{"doi":"10.1002/nbm.4907","title":"More than one‐half of the variance in in vivo proton MR spectroscopy metabolite estimates is common to all metabolites","abstract":"Abstract The present study characterized associations among brain metabolite levels, applying bivariate and multivariate (i.e., factor analysis) statistical methods to total creatine (tCr)‐referenced estimates of the major Point RESolved Spectroscopy (PRESS) proton MR spectroscopy ( 1 H‐MRS) metabolites (i.e., total NAA/tCr, total choline/tCr, myo‐inositol/tCr, glutamate + glutamine/tCr) acquired at 3 T from medial parietal lobe in a large ( n = 299), well‐characterized international cohort of healthy volunteers. Results supported the hypothesis that 1 H‐MRS–measured metabolite estimates are moderately intercorrelated ( M r = 0.42, SD r = 0.11, p s < 0.001), with more than one‐half (i.e., 57%) of the total variability in metabolite estimates explained by a single common factor. Older age was significantly associated with lower levels of the identified common metabolite variance (CMV) factor ( β = −0.09, p = 0.048), despite not being associated with levels of any individual metabolite. Holding CMV factor levels constant, females had significantly lower levels of total choline (i.e., unique metabolite variance; β = −0.19, p < 0.001), mirroring significant bivariate correlations between sex and total choline reported previously. Supplementary analysis of water‐referenced metabolite estimates (i.e., including tCr/water) demonstrated lower, although still substantial, intercorrelations among metabolites, with 37% of total metabolite variance explained by a single common factor. If replicated, these results would suggest that applied 1 H‐MRS researchers shift their analytical framework from examining bivariate associations between individual metabolites and specialty‐dependent (e.g., clinical, research) variables of interest (e.g., using t ‐tests) to examining multivariable (i.e., covariate) associations between multiple metabolites and specialty‐dependent variables of interest (e.g., using multiple regression).","journal":"NMR in Biomedicine","year":2023,"id":374111,"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":4,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.7529,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":238011,"name":"Helge J. Zöllner","orcid":"0000-0002-7148-292X","position":1,"is_corresponding":false},{"id":699065,"name":"Saipavitra Murali‐Manohar","orcid":"0000-0002-4978-0736","position":2,"is_corresponding":false},{"id":238010,"name":"Georg Oeltzschner","orcid":"0000-0003-3083-9811","position":3,"is_corresponding":false},{"id":238015,"name":"Richard A.E. Edden","orcid":"0000-0002-0671-7374","position":4,"is_corresponding":false},{"id":459004,"name":"James J. Prisciandaro","orcid":"0000-0002-8877-7871","position":0,"is_corresponding":true}],"reference_count":41,"raw_metadata":null,"created_at":"2026-07-19T01:16:11.341687Z","pmid":"36651918","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":[]}