Affiliations: [a]
Department of Neurology, Qingdao Municipal Hospital, Dalian Medical University, Dalian, China
| [b]
Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
Correspondence:
[*]
Correspondence to: Dr. Wei Xu, MD, PhD, Department of Neurology, Qingdao Municipal Hospital, Qingdao, China, Donghai Middle Road, No. 5, Qingdao, China. Tel.: +86 0532 15610091257; E-mails: [email protected] or [email protected].
Note: [1] The data used in preparation for this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in the analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
Abstract: Background:The causal relationships of late-life body mass index (BMI) with Alzheimer’s disease (AD) remains debated. Objective:We aimed to assess the associations of dynamic BMI features (ΔBMIs) with cognitive trajectories, AD biomarkers, and incident AD risk. Methods:We analyzed an 8-year cohort of 542 non-demented individuals who were aged ≥65 years at baseline and had BMI measurements over the first 4 years. ΔBMIs were defined as changing extent (change ≤ or >5%), variability (standard deviation), and trajectories over the first 4 years measured using latent class trajectory modeling. Linear mixed-effect models were utilized to examine the influence of ΔBMIs on changing rates of AD pathology biomarkers, hippocampus volume, and cognitive functions. Cox proportional hazards models were used to test the associations with AD risk. Stratified analyzes were conducted by the baseline BMI group and age. Results:Over the 4-year period, compared to those with stable BMI, individuals who experienced BMI decreases demonstrated accelerated declined memory function (p = 0.006) and amyloid-β deposition (p = 0.034) while BMI increases were associated with accelerated hippocampal atrophy (p = 0.036). Three BMI dynamic features, including stable BMI, low BMI variability, and persistently high BMI, were associated with lower risk of incident AD (p < 0.005). The associations were validated over the 8-year period after excluding incident AD over the first 4 years. No stratified effects were revealed by the BMI group and age. Conclusions:High and stable BMI in late life could predict better cognitive trajectory and lower risk of AD.
Keywords: Alzheimer’s disease, amyloid-β, body mass index, cognitive, late life, trajectory
