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Neurodegeneration
Original research
Plasma inflammation for predicting phenotypic conversion and clinical progression of autosomal dominant frontotemporal lobar degeneration
  1. Breton M Asken1,2,
  2. Peter A Ljubenkov2,
  3. Adam M Staffaroni2,
  4. Kaitlin B Casaletto2,
  5. Lawren Vandevrede2,
  6. Yann Cobigo2,
  7. Julio C Rojas-Rodriguez2,
  8. Katherine P Rankin2,
  9. John Kornak3,
  10. Hilary Heuer2,
  11. Judy Shigenaga4,
  12. Brian S Appleby5,
  13. Andrea C Bozoki6,
  14. Kimiko Domoto-Reilly7,
  15. Nupur Ghoshal8,
  16. Edward Huey9,
  17. Irene Litvan10,
  18. Joseph C Masdeu11,
  19. Mario F Mendez12,
  20. Belen Pascual11,
  21. Peter Pressman13,
  22. Maria Carmela Tartaglia14,15,
  23. Walter Kremers16,
  24. Leah K Forsberg17,
  25. Brad F Boeve17,
  26. Adam L Boxer2,
  27. Howie J Rosen2,
  28. Joel H Kramer2
  29. ALLFTD Consortium Investigators
    1. 1 Department of Clinical and Health Psychology, 1Florida Alzheimer's Disease Research Center, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, Florida, USA
    2. 2 Department of Neurology, Weill Institute for Neurosciences, Memory and Aging Center, University of California, San Francisco, San Francisco, California, USA
    3. 3 Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
    4. 4 Department of Medicine, Veterans Affairs Health Care System, San Francisco, California, USA
    5. 5 Departments of Neurology, Psychiatry, and Pathology, Case Western Reserve, Cleveland, Ohio, USA
    6. 6 Department of Neurology, University of North Carolina, Chapel Hill, North Carolina, USA
    7. 7 Department of Neurology, University of Washington School of Medicine, Seattle, Washington, USA
    8. 8 Department of Neurology, Washington University, St. Louis, Missouri, USA
    9. 9 Departments of Psychiatry and Neurology, Columbia University, New York, New York, USA
    10. 10 Department of Neurology, University of California, San Diego, La Jolla, California, USA
    11. 11 Department of Neurology, Nantz National Alzheimer Center, Houston Methodist, Houston, Texas, USA
    12. 12 Department of Neurology, University of California, Los Angeles, Los Angeles, California, USA
    13. 13 Department of Neurology, University of Colorado Denver School of Medicine, Aurora, Colorado, USA
    14. 14 Tanz Centre for Research in Neurodegenerative Disease, University of Toronto, Toronto, Ontario, Canada
    15. 15 Canadian Sports Concussion Project, Toronto, Ontario, Canada
    16. 16 Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, Minnesota, USA
    17. 17 Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
    1. Correspondence to Dr Breton M Asken, Clinical and Health Psychology, University of Florida, Gainesville, Florida, USA; basken8{at}phhp.ufl.edu

    Abstract

    Background Measuring systemic inflammatory markers may improve clinical prognosis and help identify targetable pathways for treatment in patients with autosomal dominant forms of frontotemporal lobar degeneration (FTLD).

    Methods We measured plasma concentrations of IL-6, TNFα and YKL-40 in pathogenic variant carriers (MAPT, C9orf72, GRN) and non-carrier family members enrolled in the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration consortium. We evaluated associations between baseline plasma inflammation and rate of clinical and neuroimaging changes (linear mixed effects models with standardised (z) outcomes). We compared inflammation between asymptomatic carriers who remained clinically normal (‘asymptomatic non-converters’) and those who became symptomatic (‘asymptomatic converters’) using area under the curve analyses. Discrimination accuracy was compared with that of plasma neurofilament light chain (NfL).

    Results We studied 394 participants (non-carriers=143, C9orf72=117, GRN=62, MAPT=72). In MAPT, higher TNFα was associated with faster functional decline (B=0.12 (0.02, 0.22), p=0.02) and temporal lobe atrophy. In C9orf72, higher TNFα was associated with faster functional decline (B=0.09 (0.03, 0.16), p=0.006) and cognitive decline (B=−0.16 (−0.22, −0.10), p<0.001), while higher IL-6 was associated with faster functional decline (B=0.12 (0.03, 0.21), p=0.01). TNFα was higher in asymptomatic converters than non-converters (β=0.29 (0.09, 0.48), p=0.004) and improved discriminability compared with plasma NfL alone (ΔR2=0.16, p=0.007; NfL: OR=1.4 (1.03, 1.9), p=0.03; TNFα: OR=7.7 (1.7, 31.7), p=0.007).

    Conclusions Systemic proinflammatory protein measurement, particularly TNFα, may improve clinical prognosis in autosomal dominant FTLD pathogenic variant carriers who are not yet exhibiting severe impairment. Integrating TNFα with markers of neuronal dysfunction like NfL could optimise detection of impending symptom conversion in asymptomatic pathogenic variant carriers and may help personalise therapeutic approaches.

