Inflammation-related biomarkers across Alzheimer disease severity in older adults: A retrospective single-center study

Aim: We examined how routine inflammation-related biomarkers vary across Alzheimer disease (AD) severity and whether they are independently associated with moderate-to-severe disease.

Materials and Methods: This retrospective cross-sectional study included outpatients aged ≥ 65 years with clinically diagnosed AD. Severity was staged (Clinical Dementia Rating) as mild, moderate, or severe. We evaluated 6 biomarkers: red cell distribution width-to-albumin ratio (RAR), C-reactive protein-to-albumin ratio (CAR), C-reactive protein-albumin-lymphocyte index (CALLY), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and systemic immune-inflammation index (SII). Groups were compared using nonparametric tests with multiple-comparison corrections. Cross-sectional separation was assessed using the area under the receiver operating characteristic curve (AUC). We used Firth penalized logistic regression with multiple imputation, adjusting for age, body mass index, sex, malnutrition risk, and multimorbidity.

Results: Among 151 patients (90 mild, 53 moderate, 8 severe), only CAR differed across severity in unadjusted tests (p = .03); no biomarker remained significant after multiple-comparison correction (Holm-adjusted p ≥ .18). Cross-sectional separation for moderate-to-severe disease was weak (all AUCs < 0.60). In adjusted models, no biomarker was independently associated with moderate-to-severe disease (SII [per 100-unit increment]: odds ratio [OR], 1.06 [95% confidence interval (CI), 0.95 – 1.17]; CAR: OR, 0.90 [95% CI, 0.64 – 1.08]; CALLY: OR, 0.99 [95% CI, 0.96 – 1.03]). Multimorbidity was inversely associated with severity.

Conclusion: Routine inflammation-related biomarkers were not independently associated with AD severity. Frailty and functional status, rather than blood biomarkers, most clearly indicated severity. These hypothesis-generating findings require validation in larger cohorts.