The journal of prevention of Alzheimer's disease · 2026
Associations of modifiable and non-modifiable risk factors with longitudinal white matter hyperintensities, amyloid-β and tau - a prospective cohort study.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Glans Isabelle, Mattsson-Carlgren Niklas, Strandberg Olof, Stomrud Erik, Ossenkoppele Rik, van Westen Danielle, Spotorno Nicola, Hansson Oskar, Palmqvist Sebastian
Аннотация
BACKGROUND: The global prevalence of dementia is rapidly expanding and is expected to triple by 2050. Approximately 45 % of dementia cases are estimated to be attributable to potentially modifiable risk factors. Identifying how these factors contribute to specific brain pathologies may improve strategies to reduce dementia incidence.
OBJECTIVES, DESIGN, SETTING: The aim of this study was to identify both non-modifiable and modifiable risk factors associated with longitudinal changes in white matter hyperintensities (WMH), amyloid-beta (Aβ) and tau. Data were acquired in the prospective observational Swedish BioFINDER-2 study between May 2017-January 2025. All participants underwent clinical assessments, questionnaires and at least two magnetic resonance imaging (MRI), Aβ Positron Emission Tomography (PET) and tau PET scans, respectively. Mixed-effects models were used to assess the associations between non-modifiable and modifiable risk factors and WMH (MRI), Aβ (PET) and tau (PET).
PARTICIPANTS: A total of 494 cognitively unimpaired participants were included, of whom 365 were amyloid-negative (CU Aβ-) and 129 were amyloid-positive (CU Aβ+).
MEASUREMENTS AND MAIN OUTCOMES: Non-modifiable (age, apolipoprotein E (APOE) ɛ4 genotype and sex) and modifiable risk factors (co-morbidities at baseline, such as hypertension and cardiovascular disease, BMI, and sleep duration) were analyzed with mixed-effects models, adjusted for age and sex, to predict longitudinal measurements of WMH, Aβ and tau.
RESULTS: Mean age was 64.8 (SD 13.3) years and mean follow-up was 3.9 (SD 1.5) years. Predictors represent baseline data, both predictors and outcomes are on standardized scales. Linear mixed-effects models, adjusted for age and sex, showed that higher blood pressure (β = 0.02, 95 % CI :0.01-0.02), presence of hyperlipidemia (β = 0.03, 0.01-0.05), ischemic heart disease (β = 0.06, 0.03-0.09), smoking (β = 0.02, 0.00-0.03) and lower education (β = -0.01, -0.02- -0.01) were associated with a longitudinal increase in WMH. Presence of the APOE ε4 allele was linked to faster Aβ accumulation (β = 0.03, 0.02-0.04) and tau (β = 0.01, 0.00-0.03), but only to Aβ among Aβ+ positive participants. Higher depression score (β = 0.01, 0.00-0.01) and diabetes (β = 0.02, 0.00-0.04) were associated with faster Aβ accumulation. Lower BMI was associated with faster accumulation of tau (β = -0.01, -0.02- -0.01).
CONCLUSIONS: Modifiable risk factors of future dementia primarily affect accumulation of cerebral vascular pathology, although lower BMI was associated with tau accumulation and diabetes with Aβ accumulation.
OBJECTIVES, DESIGN, SETTING: The aim of this study was to identify both non-modifiable and modifiable risk factors associated with longitudinal changes in white matter hyperintensities (WMH), amyloid-beta (Aβ) and tau. Data were acquired in the prospective observational Swedish BioFINDER-2 study between May 2017-January 2025. All participants underwent clinical assessments, questionnaires and at least two magnetic resonance imaging (MRI), Aβ Positron Emission Tomography (PET) and tau PET scans, respectively. Mixed-effects models were used to assess the associations between non-modifiable and modifiable risk factors and WMH (MRI), Aβ (PET) and tau (PET).
PARTICIPANTS: A total of 494 cognitively unimpaired participants were included, of whom 365 were amyloid-negative (CU Aβ-) and 129 were amyloid-positive (CU Aβ+).
MEASUREMENTS AND MAIN OUTCOMES: Non-modifiable (age, apolipoprotein E (APOE) ɛ4 genotype and sex) and modifiable risk factors (co-morbidities at baseline, such as hypertension and cardiovascular disease, BMI, and sleep duration) were analyzed with mixed-effects models, adjusted for age and sex, to predict longitudinal measurements of WMH, Aβ and tau.
RESULTS: Mean age was 64.8 (SD 13.3) years and mean follow-up was 3.9 (SD 1.5) years. Predictors represent baseline data, both predictors and outcomes are on standardized scales. Linear mixed-effects models, adjusted for age and sex, showed that higher blood pressure (β = 0.02, 95 % CI :0.01-0.02), presence of hyperlipidemia (β = 0.03, 0.01-0.05), ischemic heart disease (β = 0.06, 0.03-0.09), smoking (β = 0.02, 0.00-0.03) and lower education (β = -0.01, -0.02- -0.01) were associated with a longitudinal increase in WMH. Presence of the APOE ε4 allele was linked to faster Aβ accumulation (β = 0.03, 0.02-0.04) and tau (β = 0.01, 0.00-0.03), but only to Aβ among Aβ+ positive participants. Higher depression score (β = 0.01, 0.00-0.01) and diabetes (β = 0.02, 0.00-0.04) were associated with faster Aβ accumulation. Lower BMI was associated with faster accumulation of tau (β = -0.01, -0.02- -0.01).
CONCLUSIONS: Modifiable risk factors of future dementia primarily affect accumulation of cerebral vascular pathology, although lower BMI was associated with tau accumulation and diabetes with Aβ accumulation.