    • FRONTOTEMPORAL DEMENTIA
    • CLINICAL NEUROLOGY

    Data availability statement

    Data are available upon reasonable request. ALLFTD data are available upon reasonable request from qualified investigators (https://www.allftd.org/data).

    http://creativecommons.org/licenses/by-nc/4.0/

    This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

    https://doi.org/10.1136/jnnp-2022-330866

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      WHAT IS ALREADY KNOWN ON THIS TOPIC

      • Inflammation plays a key role in neurodegenerative pathophysiology, including frontotemporal lobar degeneration.

      WHAT THIS STUDY ADDS

      • Concentrations of plasma-based proinflammatory proteins such as TNFα relate to future clinical decline in patients with autosomal dominant forms of frontotemporal dementia. Inflammatory biomarkers may complement measures or neuronal or glial injury for optimising prognosis.

      HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

      • In frontotemporal dementia research, inflammatory proteins should be considered among blood biomarker panels. Pending additional studies, clinical treatments and interventions targeting inflammatory pathways may be beneficial for patients with frontotemporal dementia.

      Introduction

      Inflammation is a central component of neurodegenerative disease pathogenesis. Blood-based biomarkers offer easily obtainable, relatively non-invasive, and scalable measurement of systemic inflammation. Evaluating systemic inflammatory biomarkers in asymptomatic and mildly symptomatic disease stages may help improve prognosis and further characterise the role of peripheral immune activation in neurodegenerative disease. Individuals with autosomal dominant pathogenic variants causing frontotemporal lobar degeneration (FTLD) represent a unique model for studying whether systemic inflammatory biomarkers have clinical utility.

      FTLD is among the most common causes of dementia in adults under 65 years old.1 Up to 40% of FTLD cases have a family history of dementia and around 10% have an autosomal dominant inheritance.2 Most identified inherited FTLD cases are caused by a pathogenic variant of one of three genes: chromosome 9 open reading frame 72 (C9orf72), progranulin (GRN), or microtubule associated protein tau (MAPT).3 In vivo and in vitro models suggest both central and systemic inflammatory pathways may impact the severity of neurodegeneration in FTLD.4–9 In FTLD patients with pathogenic GRN variants, blood biomarkers of immune function correlate with clinical severity and brain structure, especially white matter.9 Additionally, systemic autoimmune diseases are disproportionately prevalent in FTLD patients compared with healthy controls and patients with Alzheimer’s disease.10 11 Few clinical studies have evaluated the role of peripheral inflammation on FTLD disease progression in humans. Quantification of key markers reflecting inflammatory state may help identify targetable pathways for treatment and inform utility of plasma inflammatory biomarkers to aid disease conversion and prognosis.

      Most recent research has focused on biomarkers sensitive to neuronal degeneration in FTLD. Accumulating evidence supports plasma neurofilament light chain (NfL) as a candidate biomarker for FTLD diagnosis, prognosis, and treatment response measurement.12 13 Plasma inflammatory markers have not been studied in familial FTLD patients followed longitudinally from asymptomatic to symptomatic disease stages.14 Identifying associations with symptom conversion and clinically meaningful FTLD outcomes like daily functioning, behaviour, and cognition might support the use of systemic inflammation measurement alongside markers of neuronal and glial dysfunction.12 14 15

      We assessed three proteins with widespread and broadly influential roles across inflammatory pathways—IL-6, YKL-40, TNFα—in plasma collected from autosomal dominant FTLD pathogenic variant carriers and controls (non-carrier family members) followed longitudinally in the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) consortium. We investigated (A) plasma inflammation levels between genetic groups (MAPT, C9orf72, GRN), (B) whether baseline plasma inflammation levels related to rates of change in clinical functioning and brain volume, (C) the ability of inflammatory markers to discriminate stable asymptomatic participants from those who phenoconvert to symptomatic disease and (D) the added prognostic value of pairing plasma inflammation levels with plasma NfL to identify asymptomatic converters.

      Methods

      Additional methods and references for subsequent sections are provided in online supplemental material.

      Supplemental material

      Participants

      The study included 394 participants in the ALLFTD consortium (ClinicalTrials.gov NCT04363684), which enrols individuals based on a family history suggestive of familial FTLD. Only participants with pathogenic C9orf72 (N=117), GRN (N=62), or MAPT (N=72) variants, or non-carrier family members (N=143) were included in the analyses reported here. Genetic screening methods are described in detail elsewhere.16 All non-carriers were functionally normal at baseline based on a global score of 0 using the Clinical Dementia Rating scale plus National Alzheimer’s Coordinating Center (NACC) FTLD module (CDR+NACC FTLD; see below). Clinical phenotype frequency for each genetic group is shown in table 1.

      View this table:
      Table 1

      Frequency of clinical phenotype diagnoses across autosomal dominant mutation carriers and non-mutation carrier family members

      Converters versus non-converters

      To inform clinical utility of inflammatory biomarkers, we used longitudinal clinical data (minimum 2 study visits, max=6) to define baseline subgroups based on their future disease trajectory. Clinical disease severity was defined using the CDR+NACC FTLD global score.17 Asymptomatic non-converter pathogenic variant carriers (N=90) were clinically normal at all study visits (CDR+NACC FTLD Global=0). Asymptomatic converters (N=19) were clinically normal at baseline and exhibited at least mild behavioural or cognitive changes at their last study visit (CDR+NACC FTLD Global >0).

