Association of the inflammation-related proteome with dementia development at older age: results from a large, prospective, population-based cohort study.

Background: Chronic inflammation is a central feature of several forms of dementia. However, few details on the associations of blood-based inflammation-related proteins with dementia incidence have been explored yet.

Methods: The Olink Target 96 Inflammation panel was measured in baseline serum samples (collected 07/2000-06/2002) of 1782 older adults from a German, population-based cohort study in a case-cohort design. Logistic regression models were used to assess the associations of biomarkers with all-cause dementia, Alzheimer's disease, and vascular dementia incidence.

Results: During 17 years of follow-up, 504 participants were diagnosed with dementia, including 163 Alzheimer's disease and 195 vascular dementia cases. After correction for multiple testing, 58 out of 72 tested (80.6%) biomarkers were statistically significantly associated with all-cause dementia, 22 with Alzheimer's disease, and 33 with vascular dementia incidence. We identified four biomarker clusters, among which the strongest representatives, CX3CL1, EN-RAGE, LAP TGF-beta-1, and VEGF-A, were significantly associated with dementia endpoints independently from other inflammation-related proteins. CX3CL1 (odds ratio [95% confidence interval] per 1 standard deviation increase: 1.41 [1.24-1.60]) and EN-RAGE (1.41 [1.25-1.60]) were associated with all-cause dementia incidence, EN-RAGE (1.51 [1.25-1.83]) and LAP TGF-beta-1 (1.46 [1.21-1.76]) with Alzheimer's disease incidence, and VEGF-A (1.43 [1.20-1.70]) with vascular dementia incidence. All named associations were stronger among APOE ε4-negative subjects.

Conclusion: With this large, population-based cohort study, we show for the first time that the majority of inflammation-related proteins measured in blood samples are associated with total dementia incidence. Future studies should concentrate not only on single biomarkers but also on the complex relationships in biomarker clusters.