The improvement of life quality and medical advances has resulted in increased life expectancy. Despite this, health status commonly worsens in the last years of life. Frailty is an intermediate and reversible state that often precedes dependency and therefore, its identification may be essential to prevent dependency. However, there is no consensus on the best tools to identify frailty. In this sense, diverse molecules have been proposed as potential biomarkers. Some investigations pointed to an increased chronic inflammation or inflammaging with frailty, while others did not report such differences. In this work, we evaluated the circulating concentration of the inflammaging markers in adults and older adults (aged over 70 years) by ELISA and Luminex techniques. The Barthel Index was applied for the evaluation of dependency and Timed up-and-go, Gait Speed, Short Physical Performance Battery, Tilburg Frailty Indicator and Gerontopole Frailty Screening Tool were used for the identification of frailty. CRP, TNF-α, IL-6 and albumin concentrations were measured, and we found that elevated inflammation is present in older adults, while no differences with frailty and dependency were reported. Our results were consistent for all the evaluated frailty scales, highlighting the need to reconsider increased inflammation as a biomarker of frailty.

Aging population is at higher risk of developing severe COVID-19, including hospitalization and death. In this work, to further understand the relationship between host age-related factors, immunosenescence/exhaustion of the immune system and the response to the virus, we characterized immune cell and cytokine responses in 58 COVID-19 patients admitted to the hospital and 40 healthy controls of different age ranges. Lymphocyte populations and inflammatory profiles were studied in blood samples, using different panels of multicolor flow cytometry. As expected, our analysis reveals differences at both the cellular and cytokine level in COVID-19 patients. Interestingly, when the age range analysis was carried out, the immunological response to the infection was found to differ with age, being especially affected in the group of 30–39 years. In this age range, an increased exhausted T cell response and a decrease of naïve T helper lymphocytes was found in patients, as well as a reduced concentration of the proinflammatory TNF, IL-1β and IL-8 cytokines. Besides, the correlation between age and the study variables was evaluated, and multiple cell types and interleukins were found to correlate with donor age. Notably, the correlations of T helper naïve and effector memory cells, T helper 1–17 cells, TNF, IL-10, IL-1β, IL-8, among others, showed differences between healthy controls and COVID-19 patients. Our findings, in the context of other previous studies, suggest that aging affects the behavior of the immune system in COVID-19 patients. They suggest that young individuals are able to mount an initial response to SARS-CoV-2, but some of them present an accelerated exhaustion of the cell response and an insufficient inflammatory response, resulting in a moderate to severe COVID-19. On the other hand, in older patients there is a smaller immune cell response to the virus, reflected in fewer differences in immune populations between COVID-19 patients and controls. Nevertheless, old patients show more evidence of an inflammatory phenotype, suggesting that the underlying inflammation associated with their age is exacerbated by the SARS-CoV-2 infection.

COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. The test has been done by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course.This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.

COVID-19 pandemic has put the protocols and the capacity of our Hospitals to the test. The management of severe patients admitted to the Intensive Care Units has been a challenge for all health systems. To assist in this challenge, various models have been proposed to predict mortality and severity, however, there is no clear consensus for their use. In this work, we took advantage of data obtained from routine blood tests performed on all individuals on the first day of hospitalization. These data has been obtained by standardized cost-effective technique available in all the hospitals. We have analyzed the results of 1082 patients with COVID19 and using artificial intelligence we have generated a predictive model based on data from the first days of admission that predicts the risk of developing severe disease with an AUC = 0.78 and an F1-score = 0.69. Our results show the importance of immature granulocytes and their ratio with Lymphocytes in the disease and present an algorithm based on 5 parameters to identify a severe course. This work highlights the importance of studying routine analytical variables in the early stages of hospital admission and the benefits of applying AI to identify patients who may develop severe disease.

No abstract