@Article{Roman_et_al_BMC_Med_2026,
  author = {{Aurora Román-Domínguez} and {Cristina Mas-Bargués} and {Virgilio Pérez} and {Miguel Medina} and {Jesús Ávila} and {Consuelo Borrás} and {José VIña}},
  title = {Peripheral blood biomarkers RCAN1, Clusterin, RAGE, and malondialdehyde for early diagnosis and progression of Alzheimer’s Disease},
  journal = {BMC Medicine},
  year = {2026},
  volume = {24},
  number = {354},
  doi = {10.1186/s12916-026-04882-0},
  url = {https://doi.org/10.1186/s12916-026-04882-0},
  abstract = {
  Background
Alzheimer’s disease (AD) diagnosis often relies on invasive or costly techniques such as cerebrospinal fluid sampling and PET imaging. Peripheral blood biomarkers could offer a minimally invasive and accessible alternative. We aimed to evaluate the diagnostic and prognostic value of four candidate biomarkers—Clusterin, RCAN1, RAGE, and MDA—in the context of cognitive decline, and to generate a predictive model for AD diagnosis.

Methods
We conducted longitudinal and cross-sectional analyses among participants in the Vallecas Project (Spain). For longitudinal analyses, 52 subjects with paired baseline and 5-year follow-up samples were classified as stable cognitively healthy controls, MCI converters, or AD progression. Cross-sectional analyses were conducted using a single observation per subject (n = 83) selected to reduce age differences between the three groups, although AD patients were significantly older. Biomarker levels were measured in plasma or serum by ELISA (Clusterin, RCAN1, RAGE) or UPLC (MDA). A predictive model for AD diagnosis was developed using penalized logistic regression based on baseline data from 76 subjects, incorporating biomarkers, age, sex, and APOE ε4 genotype.

Results
In the longitudinal analysis, RCAN1 levels decreased significantly over time in cognitively stable controls, whereas Clusterin levels decreased in the AD progression group. No significant longitudinal changes were observed in MCI converters. In the cross-sectional analysis, RCAN1 and MDA levels were significantly lower in AD patients than in cognitively healthy controls and MCI patients. RAGE levels showed a trend toward reduction in MCI but did not remain significant. At baseline, cognitively healthy individuals who later converted to MCI exhibited higher MDA levels and lower RAGE levels than stable controls. The predictive model achieved a mean cross-validated accuracy of approximately 92% and an area under the ROC curve (AUC) of 0.95 (95% CI: 0.94–0.96), with good calibration.

Conclusions
RCAN1, Clusterin, RAGE, and MDA show potential as peripheral biomarkers for monitoring and early detection of Alzheimer’s disease. Longitudinal and cross-sectional alterations in these markers suggest that biochemical changes may precede clinical symptoms. A multivariable predictive model combining biomarkers with demographic and genetic factors demonstrated robust discriminative performance, supporting the potential utility of minimally invasive blood-based screening tools for AD.}