A data‐driven disease progression model of fluid biomarkers in genetic FTD

Background Several fluid biomarkers for genetic frontotemporal dementia (FTD) have been proposed, including those reflecting neuroaxonal loss (neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH)), synapse dysfunction (neuronal pentraxin 2 (NPTX2)), gliosis (glial fibrillary acidic protein (GFAP)) and complement activation (C3b, C1q). Determining the sequence in which biomarkers become abnormal over the course of disease could facilitate disease staging in FTD and enable us to identify mutation carriers with prodromal or early‐stage FTD, which is especially important as pharmaceutical interventions emerge. We aimed to model the sequence of biomarker abnormalities in presymptomatic and symptomatic genetic FTD using cross‐sectional data from the Genetic Frontotemporal dementia Initiative (GENFI). Method 276 presymptomatic and 142 symptomatic carriers of mutations in GRN, C9orf72 or MAPT, as well as 247 non‐carriers, were selected from the GENFI cohort based on availability of one or more of the aforementioned biomarkers. Nine presymptomatic carriers developed symptoms within 18 months of data collection (‘converters’). Sequences of biomarker abnormalities were modelled for the entire group using discriminative event‐based modelling (DEBM) and for each genetic subgroup using co‐initialized DEBM. These models estimate probabilistic biomarker abnormalities in a data‐driven way and do not rely on prior diagnostic information or biomarker cut‐off points. We estimated individual disease severity scores based on the position of subjects along the disease progression timeline through cross‐validation. Result Cerebrospinal fluid (CSF) NPTX2 was the first detectable abnormal biomarker, followed by blood and CSF NfL, blood GFAP, blood pNfH and finally CSF C1q and C3b (Fig. 1). Biomarker orderings did not differ significantly between genetic subgroups. Estimated disease severity scores (Fig. 2) could distinguish symptomatic from presymptomatic carriers and non‐carriers with areas under the curve (AUC) of 0.84 and 0.90 respectively. The AUC to distinguish converters from non‐converting presymptomatic carriers was 0.85. Conclusion In our data‐driven disease progression models of genetic FTD, NPTX2 and NfL were the first biomarkers to become abnormal. Further research should focus on their utility as candidate selection tools for pharmaceutical trials. Estimating disease stages using DEBM could enable us to identify presymptomatic carriers ap