Author Of 5 Presentations
P0053 - Correlation Between Spinal Fluid and Blood Levels of Neurofilament Light, GFAP, Tau, and UCHL1: Do We Need a Correction Factor? (ID 1942)
Abstract
Background
Plasma neurofilament light(pNFL) levels account for 30-60% of the variance in CSF neurofilament light(cNFL) levels depending on the study. Age, disability, relapses, and the presence of contrast enhancing MRI lesions can increase both pNFL and cNFL. Additional nervous system biomarkers can now be studied in plasma. Understanding the factors that increase their variability in blood may be helpful in normalizing levels to better understand what levels are concerning for ongoing disease activity.
Objectives
To evaluate factors contributing to blood and cerebrospinal fluid(CSF) discordance and determine if a correction of blood levels can better estimate what is happening in the CSF compartment.
Methods
Matched plasma and CSF samples were identified in the Rocky Mountain Multiple Sclerosis Center Biorepository at the University of Colorado. Neurofilament Light(NFL), Glial Fibrillary Acidic Protein(GFAP), tau, and Ubiquitin carboxy-terminal hydrolase L1(UCHL1) levels were assessed using Single Molecule Array(SIMOA) in a Quanterix SR-X machine. Analyses were done on log transformed NFL concentrations.
Results
Fifty-seven patients had matched plasma and cerebrospinal fluid samples evaluated for neurofilament light which included 24 patients with multiple sclerosis(MS), 7 with neuromyelitis optica spectrum disorder(NMOSD), and 18 patients with headache whose opening pressures were <20cmH2O. These patients had a mean age of 46.5(+/-11.2) years, 75% female, mean albumin index of 6.3(+/-5.5), and BMI of 27.4(+/-5.8). The CSF and plasma concentrations in pg/ml were for NFL 1059.3(+/-3052.4) and 12.2(+/-32.4), GFAP 7621.5(+/-9713.4) and 52.9(+/-39.7), tau 41.5(+/-41.3) and 1.3(+/-0.8), UCHL1 1356.0(+/-1677.1) and 23.6(+/-32.8). Respectively the CSF vs plasma Spearman correlations (95% confidence intervals, p values) were: 0.79(0.67-0.87,<0.0001), 0.67(0.50-0.79,<0.0001), 0.75(0.61-0.85,<0.0001), and 0.70(0.54-0.81,<0.0001). Adjusting individually for age, BMI, or albumin index did not affect the correlation for NFL.
Conclusions
Blood and CSF levels of NFL, GFAP, tau, and UCHL1 correlated well. Models will be created that explore the relationship between Blood and CSF levels of these biomarkers.
P0061 - Determining the effect of blood anticoagulants on the detection level of neurobiomarkers in headache and control patients on the SIMOA platform (ID 1833)
Abstract
Background
In the development of the sensitive single molecule array (SIMOA) technology, serum and blood plasma from EDTA collection tubes were used to verify and validate blood levels of NFL, GFAP, Tau and UCHL1 across healthy controls[1]. As the need arises to establish extensive baseline levels of these neurobiomarkers in order to better evaluate neurodegeneration in a wide variety of patients including multiple sclerosis (MS) patients, a validation study across a variety of blood collection anticoagulants is necessary to ensure there are no significant differences in blood levels due to the additives themselves.
Objectives
To determine whether SIMOA results are reliable and comparable within patients and between cohorts, we measured blood levels of the neurobiomarkers neurofilament (NFL), glial fibrillary acidic protein (GFAP), tau and ubiquitin C-terminal hydrolase L1 (UCHL1) in samples collected with different blood collection additives.
Methods
Serum and blood plasma were collected voluntarily from either headache or healthy control patients in one of four different blood collection tubes representing the 4 most common types of anticoagulant: none (serum), EDTA, sodium heparin (hep) and sodium citrate (NaC). Plasma and serum were isolated from whole blood by centrifugation at 1500 x g for 20 minutes. Plasma was centrifuged at 400 x g for 10 minutes to further deplete cells. Biomarker levels of NFL, GFAP, Tau and UCHL1 were measured via SIMOA with the Neuro4Plex A Advantage kit in the Quanterix SR-X machine according to manufacturer instructions. Statistical comparisons were made using GraphPad Prism analysis software.
Results
No statistically significant differences in blood concentrations were found between the anticoagulants for the NFL, GFAP or UCHL1 biomarkers in either the headache patients or the healthy control patients. However, Tau levels were significantly lower in all serum samples (p value <0.0001) from both patient cohorts with average levels 65-80% lower than plasma.
