Late-onset Alzheimer’s disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the symptoms vary among patients. Genome-wide association studies (GWAS) have identified genetic risk factors for LOAD but not for LOAD subtypes. Here, we examined the genetic architecture of LOAD based on Japanese GWAS data from 1947 patients and 2192 cognitively normal controls in a discovery cohort and 847 patients and 2298 controls in an independent validation cohort. Two distinct groups of LOAD patients were identified. One was characterized by major risk genes for developing LOAD (APOC1 and APOC1P1) and immune-related genes (RELB and CBLC). The other was characterized by genes associated with kidney disorders (AXDND1, FBP1, and MIR2278). Subsequent analysis of albumin and hemoglobin values from routine blood test results suggested that impaired kidney function could lead to LOAD pathogenesis. We developed a prediction model for LOAD subtypes using a deep neural network, which achieved an accuracy of 0.694 (2870/4137) in the discovery cohort and 0.687 (2162/3145) in the validation cohort. These findings provide new insights into the pathogenic mechanisms of LOAD.
Sarcopenia is geriatric disease associated with increased mortality and disability. Early diagnosis and intervention are required to prevent it. This study investigated biomarkers for sarcopenia by using combination of comprehensive clinical data and messenger RNA sequencing (RNA-seq) analysis obtained from peripheral blood mononuclear cells. We enrolled total 114 older adults aged 66 – 94 years (52 sarcopenia diagnosed according to the Asian Working Group for Sarcopenia 2019 consensus and 62 normal older people). We used clinical data which were not included diagnosis criteria of sarcopenia, and stride length showed significance by logistic regression analysis (Bonferroni corrected p = .012, odds ratio = 0.06, 95% confidence interval [CI]: 0.05 to 0.40). RNA-seq analysis detected six differential expressed genes (FAR1, GNL2, HERC5, MRPL47, NUBP2, and S100A11). We also performed gene set enrichment analysis and detected two functional modules (i.e., hub genes, MYH9 and FLNA). By using any combination of the nine candidates and basic information (age and sex), risk-prediction models were constructed. The best model by using a combination of stride length, HERC5, S100A11, and FLNA, achieved a high area under the curve (AUC) of 0.91 in a validation cohort (95% CI: 0.78 to 0.95). The quantitative PCR results of the three genes were consistent with the trend observed in the RNA-seq results. When BMI was added, the model achieved a high AUC of 0.95 (95% CI: 0.84 to 0.99). We have discovered potential biomarkers for the diagnosis of sarcopenia. Further refinement may lead to their future practical use in clinical use.
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