The poly-omics of ageing through individual-based metabolic modelling

Abstract Background Ageing can be classified in two different ways, chronological ageing and biological ageing. While chronological age is a measure of the time that has passed since birth, biological (also known as transcriptomic) ageing is defined by how time and the environment affect an individual in comparison to other individuals of the same chronological age. Recent research studies have shown that transcriptomic age is associated with certain genes, and that each of those genes has an effect size. Using these effect sizes we can calculate the transcriptomic age of an individual from their age-associated gene expression levels. The limitation of this approach is that it does not consider how these changes in gene expression affect the metabolism of individuals and hence their observable cellular phenotype. Results We propose a method based on poly-omic constraint-based models and machine learning in order to further the understanding of transcriptomic ageing. We use normalised CD4 T-cell gene expression data from peripheral blood mononuclear cells in 499 healthy individuals to create individual metabolic models. These models are then combined with a transcriptomic age predictor and chronological age to provide new insights into the differences between transcriptomic and chronological ageing. As a result, we propose a novel metabolic age predictor. Conclusions We show that our poly-omic predictors provide a more detailed analysis of transcriptomic ageing compared to gene-based approaches, and represent a basis for furthering our knowledge of the ageing mechanisms in human cells

Improving nutritional care quality in the orthopedic ward of a Septic Surgery Center by implementing a preventive nutritional policy using the Nutritional Risk Score: a pilot study.

Septic Surgery Center (SSC) patients are at a particularly high risk of protein-energy malnutrition (PEM), with a prevalence of 35-85% found in various studies. Previous collaboration between our hospital's SSC and its Clinical Nutrition Team (CNT) only focussed on patients with severe PEM. This study aimed to determine whether it was possible to improve the quality of nutritional care in septic surgery patients with help of a nutritional policy using the Nutritional Risk Score (NRS). Nutritional practices in the SSC were observed over three separate periods: in the 3 months leading up to the implementation baseline, 6 months after implementation of preventive nutritional practices, and at 3 years. The nutritional care quality indicator was the percentage of patients whose nutritional care, as prescribed by the SSC, was adapted to their specific requirements. We determined the septic surgery team's NRS completion rate and calculated the nutritional policy's impact on SSC length of stay. Data before (T <sub>0</sub> ) and after (T <sub>1</sub> + T <sub>2</sub> ) implementation of the nutritional policy were compared. Ninety-eight patients were included. The nutritional care-quality indicator improved from 26 to 81% between T <sub>0</sub> and T <sub>2</sub> . During the T <sub>1</sub> and T <sub>2</sub> audits, septic surgery nurses calculated NRS for 100% and 97% of patients, respectively. Excluding patients with severe PEM, SSC length of stay was significantly reduced by 23 days (p = 0.005). These findings showed that implementing a nutritional policy in an SSC is possible with the help of an algorithm including an easy-to-use tool like the NRS