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Estimating the potential for dementia prevention through modifiable risk factors elimination in the real-world setting: a population-based study
BACKGROUND: Preventing dementia onset is one of the global public health priorities: around 35% of dementia cases could be attributable to modifiable risk factors. These estimates relied on secondary data and did not consider the concurrent effect of non-modifiable factors and death. Here, we aimed...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7414752/ https://www.ncbi.nlm.nih.gov/pubmed/32767997 http://dx.doi.org/10.1186/s13195-020-00661-y |
Sumario: | BACKGROUND: Preventing dementia onset is one of the global public health priorities: around 35% of dementia cases could be attributable to modifiable risk factors. These estimates relied on secondary data and did not consider the concurrent effect of non-modifiable factors and death. Here, we aimed to estimate the potential reduction of dementia incidence due to modifiable risk factors elimination, controlling for non-modifiable risk factors and for the competing risk of death. METHODS: Participants from the InveCe.Ab population-based prospective cohort (Abbiategrasso, Italy) without a baseline dementia diagnosis and attending at least one follow-up visit were included (N = 1100). Participants underwent multidimensional assessment at baseline and after 2, 4, and 8 years, from November 2009 to January 2019. Modifiable risk factors were low education, obesity, hypertension, diabetes, depression, smoking, physical inactivity, hearing loss, loneliness, heart disease, stroke, head injury, and delirium. Non-modifiable risk factors were age, sex, and APOE ε4 genotype. The primary endpoint was dementia diagnosis within the follow-up period (DSM-IV criteria). We performed competing risk regression models to obtain sub-hazard ratio (SHR) for each exposure, with death as competing risk. The exposures associated with dementia were included in a multivariable model to estimate their independent influence on dementia and the corresponding population attributable fraction (PAF). RESULTS: Within the study period (mean follow-up, 82.3 months), 111 participants developed dementia (10.1%). In the multivariable model, APOE ε4 (SHR = 1.89, 95% CI 1.22–2.92, p = 0.005), diabetes (SHR = 1.56, 95% CI 1.00–2.39, p = 0.043), heart disease (SHR = 1.56, 95% CI 1.03–2.36, p = 0.037), stroke (SHR = 2.31, 95% CI 1.35–3.95, p = 0.002), and delirium (SHR = 8.70, 95% CI 3.26–23.24, p < 0.001) were independently associated with increased dementia risk. In the present cohort, around 40% of dementia cases could be attributable to preventable comorbid diseases. CONCLUSIONS: APOE ε4, diabetes, heart disease, stroke, and delirium independently increased the risk of late-life dementia, controlling for the competing risk of death. Preventive intervention addressed to these clinical populations could be an effective approach to reduce dementia incidence. Further studies on different population-based cohort are needed to obtain more generalizable findings of the potential of dementia prevention in the real-world setting. TRIAL REGISTRATION: ClinicalTrials.gov, NCT01345110. |
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