Leader: Licia Iacoviello (NEUROMED); Other collaborator(s): Rosa Campopiano (Neuromed) Carla Letizia Busceti (Neuromed)
Molisani biobank will be tested for telomeric ncRNA accumulation during aging and disease
Brief description of the activities and of the intermediate results: finalization of the DNN algorithm used for Heart Age estimation, based on dichotomous ECG tracing alteration features (Minnesota coding system). Performance evaluation in terms of loss (root mean squared error, RMSE) and accuracy (mean absolute error, MAE) with respect to the label variable (chronological age). The best performing algorithm shows RMSE = 84.86 and MAE = 7.27 years, Pearson correlation coefficient r = 0.62 and R2 = 0.39 in the test set. A comparative analysis of the literature in the field of heart age estimation based on similar algorithms revealed how the algorithm thus produced performs comparably – if not better – than neural networks trained on cohorts 1.5x to 65x times larger ( doi: 10.1038/s41467-021-25351-7; 10.1161/CIRCEP.119.007284; Calculation of the discrepancy between biological and chronological age as an index of biological aging in the test set.
Update: validation of the Heart Age measure and the relative difference between HeartAge and chronological age (hereinafter DeltaHeartAge), through comparison between men and women, subjects with prevalent cardiovascular disease and unaffected subjects, as well as through Cox proportional hazards models for the prediction of the incident risk of death from all causes and from specific causes (cardiovascular, cancer, other cause), hospitalizations for specific causes (cardiovascular, cancer), as well as for incident fatal/non-fatal cases of type 2 diabetes and various types of cancer (breast, prostate, lung and colorectal). To this end, incremental Cox models were constructed, adjusted for i) age and sex (Model 1), ii) Model 1 + prevalent diseases (cardiovascular, diabetes, dyslipidemia, hypertension, cancer and BMI) (Model 2), iii) Model 2 + education level (Model 3), iv) Model 4 + lifestyle factors (adherence to the Mediterranean diet, physical activity, daily caloric and alcohol intake). These analyses revealed significant risk associations of increasing DeltaHeartAge with all-cause mortality, coronary heart disease and stroke, hospitalizations for atrial fibrillation and with incident risk of occurrence of fatal/non-fatal type 2 diabetes.
For the same purpose, it was decided to invest in estimating epigenetic clocks, which aim to assess the degree of aging based on methylation patterns in the genome and are the most tested and successful estimators of biological aging in the past 10 years. Specifically, after extracting DNA from buffy coat samples stored at the Neuromed biobanking Center and bisulfiting the DNA, methylation levels at approximately 935,000 CpG sites in the epigenome began to be tested using the Illumina MethylationEPIC V2.0 Kit array, for about 1,400 participants from the study out of the ~2,500 with data available from both baseline recruitment (2005-2010) and the first active cohort follow-up (2017-2020). These subjects will allow the analysis of how epigenetic aging correlates with biological aging measured through other blood-based clocks (BioAge and PhenoAge) and with organ-specific estimators (e.g., HeartAge). Additionally, it will be possible to test whether these clocks independently predict fractions of risk for mortality and age-related chronic diseases. To better understand the mechanisms and pathways of the biological aging process at the brain level, we have initiated studies on induced stem cells obtained from a cohort of subjects over the age of 65, consisting of healthy individuals and patients with neurodegenerative diseases. These cells are differentiated into different neuronal populations in order to study the neuronal connection network under physiological and pathological conditions and their ability to respond to external stimuli. These studies will be conducted using advanced technological platforms based on MEA technology and confocal fluorescence microscopy.
We have made progress in deploying a novel algorithm for the estimation of HeartAge. In particular, we improved training by implementing hyperparamter tuning of DNN models through Bayesian approaches using ‘rBayesianOptimization’ package (v. 1.2.1) in R. This approach is expected to be more efficient and to outperform other optimization strategies (grid and random searches). Moreover, we have investigated the inclusion of novel potential features for the prediction of HeartAge, like Heart Rate variability.
Two lines of iPSCs derived from healthy individuals and two derived from PD patients were selected. Differentiation of iPSCs into dopaminergic neurons was performed as described (PMID 28858290), and the expression of specific markers were analyzed through Immunofluorescence and Confocal Imaging, at various stages of differentiation. Specifically, we analyzed: on day 0, pluripotency markers NANOG, OCT4, and SOX2, before initiating neuronal differentiation; on day 16, LMX1a/b and FOXA2, markers of dopaminergic precursor neurons of the substantia nigra; on day 45, at the end of differentiation, the expression of dopaminergic neuron marker TH and pan-neural marker MAP2. Additionally, we have set up plating conditions on MEA chips to evaluate the neuronal activity of PD cells and control cells at different stages of differentiation.
Coming soon