated syndrome. BDNF was assessed in serum and CSF with a single molecule array (SIMOA) HD-1 analyser (Quanterix). Twelve months later EDSS and cognitive testing were repeated. BDNF concentrations of patients with vs. without disability or cognitive improvement (disability improvement: decrease in EDSS ≥ 0.5; cognitive improvement: average z-score increase in neuropsychological performance ≥ 0.5) were compared using univariate ANOVAs adjusting for covariates. RESULTS: Compared to subjects without, patients with disability improvement had higher sBDNF at baseline (q = 0.04). Subjects with cognitive improvement had higher cBDNF at baseline than those without cognitive improvement (q = 0.004). Secondary analysis demonstrated significant correlations between sBDNF and EDSS change (q = 0.036), cBDNF and average z-score change (q = 0.04) and cBDNF and number of cognitive tests with improvement (q = 0.04), while controlling for covariates. CONCLUSIONS: Our findings suggest a possible role for BDNF in neurological and cognitive improvement in MS. These findings have to be confirmed in a larger sample but they already highlight the potential of BDNF as a biomarker for disability improvement and neuroplasticity in MS.

Brain-derived neurotrophic factor and neurofilament light chain in cerebrospinal fluid are inversely correlated with cognition in Multiple Sclerosis at the time of diagnosis.

BACKGROUND: Cognitive performance may be impaired in MS even at the earliest stages of disease. We tested whether brain-derived neurotrophic factor and neurofilament light chain levels in serum and cerebrospinal fluid (CSF) samples (sNfL/cNfL/sBDNF/cBDNF) collected at the time of diagnosis are associated with cognitive performance. METHODS: We measured sNfL/cNfL/sBDNF/cBDNF using single-molecule array (Simoa) in 47 newly diagnosed patients (32 relapsing-remitting MS/6 primary progressive MS/9 clinically isolated syndrome). Partial correlations between average z-score on neuropsychological tests and sNfL/sBDNF/cNfL/cBDNF were computed after adjusting for covariates. Multivariate analysis of covariance determined the effect of cognitive status on biomarker levels. A composite measure of NfL and BDNF was submitted to similar exploratory analysis. RESULTS: Cognitive performance correlated inversely with cNfL (r=-0.451/q=0.032) and cBDNF (r=-0.406/q=0.034). Impairment in at least two different tests was linked to higher cNfL (p=0.011) and cBDNF (p=0.035) levels compared to impairment in only one test and for cNfL also compared to no impairment at all (p=0.01). Composite CSF biomarker measure accounting for both cNfL and cBDNF correlated more strongly with tests of information processing (p=0.048) and verbal learning/memory consolidation (p = 0.02) as compared to the single CSF biomarkers. CONCLUSIONS: CSF BDNF and NfL levels measured at the time of diagnosis are inversely associated with cognitive performance in MS. Our findings suggest that CSF biomarkers linked to different pathophysiological processes reflect neuropsychological impairment in the earliest stages of the disease. Combining different CSF measures might facilitate the developing of a better biomarker of cognition in MS.

C-Reactive Protein Levels and Gadolinium-Enhancing Lesions Are Associated With the Degree of Depressive Symptoms in Newly Diagnosed Multiple Sclerosis.

Background: Inflammation is essential for the pathogenesis of multiple sclerosis (MS). While the immune system contribution to the development of neurological symptoms has been intensively studied, inflammatory biomarkers for mental symptoms such as depression are poorly understood in the context of MS. Here, we test if depression correlates with peripheral and central inflammation markers in MS patients as soon as the diagnosis is established. Methods: Forty-four patients were newly diagnosed with relapsing-remitting MS, primary progressive MS or clinically isolated syndrome. Age, gender, EDSS, C-reactive protein (CRP), albumin, white blood cells count in cerebrospinal fluid (CSF WBC), presence of gadolinium enhanced lesions (GE) on T1-weighted images and total number of typical MS lesion locations were included in linear regression models to predict Beck Depression Inventory (BDI) score and the depression dimension of the Symptoms Checklist 90-Revised (SCL90RD). Results: CRP elevation and GE predicted significantly BDI (CRP: p = 0.007; GE: p = 0.019) and SCL90RD (CRP: p = 0.004; GE: p = 0.049). The combination of both factors resulted in more pronounced depressive symptoms (p = 0.04). CSF WBC and EDSS as well as the other variables were not correlated with depressive symptoms. Conclusions: CRP elevation and GE are associated with depressive symptoms in newly diagnosed MS patients. These markers can be used to identify MS patients exhibiting a high risk for the development of depressive symptoms in early phases of the disease.