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The age-specific comorbidity burden of mild cognitive impairment: a US claims database study.
Li, Gang; Toschi, Nicola; Devanarayan, Viswanath; Batrla, Richard; Boccato, Tommaso; Cho, Min; Ferrante, Matteo; Frech, Feride; Galvin, James E; Henley, David; Mattke, Soeren; De Santi, Susan; Hampel, Harald.
Afiliação
  • Li G; Eisai Inc., 200 Metro Boulevard, Nutley, NJ, 07110, USA. Gang_Li@eisai.com.
  • Toschi N; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133, Rome, Italy.
  • Devanarayan V; A.A. Martino's Center for Biomedical Imagin, Harvard Medical School, Boston, USA.
  • Batrla R; Eisai Inc., 200 Metro Boulevard, Nutley, NJ, 07110, USA.
  • Boccato T; Eisai Inc., 200 Metro Boulevard, Nutley, NJ, 07110, USA.
  • Cho M; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133, Rome, Italy.
  • Ferrante M; Eisai Inc., 200 Metro Boulevard, Nutley, NJ, 07110, USA.
  • Frech F; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, 00133, Rome, Italy.
  • Galvin JE; Eisai Inc., 200 Metro Boulevard, Nutley, NJ, 07110, USA.
  • Henley D; Miller School of Medicine, University of Miami, 7700 W Camino Real, Suite 200, Boca Raton, FL, 33433, USA.
  • Mattke S; Research and Development, Janssen Pharmaceuticals, Inc., 1125 Bear Tavern Rd, Titusville, NJ, 08560, USA.
  • De Santi S; The USC Brain Health Observatory, University of Southern California, 635 Downey Way, Los Angeles, CA, 90089, USA.
  • Hampel H; Eisai Inc., 200 Metro Boulevard, Nutley, NJ, 07110, USA.
Alzheimers Res Ther ; 15(1): 211, 2023 12 06.
Article em En | MEDLINE | ID: mdl-38057937
BACKGROUND: Identifying individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer's disease and related dementia disorders (ADRD) would facilitate the development of individualized prevention plans. We investigated the association between MCI and comorbidities of ADRD. We examined the predictive potential of these comorbidities for MCI risk determination using a machine learning algorithm. METHODS: Using a retrospective matched case-control design, 5185 MCI and 15,555 non-MCI individuals aged ≥50 years were identified from MarketScan databases. Predictive models included ADRD comorbidities, age, and sex. RESULTS: Associations between 25 ADRD comorbidities and MCI were significant but weakened with increasing age groups. The odds ratios (MCI vs non-MCI) in 50-64, 65-79, and ≥ 80 years, respectively, for depression (4.4, 3.1, 2.9) and stroke/transient ischemic attack (6.4, 3.0, 2.1). The predictive potential decreased with older age groups, with ROC-AUCs 0.75, 0.70, and 0.66 respectively. Certain comorbidities were age-specific predictors. CONCLUSIONS: The comorbidity burden of MCI relative to non-MCI is age-dependent. A model based on comorbidities alone predicted an MCI diagnosis with reasonable accuracy.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Disfunção Cognitiva Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doença de Alzheimer / Disfunção Cognitiva Idioma: En Ano de publicação: 2023 Tipo de documento: Article