{"doi":"10.1111/dom.14788","title":"Obesity‐associated metabolites in relation to type 2 diabetes risk: A prospective nested <scp>case‐control</scp> study of the <scp>CARRS</scp> cohort","abstract":"Abstract Aims To determine whether obesity‐associated metabolites are associated with type 2 diabetes (T2DM) risk among South Asians. Materials and Methods Serum‐based nuclear magnetic resonance imaging metabolomics data were generated from two South Asian population‐based prospective cohorts from Karachi, Pakistan: CARRS1 (N = 4017) and CARRS2 (N = 4802). Participants in both cohorts were followed up for 5 years and incident T2DM was ascertained. A nested case‐control study approach was developed to select participants from CARRS1 (N cases = 197 and N controls = 195) and CARRS2 (N cases = 194 and N controls = 200), respectively. First, we investigated the association of 224 metabolites with general obesity based on body mass index and with central obesity based on waist‐hip ratio, and then the top obesity‐associated metabolites were studied in relation to incident T2DM. Results In a combined sample of the CARRS1 and CARRS2 cohorts, out of 224 metabolites, 12 were associated with general obesity and, of these, one was associated with incident T2DM. Fifteen out of 224 metabolites were associated with central obesity and, of these, 10 were associated with incident T2DM. The higher level of total cholesterol in high‐density lipoprotein (HDL) was associated with reduced T2DM risk (odds ratio [OR] 0.68, 95% confidence interval [CI] 0.53, 0.86; P = 1.2 × 10 −3 ), while higher cholesterol esters in large very‐low‐density lipoprotein (VLDL) particles were associated with increased T2DM risk (OR 1.90, 95% CI 1.40, 2.58; P = 3.5 × 10 −5 ). Conclusion Total cholesterol in HDL and cholesterol esters in large VLDL particles may be an important biomarker in the identification of early development of obesity‐associated T2DM risk among South Asian adults.","journal":"Diabetes Obesity and Metabolism","year":2022,"id":279224,"datarank":0.37251626051827236,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.10375234013406406,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.10375234013406406,"corpus_percentile":51.72893942910188,"corpus_rank":6241,"citation_count":5,"citer_count":4,"citers_with_citation_signal":4,"citers_with_endowment":4,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.8754,"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":4.1667,"fair_percentile":4.891470498318557,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":952812,"name":"M. Masood Kadir","orcid":null,"position":1,"is_corresponding":false},{"id":478824,"name":"Unjali P. Gujral","orcid":"0000-0002-0352-813X","position":2,"is_corresponding":false},{"id":103586,"name":"Syeda Sadia Fatima","orcid":"0000-0002-3164-0225","position":3,"is_corresponding":false},{"id":246293,"name":"Romaina Iqbal","orcid":"0000-0002-5364-4366","position":4,"is_corresponding":false},{"id":22017,"name":"Yan V. Sun","orcid":"0000-0002-2838-1824","position":5,"is_corresponding":false},{"id":245134,"name":"K.M. Venkat Narayan","orcid":"0000-0001-8621-5405","position":6,"is_corresponding":false},{"id":952292,"name":"Shafqat Ahmad","orcid":"0000-0002-8844-6845","position":7,"is_corresponding":false},{"id":22946,"name":"Mohammed K. Ali","orcid":"0000-0001-7266-2503","position":0,"is_corresponding":true}],"reference_count":33,"raw_metadata":null,"created_at":"2026-07-19T00:28:51.322666Z","pmid":"35676808","pmcid":"PMC9543742","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":22.2222,"fair_a":18.75,"fair_i":0.0,"fair_r":25.0,"fair_zscore":-1.1986,"fair_rationale":{"fair_score":4.17,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":22.22,"criteria":[{"key":"f_dataset_pid","label":"Persistent identifier for the data","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":null,"grounded":false,"rationale":"No PID-scheme string or data URL is given for the dataset.","anchors":["RDA-F1-01D — FAIR Data Maturity Model: 'Data is identified by a persistent identifier' (priorit","RDA-F1-02D — FAIR Data Maturity Model: 'Data is identified by a globally unique identifier'","FsF-F1-02D — F-UJI/FAIRsFAIR: 'Data is assigned a persistent identifier'"],"scored":true,"signal":null},{"key":"f_repository_named","label":"Named repository","kind":"llm","weight":2.0,"fraction":0.0,"verdict":"no","evidence":"The raw data are not publicly available owing to the study's data access policies.","grounded":true,"rationale":"No repository is named; the data are held by the authors.","anchors":["RDA-F4-01M — FAIR Data Maturity Model: metadata is offered so it can be harvested and indexed (","NIH DMS Policy Element 4 (NOT-OD-21-014) — name the repository where data will be archived","NSTC Desirable Characteristics of Data Repositories (2022) — 'Long-Term Sustainability', 'Reten"],"scored":true,"signal":null},{"key":"f_data_availability_statement","label":"Data-availability statement","kind":"llm","weight":2.0,"fraction":0.5,"verdict":"partial","evidence":"The raw data are not publicly available owing to the study's data access policies. 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For metabolomics data, deposit in MetaboLights (MTBLS accession) or Metabolomics Workbench.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No PID-scheme string or data URL is given for the dataset.","gain":16.67,"priority":"essential","scored":true},{"key":"f_repository_named","dimension":"F","label":"Named repository","action":"Deposit the data in a repository registered in re3data/FAIRsharing (a domain repository such as GEO, SRA, dbGaP, PRIDE, or a generalist such as Zenodo, Dryad, Dataverse) and name it explicitly in the paper. A lab website is not an archive: it has no retention commitment and no accession. 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Prefer open metabolomics formats such as mzML or nmrML.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No file format for the released data is named in the text.","gain":8.33,"priority":"important","scored":true},{"key":"x_code_availability","dimension":"R","label":"Analysis code available","action":"Publish the analysis code in a public forge, archive a tagged release with a DOI (Zenodo/Software Heritage), and cite that DOI in the paper. NIH DMS Element 2 asks for the tools and code, not only the data — and 'available on request' is not a locator. 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For metabolomics data, deposit in MetaboLights (MTBLS accession) or Metabolomics Workbench.","Remove the precondition or justify it. Release the data at publication with no embargo, no registration wall, and no approval step — NIH's zero-embargo public- access rule (NOT-OD-25-101) has already made 'available at publication' the federal baseline for the article; the data should not lag behind it. For metabolomics data, deposit in MetaboLights (MTBLS accession) or Metabolomics Workbench.","Attach a standard, machine-readable open licence to the deposit — CC0 or CC BY, which is what Horizon Europe and most funders expect — and print the licence identifier in the paper. 'Free to use' is not a licence: it grants nothing a reuser's institution can rely on.","Cite the dataset in the reference list like a publication — creator, year, title, repository, DOI/accession — and cite it in-text where it is used. Only a reference- list entry is machine-readable to Crossref/DataCite, and only a citation lets the data earn credit. Cite the metabolomics repository accession (e.g. from MetaboLights (MTBLS accession) or Metabolomics Workbench) in the reference list."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"epmc_xml"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"epmc_xml","fair_has_llm":true,"fair_computed_at":"2026-07-20T12:59:04.831049Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}