      Plasma collection and protein measurement

      Blood samples were collected and stored following standardised procedures for the ALLFTD consortium. Plasma IL-6, YKL-40, and TNFα concentrations were quantified in duplicate using Meso Scale Discovery (Acrobiosystems, Newark, Delaware, USA) chemiluminescence assays. All participants had at least one inflammatory biomarker measured (IL-6, N=375; YKL-40, N=394; TNFα, N=389). Samples with coefficient of variation >25% were excluded from all analyses (IL-6, N=35; YKL-40, N=0; TNFα, N=21). A total of 334 participants had all three inflammatory biomarkers measured and eligible for analyses (non-carriers, N=120; C9orf72, N=98; GRN, N=57; MAPT, N=59; online supplemental table 1). As reported in a separate study from this cohort, plasma NfL concentrations were measured with single-molecule array technology (Quanterix Simoa; Lexington, Massachusetts, USA).12

      Disease outcomes

      Clinical Outcomes: Clinical Disease Severity, Socioemotional Sensitivity and Cognition

      Our primary longitudinal clinical outcome was based on the CDR+NACC FTLD rating scale. The CDR+NACC FTLD is a measure of clinical disease severity optimised for FTD spectrum cohorts.17 18 The ‘Global’ score categorises each participant as asymptomatic (global score=0), prodromal mild cognitive or behavioural symptoms of neurodegenerative disease18 (global score=0.5), or clear functionally impairing symptoms consistent with dementia (‘overtly symptomatic;’ global score >1). For analysing longitudinal clinical disease severity, we used the CDR+NACC FTLD Sum of Boxes (SB) score (range 0–24; higher scores indicate worse severity).

      Secondarily, we evaluated longitudinal changes in socioemotional sensitivity and cognition. Socioemotional sensitivity was measured using the Revised Self-Monitoring Scale (RSMS) total score.19 The RSMS is completed by a study informant about the participant and measures sensitivity and responsiveness to subtle emotional expressions during face-to-face interactions. Lower scores representing more severe dysfunction (ie, less socioemotional sensitivity). Given the common early changes in executive functions in FTLD spectrum diseases, we used the NACC Uniform Data Set (V.3.0) executive function composite score (UDS3-EF) as the primary cognitive outcome.20 Higher UDS3-EF scores reflect better executive functioning.

      Brain imaging

      Volumetric T1-weighted images were acquired according to the LEFFTDS protocol.21 All T1-weighted images were visually inspected for quality control before bias field correction and segmentation. An intrasubject template was created by non-linear diffeomorphic and rigid-body registration and then a within-subject modulation was applied. A customised group template was generated from the within-subject average grey and white matter tissues and cerebrospinal fluid by non-linear registration template generation. Modulated intrasubject grey and white matter were geometrically normalised to the group template and then smoothed. Every step of the transformation was carefully inspected from the native space to the group template. Linear and non-linear transformations between the group template space and International Consortium of Brain Mapping were applied.

      Data analyses

      Analyses were performed using SPSS (IBM; V.25.0 andV. 27.0). Group differences in potentially confounding variables (age, sex, education as outcomes) between pathogenic variant carriers and non-carriers (predictors) were analysed with linear regression models. We further assessed differences in global cognition (Montreal Cognitive Assessment; MoCA), CDR+NACC FTLD SB, RSMS total score and UDS3-EF score, and frequency of asymptomatic status (CDR+NACC FTLD Global=0) between the three genetic groups at baseline.

      Cross-sectional group comparisons

      We first evaluated baseline plasma inflammation differences between pathogenic variant carriers and non-carriers and between the three genotypes using linear regression. To determine whether presence of a pathogenic variant (vs disease severity) was associated with differences in inflammatory protein levels, we compared asymptomatic non-converter pathogenic variant carriers to non-carrier family members who also had 2+ study visits (N=112). We compared inflammatory protein levels between genetic groups (C9orf72 vs GRN vs MAPT) while controlling for disease severity (CDR+NACC FTLD SB). We then evaluated the effect of disease severity on plasma inflammatory proteins among all pathogenic variant carriers (N=251; CDR+NACC FTLD Global=0 vs 0.5 vs 1+).

      Baseline inflammation and rate of functional, socioemotional, and cognitive changes

      We used linear mixed effects models with random slopes and intercepts to evaluate the association between baseline inflammation levels and longitudinal changes in our clinical outcomes. Longitudinal models excluded pathogenic variant carriers with baseline CDR+NACC FTLD Global >1 to limit ceiling effects associated with severe impairment at baseline. We evaluated the interaction between baseline inflammation level and time since baseline visit (years) to estimate the longitudinal trajectory differences according to baseline inflammation level. We present standardised regression estimates controlling for baseline age, sex and years of education among pathogenic variant carriers. Each genotype was analysed in separate models. Statistical significance (p values) is reported unadjusted for multiple comparisons. Accounting for the three different clinical outcomes analysed in each model (CDR+NACC FTLD SB, RSMS total score, UDS3-EF), unadjusted p values <0.017 would survive a conservative Bonferroni correction (0.05/3=0.017).

