Background Health-related factors may influence blood-based biomarkers (BBBM) of Alzheimer's disease (AD). In this analysis, associations between modifiable factors and plasma biomarkers of neurodegeneration were investigated across the Alzheimer's disease spectrum and in cognitively healthy controls in a cerebrospinal fluid–confirmed (CSF) cohort. Methods Plasma biomarkers included the Aβ1–42/1–40 ratio, pTau181, GFAP, NFL and ApoE4. Multiple linear regression was used to test associations with lifestyle factors (physical activity and sleep), physiological factors (including renal and lipid metabolism markers), genetic factors (APOE ε4), age and sex. Percentage effect sizes and confidence intervals were calculated. Results The study included CSF-characterized individuals with AD (mild cognitive impairment due to AD and AD dementia) and cognitively healthy controls (n = 116; mean age 71.2 years). Overall, the associations were modest, with wide confidence intervals reflecting variability in the outcomes and the limited range of predictors in this relatively healthy sample. In CSF-confirmed participants, age emerged as the most consistent predictor of plasma biomarker levels, particularly NFL and pTau181. APOE ε3/ε4 genotype was additionally associated with higher pTau181 levels. Other demographic, metabolic and lifestyle-related variables showed only weak or inconsistent associations. Conclusion To implement BBBM in broader populations, a systematic evaluation of confounders is required. As aging cohorts present with mixed pathologies, strategies to address heterogeneity will be essential. The limited number of robust associations observed suggests that plasma biomarkers are influenced primarily by age and genetic background rather than by metabolic factors in this cohort. Validation in more diverse populations remains warranted.
Carolin I. Kurz, Marleen Taute, Paulina Tegethoff et al.· Aging Brain· 0 citations
White matter hyperintensities (WMH) are a highly prevalent finding on FLAIR MRI scans and a prominent feature of white matter pathology across cerebrovascular and neurodegenerative diseases. Currently, WMH are assessed with visual rating scales such as the Fazekas scale or with their volume, as calculated from automatic or manual segmentations. Both methods have limitations: Visual rating scales are rater-dependent and coarse, while WMH volume does not take the confluence of lesions into account and thus disregards their spatial organisation. As an alternative, here we propose a novel automated method for quantifying the confluence of white matter hyperintensities on a continuous standardised scale between 0 and 1. The metric is based on WMH segmentations from routine MRI and quantifies the extent to which individual WMH merge into coherent lesions, independently of total lesion volume. We apply the method to QMIN-MC, a large UK memory clinic cohort, and show associations of the confluence metric with age, cognitive performance across domains, and Fazekas ratings. Participants with vascular and mixed dementia showed higher confluence than other diagnostic groups, whereas cognitively unimpaired participants showed lower confluence. However, confluence did not explain additional cognitive variance after accounting for log-transformed WMH volume. Findings were validated in DELCODE, an independent cohort of individuals with neurodegenerative disorders, replicating our original results. In this validation cohort, periventricular WMH confluence remained associated with cognition after adjustment for WMH volume. These findings introduce WMH confluence as a reproducible, automated, and fine-grained measure of lesion spatial organisation. It provides complementary information about morphological WMH severity beyond volume and is an alternative to visual rating scales. Although related to WMH volume in memory-clinic populations, confluence captures clinically interpretable information and may complement existing WMH measures for improved lesion characterisation in studies of white matter disease, ageing, and cognitive impairment.
Tatjana Schmidt, Robert Salzmann, M. Montagnese et al.· bioRxiv· 0 citations
BAG is a reliable non-invasive marker of structural brain health sensitive to AD pathology and to modifiable AD risk and supports its relevance for early risk stratification and prevention-oriented research.
E. Kuhn, G. Antopoulos, L. Kleineidam et al.· medRxiv· 0 citations
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