Identification of Potential Blood-Based Biomarkers for Frailty by Using an Integrative Approach.

INTRODUCTION: Although frailty is a geriatric syndrome that is associated with disability, hospitalization, and mortality, it can be reversible and preventable with the appropriate interventions. Additionally, as the current diagnostic criteria for frailty include only physical, psychological, cognitive, and social measurements, there is a need for promising blood-based molecular biomarkers to aid in the diagnosis of frailty. METHODS: To identify candidate blood-based biomarkers that can enhance current diagnosis of frailty, we conducted a comprehensive analysis of clinical data, messenger RNA-sequencing (RNA-seq), and aging-related factors using a total of 104 older adults aged 65-90 years (61 frail subjects and 43 robust subjects) in a cross-sectional case-control study. RESULTS: We identified two candidate biomarkers of frailty from the clinical data analysis, nine from the RNA-seq analysis, and six from the aging-related factors analysis. By using combinations of the candidate biomarkers and clinical information, we constructed risk prediction models. The best models used combinations that included skeletal muscle mass index measured by dual-energy X-ray absorptiometry (adjusted p = 0.026), GDF15 (adjusted p = 1.46E-03), adiponectin (adjusted p = 0.012), CXCL9 (adjusted p = 0.011), or apelin (adjusted p = 0.020) as the biomarker. These models achieved a high area under the curve of 0.95 in an independent validation cohort (95% confidence interval: 0.79-0.97). Our risk prediction models showed significantly higher areas under the curve than did models constructed using only basic clinical information (Welch's t test p < 0.001). CONCLUSION: All five biomarkers showed statistically significant correlations with components of the frailty diagnostic criteria. We discovered several potential biomarkers for the diagnosis of frailty. Further refinement may lead to their future clinical use.

Conduction velocity mapping in atrial fibrillation using omnipolar technology.

BACKGROUND: Recent studies have shown that atrial slow conduction velocity (CV) is associated with the perpetuation of atrial fibrillation (AF). However, the criteria of CV measurement have not been standardized. The aim of this study was to evaluate the relationship between the slow CV area (SCVA) measured by novel omnipolar technology (OT) and AF recurrence. METHODS: This study included 90 patients with AF who underwent initial pulmonary vein isolation (PVI). The segmented surface area of the SCVA was measured by left atrial (LA) electrophysiological mapping using OT before the PVI. The proportion of the SCVA at each cutoff value of CV (from < 0.6 to < 0.9 m/s) was compared between the patients with and without AF recurrence. RESULTS: During a mean follow-up period of 516 ± 197 days, the recurrence of AF after the initial PVI was observed in 23 (25.5%) patients. In patients with AF recurrence, the proportion of the SCVA in the LA posterior, LA appendage (LAA), and LA anterior were significantly higher than those without AF recurrence. The multivariate analysis indicated that the proportion of the low voltage area and the SCVA in the LA anterior (local CV < 0.7 m/s) were independent predictors of AF recurrence (hazard ratio [HR], 1.07; 95% confidence interval [CI], 1.01-1.14; p = 0.03; HR, 1.40; 95% CI, 1.07-1.83; p = 0.01, respectively). CONCLUSION: By evaluating the local CV using OT, it was indicated that SCVA with CV < 0.7 m/s in the LA anterior is strongly associated with AF recurrence after PVI.

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

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.

RNA-Sequencing Analysis Identification of Potential Biomarkers for Diagnosis of Sarcopenia.

Sarcopenia is a 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 a combination of comprehensive clinical data and messenger RNA-sequencing (RNA-seq) analysis obtained from peripheral blood mononuclear cells. We enrolled a total of 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 = 0.14, 95% confidence interval [CI]: 0.05-0.40). RNA-seq analysis detected 6 differential expressed genes (FAR1, GNL2, HERC5, MRPL47, NUBP2, and S100A11). We also performed gene-set enrichment analysis and detected 2 functional modules (ie, hub genes, MYH9, and FLNA). By using any combination of the 9 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: 0.78-0.95). The quantitative PCR results of the 3 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: 0.84-0.99). We have discovered potential biomarkers for the diagnosis of sarcopenia. Further refinement may lead to their future practical use in clinical use.