Claudio Franceschi

Centenarians — individuals aged ≥100 years — provide a unique human model for understanding resilience to cardiovascular ageing. Although cardiovascular and metabolic diseases, including hypertension, heart failure, atrial fibrillation and chronic kidney disease, are common in centenarians, their onset is typically delayed relative to that in individuals aged 80–100 years. Centenarians characteristically have structural changes in the heart and vessels, including concentric left ventricular remodelling, increased myocardial stiffness and vascular ageing, but often without the expected burden of myocardial infarction or overt heart failure. Their cardiometabolic profile is generally favourable, marked by preserved insulin sensitivity in cohorts with a low prevalence of diabetes mellitus, mid-range lipid levels and a low prevalence of obesity. Centenarians also have a distinctive inflammatory and gastrointestinal phenotype, characterized by chronic low-grade inflammation with elevated anti-inflammatory signalling, a gut microbiome that more closely resembles that of younger adults, and preserved intestinal barrier integrity. Genetically and epigenetically, exceptional longevity reflects highly polygenic contributions, selective maintenance of genomic stability and markedly younger epigenetic age compared with chronological age. In this Review, we summarize data on the cardiovascular system among centenarians by framing observations of longevity as a composite of delayed pathology, adaptive compensation and survivorship selection. Understanding the biological, behavioural and environmental mechanisms underlying cardiovascular resilience in extreme longevity might inform strategies to extend cardiovascular healthspan across all ages.

Centenarians — individuals aged 100 years or older — constitute a biologically distinct human population that achieves exceptional longevity while frequently retaining functional independence and avoiding major age-related diseases or postponing their onset. Despite their advanced age, many centenarians show relatively preserved immune function and resistance to conditions linked to immunosenescence and chronic low-grade inflammation (inflammageing). These features are especially pronounced in semi-supercentenarians (105–109 years) and supercentenarians (≥110 years), whose immune profiles often resemble those of much younger individuals. In this Review, we explore how centenarians modulate key hallmarks of immune ageing across innate and adaptive immune compartments. We discuss evidence that they limit the pathological effects of inflammageing, potentially through reduced NLRP3 inflammasome activation, enhanced autophagy and a tempered senescence-associated secretory phenotype. Omics studies further reveal transcriptomic, epigenetic and microbial signatures consistent with preserved immune function, including youth-like gene expression patterns in circulating immune cells and beneficial shifts in gut microbiome composition. Together, these findings suggest that centenarians achieve longevity through coordinated adaptations that maintain immune homeostasis and disease resistance and may inform strategies to enhance healthspan in ageing societies.

This Review discusses the current state of knowledge on the contribution of the gut microbiome as a potential key actor in defining how we age. The gut microbiome is a complex ecosystem that establishes lifelong dynamic interactions with the host at multiple levels (several gut–organ axes), differently influencing ageing patterns and age-related disease onset and progression across populations. Accordingly, the definition of a ‘normative’ gut microbiome remains elusive, depending largely on the interaction with the external environment. In this complex scenario, the causal role of the gut microbiome in defining the ageing trajectory and its precise contribution to various organ-specific age-related diseases is still uncertain in clinical terms and could be context specific. Multiparametric and uniqueness indexes within a given population have shown a certain capacity for predicting disability and mortality. However, the gut microbiome is shaped over time by exposure to different intrinsic and environmental factors, resulting in a high degree of inter-individual variability, a key phenomenon that should be considered to develop novel personalized strategies to counteract age-related disease and frailty.

The article presents an assessment of body composition markers measured by imaging in clinical practice for postmenopausal and aging women. Body composition changes with aging and is specifically affected by endocrinological changes occurring with menopause. Several imaging markers have been proposed and used in the assessment of body composition status. The associations of different imaging markers with cardiometabolic risk and risks for other diseases, with impact on morbidity/mortality, functional impairment, and frailty, are discussed. The imaging markers confirmed by evidence and are applicable to clinical practice are highlighted. With this purpose, the current level of evidence in the literature on reliability and potential associations of each relevant marker was reviewed. This review describes what can and should be done with available imaging tools (e.g., dual-energy x-ray absorptiometry, ultrasound, computed tomography, and magnetic resonance imaging) in dedicated and opportunistic settings (i.e., tests for assessing body composition vs those for other clinical reasons but wherein exploitation of imaging data is possible) to improve the management and understanding of lifestyle needs of postmenopausal women and thus to prevent or decrease unhealthy aging and rate of women with aging-related diseases.

