Associations of blood cell indices and anemia with risk of incident dementia: A prospective cohort study of 313,448 participants.

INTRODUCTION: Low hemoglobin and anemia are associated with cognitive impairment and Alzheimer's disease (AD). However, the associations of other blood cell indices with incident dementia risk and the underlined mechanisms are unknown. METHODS: Three hundred thirteen thousand four hundred forty-eight participants from the UK Biobank were included. Cox and restricted cubic spline models were used to investigate linear and non-linear longitudinal associations. Mendelian randomization analysis was used to identify causal associations. Linear regression models were used to explore potential mechanisms driven by brain structures. RESULTS: During a mean follow-up of 9.03 years, 6833 participants developed dementia. Eighteen indices were associated with dementia risk regarding erythrocytes, immature erythrocytes, and leukocytes. Anemia was associated with a 56% higher risk of developing dementia. Hemoglobin and red blood cell distribution width were causally associated with AD. Extensive associations exist between most blood cell indices and brain structures. DISCUSSION: These findings consolidated associations between blood cells and dementia. HIGHLIGHT: Anemia was associated with 56% higher risk for all-cause dementia. Hematocrit percentage, mean corpuscular volume, platelet crit, and mean platelet volume had U-shaped associations with incident dementia risk. Hemoglobin (HGB) and red blood cell distribution width had causal effects on Alzheimer's risk. HGB and anemia were associated with brain structure alterations.

Plasma metabolic profiles predict future dementia and dementia subtypes: a prospective analysis of 274,160 participants.

BACKGROUND: Blood-based biomarkers for dementia are gaining attention due to their non-invasive nature and feasibility in regular healthcare settings. Here, we explored the associations between 249 metabolites with all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VaD) and assessed their predictive potential. METHODS: This study included 274,160 participants from the UK Biobank. Cox proportional hazard models were employed to investigate longitudinal associations between metabolites and dementia. The importance of these metabolites was quantified using machine learning algorithms, and a metabolic risk score (MetRS) was subsequently developed for each dementia type. We further investigated how MetRS stratified the risk of dementia onset and assessed its predictive performance, both alone and in combination with demographic and cognitive predictors. RESULTS: During a median follow-up of 14.01 years, 5274 participants developed dementia. Of the 249 metabolites examined, 143 were significantly associated with incident ACD, 130 with AD, and 140 with VaD. Among metabolites significantly associated with dementia, lipoprotein lipid concentrations, linoleic acid, sphingomyelin, glucose, and branched-chain amino acids ranked top in importance. Individuals within the top tertile of MetRS faced a significantly greater risk of developing dementia than those in the lowest tertile. When MetRS was combined with demographic and cognitive predictors, the model yielded the area under the receiver operating characteristic curve (AUC) values of 0.857 for ACD, 0.861 for AD, and 0.873 for VaD. CONCLUSIONS: We conducted the largest metabolome investigation of dementia to date, for the first time revealed the metabolite importance ranking, and highlighted the contribution of plasma metabolites for dementia prediction.