CD4+/CD8+ Ratio and Growth Differentiation Factor 8 Levels in Peripheral Blood of Large Canine Males Are Useful Parameters to Build an Age Prediction Model

Background: The risk of cardiovascular (CV) and fatal events remains extremely high in patients with maintenance hemodialysis (MHD), and the growth differentiation factor 15 (GDF15) has emerged as a valid risk stratification biomarker. We aimed to develop a GDF15-based risk score as a death prediction model for MHD patients. Methods: Age, biomarker levels, and clinical parameters were evaluated at study entry. One hundred and seventy patients with complete information were finally included for data analysis. We performed the Cox regression analysis of various prognostic factors for mortality. Then, age, GDF15, and robust clinical predictors were included as a risk score model to assess the predictive accuracy for all-cause and CV death in the receiver operating characteristic (ROC) curve analysis. Results: Age, GDF15, and albumin were significantly associated with higher all-cause and CV mortality risk that were combined as a risk score model. The highest tertile of GDF-15 (>1707.1 pg/mL) was associated with all-cause mortality (adjusted hazard ratios (aHRs): 3.06 (95% confidence interval (CI): 1.20–7.82), p < 0.05) and CV mortality (aHRs: 3.11 (95% CI: 1.02–9.50), p < 0.05). The ROC analysis of GDF-15 tertiles for all-cause and CV mortality showed 0.68 (95% CI = 0.59 to 0.77) and 0.68 (95% CI = 0.58 to 0.79), respectively. By contrast, the GDF15-based prediction model for all-cause and CV mortality showed 0.75 (95% CI: 0.67–0.82) and 0.72 (95% CI: 0.63–0.81), respectively. Conclusion: Age, GDF15, and hypoalbuminemia predict all-cause and CV death in MHD patients, yet a combination scoring system provides more robust predictive powers. An elevated GDF15-based risk score warns clinicians to determine an appropriate intervention in advance. In light of this, the GDF15-based death prediction model could be developed in the artificial intelligence-based precision medicine.

Download Full-text

Improving Individual Brain Age Prediction Using an Ensemble Deep Learning Framework

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.626677 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chen-Yuan Kuo ◽

Tsung-Ming Tai ◽

Pei-Lin Lee ◽

Chiu-Wang Tseng ◽

Chieh-Yu Chen ◽

...

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Prediction Accuracy ◽

Analytical Framework ◽

Support Vector ◽

Feature Sets ◽

Input Feature ◽

Brain Age ◽

Analytical Frameworks ◽

Age Prediction

Brain age is an imaging-based biomarker with excellent feasibility for characterizing individual brain health and may serve as a single quantitative index for clinical and domain-specific usage. Brain age has been successfully estimated using extensive neuroimaging data from healthy participants with various feature extraction and conventional machine learning (ML) approaches. Recently, several end-to-end deep learning (DL) analytical frameworks have been proposed as alternative approaches to predict individual brain age with higher accuracy. However, the optimal approach to select and assemble appropriate input feature sets for DL analytical frameworks remains to be determined. In the Predictive Analytics Competition 2019, we proposed a hierarchical analytical framework which first used ML algorithms to investigate the potential contribution of different input features for predicting individual brain age. The obtained information then served as a priori knowledge for determining the input feature sets of the final ensemble DL prediction model. Systematic evaluation revealed that ML approaches with multiple concurrent input features, including tissue volume and density, achieved higher prediction accuracy when compared with approaches with a single input feature set [Ridge regression: mean absolute error (MAE) = 4.51 years, R2 = 0.88; support vector regression, MAE = 4.42 years, R2 = 0.88]. Based on this evaluation, a final ensemble DL brain age prediction model integrating multiple feature sets was constructed with reasonable computation capacity and achieved higher prediction accuracy when compared with ML approaches in the training dataset (MAE = 3.77 years; R2 = 0.90). Furthermore, the proposed ensemble DL brain age prediction model also demonstrated sufficient generalizability in the testing dataset (MAE = 3.33 years). In summary, this study provides initial evidence of how-to efficiency for integrating ML and advanced DL approaches into a unified analytical framework for predicting individual brain age with higher accuracy. With the increase in large open multiple-modality neuroimaging datasets, ensemble DL strategies with appropriate input feature sets serve as a candidate approach for predicting individual brain age in the future.

Download Full-text

Development of Biological Age Prediction Model Based on the Osseous Parameters and the Effects of Endurance Exercise Duration and Falling on Aging

Exercise Science ◽

10.15857/ksep.2016.25.1.60 ◽

2016 ◽

Vol 25 (1) ◽

pp. 60-67 ◽

Cited By ~ 1

Author(s):

Haemi Jee

Keyword(s):

Prediction Model ◽

Endurance Exercise ◽

Exercise Duration ◽

Biological Age ◽

Model Based ◽

Age Prediction

Download Full-text

DNA Methylation-Based Age Prediction and Telomere Length Reveal an Accelerated Aging in Induced Sputum Cells Compared to Blood Leukocytes: A Pilot Study in COPD Patients

Frontiers in Medicine ◽

10.3389/fmed.2021.690312 ◽

2021 ◽

Vol 8 ◽

Author(s):

Manuela Campisi ◽

Filippo Liviero ◽

Piero Maestrelli ◽

Gabriella Guarnieri ◽

Sofia Pavanello

Keyword(s):

Dna Methylation ◽

Telomere Length ◽

Peripheral Blood ◽

Induced Sputum ◽

Biological Aging ◽

Peripheral Blood Leukocytes ◽

Blood Leukocytes ◽

Copd Patients ◽

New Findings ◽

Age Prediction

Aging is the predominant risk factor for most degenerative diseases, including chronic obstructive pulmonary disease (COPD). This process is however very heterogeneous. Defining the biological aging of individual tissues may contribute to better assess this risky process. In this study, we examined the biological age of induced sputum (IS) cells, and peripheral blood leukocytes in the same subject, and compared these to assess whether biological aging of blood leukocytes mirrors that of IS cells. Biological aging was assessed in 18 COPD patients (72.4 ± 7.7 years; 50% males). We explored mitotic and non-mitotic aging pathways, using telomere length (TL) and DNA methylation-based age prediction (DNAmAge) and age acceleration (AgeAcc) (i.e., difference between DNAmAge and chronological age). Data on demographics, life style and occupational exposure, lung function, and clinical and blood parameters were collected. DNAmAge (67.4 ± 5.80 vs. 61.6 ± 5.40 years; p = 0.0003), AgeAcc (−4.5 ± 5.02 vs. −10.8 ± 3.50 years; p = 0.0003), and TL attrition (1.05 ± 0.35 vs. 1.48 ± 0.21 T/S; p = 0.0341) are higher in IS cells than in blood leukocytes in the same patients. Blood leukocytes DNAmAge (r = 0.927245; p = 0.0026) and AgeAcc (r = 0.916445; p = 0.0037), but not TL, highly correlate with that of IS cells. Multiple regression analysis shows that both blood leukocytes DNAmAge and AgeAcc decrease (i.e., younger) in patients with FEV1% enhancement (p = 0.0254 and p = 0.0296) and combined inhaled corticosteroid (ICS) therapy (p = 0.0494 and p = 0.0553). In conclusion, new findings from our work reveal a differential aging in the context of COPD, by a direct quantitative comparison of cell aging in the airway with that in the more accessible peripheral blood leukocytes, providing additional knowledge which could offer a potential translation into the disease management.

Download Full-text