Chitinases in tear fluid of patients with Amyotrophic Lateral Sclerosis
Wenz, L.; Scholl, L.-S.; Reinhardt, N.; von Heynitz, R.; Gmeiner, V.; Rau, P.; Mueller, P. J.; Feneberg, E.; Demleitner, A. F.; Lingor, P.
Show abstract
BackgroundChitinases, including chitotriosidase (CHIT1) and chitinase-3-like protein 1 (CHI3L1), are markers of neuroinflammation, a key process in amyotrophic lateral sclerosis (ALS). Tear fluid (TF) can be collected non-invasively and may represent a promising alternative to CSF or blood to study chitinases. MethodsTF was collected from 50 ALS patients and 50 control subjects using Schirmer strips. CHIT1 and CHI3L1 levels in TF, serum, and CSF were quantified using ELISA. Serum NfL was measured using SIMOA. The frequency of a 24 bp-duplication polymorphism in the CHIT1 gene influencing CHIT1 expression was assessed by PCR. ResultsNo group differences in the distribution of the CHIT1 polymorphism were detected. Carriers of the polymorphism in both ALS and controls showed lower CHIT1 levels in serum and TF. CHI3L1 levels in TF were higher in ALS patients compared to controls (p = 0.007), consistent with changes in CSF but not serum. In ALS, males showed higher TF CHIT1-values compared to females (p = 0.009). Combining TF chitinase values with serum NfL values improved discrimination between ALS and controls. ConclusionsChitinases are detectable in TF, and CHI3L1 levels recapitulate changes observed in CSF, highlighting its potential for non-invasive longitudinal assessment. Furthermore, chitinase values in TF, together with serum NfL, may act complementary by capturing distinct aspects of the disease, neuroinflammation and axonal damage. These results suggest TF chitinases and serum NfL could complementarily contribute to the diagnosis and monitoring of the disease, and call for further evaluation of TF as a biomarker source in ALS.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- SFPQ intron retention, reduced expression and aggregate formation in central nervous system tissue are pathological features of amyotrophic lateral sclerosis 94%
- miRNA biomarkers for diagnosis of ALS and FTD, developed by a nonlinear machine learning approach 92%
- Refining Muscle Morphometry Through Machine Learning and Spatial Analysis 92%
Similar papers in this journal
- Neuronal TDP-43 aggregation drives changes in microglial morphology prior to immunophenotype in amyotrophic lateral sclerosis 94%
- Blood-spinal cord barrier leakage is independent of motor neuron pathology in ALS 94%
- White adipose tissue undergoes pathological dysfunction in the TDP-43A315T mouse model of amyotrophic lateral sclerosis (ALS) 94%
Similar papers in this journal
- The oligogenic structure of amyotrophic lateral sclerosis has genetic testing, counselling, and therapeutic implications 93%
- Spinal Cord Motor Neuron Phenotypes and Polygenic Risk Scores in Sporadic Amyotrophic Lateral Sclerosis: Deciphering the Disease Pathology and Therapeutic Potential of Ropinirole Hydrochloride 93%
- Neuroinflammation predicts disease progression in progressive supranuclear palsy 91%
Similar papers in this journal
- Nuclear depletion of RNA binding protein ELAVL3 (HuC) in sporadic and familial amyotrophic lateral sclerosis 94%
- TMEM106B modifies TDP-43 pathology in human ALS brain and cell-based models of TDP-43 proteinopathy 93%
- Single-cell transcriptomic landscape of the neuroimmune compartment in amyotrophic lateral sclerosis brain and spinal cord 92%
Similar papers in this journal
- Network Analysis of the Cerebrospinal Fluid Proteome Reveals Shared and Unique Differences Between Sporadic and Familial Forms of Amyotrophic Lateral Sclerosis 93%
- A Microglial Activity State Biomarker Panel Differentiates Ftd-Granulin And Ad From Control Cases 92%
- Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.