      We also aimed to inform whether significant associations between baseline inflammation and longitudinal clinical outcomes were specific to pathogenic variant carriers. We incorporated healthy non-carriers into our models and evaluated the three-way interaction between baseline inflammation level, time since baseline, and pathogenic variant status (pathogenic variant carriers vs non-carriers). A statistically significant three-way interaction would indicate that the association between baseline inflammation and longitudinal clinical outcomes observed in pathogenic variant carriers was statistically significantly stronger (or weaker) than the effect of baseline inflammation observed in non-carriers.

      Models with the CDR+NACC FTLD SB as the outcome had residuals with statistically significant departures from normality (positive skew, Kolmogorov-Smirnov test p<0.001). Even though departures from normality were reduced after log transformation, we report results based on the original scale because they are more interpretable, and the pattern of results were consistent with those after transformation.

      Neuroimaging analyses

      Voxel-based morphometry analyses were conducted using FSL22 for baseline visits (cross-sectional). Familywise error correction was performed using 5000 permutations with threshold free cluster enhancement23 and models adjusted for age, sex, and total intracranial volume. Longitudinal analyses were performed in the Bayesian linear mixed-effect model framework.24 The interaction between inflammation at baseline and rate of cortical atrophy over time was thresholded using the posterior probability maps with an alpha=5%. Neuroimaging analyses were restricted to pathogenic variant carriers and stratified by genotype given regional atrophy pattern differences expected between groups.25 26

      Identifying asymptomatic converter pathogenic variant carriers

      Among pathogenic variant carriers, we evaluated the classification accuracy of plasma inflammatory markers between asymptomatic non-converters and asymptomatic converters evaluated using area under the receiver operating characteristic curve analyses (AUC). These analyses were repeated in a subset of participants who also had plasma NfL levels measured previously. Binary logistic regression models with OR additionally adjusting for age, sex and education were used to evaluate the added value of plasma inflammatory markers beyond the expected prognostic utility of NfL.12

      Results

      Descriptive differences and baseline group comparisons are provided in online supplemental results (text and online supplemental figures 1 and 2) and in table 2.

      View this table:
      Table 2

      Descriptive baseline characteristics for the mutation carriers and non-mutation carriers in the study

      Baseline plasma inflammation associations with longitudinal disease outcomes

      All genotypes

      Among all pathogenic variant carriers, higher TNFα was associated with more rapid cognitive decline (B=−0.12 (−0.20, −0.05), p=0.002). Higher baseline YKL-40 was associated with faster decline in socioemotional sensitivity among all pathogenic variant carriers (B=−0.09 (−0.14, −0.04), p<0.001). All other associations between baseline plasma inflammation and longitudinal clinical or neuroimaging outcomes were not statistically significant in the combined genotype analyses (online supplemental figure 3).

      MAPT

      Among pathogenic MAPT variant carriers, higher baseline TNFα was associated with more rapid worsening of disease severity (CDR+NACC FTLD SB; B=0.12 (0.02, 0.22), p=0.02; figure 1). When including non-carriers, there was a three-way interaction, suggesting that the relationship between TNFα and disease severity trajectory was stronger in MAPT carriers than non-carriers (p=0.001). Higher baseline TNFα corresponded with lower inferior temporal lobe volume (predominantly left) at baseline and a faster rate of brain volume loss in widespread cortical regions including, but not limited to, the temporal lobes (online supplemental figure 4).

      Figure 1
      Figure 1

      Association of baseline plasma TNFα with rate of change in disease severity among pathogenic MAPT variant carriers. (A) Participants with high baseline TNFα (>75th percentile, blue line) had a more rapid clinical disease progression (CDR+NACC FTLD sum of box score increase) over time than those with low baseline TNFα (<25th percentile, green line). (B) At baseline (cross-sectionally), voxel-based morphometry analysis revealed higher plasma TNFα was associated with lower brain volume in the left anterior temporal lobe (blue=p<0.05 after familywise error correction using 5000 permutations with threshold-free cluster enhancement. Longitudinally, Bayesian linear mixed-effects analyses revealed widespread regions where higher baseline plasma TNFα was associated with significantly faster atrophy rates, with the most rapid atrophy (red areas) occurring in medial temporal structures (threshold using posterior probability maps and p<0.05 alpha). CDR, Clinical Dementia Rating; FTLD, frontotemporal lobar degeneration; NACC, National Alzheimer’s Coordinating Center; SB, Sum of Boxes.

      C9orf72

      Among pathogenic C9orf72 carriers, higher TNFα was associated with more rapid worsening of disease severity (B=0.09 (0.03, 0.16), p=0.006) and cognitive decline (B=−0.16 (−0.22, −0.10), p<0.001). Higher baseline IL-6 was associated with steeper decline in disease severity (B=0.12 (0.03, 0.21), p=0.01; figure 2), and higher baseline YKL-40 was associated with faster decline in socioemotional sensitivity (B=−0.42 (−0.67, −0.17), p=0.001). Again, when including non-carriers, effects evidenced a three-way interaction such that estimates were significantly stronger in C9orf72 carriers than in non-carriers. Inflammatory markers did not strongly correlate with brain volume trajectories, though we saw a trend-level association (p<0.1) of higher IL-6 with lower brain volume in dorsal and lateral parietal lobes (predominantly left) at baseline (cross-sectionally; (online supplemental figure 5).

