Most older individuals develop inflammageing, a condition characterized by elevated levels of blood inflammatory markers that carries high susceptibility to chronic morbidity, disability, frailty, and premature death. Potential mechanisms of inflammageing include genetic susceptibility, central obesity, increased gut permeability, changes to microbiota composition, cellular senescence, NLRP3 inflammasome activation, oxidative stress caused by dysfunctional mitochondria, immune cell dysregulation, and chronic infections. Inflammageing is a risk factor for cardiovascular diseases (CVDs), and clinical trials suggest that this association is causal. Inflammageing is also a risk factor for chronic kidney disease, diabetes mellitus, cancer, depression, dementia, and sarcopenia, but whether modulating inflammation beneficially affects the clinical course of non-CVD health problems is controversial. This uncertainty is an important issue to address because older patients with CVD are often affected by multimorbidity and frailty — which affect clinical manifestations, prognosis, and response to treatment — and are associated with inflammation by mechanisms similar to those in CVD. The hypothesis that inflammation affects CVD, multimorbidity, and frailty by inhibiting growth factors, increasing catabolism, and interfering with homeostatic signalling is supported by mechanistic studies but requires confirmation in humans. Whether early modulation of inflammageing prevents or delays the onset of cardiovascular frailty should be tested in clinical trials.

Purpose of review Older obese persons with decreased muscle mass or strength are at special risk for adverse outcomes. We discuss potential pathways to muscle impairment in obese individuals and the consequences that joint obesity and muscle impairment may have on health and disability. Tantamount to this discussion is whether low muscle mass or, rather, muscle weakness should be used for the definition. Recent findings Excess energy intake, physical inactivity, low-grade inflammation, insulin resistance and changes in hormonal milieu may lead to the development of so-called ‘sarcopenic obesity’. It was originally believed that the culprit of age-related muscle weakness was a reduction in muscle mass, but it is now clear that changes in muscle composition and quality are predominant. We propose that the risk of adverse outcomes, such as functional limitation and mortality, is better estimated by considering jointly obesity and muscle strength rather than obesity and muscle mass and the term ‘sarcopenic obesity’ should be revisited. Summary Recognition of obese patients who have associated muscle problems is an essential goal for clinicians. Further research is needed to identify new target for prevention and cure of this important geriatric syndrome.

The global population of individuals over the age of 65 is growing at an unprecedented rate and is expected to reach 1.6 billion by 2050. Most older individuals are affected by multiple chronic diseases, leading to complex drug treatments and increased risk of physical and cognitive disability. Improving or preserving the health and quality of life of these individuals is challenging due to a lack of well‐established clinical guidelines. Physicians are often forced to engage in cycles of “trial and error” that are centered on palliative treatment of symptoms rather than the root cause, often resulting in dubious outcomes. Recently, geroscience challenged this view, proposing that the underlying biological mechanisms of aging are central to the global increase in susceptibility to disease and disability that occurs with aging. In fact, strong correlations have recently been revealed between health dimensions and phenotypes that are typical of aging, especially with autophagy, mitochondrial function, cellular senescence, and DNA methylation. Current research focuses on measuring the pace of aging to identify individuals who are “aging faster” to test and develop interventions that could prevent or delay the progression of multimorbidity and disability with aging. Understanding how the underlying biological mechanisms of aging connect to and impact longitudinal changes in health trajectories offers a unique opportunity to identify resilience mechanisms, their dynamic changes, and their impact on stress responses. Harnessing how to evoke and control resilience mechanisms in individuals with successful aging could lead to writing a new chapter in human medicine.

We hypothesized that the rising levels of inflammatory markers with aging is explained by cardiovascular risk factors and morbidity becoming progressively more prevalent in older persons. Information on inflammatory markers, cardiovascular risk factors, and diseases was collected in 595 men and 748 women sampled from the general population (age, 20-102 years). In both men and women, older age was associated with higher levels of interleukin-6 (IL-6), IL-1 receptor antagonist (IL-1ra), IL-18, C-reactive protein (CRP), and fibrinogen, while soluble IL-6 receptor (sIL-6r) increased significantly with age only in men. Adjusting for cardiovascular risk factors and morbidity, the age regression coefficients became substantially smaller in models predicting IL-6, IL-1ra, IL-18, and fibrinogen and larger in the model predicting sIL6r. Adjustment for cardiovascular morbidity substantially reduced the effect of age on CRP in men but not in women. Findings were confirmed in a subgroup of 51 men and 45 women with low risk profile and no cardiovascular morbidity. Part of the “proinflammatory state” in older persons is related to the high prevalence of cardiovascular risk factor and morbidity.

A person’s rate of aging has important implications for his/her risk of death and disease; thus, quantifying aging using observable characteristics has important applications for clinical, basic, and observational research. Based on routine clinical chemistry biomarkers, we previously developed a novel aging measure, Phenotypic Age, representing the expected age within the population that corresponds to a person’s estimated mortality risk. The aim of this study was to assess its applicability for differentiating risk for a variety of health outcomes within diverse subpopulations that include healthy and unhealthy groups, distinct age groups, and persons with various race/ethnic, socioeconomic, and health behavior characteristics. Phenotypic Age was calculated based on a linear combination of chronological age and 9 multi-system clinical chemistry biomarkers in accordance with our previously established method. We also estimated Phenotypic Age Acceleration (PhenoAgeAccel), which represents Phenotypic Age after accounting for chronological age (i.e., whether a person appears older [positive value] or younger [negative value] than expected, physiologically). All analyses were conducted using NHANES IV (1999–2010, an independent sample from that originally used to develop the measure). Our analytic sample consisted of 11,432 adults aged 20–84 years and 185 oldest-old adults top-coded at age 85 years. We observed a total of 1,012 deaths, ascertained over 12.6 years of follow-up (based on National Death Index data through December 31, 2011). Proportional hazard models and receiver operating characteristic curves were used to evaluate all-cause and cause-specific mortality predictions. Overall, participants with more diseases had older Phenotypic Age. For instance, among young adults, those with 1 disease were 0.2 years older phenotypically than disease-free persons, and those with 2 or 3 diseases were about 0.6 years older phenotypically. After adjusting for chronological age and sex, Phenotypic Age was significantly associated with all-cause mortality and cause-specific mortality (with the exception of cerebrovascular disease mortality). Results for all-cause mortality were robust to stratifications by age, race/ethnicity, education, disease count, and health behaviors. Further, Phenotypic Age was associated with mortality among seemingly healthy participants—defined as those who reported being disease-free and who had normal BMI—as well as among oldest-old adults, even after adjustment for disease prevalence. The main limitation of this study was the lack of longitudinal data on Phenotypic Age and disease incidence. In a nationally representative US adult population, Phenotypic Age was associated with mortality even after adjusting for chronological age. Overall, this association was robust across different stratifications, particularly by age, disease count, health behaviors, and cause of death. We also observed a strong association between Phenotypic Age and the disease count an individual had. These findings suggest that this new aging measure may serve as a useful tool to facilitate identification of at-risk individuals and evaluation of the efficacy of interventions, and may also facilitate investigation into potential biological mechanisms of aging. Nevertheless, further evaluation in other cohorts is needed.