Inflammatory plasma profile in genetic symptomatic and presymptomatic Frontotemporal Dementia − A GENFI study
Bibliographic record
Abstract
BACKGROUND: Inflammation has been proposed as a crucial player in neurodegeneration, including Frontotemporal Dementia (FTD). A few studies on sporadic FTD lead to inconclusive results, whereas large studies on genetic FTD are lacking. The aim of this study is to determine cytokine and chemokine plasma circulating levels in a large cohort of genetic FTD, collected within the GENetic Frontotemporal dementia Initiative (GENFI). METHODS: Mesoscale technology was used to analyse levels of 30 inflammatory factors in 434 plasma samples, including 94 Symptomatic Mutation carriers [(SMC); 15 with mutations in Microtubule Associated Protein Tau (MAPT) 34 in Progranulin (GRN) and 45 in Chromosome 9 Open Reading Frame (C9ORF)72], 168 Presymptomatic Mutation Carriers (PMC; 34 MAPT, 70 GRN and 64 C9ORF72) and 173 Non-carrier Controls (NC)]. RESULTS: The following cytokines were significantly upregulated (P<0.05) in MAPT and GRN SMC versus NC: Tumor Necrosis Factor (TNF)α, Interleukin (IL)-7, IL-15, IL-16, IL-17A. Moreover, only in GRN SMC, additional factors were upregulated, including: IL-1β, IL-6, IL-10, IL-12/IL-23p40, eotaxin, eotaxin-3, Interferon γ-induced Protein (IP-10), Monocyte Chemotactic Protein (MCP)4. On the contrary, IL-1α levels were decreased in SMC compared with NC. Significantly decreased levels of this cytokine were also found in PMC, independent of the type of mutation. In SMC, no correlations between disease duration and cytokine and chemokine levels were found. Considering NfL and GFAP levels, as expected, significant increases were observed in SMC as compared to NC. These differences in mean values remain significant even when stratifying symptomatic patients by the mutated gene (P<0.0001). Considering instead the levels of NfL, GFAP, and the altered inflammatory molecules, no significant correlations emerged. CONCLUSION: We showed that inflammatory proteins are upregulated in MAPT and GRN SMC, with some specific factors altered in GRN only, whereas no changes were seen in C9ORF72 carriers. Notably, only IL-1α levels were decreased in both SMC and PMC, independent of the type of causal mutation, suggesting common modifications occurring in the preclinical phase of the disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".