      Figure 2
      Figure 2

      Association baseline plasma IL-6 with rate of change in disease severity among pathogenic C9orf72 variant carriers. (A) Participants with high baseline IL-6 (>75th percentile, blue line) had a more rapid clinical disease progression (CDR+NACC FTLD sum of box score increase) over time than those with low baseline IL-6 (<25th percentile, green line). (B) At baseline (cross-sectionally), voxel-based morphometry analyses revealed regions with a trend towards an association of higher IL-6 with lower brain volume predominantly in lateral parietal and medial parietal/precuneus regions (blue=p<0.10 after familywise error correction using 5000 permutations with threshold-free cluster enhancement). Bayesian linear mixed-effects analyses did not support a significant association of baseline plasma IL-6 concentrations with the rates of brain atrophy longitudinally. CDR, Clinical Dementia Rating; FTLD, frontotemporal lobar degeneration; NACC, National Alzheimer’s Coordinating Center; SB, Sum of Boxes.

      GRN

      Among pathogenic GRN variant carriers, higher baseline YKL-40 was associated with faster decline in socioemotional sensitivity (B=−0.08 (−0.15, −0.01), p=0.04), though IL-6 and TNFα did not correlate with clinical trajectories in GRN carriers. Higher baseline IL-6 associated with faster rates of longitudinal atrophy in clusters predominantly within the insula and temporal lobes (online supplemental figure 6), but no other inflammatory marker evidenced a statistically significant association with grey matter atrophy in the GRN group.

      All associations between baseline plasma inflammatory marker concentrations and longitudinal clinical outcomes for each gene group are shown in online supplemental figures 7−9.

      Non-converters versus converters

      Plasma TNFα was significantly higher in converters than non-converters (β=0.29 (0.09, 0.48), p=0.004; figure 3A). Converters, on average, had longer study follow-up than non-converters (~6 months). Baseline plasma TNFα levels showed fair discrimination of asymptomatic converters and non-converters (AUC=0.72 (0.56–0.88), p=0.004; Youden cut-off 2.2 pg/mL: 72% sensitivity, 80% specificity to detecting converters) (figure 3B). Results remained statistically significant after further adjusting for follow-up duration Neither plasma IL-6 levels (β=0.11 (−0.10, 0.32), p=0.31; figure 3C,D) nor YKL-40 levels (β=0.01 (−0.16, 0.18), p=0.9; figure 3E,F) differed statistically significantly between asymptomatic non-converters and asymptomatic converters.

      Figure 3
      Figure 3

      Baseline plasma inflammatory marker concentration differences between asymptomatic non-converter participants (CDR+NACC FTLD Global=0 at all visits) and asymptomatic converter participants (CDR+NACC FTLD Global=0 at baseline and >0.5 at a future visit). (A, B) Baseline plasma TNFα was statistically significantly higher in asymptomatic converters than asymptomatic non-converters (β=0.29 (0.09, 0.48), p=0.004) and showed fair discrimination accuracy between groups (AUC=0.72). There were no statistically significant differences in plasma IL-6 (C, D) nor YKL-40 (E, F) between asymptomatic converters and asymptomatic non-converters, and discrimination accuracy was insufficient (AUC <0.70) for both. Separate box-and-whisker plots are shown for each gene group for visualisation only (figure 4A,C,E). Gene groups were combined for all analyses. AUC, area under the curve; CDR, Clinical Dementia Rating; FTLD, frontotemporal lobar degeneration; NACC, National Alzheimer’s Coordinating Centre; SB, Sum of Boxes.

      In a subset of participants who also had plasma NfL levels quantified (N=13 converters, N=55 non-converters), both plasma NfL (AUC=0.80 (0.67–0.93), p=0.001) and plasma TNFα (AUC=0.75 (0.58–0.92), p=0.005) independently discriminated converters from non-converters (figure 4). The AUC for NfL and TNFα combined increased to 0.88 (0.77–0.99) (p<0.001). Plasma TNFα significantly improved classification accuracy above and beyond plasma NfL (ΔR2=0.16) when simultaneously evaluated in a binary logistic regression model (NfL: OR=1.4 (1.03, 1.9), p=0.03; TNFα: OR=7.7 (1.7, 31.7), p=0.007).

      Figure 4
      Figure 4

      Added value of plasma TNFα to plasma NfL for discriminating asymptomatic converters from asymptomatic non-converters (all three gene groups combined). In a subset of participants with both markers (N=13 converters, N=55 non-converters), both plasma NfL (AUC=0.80) and plasma TNFα (AUC=0.75) independently discriminated asymptomatic converters from non-converters. The AUC for NfL and TNFα combined increased to 0.88. There was a statistically significant increase in classification accuracy after adding plasma TNFα to the model with plasma NfL only (ΔR2=0.16; NfL: OR=1.4 (1.03, 1.9), p=0.03; TNFα: OR=7.7 (1.7, 31.7), p=0.007). AUC, area under the curve; NfL, neurofilament light.

