Markers help to predict dementia with Lewy bodies
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Late-life depression (LLD) has been linked to increased likelihood of dementia, although mechanisms responsible for this association remain largely unknown. One feature frequently observed in both LLD and dementia is elevated levels of plasma inflammatory markers. The present study aimed to compare the levels of 12 plasma inflammatory markers between older people with LLD and controls, and to explore whether these markers, along with clinical characteristics, can predict dementia in patients with LLD within 3 years of follow-up. Using multiple linear regression with stepwise adjustment, we compared levels of plasma inflammatory markers (IL-1β, IL-1ra, IL-6, IL-10, IL-17a, IL-18, IL-33, TNFα, CD40L, IFN-γ, CCL-2 and CCL-4) between 136 inpatients with LLD (PRODE cohort) and 103 cognitively healthy non-depressed controls (COGNORM cohort). In the PRODE cohort, follow-up data was available for 139 patients (of them 123 had data on baseline plasma inflammatory markers); 36 (25.9%) developed dementia by Year 3 (n = 31 for those with cytokine data). Using Cox proportional hazards regression, we explored whether inflammatory markers and clinical characteristics of LLD (age of onset, treatment response, number of episodes) predicted progression to dementia during follow-up. Levels of IL-1ra, CCL-2, CCL-4, IFN-γ and IL-17a were significantly higher in LLD patients compared to controls in the majority of models. However, none of the inflammatory markers predicted progression from LLD to dementia in the PRODE cohort. Among clinical features, only poor response to treatment significantly predicted higher risk of progression to dementia.
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Neuroinflammation is associated with both early and late stages of the pathophysiology of Alzheimer’s disease (AD). Fluid biomarkers are gaining significance in clinical practice for diagnosis in presymptomatic stages, monitoring, and disease prognosis. This systematic literature review (SLR) aimed to identify fluid biomarkers for neuroinflammation related to clinical stages across the AD continuum and examined long-term outcomes associated with changes in biomarkers.
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Depression is a risk factor for the later development of Alzheimer’s disease (AD), but evidence for the genetic relationship is mixed. Assessing depression symptom-specific genetic associations may better clarify this relationship. To address this, we conducted genome-wide meta-analysis (a genome-wide association study, GWAS) of the nine depression symptom items, plus their sum score, on the Patient Health Questionnaire (PHQ-9) (GWAS-equivalent N: 224,535–308,421) using data from UK Biobank, the GLAD study and PROTECT, identifying 37 genomic risk loci. Using six AD GWASs with varying proportions of clinical and proxy (family history) case ascertainment, we identified 20 significant genetic correlations with depression/depression symptoms. However, only one of these was identified with a clinical AD GWAS. Local genetic correlations were detected in 14 regions. No statistical colocalization was identified in these regions. However, the region of the transmembrane protein 106B gene (TMEM106B) showed colocalization between multiple depression phenotypes and both clinical-only and clinical + proxy AD. Mendelian randomization and polygenic risk score analyses did not yield significant results after multiple testing correction in either direction. Our findings do not demonstrate a causal role of depression/depression symptoms on AD and suggest that previous evidence of genetic overlap between depression and AD may be driven by the inclusion of family history-based proxy cases/controls. However, colocalization at TMEM106B warrants further investigation.
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