Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies—including genomics, transcriptomics, epigenomics, proteomics, and metabolomics—in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.

Sarcopenia, an age-related decline in skeletal muscle mass, strength, and function, is increasingly recognized as a significant condition in the aging population, particularly among those with cardiovascular diseases (CVD). This review provides a comprehensive synthesis of the interplay between sarcopenia and cardiogeriatrics, emphasizing shared mechanisms such as chronic low-grade inflammation (inflammaging), hormonal dysregulation, oxidative stress, and physical inactivity. Despite advancements in diagnostic frameworks, such as the EWGSOP2 and AWGS definitions, variability in criteria and assessment methods continues to challenge standardization. Key diagnostic tools include dual-energy X-ray absorptiometry (DXA) and bioimpedance analysis (BIA) for muscle mass, alongside functional measures such as grip strength and gait speed. The review highlights the bidirectional relationship between sarcopenia and cardiovascular conditions such as heart failure, aortic stenosis, and atherosclerotic cardiovascular disease, which exacerbate each other through complex pathophysiological mechanisms. Emerging therapeutic strategies targeting the mTOR pathway, NAD+ metabolism, and senescence-related processes offer promise in mitigating sarcopenia’s progression. Additionally, integrated interventions combining resistance training, nutritional optimization, and novel anti-aging therapies hold significant potential for improving outcomes. This paper underscores critical gaps in the evidence, including the need for longitudinal studies to establish causality and the validation of advanced therapeutic approaches in clinical settings. Future research should leverage multi-omics technologies and machine learning to identify biomarkers and personalize interventions. Addressing these challenges is essential to reducing sarcopenia’s burden and enhancing the quality of life for elderly individuals with comorbid cardiovascular conditions. This synthesis aims to guide future research and promote effective, individualized management strategies.

This nationwide study aims to analyze mortality trends for all individual causes in Greece from 2001 to 2020, with a specific focus on 2020 - a year influenced by the coronavirus pandemic. As Greece is the fastest-aging country in Europe, the study's findings can be generalized guiding the re-evaluation of global health policies. Data on the population and the number of deaths were retrieved from the Hellenic Statistical Authority. We calculated age-standardized mortality rates (ASMR) and cause-specific mortality rates by gender in three age groups (0-64, 65-79, and 80+) from 2001 to 2020. Proportional mortalities for 2020 were determined. Statistical analysis used generalized linear models with Python Programming Language. From 2001 to 2020, cardiovascular diseases (CVD) ASMR decreased by 42,7% (p<0,0001), with declines in most sub-causes, except for hypertensive diseases, which increased by 2,8-fold (p<0,0001). In 2020, the proportional mortalities of the three leading causes were 34,9% for CVD, 23,5% for neoplasms, and 9,6% for respiratory diseases (RD). In 2020, CVD dominate in ages 80+ (39,3%), while neoplasms prevail in ages 0-79 (37,7%). Among cardiovascular sub-causes, cerebrovascular diseases dominate in ages 80+ (30,3%), while ischemic heart diseases prevail in ages 0-79 (up to 60,0%). The global phenomenon of population aging necessitates a re-framing of health policies in our aging societies, focusing on diseases with either a high mortality burden, such as CVD, neoplasms, and RD, or those experiencing increasing trends, such as hypertensive diseases.

Obesity in elderly individuals is associated with increased levels of inflammatory biomarkers, indicating a state of chronic low-grade inflammation, which has been recently termed as adipaging. Several studies have demonstrated this relationship: overweight and obese middle-aged and elderly individuals show elevated levels of inflammatory markers like CXCL-16, IL-6, and adipokines compared to normal weight counterparts. These markers positively correlate with anthropometric parameters indicating increased cardiovascular risk. C-reactive protein (CRP) and fibrinogen levels increase progressively with higher obesity classes in the general population, including the elderly. For instance, CRP levels nearly double with each increase in weight class compared to normal weight individuals. Additionally, the presence of obesity-related comorbidities like hypertension or diabetes further elevates these inflammatory markers. In conclusion, obesity in the elderly is characterized by elevated levels of various inflammatory biomarkers, reflecting a state of chronic low-grade inflammation. This inflammatory state may contribute to the development of obesity-related comorbidities. The clarification of the complementary or independent role of these biomarkers in aging and obesity could lead to targeted therapeutic interventions in this vulnerable population group.

Objectives: To identify clinical, functional, laboratory, and patient-reported parameters associated with medium-term risk of hospitalization or death among older adults attending a multidisciplinary outpatient clinic, and to assess the predictive performance of these measures for individual risk stratification. Methods: In this cohort study, 350 adults aged ≥65 years were assessed at baseline and followed for an average of 8 months. The primary outcome was a composite of hospitalization or all-cause mortality. Parameters assessed included frailty and comorbidity measures, functional parameters, such as gait speed and grip strength, laboratory biomarkers, and patient-reported measures, such as quality of life (QoL, assessed on a Likert scale) and the presence of depressive symptoms. Predictive performance was evaluated using univariable logistic regression and multivariable modeling. Discriminative ability was assessed via area under the ROC curve (AUC), and selected models were internally validated using repeated k-fold cross-validation. Results: Overall, 40 participants (11.4%) experienced hospitalization or death. Traditional clinical risk indicators, including frailty and comorbidity scores, were significantly associated with the outcome. Patient-reported QoL (AUC = 0.74) and Geriatric Depression Scale (GDS) scores (AUC = 0.67) demonstrated useful overall discriminatory ability, with high specificities at optimal cut-offs, suggesting they could act as “red flags” for adverse outcomes. However, the limited sensitivities of individual predictors underscore the need for more comprehensive screening instruments with improved ability to identify at-risk individuals earlier. A multivariable model that incorporated several predictors did not outperform QoL alone (AUC = 0.79), with cross-validation confirming comparable discriminative performance. Conclusions: Patient-reported measures—particularly quality of life and depressive symptoms—are valuable predictors of hospitalization or death and may enhance traditional frailty and comorbidity assessments in outpatient geriatric care. Future work should focus on developing or integrating screening tools with greater sensitivity to optimize early risk detection and guide preventive interventions.