      Discussion

      We evaluated plasma levels of TNFα, IL-6, and YKL-40 in FTLD pathogenic variant carriers in the ALLFTD consortium. The most consistent findings related to plasma TNFα, a proinflammatory cytokine. Higher baseline plasma TNFα was associated with faster clinical progression in both MAPT and C9orf72 carriers, including executive functioning decline in C9orf72 carriers. In the MAPT group, higher baseline TNFα was also associated with lower inferior and medial temporal lobe volume at baseline and more rapid atrophy rates in widespread regions longitudinally. Further, plasma TNFα discriminated converter from non-converter pathogenic variant carriers, as a whole, and improved detection of converters beyond plasma NfL alone. Collectively, study results suggest that peripheral inflammation, especially plasma TNFα, may contribute to FTLD disease pathogenesis and inform disease prognosis.

      There is converging and complementary evidence that the peripheral immune system contributes to FTLD pathogenesis. For example, patients with FTLD due to transactive response DNA-binding protein 43 aggregation (FTLD-TDP) have a higher frequency of systemic autoimmune disorders like inflammatory arthritis, cutaneous disorders, and gastrointestinal conditions.10 11 However, others have reported lower frequency of autoimmune diseases in C9orf72 carriers than non-carriers.27 Of note, associations between peripheral inflammation levels and longitudinal markers of clinical disease severity were relatively consistent among the C9orf72 group, a pathogenic variant associated with FTLD-TDP pathology. Prior work in C9orf72 implicates other pro- (RANTES, MCP-1) and anti-inflammatory (IL-10) markers relating to different disease trajectories and clinical profiles.28

      Correlations between baseline inflammation and brain atrophy reflected previously reported patterns of regional volume loss and atrophy rates across the different gene variants. Strongest associations were noted for higher baseline TNFα with lower temporal lobe volume in the MAPT group at baseline, and with faster rate of atrophy in widespread cortical regions longitudinally. MAPT pathogenic variant carriers have accelerated volume loss initially in temporal regions during the asymptomatic and prodromal disease stage, followed by global spread with symptom progression.26 In C9orf72, we only saw an association of baseline inflammation (IL-6) and lower brain volume cross-sectionally in dorsal and lateral parietal lobes, a region implicated in other studies of C9orf72 expansion carriers.29 The lack of statistically significant association with longitudinal atrophy rates may be due to the minimal increase in rate of atrophy among C9orf72 expansion carriers as symptoms progress.26 It may be that our study was best powered to detect effects in the MAPT group due to the particularly rapid atrophy compared with C9orf72 or GRN carriers.

      Blood-based inflammatory protein levels do not solely or completely reflect neuroinflammation in the brain.30 There likely are bidirectional influences of the peripheral-central immune responses. Microglia regulate the brain’s immune response by functioning along a phenotypic spectrum spanning a surveillant, phagocytic state to an activated, proinflammatory state.31 Animal models show that peripheral inflammation can lead to microglial activation via monocyte infiltration across the blood–brain barrier.8 Activated microglia also recruit peripheral monocytes in response to brain injury or disease.5 6 31 This may be particularly relevant for patients with pathogenic variants of C9orf72 or MAPT. C9 expression is higher in microglia than other cell types and plays a direct role in immune response homeostasis, but C9-deficient microglia observed in pathogenic variants of C9orf72 are associated with a proinflammatory phenotype.5 6 Pathogenic MAPT is also linked with microglial activation and excessive production of proinflammatory mediators32, and TNFα inhibitors may reduce microgliosis and neuronal loss in transgenic mouse models of tauopathy.33

      We did not find statistically significant evidence of plasma cytokines (TNFα, IL-6 or YKL-40) being elevated in asymptomatic pathogenic variant carriers compared with healthy, non-carrier family members, underscoring the diagnostic limitations of plasma inflammatory biomarkers. However, a prognostic biomarker that accurately identifies asymptomatic carrier patients at-risk for nearer term symptomatic conversion would be valuable both to inform clinical prognosis and to refine clinical trial enrolment. Patients and their families may also be able to use such information to inform longer-term care planning. Towards this end, we found that plasma TNFα was elevated in patients with mild symptoms compared with asymptomatic carriers, and that higher baseline plasma TNFα differentiated asymptomatic converters from non-converters. Further, while plasma NfL has shown promise for identifying converters while asymptomatic,12 13 we demonstrate incremental improvement when pairing plasma TNFα with NfL. FTLD prognosis and disease monitoring may ultimately be optimised through a patient-specific prediction model that combines relevant blood-based biomarkers,12 15 brain atrophy patterns,25 26 29 cognitive testing,34 and behavioural characterisation.35

      Lack of consistent associations between inflammatory markers and neurobehavioural outcomes in the GRN pathogenic variant carriers was unexpected given the well-established impact of GRN haploinsufficiency on inflammatory pathways.36 37 In GRN, peripheral markers of key regulators within monocyte activation pathways were shown previously to be elevated cross-sectionally compared with controls and associated with worse white matter integrity.9 Inconsistent findings between genetic groups in our study may partly reflect the specific inflammation markers studied, variability in clinical phenotypes, or the clinical outcomes used. For example, the GRN group had the greatest diversity of clinical phenotypes and the lowest proportion of patients diagnosed with bvFTD. Scales like the CDR+NACC FTLD and RSMS that rely on caregiver report may be more sensitive to behavioural changes observed in bvFTD than the breadth of cognitive changes associated with other GRN clinical phenotypes.38