Conclusions
Use of serum should be avoided when establishing baseline blood levels of the neurobiomarker Tau on the SIMOA platform.
P0111 - Monitoring Multiple Sclerosis Treatment with Plasma Biomarkers: NfL, GFAP, UCH-L1, and Tau (ID 909)
Abstract
Background
Blood neurofilament light (NfL) levels have been linked to multiple sclerosis (MS) activity and progression but are affected by factors such as age and body mass index (BMI). Less is known about what factors affect blood levels of Glial Fibrillary Acid Protein (GFAP), Ubiquitin Carboxy-Terminal Hydrolase L1 (UCH-L1), and Tau. Single Molecule Array (SIMOA) platforms allow multiplex measurement of these biomarkers with high sensitivity.
Objectives
To evaluate factors associated with higher levels of four plasma biomarkers—NfL, GFAP, UCH-L1, and Tau—in individuals with MS on immunotherapy.
Methods
Subjects with MS between 18-65 years taking dimethyl fumarate (n=40), fingolimod (n=37), natalizumab (n=47), or rituximab (n=90) for at least 1 year were identified from Rocky Mountain MS Center Biorepository. Neuro 4-plex A plasma assays were conducted on the Quanterix SR-X SIMOA platform. Biomarker concentrations were log transformed. For each biomarker, summary statistics were generated, and logistic regressions on the probability of having a level in the top quartile, adjusting for age, were performed with different explanatory variables including gender, BMI, disease duration, length on disease modifying therapy (DMT), DMT type, and MS subtype (relapsing MS [RMS] or progressive MS [PMS]). All statistics were generated in SAS.
Results
Included were 214 subjects (194 RMS, 20 PMS; 70.3% female; 86.9% Caucasian) with mean age of 44.1(SD 9.8). Mean disease and treatment durations were 150.7(SD 95.7) and 49.7(SD 33.5) months, respectively. Means (IQR) for NfL, GFAP, UCH-L1 and Tau were 6.6(3.9-7.1), 66.9(45.5-81.4), 11.5(7.1-13.8), and 1.2(0.8-1.5) pg/ml, respectively. NfL, GFAP, and UCH-L1 increased with age. (Remainder of results are given age-adjusted.) PMS was more likely than RMS to be in the top quartile for NfL (OR 3.5,p=0.01), GFAP (OR 2.6,p=0.06), and UCH-L1 (OR 2.8,p=0.04). Longer disease duration (5 years) increased the likelihood of elevated NfL (OR 1.3,p=0.02) and elevated GFAP (OR 1.3,p=0.03). Higher BMI (5 units) decreased the likelihood of elevated NfL (OR 0.6,p=0.0007) and GFAP (OR 0.8,p=0.03) but increased the likelihood of having an elevated Tau (OR 1.4,p=0.002). Ethnicity and treatment duration had no effect, but men were more likely to have elevated UCH-L1 (OR 2.1,p=0.03) and lower Tau (OR 0.2,p=0.0004). Comparing DMTs, no biomarker differences were observed except subjects on rituximab and dimethyl fumarate were less likely to have elevated Tau.
Conclusions
Plasma NfL, GFAP, and UCH-L1 are promising biomarkers to differentiate relapsing from progressive MS. Age and BMI should be incorporated into biomarker models to determine normal thresholds. No differences were observed between treatments for NFL, GFAP, or UCH-L1, but subjects on dimethyl fumarate and rituximab were less likely to have elevated Tau. Lack of randomization or repeated measures limited comparative effectiveness analyses.
P0364 - Ocrelizumab real-world safety and effectiveness in the two years of treatment in multiple sclerosis. (ID 1855)
Abstract
Background
Ocrelizumab (OCR), used in the treatment of multiple sclerosis (MS), is a monoclonal antibody targeting CD20, resulting in B-cell depletion.
Objectives
To describe the patient two-year experience of MS patients treated with OCR at the Rocky Mountain MS Center at the University of Colorado.
Methods
94 randomly selected MS patients prescribed OCR prior to May 2018 at the Rocky Mountain MS Center at the University of Colorado were retrospectively followed for up to two years from OCR start date. Lab data, relapse history, adverse events, MRI outcomes, disease history and patient characteristics were collected. Descriptive statistics were used to describe the sample group.
