Heliyon · 7 authors, 1 centre
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This study identified seven shared blood biomarkers (SMC4, CDC27, HNF1A, RHOD, CUX1, PDLIM5, TTR) for Alzheimer's disease, type 2 diabetes, and major depressive disorder through bioinformatics analysis. Clinical survey data from 1094 elderly individuals confirmed that type 2 diabetes (OR=1.839) and depression (OR=3.049) significantly increase dementia risk. These findings suggest common pathogenic pathways and potential early diagnostic markers for these interconnected conditions.
**Background:** Alzheimer's disease (AD), type 2 diabetes mellitus (T2DM), and major depressive disorder (MDD) are common in the elderly and have increasing prevalence. Evidence suggests close associations among them, but shared mechanisms remain unclear. This study aimed to explore common pathogenesis and peripheral blood biomarkers for AD, MDD, and T2DM.
**Methods:** Microarray data were downloaded from the Gene Expression Omnibus (GEO) database: AD datasets GSE18309 (3 AD, 3 controls) and GSE97760 (10 AD, 9 controls); T2DM datasets GSE26168 (8 T2DM, 9 controls) and GSE19532 (8 T2DM, 8 controls); MDD datasets GSE32280 (8 MDD, 8 controls) and GSE98793 (64 MDD, 64 controls). All samples were blood. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to construct co-expression networks. Differentially expressed genes (DEGs) were identified using the limma package (p-value<0.05). Intersection of DEGs from all three diseases yielded co-DEGs. GO and KEGG enrichment analyses were performed using ClusterProfiler. Protein-protein interaction (PPI) networks were built using STRING and Cytoscape. Hub genes were identified. Receiver operating characteristic (ROC) curves were constructed to evaluate diagnostic value. Drug prediction was performed using the L1000 platform. A clinical survey of 1094 elderly individuals (aged >60) in Zhuhai used MMSE, MoCA, HAMA, HAMD, and ADL scales. Logistic regression and Pearson correlation analyses were conducted.
**Key Results:** Bioinformatics analysis identified 127 co-DEGs, including 19 upregulated and 25 downregulated genes. Seven hub genes were identified: SMC4, CDC27, HNF1A, RHOD, CUX1, PDLIM5, and TTR. ROC analysis showed good diagnostic efficiency for AD (AUC 0.7308-0.8462) and T2DM (AUC 0.8015-0.9118), and moderate for MDD (AUC 0.6117-0.717). KEGG enrichment of co-DEGs included metabolic diseases (AGE-RAGE signaling pathway, p=0.00902; lipid and atherosclerosis, p=0.008) and signaling pathways (cell cycle, p=0.0036; chemokine signaling, p=0.0048). Clinical survey: dementia prevalence was 24.63%, depression 61.57%, anxiety and depression 43.28%, MCI 23.3%. Multivariate logistic regression showed T2DM (OR=1.839, 95%CI: 1.215-2.783, p=0.004) and depression (OR=3.049, 95%CI: 1.447-6.428, p=0.003) as significant risk factors for dementia. Pearson correlation showed MoCA score negatively associated with diabetes (r=-0.172, p<0.001) and depression (r=-0.433, p<0.001). Drug prediction identified RO-3306 (CDK inhibitor) as a potential therapeutic agent.
**Clinical Implications:** The seven hub genes may serve as peripheral blood biomarkers for early diagnosis of AD, especially in patients with T2DM and MDD. The shared pathways suggest that antidepressant and hypoglycemic therapy could play a role in AD treatment. Early intervention in diabetic patients with depressive symptoms may delay dementia onset. However, limitations include small sample sizes in GEO datasets, lack of multi-center validation, and potential bias from one community being a pension institution. Future large-scale, multi-center studies are needed.