      Lastly, the relevance of peripheral inflammation for FTLD pathogenesis could provide insights regarding therapeutic interventions aiming to slow symptom progression. Both emerging pharmacological (eg, CSF1R inhibitors) and behavioural modifications of inflammatory pathways may be relevant in FTLD. For instance, physical activity, which is linked with lower inflammation, is associated with slower clinical decline in autosomal dominant FTLD.39 These findings highlight avenues for future research examining whether systemic inflammation mediates the benefits of lifestyle interventions on symptom progression in FTLD. Other interventions modulating inflammatory response, including those targeting peripheral mechanisms like the gut–brain axis, may also be beneficial.40 Additional work is needed to identify patients who would benefit most and to optimise intervention timing.

      The longitudinal clinical characterisation of asymptomatic and symptomatic autosomal dominant FTLD pathogenic variant carriers is a key strength of the ALLFTD consortium. Regarding study limitations, autosomal dominant FTLD pathogenic variants are rare, so our sample size was modest, especially for analyses of specific gene groups and incorporating three-way interactions with non-carrier family members. We did not have a replication cohort and focused on just a subset of possible inflammatory markers. Plasma inflammatory markers are neither disease-specific nor necessarily direct measures of neuroinflammation. Potentially important details like co-occurring inflammatory or autoimmune conditions, or the use of anti-inflammatory medications, were not known but may influence either the measurement of blood-based inflammation markers or independently contribute to disease progression. Plasma inflammatory biomarker levels may not be as stable as other protein measurements (eg, NfL) and several other factors, such as time of day when samples were obtained, may contribute to measurement variability and secretion dynamics. Future work pairing plasma with cerebrospinal fluid could help contextualise these findings. The inflammatory proteins were measured at a single time point to assess prognostic value. Longitudinal measurement would improve our understanding of disease-related biomarker dynamics or potential for treatment response indicators. Larger longitudinal samples with similar follow-up duration would reduce bias associated with defining baseline cohorts (ie, asymptomatic converters and non-converters) using post-baseline data. Our sample was predominantly white/Caucasian and results may not generalise to patient groups more racially/ethnically representative of the increasing sociodemographic diversity of the ageing population.

      Conclusions

      Systemic inflammatory protein measurement may improve clinical prognosis in autosomal dominant FTLD pathogenic variant carriers who are not yet exhibiting severe impairment. Higher baseline systemic inflammation, particularly TNFα, may relate to with faster disease progression. Integrating TNFα with markers of neuronal dysfunction like NfL could optimise detection of impending symptom conversion in asymptomatic pathogenic variant carriers. The peripheral immune system warrants continued study as a targetable and readily measurable biological pathway.

      Data availability statement

      Data are available upon reasonable request. ALLFTD data are available upon reasonable request from qualified investigators (https://www.allftd.org/data).

      Ethics statements

      Patient consent for publication

      Ethics approval

      This study involves human participants and was approved by Johns Hopkins Medicine IRB serves as the Single IRB for the ALLFTD Consortium (CR00042454 / IRB00227492; local PI: Chiadi Onyike). All participating sites additionally obtain local IRB approvals. Participants gave informed consent to participate in the study before taking part.

      Acknowledgments

      The manuscript has been reviewed by the ALLFTD Executive Committee for scientific content. The authors acknowledge the invaluable contributions of the study participants and families as well as the assistance of the support staffs at each of the participating sites.

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      Supplementary materials

      • Supplementary Data

        This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

      • Supplementary Data

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      Footnotes

      • Twitter @basken8, @NupurGhoshal2

      • Collaborators ALLFTD Consortium Investigators: Liana Apostolova, Sami Bamada, David Clark, R. Ryan Darby, Douglas Galasko, Neill Graff-Radford, Ian M. Grant, Murray Grossman, Ging-Yuek (Robin) Hsiuang, David Irwin, David Knopman, Gabriel Leger, Chiadi Onyike, Aaron Ritter, Erik D. Roberson, Emily Rogalski, Sandra Weintraub, Dylan Wint.

      • Contributors Guarantor: BA. Planning, Conduct, and Reporting of the work: BA, PAL, AS, KBC, LV, AB and JHK. Concept and Design: BA, PAL, AS, KBC, LV, AB and JHK. Data Acquisition: HH, JS, BFB, HR, AB and JHK, ALLFTD site investigators. Data Analysis: BA, PAL, AS, KBC, YC and JK. Manuscript Drafting: BA, PAL, AS, KBC, LV and JHK. Critical Review and Final Approval of Manuscript: All authors.

      • Funding Data collection and dissemination of the data presented in this manuscript was supported by the ALLFTD Consortium (U19: AG063911, funded by the National Institute on Aging and the National Institute of Neurological Diseases and Stroke) and the former ARTFL & LEFFTDS Consortia (ARTFL: U54 NS092089, funded by the National Institute of Neurological Diseases and Stroke and National Center for Advancing Translational Sciences; LEFFTDS: U01 AG045390, funded by the National Institute on Aging and the National Institute of Neurological Diseases and Stroke). This project was further supported through funding from the Rainwater Charitable Foundation.

