{
  "abstract": "ObjectiveTo analyze the latent profiles of cognitive function among middle⁃aged and older adults in China and their influencing factors,so as to provide a reference for identifying high⁃risk groups and developing targeted intervention strategies.MethodsBased on data from the CHARLS 2020 wave,8,848 participants aged 45 years and above were included.Assessments were conducted using a general information questionnaire,the Mini⁃Mental State Examination,the short form of the Center for Epidemiologic Studies Depression Scale,and the Activities of Daily Living scale.Latent profile analysis was used to identify heterogeneous subgroups of cognitive function,and multiple machine learning algorithms including random forest were employed to construct predictive models.ResultsThree latent profiles of cognitive function were identified:the subjective⁃objective memory dissociation group(43.03%),the memory and executive impairment group(27.17%),and the multidimensional cognitive advantage group(29.80%).Educational level,age,internet use,depressive symptoms,and type of pension insurance were factors influencing the different latent profiles.The random forest model demonstrated general predictive performance.ConclusionsCognitive function in middle⁃aged and older adults in China exhibits population heterogeneity.Stratified management and interventions should be implemented based on the characteristics and influencing factors of each profile.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "ZHANG Libing"
    },
    {
      "name": "FANG Juan"
    },
    {
      "name": "LU Dongyan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8edfkcf1tdzqe.bib",
  "cited_by": 0,
  "doi": null,
  "fulltext_url": "http://hlyj.suo1.cn/thesisDetails#10.12102/j.issn.1009-6493.2026.21.011",
  "id": "8edfkcf1tdzqe",
  "issns": [
    "1009-6493"
  ],
  "landing_url": "http://hlyj.suo1.cn/thesisDetails#10.12102/j.issn.1009-6493.2026.21.011",
  "language": "zh",
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "RT1-120"
    ]
  ],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-30",
  "publisher": "Shanxi Medical Periodical Press",
  "ris": "https://research.draconitic.com/paper/8edfkcf1tdzqe.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T13:44:45Z",
      "native_id": "679b51eaa36346c89c8ef70a2ec14530",
      "source": "doaj",
      "url": "http://hlyj.suo1.cn/thesisDetails#10.12102/j.issn.1009-6493.2026.21.011"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "lcc:RT1-120",
      "subfield": "general"
    }
  ],
  "title": "Latent profile analysis of cognitive function in middle⁃aged and older adults and its influencing factors",
  "title_text": "Latent profile analysis of cognitive function in middle⁃aged and older adults and its influencing factors",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T13:44:45Z",
  "url": "https://research.draconitic.com/paper/8edfkcf1tdzqe",
  "venue": "Huli yanjiu",
  "versions": []
}