Results
Patients had a mean age of 44.2 years at date of first infusion; were predominantly female (75.5%); and had a mean MS disease duration of 10.4 years. Of the sample group, 76 (80.9%), 16 (17.0%), and 2 (2.1%) were relapsing-remitting, secondary progressive, and primary progressive MS, respectively. Two (2.1%), 1 (1.2%), and 6 (7.4%) patients experienced a clinical relapse, enhancing lesion and new T2 lesion, respectively. Of 48 patients with available MRI data for re-baselining after initiation of OCR, 1 (2.1%) patient had a new T2 lesion. Twenty (21.3%) patients discontinued OCR at our center at <24 months. Nine patients were lost to follow-up or relocated care, 7 patients discontinued due to issues with insurance, 1 patient discontinued due to adverse events, specifically hypogammaglobulinemia, and 3 patients discontinued due to other reasons, such as family planning and concern for cancer. During the first and second infusion course, 19 (20.2%) and 7 (7.4%) experienced an infusion reaction that interrupted the OCR infusion, respectively, and none experienced a life-threatening reaction or were hospitalized. After initiating OCR, 3 patients were diagnosed with basal cell carcinoma. Infections resulting in an emergency department visit or hospitalization occurred in 11 (11.7%) and 1 (1.1%) patients, respectively. Eleven (11.7%) patients experienced lymphopenia ≤500/mm3, and 2 (2.1%) experienced neutropenia ≤1000/mm3. Seven (7.4%) patients experienced IgG levels ≤500, 25 (26.6%) experienced IgM levels ≤40.
Conclusions
Our data suggests OCR is safe and effective in the treatment of MS. Additional data on an increased sample size will be presented.
P1044 - Patient demographics and disease characteristics predict likelihood of clinical benefit on patient-reported outcome measures in multiple sclerosis (ID 278)
Abstract
Background
Multiple sclerosis (MS) treatment has shifted away from injectable agents, toward oral/infusible disease-modifying therapies (DMTs) that show greater efficacy in reducing disease activity. Clinical benefit has been observed in some patients on these high-efficacy DMTs, but factors that contribute to the likelihood of benefit are unknown.
Objectives
To assess the impact of patient demographics, MS disease characteristics, and brain volumes on likelihood of clinical benefit in patients treated with high-efficacy DMTs, as assessed by patient-reported outcome (PRO) measures.
Methods
This retrospective chart review included adults with MS who completed 2 Patient-Determined Disease Steps (PDDS) measures and at least 2/10 Neurology Quality of Life (NeuroQOL) Short Form scales across 2 time points ≥10 months apart, taking a high-efficacy DMT at baseline. Qualifying DMTs included fingolimod, dimethyl fumarate, natalizumab, rituximab, and ocrelizumab. We examined the influence of various demographics, disease characteristics, and normalized brain volumes on likelihood of clinical benefit. PRO measures included the PDDS and 10 NeuroQOL domains. Patients were grouped as Clinical Benefit vs. Clinical Worsening by change in PDDS score over time (clinically significant change = +/- 1 point). Clinical Benefit was defined as No Change or Improvement on PDDS. Influence of NeuroQOL baseline and change scores was also investigated. NeuroQuant MRI reports provided volumetric data. Statistical analyses used Spearman correlations and logistic regression.
Results
314 patients met inclusion criteria. Factors significantly predicting likelihood of clinical benefit included smoking history (Current v. Former: Odds Ratio (OR)=1.251, CI 5, 95=0.520, 3.008; Current v. Never: OR=2.332, CI 5, 95=1.017, 5.350; Former v. Never: OR=1.864, CI 5, 95=1.070, 3.249; p=.029), body mass index (Odds Ratio (OR)=1.049; CI 5, 95=1.009, 1.089; p=.015), and number of clinical relapses within the study period (OR=1.638; CI 5, 95=1.071, 2.505; p=.023). NeuroQOL scores significantly influencing likelihood of clinical benefit included baseline Fatigue (OR=1.043; CI 5, 95=1.014, 1.073; p=0.004), Sleep Disturbance (OR=1.045; CI 5, 95=1.014, 1.076; p=0.004), and Emotional and Behavioral Dyscontrol (OR=1.030; CI 5, 95=1.002, 1.058; p=0.033); and Social Participation change score (OR=0.918; CI 5, 95=0.876, 0.962; p<0.001).
Conclusions
Patient demographic and disease characteristics appear to better predict clinical benefit than brain volumes. As better baseline and follow-up functioning in several NeuroQOL domains appears to be associated with clinical benefit, clinicians who actively treat these symptoms may see enhanced patient outcomes.