      • Competing interests JCR and JCM report being site PIs for clinical trials sponsored by Eli Lilly and Eisai. BA (Appleby) receives research support from the Centers for Disease Control and Prevention, the National Institutes of Health (NIH), Ionis, Alector and the CJD Foundation; he has provided consultation to Acadia, Ionis and Sangamo. BCD is a consultant for Acadia, Alector, Arkuda, Biogen, Denali, Eisai, Genentech, Lilly, Merck, Novartis, Takeda and Wave Lifesciences; receives royalties from Cambridge University Press, Elsevier and Oxford University Press; and receives grant funding from the NIA, the National Institute of Neurological Disorders and Stroke, the National Institute of Mental Health and the Bluefield Foundation. NG has participated or is currently participating in clinical trials of anti-dementia drugs sponsored by Bristol Myers Squibb, Eli Lilly/Avid Radiopharmaceuticals, Janssen Immunotherapy, Novartis, Pfizer, Wyeth, SNIFF (The Study of Nasal Insulin to Fight Forgetfulness) and the A4 (The Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease) trial; she receives research support from Tau Consortium and the Association for Frontotemporal Dementia and is funded by the NIH. IL reports funding support from the National Institutes of Health, the Michael J Fox Foundation, Parkinson Foundation, Lewy Body Association, CurePSP, Roche, Abbvie, Biogen, Centogene. EIP-Pharma, Biohaven Pharmaceuticals, Novartis, Brain Neurotherapy Bio and United Biopharma SRL – UCB; she is a Scientific advisor for Amydis and Rossy Center for Progressive Supranuclear Palsy University of Toronto . She receives her salary from the University of California San Diego and as Chief Editor of Frontiers in Neurology. AB reports research support from the NIH, the Tau Research Consortium, the Association for Frontotemporal Degeneration, Bluefield Project to Cure Frontotemporal Dementia, Corticobasal Degeneration Solutions, the Alzheimer’s Drug Discovery Foundation and the Alzheimer’s Association; he has served as a consultant for Aeovian, AGTC, Alector, Arkuda, Arvinas, Boehringer Ingelheim, Denali, GSK, Life Edit, Humana, Oligomerix, Oscotec, Roche, TrueBinding and Wave and received research support from Biogen, Eisai and Regeneron. BFB has served as an investigator for clinical trials sponsored by EIP Pharma, Alector and Biogen; he receives royalties from the publication of a book entitled Behavioral Neurology of Dementia (Cambridge Medicine, 2009, 2017). He serves on the Scientific Advisory Board of the Tau Consortium; he receives research support from the NIH, the Mayo Clinic Dorothy and Harry T. Mangurian Jr. Lewy Body Dementia Program and the Little Family Foundation. AS reports research support from the NIA/NIH, the Bluefield Project to Cure FTD and the Larry L. Hillblom Foundation; he has provided consultation to Passage Bio and Takeda. PL is a site primary investigator for clinical trials by Alector, AbbVie and Woolsey; he serves as an advisor for Retrotrope; he receives research and salary support from the NIH-NIA and the Alzheimer’s Association-Part the Cloud partnership. KBC reports research support from NIH. LV reports research support from the Alzheimer’s Association, the American Academy of Neurology, the American Brain Foundation and the NIH and has provided consultation for Retrotope. KPR reports research support from the NIH and the National Science Foundation and serves on a medical advisory board for Eli Lilly. MCT has served as an investigator for clinical trials sponsored by Biogen, Avanex, Green Valley, Roche/Genentech, Bristol Myers Squibb, Eli Lilly/Avid Radiopharmaceuticals and Janssen; she receives research support from the Canadian Institutes of Health Research. KD-R. receives research support from the NIH and serves as an investigator for a clinical trial sponsored by Lawson Health Research Institute. J. Kornak has provided expert witness testimony for Teva Pharmaceuticals in Forest Laboratories Inc. et al. v. Teva Pharmaceuticals USA, Inc., case numbers 1:14-cv00121 and 1:14-cv-00686 (D. Del. filed 31 January 2014 and 30 May 2014 regarding the drug Memantine) and for Apotex/HEC/Ezra in Novartis AG et al. v. Apotex Inc., case number 1:15-cv-975 (D. Del. filed 26 October 2015 regarding the drug Fingolimod); he has also given testimony on behalf of Puma Biotechnology in Hsingching Hsu et al, vs. Puma Biotechnology, Inc., et al. 2018 regarding the drug Neratinib; he receives research support from the NIH. WK reports research funding from AstraZeneca, Biogen, Roche, the Department of Defense and the NIH. LF reports research funding from NIH. HJR reports research support from Biogen Pharmaceuticals, has consulting agreements with Wave Neuroscience and Ionis Pharmaceuticals and receives research support from the NIH. JHK reports research support from NIH and receives royalties from Pearson Inc.

      • Provenance and peer review Not commissioned; externally peer reviewed.

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