| Autor: |
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 |
| Předmět: |
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| Zdroj: |
Alzheimer's Research & Therapy; 12/6/2023, Vol. 15 Issue 1, p1-11, 11p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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