Association of serum neurofilament light with microglial activation in multiple sclerosis

Abstract

Background: Translocator protein (TSPO)-PET and neurofilament light (NfL) both report on brain pathology, but their potential association has not yet been studied in multiple sclerosis (MS) in vivo. We aimed to evaluate the association between serum NfL (sNfL) and TSPO-PET-measurable microglial activation in the brain of patients with MS.

Methods: Microglial activation was detected using PET and the TSPO-binding radioligand [¹¹C]PK11195. Distribution volume ratio (DVR) was used to evaluate specific [¹¹C]PK11195-binding. sNfL levels were measured using single molecule array (Simoa). The associations between [¹¹C]PK11195 DVR and sNfL were evaluated using correlation analyses and false discovery rate (FDR) corrected linear regression modelling.

Results: 44 patients with MS (40 relapsing-remitting and 4 secondary progressive) and 24 age-matched and sex-matched healthy controls were included. In the patient group with elevated brain [¹¹C]PK11195 DVR (n=19), increased sNfL associated with higher DVR in the lesion rim (estimate (95% CI) 0.49 (0.15 to 0.83), p(FDR)=0.04) and perilesional normal appearing white matter (0.48 (0.14 to 0.83), p(FDR)=0.04), and with a higher number and larger volume of TSPO-PET-detectable rim-active lesions defined by microglial activation at the plaque edge (0.46 (0.10 to 0.81), p(FDR)=0.04 and 0.50 (0.17 to 0.84), p(FDR)=0.04, respectively). Based on the multivariate stepwise linear regression model, the volume of rim-active lesions was the most relevant factor affecting sNfL.

Conclusions: Our demonstration of an association between microglial activation as measured by increased TSPO-PET signal, and elevated sNfL emphasises the significance of smouldering inflammation for progression-promoting pathology in MS and highlights the role of rim-active lesions in promoting neuroaxonal damage.

Keywords: Microglia; Multiple Sclerosis; Neurofilament light; PET; TSPO.

Figure 1

[¹¹C]PK11195 DVR values of patients with MS and HCs in the whole brain and normal appearing white matter. The [¹¹C]PK11195 DVR values reflecting innate immune cell activation were increased in the whole brain (A) and in the NAWM (B) of patients with MS compared with HCs. Wilcoxon rank-sum test was used for statistical analyses. In boxplots, the thick horizontal lines represent the medians, the boxes represent the IQRs and the end of the whiskers represent the minimum and maximum values, if outliers represented as black dots do not exist. DVR, distribution volume ratio; HC, healthy control; MS, multiple sclerosis; NAWM, normal appearing white matter.

Figure 2

Pearson correlations between serum NfL and [¹¹C]PK11195 DVR values of the cerebral white matter regions of interest. Among patients with MS having increased innate immune cell activation within the brain (n=19) increased sNfL levels correlated with higher [¹¹C]PK11195 DVR values in the whole NAWM (A), in T1 hypo lesions (B), at the lesion rim (C) and in the perilesional NAWM (D). Logarithm of sNfL was used in order to achieve normally distributed data. DVR, distribution vol ratio (represents specific binding of [¹¹C]PK11195); MS, multiple sclerosis; NAWM, normal appearing white matter; NfL, neurofilament light; r, Pearson correlation coefficient; sNfL, serum NfL.

Figure 3

Spearman correlations between serum NfL and number and volume of active lesions. Among brain DVR(high) patients increased sNfL levels correlated with higher number and larger volume of rim-active lesions (A, B). Similarly, increased NfL correlated with higher number and larger volume of overall-active lesions (C, D). DVR, distribution vol ratio (represents specific binding of [¹¹C]PK11195); NAWM, normal appearing white matter; NfL, neurofilament light; ρ, Spearman correlation coefficient; sNfL, serum NfL.

Figure 4

Forest plot illustrating the results of the linear regression modelling of sNfL with TSPO-PET related, demographical and clinical parameters. (A) Univariate linear regression models with continuous variables. The p values of univariate models were corrected using false discovery rate (FDR) method for the number of investigated variables (n=21). After FDR correction serum NfL associated with most of the studied TSPO-PET-related parameters, that is, perilesional NAWM and lesion rim DVRs, number of rim-active and overall-active lesions, and volume of rim-active lesions. Of the nine studied demographical or clinical parameters, NfL associated with T1 lesion load. *Denotes associations that remained significant in FDR correction. (B) Univariate linear regression models with categorical variables. NfL did not associate with DMT status at the time of study onset, sex or MS disease type. (C) Multivariate stepwise linear regression model. The model building started with a model without any predictors, and in each step the most suitable variable according to Bayesian information criterion was added to the model. All variables that were used in univariate models were considered, when multivariate model was build. The final model included the volume of rim-active lesions and DMT status. Results among brain DVR(high) patients (n=19) are shown. Dots represent standardised regression coefficients and lines represent the confidence intervals of these estimates. Logarithm of NfL was used as a response in all models as non-transformed values led to non-normality of residuals. Similarly, in model-building, logarithm of lesion numbers and lesion volumes were used as predictors. To each value representing the number of lesions, +1 was added and to each value representing the lesion volume +0.01 was added before logarithmic transformation to avoid a logarithm of 0, which cannot be calculated. ARR, annualised relapse rate between disease and study onset; BMI, body mass index; DMT, disease-modifying treatment; DVR, distribution volume ratio (represents specific binding of [¹¹C]PK11195); EDSS, expanded disability status scale; GM, grey matter; MSSS, Multiple Sclerosis Status Scale; NAWM, normal appearing white matter; PET, positron emission tomography; RRMS, relapsing remitting multiple sclerosis; SPMS, secondary progressive multiple sclerosis; TPSO, translocator protein.