Inflammaging describes a chronic, systemic, low-grade inflammatory state that is recognized as a major risk factor for age-related diseases (ARDs) and a pivotal convergence point of multiple biological mechanisms involved in aging. Here, we discuss the heterogeneity of inflammaging, proposing that it emerges as a consequence of each individual's lifelong exposures to inflammatory stimuli, shaped by a unique combination of genetics, lifestyle, socioeconomic conditions and environmental factors such as infections and pollution. Through this lens, we then discuss measuring inflammaging, describing the development of inflammatory clocks that quantify inflammatory age and show strong associations with ARD incidence as well as how other aging clocks intersect with inflammaging. Finally, we consider interventions that may counteract inflammaging, including nutritional interventions, physical activity and gerotherapies such as senolytics. We propose that deepening our knowledge of the individual nature of inflammaging stands to enhance our understanding of personalized aging trajectories and inform precision interventions.

Genomic instability markers are important hallmarks of aging, as previously evidenced within the European study of biomarkers of human aging, MARK-AGE; however, establishing the specific metabolic determinants of vascular aging is challenging. The objective of the present study was to evaluate the impact of the susceptibility to oxidation of serum LDL particles (LDLox) and the plasma metabolization products of nitric oxide (NOx) on relevant genomic instability markers. The analysis was performed on a MARK-AGE cohort of 1326 subjects (635 men and 691 women, 35–75 years old) randomly recruited from the general population. The Inverse Probability of Treatment Weighting causal inference algorithm was implemented in order to assess the potential causal relationship between the LDLox and NOx octile-based thresholds and three genomic instability markers measured in mononuclear leukocytes: the percentage of telomeres shorter than 3 kb, the initial DNA integrity, and the DNA damage after irradiation with 3.8 Gy. The results showed statistically significant telomere shortening for LDLox, while NOx yielded a significant impact on DNA integrity. Overall, the effect on the genomic instability markers was higher than for the confirmed vascular aging determinants, such as low HDL cholesterol levels, indicating a meaningful impact even for small changes in LDLox and NOx values.

Inflammaging, an age-associated increase in chronic inflammation, is considered a hallmark of aging. However, there is no consensus approach to measuring inflammaging based on circulating cytokines. Here we assessed whether an inflammaging axis detected in the Italian InCHIANTI dataset comprising 19 cytokines could be generalized to a different industrialized population (Singapore Longitudinal Aging Study) or to two indigenous, nonindustrialized populations: the Tsimane from the Bolivian Amazon and the Orang Asli from Peninsular Malaysia. We assessed cytokine axis structure similarity and whether the inflammaging axis replicating the InCHIANTI result increased with age or was associated with health outcomes. The Singapore Longitudinal Aging Study was similar to InCHIANTI except for IL-6 and IL-1RA. The Tsimane and Orang Asli showed markedly different axis structures with little to no association with age and no association with age-related diseases. Inflammaging, as measured in this manner in these cohorts, thus appears to be largely a byproduct of industrialized lifestyles, with major variation across environments and populations.

We present EpInflammAge, an explainable deep learning tool that integrates epigenetic and inflammatory markers to create a highly accurate, disease-sensitive biological age predictor. This novel approach bridges two key hallmarks of aging—epigenetic alterations and immunosenescence. First, epigenetic and inflammatory data from the same participants was used for AI models predicting levels of 24 cytokines from blood DNA methylation. Second, open-source epigenetic data (25 thousand samples) was used for generating synthetic inflammatory biomarkers and training an age estimation model. Using state-of-the-art deep neural networks optimized for tabular data analysis, EpInflammAge achieves competitive performance metrics against 34 epigenetic clock models, including an overall mean absolute error of 7 years and a Pearson correlation coefficient of 0.85 in healthy controls, while demonstrating robust sensitivity across multiple disease categories. Explainable AI revealed the contribution of each feature to the age prediction. The sensitivity to multiple diseases due to combining inflammatory and epigenetic profiles is promising for both research and clinical applications. EpInflammAge is released as an easy-to-use web tool that generates the age estimates and levels of inflammatory parameters for methylation data, with the detailed report on the contribution of input variables to the model output for each sample.