{"doi":"10.1177/13524585221106290","title":"Mitochondrial measures in neuronally enriched extracellular vesicles predict brain and retinal atrophy in multiple sclerosis","abstract":"BACKGROUND: Mitochondrial dysfunction plays an important role in multiple sclerosis (MS) disease progression. Plasma extracellular vesicles are a potential source of novel biomarkers in MS, and some of these are derived from mitochondria and contain functional mitochondrial components. OBJECTIVE: To evaluate the relationship between levels of mitochondrial complex IV and V activity in neuronally enriched extracellular vesicles (NEVs) and brain and retinal atrophy as assessed using serial magnetic resonance imaging (MRI) and optical coherence tomography (OCT). METHODS: Our cohort consisted of 48 people with MS. NEVs were immunocaptured from plasma and mitochondrial complex IV and V activity levels were measured. Subjects underwent OCT every 6 months and brain MRI annually. The associations between baseline mitochondrial complex IV and V activities and brain substructure and retinal thickness changes were estimated utilizing linear mixed-effects models. RESULTS: We found that higher mitochondrial complex IV activity and lower mitochondrial complex V activity levels were significantly associated with faster whole-brain volume atrophy. Similar results were found with other brain substructures and retinal layer atrophy. CONCLUSION: Our results suggest that mitochondrial measures in circulating NEVs could serve as potential biomarkers of disease progression and provide the rationale for larger follow-up longitudinal studies.","journal":"Multiple Sclerosis Journal","year":2022,"id":270977,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9611,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":393446,"name":"Pamela J. Yao","orcid":"0000-0003-1520-1617","position":1,"is_corresponding":false},{"id":622896,"name":"Michael Vreones","orcid":"0009-0003-1719-8156","position":2,"is_corresponding":false},{"id":741381,"name":"Joseph Blommer","orcid":"0000-0002-5551-9219","position":3,"is_corresponding":false},{"id":492664,"name":"Grigorios Kalaitzidis","orcid":"0000-0001-7680-3609","position":4,"is_corresponding":false},{"id":241641,"name":"Elias S. Sotirchos","orcid":"0000-0002-8812-1637","position":5,"is_corresponding":false},{"id":241632,"name":"Kathryn C. Fitzgerald","orcid":"0000-0003-3137-0322","position":6,"is_corresponding":false},{"id":280480,"name":"Shiv Saidha","orcid":"0000-0001-6387-0714","position":7,"is_corresponding":false},{"id":241645,"name":"Peter A. Calabresi","orcid":"0000-0002-7776-6472","position":8,"is_corresponding":false},{"id":256661,"name":"Dimitrios Kapogiannis","orcid":"0000-0003-2181-3118","position":9,"is_corresponding":false},{"id":241629,"name":"Pavan Bhargava","orcid":"0000-0002-7947-9418","position":10,"is_corresponding":false},{"id":935756,"name":"Dimitrios C. Ladakis","orcid":"0000-0002-5764-2114","position":0,"is_corresponding":true}],"reference_count":26,"raw_metadata":null,"created_at":"2026-07-19T00:27:35.206187Z","pmid":"35787218","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}