{"doi":"10.3389/fnut.2022.922824","title":"Association of obesity profiles with type 2 diabetes in Chinese adults: Findings from the China health and nutrition survey","abstract":"Aims To examine longitudinal associations of obesity profiles, continuous BMI, and waist circumference (WC) with the risk of type 2 diabetes in Chinese adults. Methods Data were derived from three waves (2009, 2015, and 2018) of the China Health and Nutrition Survey, and 3,595 adults aged 18–65 years who participated in at least two waves of the survey and had completed data were analyzed. Obesity profiles included BMI- or WC-related single obesity and combined obesity. Combined obesity was categorized into six groups including Group 1 with normal BMI and WC, Group 2 with normal BMI but pre-abdominal obesity, Group 3 with normal BMI but abdominal obesity, Group 4 with abnormal BMI (overweight and general obesity) and normal WC, Group 5 with abnormal BMI and pre-abdominal obesity, and Group 6 with abnormal BMI and abdominal obesity. Three-level mixed-effects logistic regressions with random intercept stratified by gender and restricted cubic splines were performed to examine ORs and 95%CIs for the risk of type 2 diabetes. Results In men, compared with subjects of Group 1, those in Group 3 had higher risk, with an OR of 4.83 (95% CI: 1.99–11.74), followed by those in Group 6 (OR = 4.05, 95%CI: 2.32–7.08) and Group 5 (OR = 2.98, 95%CI: 1.51–5.87) after adjusting for all potential confounders. For women, the subject of Group 6 had highest risk (OR = 8.79, 95%CI: 4.04–19.12), followed by Group 3 (OR = 3.30, 95%CI: 1.23–8.86) and Group 5 (OR = 3.16, 95%CI: 1.21–8.26). No significant association between abnormal BMI and normal WC (Group 4) was observed in both genders. Type 2 diabetes risk increased steeply at BMI of 23.5 kg/m 2 and 22.5 kg/m 2 or higher, and WC of 82.0 cm and 83.0 cm or higher in Chinese adult men and women, respectively ( p for overall <0.001). Conclusion Chinese adults with pre-abdominal or abdominal obesity had a relative high risk of type 2 diabetes independent of BMI levels. Lower BMI (≤23.5 kg/m 2 for men and ≤22.5 kg/m 2 for women) and lower WC (82.0 cm for men and ≤83.0 cm for women) values than the current Chinese obesity cut-offs were found to predict the risk of type 2 diabetes. These findings urge to inform WC modification and optimization of early screening guidelines.","journal":"Frontiers in Nutrition","year":2022,"id":268080,"datarank":0.31475103645308894,"base_score":1.9459101490553132,"endowment":1.9459101490553132,"self_citation_contribution":0.29188652235829704,"citation_network_contribution":0.022864514094791913,"self_endowment_contribution":0.29188652235829704,"citer_contribution":0.022864514094791913,"corpus_percentile":46.45316005260308,"corpus_rank":6923,"citation_count":6,"citer_count":6,"citers_with_citation_signal":2,"citers_with_endowment":2,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.6574,"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":29.1667,"fair_percentile":43.01436869458881,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":869066,"name":"Weiyi Li","orcid":"0000-0002-9988-797X","position":1,"is_corresponding":false},{"id":240192,"name":"Xiaofang Jia","orcid":"0000-0002-7127-0011","position":2,"is_corresponding":false},{"id":240189,"name":"Jiguo Zhang","orcid":"0000-0003-3266-8999","position":3,"is_corresponding":false},{"id":240196,"name":"Hongru Jiang","orcid":"0000-0002-0747-6111","position":4,"is_corresponding":false},{"id":869067,"name":"Liusen Wang","orcid":"0000-0002-9426-3754","position":5,"is_corresponding":false},{"id":240188,"name":"Huijun Wang","orcid":"0000-0002-8064-4564","position":6,"is_corresponding":false},{"id":240197,"name":"Bing Zhang","orcid":"0000-0002-8399-9061","position":7,"is_corresponding":false},{"id":144481,"name":"Zhihong Wang","orcid":null,"position":8,"is_corresponding":false},{"id":490965,"name":"Gangqiang Ding","orcid":"0000-0003-3478-5910","position":9,"is_corresponding":false},{"id":925168,"name":"Siting Zhang","orcid":null,"position":0,"is_corresponding":true}],"reference_count":40,"raw_metadata":null,"created_at":"2026-07-19T00:27:10.233250Z","pmid":"36176634","pmcid":"PMC9513418","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":11.1111,"fair_a":50.0,"fair_i":0.0,"fair_r":41.6667,"fair_zscore":-0.2091,"fair_rationale":{"fair_score":29.17,"has_llm":true,"taxonomy_version":"fair_taxonomy_v5","dimensions":{"F":{"name":"Findable","score":11.11,"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 persistent identifier 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 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[majority verdict 'no' (3/4 passes agreed)]","gain":0.0,"priority":"useful","scored":false},{"key":"i_qualified_references","dimension":"I","label":"Identifiers for the resources the data depend on","action":"Cite by identifier every resource the data depend on — the source datasets' accessions, the reference build (GRCh38 / GCA_000001405.28), the cohort application number, the code DOI — and register those relations on the dataset record (IsDerivedFrom, IsSupplementTo). A name is not a link: it cannot be resolved, versioned, or followed by a machine.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No identifier for external resources beyond the paper's own dataset is given.","gain":0.0,"priority":"useful","scored":false},{"key":"a_timeline_retention","dimension":"A","label":"Availability timing & retention","action":"State when the data become available AND how long they will be retained — cite the repository's preservation policy. NIH DMS Element 4 asks for both; most papers give neither.","anchors":["yes","partial","no"],"verdict":"no","current":0.0,"evidence":null,"why":"No statement about persistence or availability timing of the data. [majority verdict 'no' (3/4 passes agreed)]","gain":0.0,"priority":"useful","scored":false}],"suggestions":["Mint or cite a persistent identifier for the dataset — a repository DOI or an accession from a registered repository — and print it in the paper. A bare URL is not persistent: it is the single most common cause of a dead data link five years after publication. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","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. For clinical / human-subjects data, deposit in dbGaP or the European Genome-phenome Archive (EGA).","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 clinical / human-subjects repository accession (e.g. from dbGaP or the European Genome-phenome Archive (EGA)) in the reference list.","Release the data in an open, community-standard format (CSV/TSV, JSON, HDF5, NetCDF, FASTQ, VCF, NIfTI…) instead of — or alongside — any proprietary or instrument-native format, and name the format in the paper. A dataset that needs a €2,000 licence to open is not reusable."],"model":"deepseek/deepseek-v4-flash","agent_version":"fair_agent_v8","fulltext_source":"unpaywall_pdf"},"fair_model":"deepseek/deepseek-v4-flash","fair_agent_version":"fair_agent_v8","fair_fulltext_source":"unpaywall_pdf","fair_has_llm":true,"fair_computed_at":"2026-07-20T12:53:13.945619Z","clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}